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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Gu, W"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11728">arXiv:2502.11728</a> <span> [<a href="https://arxiv.org/pdf/2502.11728">pdf</a>, <a href="https://arxiv.org/format/2502.11728">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Matrix Low-dimensional Qubit Casting Based Quantum Electromagnetic Transient Network Simulation Program </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lou%2C+Q">Qi Lou</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yijun Xu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11728v1-abstract-short" style="display: inline;"> In modern power systems, the integration of converter-interfaced generations requires the development of electromagnetic transient network simulation programs (EMTP) that can capture rapid fluctuations. However, as the power system scales, the EMTP's computing complexity increases exponentially, leading to a curse of dimensionality that hinders its practical application. Facing this challenge, qua… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11728v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11728v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11728v1-abstract-full" style="display: none;"> In modern power systems, the integration of converter-interfaced generations requires the development of electromagnetic transient network simulation programs (EMTP) that can capture rapid fluctuations. However, as the power system scales, the EMTP's computing complexity increases exponentially, leading to a curse of dimensionality that hinders its practical application. Facing this challenge, quantum computing offers a promising approach for achieving exponential acceleration. To realize this in noisy intermediate-scale quantum computers, the variational quantum linear solution (VQLS) was advocated because of its robustness against depolarizing noise. However, it suffers data inflation issues in its preprocessing phase, and no prior research has applied quantum computing to high-frequency switching EMT networks.To address these issues, this paper first designs the matrix low-dimension qubit casting (MLQC) method to address the data inflation problem in the preprocessing of the admittance matrix for VQLS in EMT networks. Besides, we propose a real-only quantum circuit reduction method tailored to the characteristics of the EMT network admittance matrices. Finally, the proposed quantum EMTP algorithm (QEMTP) has been successfully verified for EMT networks containing a large number of high-frequency switching elements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11728v1-abstract-full').style.display = 'none'; document.getElementById('2502.11728v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20485">arXiv:2410.20485</a> <span> [<a href="https://arxiv.org/pdf/2410.20485">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+L">Longyan Li</a>, <a href="/search/eess?searchtype=author&query=Ning%2C+C">Chao Ning</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+G">Guangsheng Pan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+L">Leiqi Zhang</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+L">Liang Zhao</a>, <a href="/search/eess?searchtype=author&query=Du%2C+W">Wenli Du</a>, <a href="/search/eess?searchtype=author&query=Shahidehpour%2C+M">Mohammad Shahidehpour</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20485v1-abstract-short" style="display: inline;"> This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20485v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20485v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20485v1-abstract-full" style="display: none;"> This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20485v1-abstract-full').style.display = 'none'; document.getElementById('2410.20485v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20058">arXiv:2410.20058</a> <span> [<a href="https://arxiv.org/pdf/2410.20058">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Optimal demand-responsive connector design: Comparing fully-flexible routing and semi-flexible routing strategies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhen%2C+L">Li Zhen</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Weihua Gu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20058v1-abstract-short" style="display: inline;"> Demand-responsive connector (DRC) services are increasingly recognized for their convenience, comfort, and efficiency, offering seamless integrations between travelers' origins/destinations and major transportation hubs such as rail stations. Past analytical models for DRC optimization often failed to distinguish between two commonly used DRC operating strategies: (i) the "fully-flexible routing"… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20058v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20058v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20058v1-abstract-full" style="display: none;"> Demand-responsive connector (DRC) services are increasingly recognized for their convenience, comfort, and efficiency, offering seamless integrations between travelers' origins/destinations and major transportation hubs such as rail stations. Past analytical models for DRC optimization often failed to distinguish between two commonly used DRC operating strategies: (i) the "fully-flexible routing" strategy, where a vehicle serves only the requests received before its dispatch through an optimal tour, and (ii) the "semi-flexible routing" strategy, where a vehicle follows a predefined path through a swath to serve requests received en route. Additionally, these models often adopted oversimplified approaches for estimating local tour lengths and capturing the stochastic nature of demand. This paper distinctly identifies and analyzes the two DRC operating strategies, developing analytical models for each that accurately incorporate the second-order effects of stochastic demand and utilize refined local tour length formulas. Numerical experiments demonstrate that our models reduce cost estimation errors to within 2% for fully-flexible routing and to 0.25% for semi-flexible routing, a significant improvement over the previous errors of 8-12% and 6.3%, respectively. These enhanced models allow for more precise determination of critical demand densities for selecting between the two DRC strategies and the fixed-route feeder service. Our extensive numerical analysis offers many insights, particularly highlighting the transition from fully-flexible to semi-flexible routing as demand and region size increase, before ultimately shifting to fixed-route service. Additionally, zoning is identified as pivotal in DRC service design, with fully-flexible routing favoring square-shaped zones and semi-flexible routing preferring elongated rectangular zones. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20058v1-abstract-full').style.display = 'none'; document.getElementById('2410.20058v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">31 pages, 10 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.09464">arXiv:2410.09464</a> <span> [<a href="https://arxiv.org/pdf/2410.09464">pdf</a>, <a href="https://arxiv.org/format/2410.09464">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Quantify Gas-to-Power Fault Propagation Speed:A Semi-Implicit Simulation Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yu%2C+R">Ruizhi Yu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Suhan Zhang</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shuai Lu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.09464v1-abstract-short" style="display: inline;"> Relying heavily on the secure supply of natural gas, the modern clean electric power systems are prone to the gas disturbances induced by the inherent rupture and leakage faults. For the first time, this paper studies the cross-system propagation speed of these faults using a simulation-based approach. Firstly, we establish the differential algebraic equation models of the rupture and leakage faul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09464v1-abstract-full').style.display = 'inline'; document.getElementById('2410.09464v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09464v1-abstract-full" style="display: none;"> Relying heavily on the secure supply of natural gas, the modern clean electric power systems are prone to the gas disturbances induced by the inherent rupture and leakage faults. For the first time, this paper studies the cross-system propagation speed of these faults using a simulation-based approach. Firstly, we establish the differential algebraic equation models of the rupture and leakage faults respectively. The boundary conditions at the fault locations are derived using the method of characteristics. Secondly, we propose utilizing a semi-implicit approach to perform post-fault simulations. The approach, based on the stiffly-accurate Rosenbrock scheme, possesses the implicit numerical stability and explicit computation burdens. Therefore, the high-dimensional and multi-time-scale stiff models can be solved in an efficient and robust way. Thirdly, to accurately locate the simulation events, which can not be predicted a priori, we propose a critical-time-location strategy based on the continuous Runge-Kutta approach. In case studies, we verified the accuracy and the efficiency superiority of the proposed simulation approach. The impacts of gas faults on gas and power dynamics were investigated by simulation, where the critical events were identified accurately. We found that the fault propagation speed mainly depends on the fault position and is influenced by the pipe frictions. The bi-directional coupling between gas and power may lead to cascading failures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09464v1-abstract-full').style.display = 'none'; document.getElementById('2410.09464v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.01222">arXiv:2409.01222</a> <span> [<a href="https://arxiv.org/pdf/2409.01222">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Nonlinear PDE Constrained Optimal Dispatch of Gas and Power: A Global Linearization Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yuan Li</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shuai Lu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yijun Xu</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+R">Ruizhi Yu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Suhan Zhang</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Z">Zhikai Huang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.01222v1-abstract-short" style="display: inline;"> The coordinated dispatch of power and gas in the electricity-gas integrated energy system (EG-IES) is fundamental for ensuring operational security. However, the gas dynamics in the natural gas system (NGS) are governed by the nonlinear partial differential equations (PDE), making the dispatch problem of the EG-IES a complicated optimization model constrained by nonlinear PDE. To address it, we pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.01222v1-abstract-full').style.display = 'inline'; document.getElementById('2409.01222v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.01222v1-abstract-full" style="display: none;"> The coordinated dispatch of power and gas in the electricity-gas integrated energy system (EG-IES) is fundamental for ensuring operational security. However, the gas dynamics in the natural gas system (NGS) are governed by the nonlinear partial differential equations (PDE), making the dispatch problem of the EG-IES a complicated optimization model constrained by nonlinear PDE. To address it, we propose a globally linearized gas network model based on the Koopman operator theory, avoiding the commonly used local linearization and spatial discretization. Particularly, we propose a data-driven Koopman operator approximation approach for the globally linearized gas network model based on the extended dynamic mode decomposition, in which a physics-informed stability constraint is derived and embedded to improve the generalization ability and accuracy of the model. Based on this, we develop an optimal dispatch model for the EG-IES that first considers the nonlinear gas dynamics in the NGS. The case study verifies the effectiveness of this work. Simulation results reveal that the commonly used locally linearized gas network model fails to accurately capture the dynamic characteristics of NGS, bringing potential security threats to the system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.01222v1-abstract-full').style.display = 'none'; document.getElementById('2409.01222v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.11336">arXiv:2406.11336</a> <span> [<a href="https://arxiv.org/pdf/2406.11336">pdf</a>, <a href="https://arxiv.org/format/2406.11336">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A General Framework for Load Forecasting based on Pre-trained Large Language Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+M">Mingyang Gao</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+S">Suyang Zhou</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zhi Wu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Haiquan Liu</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+A">Aihua Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.11336v2-abstract-short" style="display: inline;"> Accurate load forecasting is crucial for maintaining the power balance between generators and consumers,particularly with the increasing integration of renewable energy sources, which introduce significant intermittent volatility. With the advancement of data-driven methods, machine learning and deep learning models have become the predominant approaches for load forecasting tasks. In recent years… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11336v2-abstract-full').style.display = 'inline'; document.getElementById('2406.11336v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11336v2-abstract-full" style="display: none;"> Accurate load forecasting is crucial for maintaining the power balance between generators and consumers,particularly with the increasing integration of renewable energy sources, which introduce significant intermittent volatility. With the advancement of data-driven methods, machine learning and deep learning models have become the predominant approaches for load forecasting tasks. In recent years, pre-trained large language models (LLMs) have achieved significant progress, demonstrating superior performance across various fields. This paper proposes a load forecasting method based on LLMs, offering not only precise predictive capabilities but also broad and flexible applicability. Additionally, a data modeling method is introduced to effectively transform load sequence data into natural language suitable for LLM training. Furthermore, a data enhancement strategy is designed to mitigate the impact of LLM hallucinations on forecasting results. The effectiveness of the proposed method is validated using two real-world datasets. Compared to existing methods, our approach demonstrates state-of-the-art performance across all validation metrics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11336v2-abstract-full').style.display = 'none'; document.getElementById('2406.11336v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 3 figures and 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.15029">arXiv:2403.15029</a> <span> [<a href="https://arxiv.org/pdf/2403.15029">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> On the Solution Uniqueness of Data-Driven Modeling of Flexible Loads (with Supplementary Material) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shuai Lu</a>, <a href="/search/eess?searchtype=author&query=Ding%2C+J">Jiayi Ding</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+M">Mingji Chen</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+J">Junpeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yijun Xu</a>, <a href="/search/eess?searchtype=author&query=Dong%2C+Z">Zhaoyang Dong</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Z">Zezheng Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.15029v3-abstract-short" style="display: inline;"> This letter first explores the solution uniqueness of the data-driven modeling of price-responsive flexible loads (PFL). The PFL on the demand side is critical in modern power systems. An accurate PFL model is fundamental for system operations. However, whether the PFL model can be uniquely and correctly identified from operational data remains unclear. To address this, we analyze the structural a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15029v3-abstract-full').style.display = 'inline'; document.getElementById('2403.15029v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.15029v3-abstract-full" style="display: none;"> This letter first explores the solution uniqueness of the data-driven modeling of price-responsive flexible loads (PFL). The PFL on the demand side is critical in modern power systems. An accurate PFL model is fundamental for system operations. However, whether the PFL model can be uniquely and correctly identified from operational data remains unclear. To address this, we analyze the structural and practical identifiability of the PFL model, deriving the dataset condition that guarantees the solution uniqueness. Besides, we point out the practical implications of the results. Numerical tests validate this work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.15029v3-abstract-full').style.display = 'none'; document.getElementById('2403.15029v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.09627">arXiv:2401.09627</a> <span> [<a href="https://arxiv.org/pdf/2401.09627">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jiasong Chen</a>, <a href="/search/eess?searchtype=author&query=Qian%2C+L">Linchen Qian</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+L">Linhai Ma</a>, <a href="/search/eess?searchtype=author&query=Urakov%2C+T">Timur Urakov</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Weiyong Gu</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+L">Liang Liang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.09627v4-abstract-short" style="display: inline;"> Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (disks and vertebrae… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.09627v4-abstract-full').style.display = 'inline'; document.getElementById('2401.09627v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.09627v4-abstract-full" style="display: none;"> Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (disks and vertebrae) of the lumbar spine in an automated way, which is termed as instance image segmentation. In this work, we proposed SymTC, an innovative lumbar spine MR image segmentation model that combines the strengths of Transformer and Convolutional Neural Network (CNN). Specifically, we designed a parallel dual-path architecture to merge CNN layers and Transformer layers, and we integrated a novel position embedding into the self-attention module of Transformer, enhancing the utilization of positional information for more accurate segmentation. To further improves model performance, we introduced a new data augmentation technique to create synthetic yet realistic MR image dataset, named SSMSpine, which is made publicly available. We evaluated our SymTC and the other 15 existing image segmentation models on our private in-house dataset and the public SSMSpine dataset, using two metrics, Dice Similarity Coefficient and 95% Hausdorff Distance. The results show that our SymTC has the best performance for segmenting vertebral bones and intervertebral discs in lumbar spine MR images. The SymTC code and SSMSpine dataset are available at https://github.com/jiasongchen/SymTC. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.09627v4-abstract-full').style.display = 'none'; document.getElementById('2401.09627v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.02809">arXiv:2312.02809</a> <span> [<a href="https://arxiv.org/pdf/2312.02809">pdf</a>, <a href="https://arxiv.org/format/2312.02809">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Semi-implicit Continuous Newton Method for Power Flow Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yu%2C+R">Ruizhi Yu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yijun Xu</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shuai Lu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Suhan Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.02809v2-abstract-short" style="display: inline;"> As an effective emulator of ill-conditioned power flow, continuous Newton methods (CNMs) have been extensively investigated using explicit and implicit numerical integration algorithms. Explicit CNMs are prone to non-convergence issues due to their limited stable region, while implicit CNMs introduce additional iteration-loops of nonlinear equations. Faced with this, we propose a semi-implicit ver… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02809v2-abstract-full').style.display = 'inline'; document.getElementById('2312.02809v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.02809v2-abstract-full" style="display: none;"> As an effective emulator of ill-conditioned power flow, continuous Newton methods (CNMs) have been extensively investigated using explicit and implicit numerical integration algorithms. Explicit CNMs are prone to non-convergence issues due to their limited stable region, while implicit CNMs introduce additional iteration-loops of nonlinear equations. Faced with this, we propose a semi-implicit version of CNM. We formulate the power flow equations as a set of differential algebraic equations (DAEs), and solve the DAEs with the stiffly accurate Rosenbrock type method (SARM). The proposed method succeeds the numerical robustness from the implicit CNM framework while prevents the iterative solution of nonlinear systems, hence revealing higher convergence speed and computation efficiency. A new 4-stage 3rd-order hyper-stable SARM, together with a 2nd-order embedded formula to control the step size, is constructed to further accelerate convergence by tuning the damping factor. Case studies on ill-conditioned systems verified the alleged performance. An algorithm extension for MATPOWER is made available on Github for benchmarking. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02809v2-abstract-full').style.display = 'none'; document.getElementById('2312.02809v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.08418">arXiv:2310.08418</a> <span> [<a href="https://arxiv.org/pdf/2310.08418">pdf</a>, <a href="https://arxiv.org/ps/2310.08418">ps</a>, <a href="https://arxiv.org/format/2310.08418">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSG.2024.3420743">10.1109/TSG.2024.3420743 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Privacy-Preserved Aggregate Thermal Dynamic Model of Buildings </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+Z">Zeyin Hou</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shuai Lu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yijun Xu</a>, <a href="/search/eess?searchtype=author&query=Qiu%2C+H">Haifeng Qiu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Dong%2C+Z">Zhaoyang Dong</a>, <a href="/search/eess?searchtype=author&query=Ding%2C+S">Shixing Ding</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.08418v1-abstract-short" style="display: inline;"> The thermal inertia of buildings brings considerable flexibility to the heating and cooling load, which is known to be a promising demand response resource. The aggregate model that can describe the thermal dynamics of the building cluster is an important interference for energy systems to exploit its intrinsic thermal inertia. However, the private information of users, such as the indoor temperat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.08418v1-abstract-full').style.display = 'inline'; document.getElementById('2310.08418v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.08418v1-abstract-full" style="display: none;"> The thermal inertia of buildings brings considerable flexibility to the heating and cooling load, which is known to be a promising demand response resource. The aggregate model that can describe the thermal dynamics of the building cluster is an important interference for energy systems to exploit its intrinsic thermal inertia. However, the private information of users, such as the indoor temperature and heating/cooling power, needs to be collected in the parameter estimation procedure to obtain the aggregate model, causing severe privacy concerns. In light of this, we propose a novel privacy-preserved parameter estimation approach to infer the aggregate model for the thermal dynamics of the building cluster for the first time. Using it, the parameters of the aggregate thermal dynamic model (ATDM) can be obtained by the load aggregator without accessing the individual's privacy information. More specifically, this method not only exploits the block coordinate descent (BCD) method to resolve its non-convexity in the estimation but investigates the transformation-based encryption (TE) associated with its secure aggregation protocol (SAP) techniques to realize privacy-preserved computation. Its capability of preserving privacy is also theoretically proven. Finally, simulation results using real-world data demonstrate the accuracy and privacy-preserved performance of our proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.08418v1-abstract-full').style.display = 'none'; document.getElementById('2310.08418v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.14688">arXiv:2309.14688</a> <span> [<a href="https://arxiv.org/pdf/2309.14688">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Feeder bus service design under spatially heterogeneous demand </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhen%2C+L">Li Zhen</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Weihua Gu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.14688v1-abstract-short" style="display: inline;"> In rapidly sprawling urban areas and booming intercity express rail networks, efficiently designed feeder bus systems are more essential than ever to transport passengers to and from trunk-line rail terminals. When the feeder service region is sufficiently large, the spatial heterogeneity in demand distribution must be considered. This paper develops continuous approximation models for optimizing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.14688v1-abstract-full').style.display = 'inline'; document.getElementById('2309.14688v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.14688v1-abstract-full" style="display: none;"> In rapidly sprawling urban areas and booming intercity express rail networks, efficiently designed feeder bus systems are more essential than ever to transport passengers to and from trunk-line rail terminals. When the feeder service region is sufficiently large, the spatial heterogeneity in demand distribution must be considered. This paper develops continuous approximation models for optimizing a heterogeneous fixed-route feeder network in a rectangular service region next to a rail terminal. Our work enhances previous studies by: (i) optimizing heterogeneous stop spacings along with line spacings and headways; (ii) accounting for passenger boarding and alighting numbers on bus dwell times and patron transfer delays at the rail terminal; and (iii) examining the advantages of asymmetric coordination between trunk and feeder schedules in both service directions. To tackle the increased modeling complexity, we introduce a semi-analytical method that combines analytically derived properties of the optimal solution with an iterative search algorithm. Local transit agencies can readily utilize this approach to design a real fixed-route feeder system. This paper reveals many findings and insights not previously reported. For instance, integrating the heterogeneous stop spacing optimization further reduces the system cost (by 4% under specific operating conditions). The cost savings increase with demand heterogeneity but decrease with the demand rate and service region size. Choosing the layout of feeder lines where buses pick up and drop off passengers along the service region's shorter side also significantly lowers the system cost (by 6% when the service region's aspect ratio is 1 to 2). Furthermore, coordinating trunk and feeder schedules in both service directions yields an additional cost saving of up to 20%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.14688v1-abstract-full').style.display = 'none'; document.getElementById('2309.14688v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages, 9 Figures, 8 Tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.10483">arXiv:2308.10483</a> <span> [<a href="https://arxiv.org/pdf/2308.10483">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSTE.2024.3383062">10.1109/TSTE.2024.3383062 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lu%2C+S">Shuai Lu</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Z">Zihang Gao</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yong Sun</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Suhan Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+B">Baoju Li</a>, <a href="/search/eess?searchtype=author&query=Hao%2C+C">Chengliang Hao</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yijun Xu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.10483v2-abstract-short" style="display: inline;"> The district heating network (DHN) is essential in enhancing the operational flexibility of integrated energy systems (IES). Yet, it is hard to obtain an accurate and concise DHN model for the operation owing to complicated network features and imperfect measurements. Considering this, this paper proposes a physical-ly informed data-driven aggregate model (AGM) for the DHN, providing a concise des… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10483v2-abstract-full').style.display = 'inline'; document.getElementById('2308.10483v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.10483v2-abstract-full" style="display: none;"> The district heating network (DHN) is essential in enhancing the operational flexibility of integrated energy systems (IES). Yet, it is hard to obtain an accurate and concise DHN model for the operation owing to complicated network features and imperfect measurements. Considering this, this paper proposes a physical-ly informed data-driven aggregate model (AGM) for the DHN, providing a concise description of the source-load relationship of DHN without exposing network details. First, we derive the analytical relationship between the state variables of the source and load nodes of the DHN, offering a physical fundament for the AGM. Second, we propose a physics-informed estimator for the AGM that is robust to low-quality measurements, in which the physical constraints associated with the parameter normalization and sparsity are embedded to improve the accuracy and robustness. Finally, we propose a physics-enhanced algorithm to solve the nonlinear estimator with non-closed constraints efficiently. Simulation results verify the effectiveness of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10483v2-abstract-full').style.display = 'none'; document.getElementById('2308.10483v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.08539">arXiv:2307.08539</a> <span> [<a href="https://arxiv.org/pdf/2307.08539">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSTE.2019.2914089">10.1109/TSTE.2019.2914089 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Image-Based Abnormal Data Detection and Cleaning Algorithm via Wind Power Curve </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Long%2C+H">Huan Long</a>, <a href="/search/eess?searchtype=author&query=Sang%2C+L">Linwei Sang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zaijun Wu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.08539v1-abstract-short" style="display: inline;"> This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points, and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction, and data marking. The negative abnormal points, whose wind spe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.08539v1-abstract-full').style.display = 'inline'; document.getElementById('2307.08539v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.08539v1-abstract-full" style="display: none;"> This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points, and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction, and data marking. The negative abnormal points, whose wind speed is greater than cut-in speed and power is below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k-means, local outlier factor, combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency, and universality of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.08539v1-abstract-full').style.display = 'none'; document.getElementById('2307.08539v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.17797">arXiv:2306.17797</a> <span> [<a href="https://arxiv.org/pdf/2306.17797">pdf</a>, <a href="https://arxiv.org/format/2306.17797">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pang%2C+L">Li Pang</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Weizhen Gu</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+X">Xiangyong Cao</a>, <a href="/search/eess?searchtype=author&query=Rui%2C+X">Xiangyu Rui</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+J">Jiangjun Peng</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+S">Shuang Xu</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+G">Gang Yang</a>, <a href="/search/eess?searchtype=author&query=Meng%2C+D">Deyu Meng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.17797v1-abstract-short" style="display: inline;"> Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, current deep learning-based approaches ignore this fact and restore the clean image with deterministic mapping (i.e., the network receives a noisy HSI and outputs a clean HSI). To alleviate this issue, this paper proposes a flow-based HSI denoising network (HIDFlowNet)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.17797v1-abstract-full').style.display = 'inline'; document.getElementById('2306.17797v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.17797v1-abstract-full" style="display: none;"> Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, current deep learning-based approaches ignore this fact and restore the clean image with deterministic mapping (i.e., the network receives a noisy HSI and outputs a clean HSI). To alleviate this issue, this paper proposes a flow-based HSI denoising network (HIDFlowNet) to directly learn the conditional distribution of the clean HSI given the noisy HSI and thus diverse clean HSIs can be sampled from the conditional distribution. Overall, our HIDFlowNet is induced from the flow methodology and contains an invertible decoder and a conditional encoder, which can fully decouple the learning of low-frequency and high-frequency information of HSI. Specifically, the invertible decoder is built by staking a succession of invertible conditional blocks (ICBs) to capture the local high-frequency details since the invertible network is information-lossless. The conditional encoder utilizes down-sampling operations to obtain low-resolution images and uses transformers to capture correlations over a long distance so that global low-frequency information can be effectively extracted. Extensive experimental results on simulated and real HSI datasets verify the superiority of our proposed HIDFlowNet compared with other state-of-the-art methods both quantitatively and visually. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.17797v1-abstract-full').style.display = 'none'; document.getElementById('2306.17797v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.14527">arXiv:2306.14527</a> <span> [<a href="https://arxiv.org/pdf/2306.14527">pdf</a>, <a href="https://arxiv.org/format/2306.14527">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Computationally Enhanced Approach for Chance-Constrained OPF Considering Voltage Stability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+Y">Yuanxi Wu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zhi Wu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yijun Xu</a>, <a href="/search/eess?searchtype=author&query=Long%2C+H">Huan Long</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+S">Shu Zheng</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+J">Jingtao Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.14527v3-abstract-short" style="display: inline;"> The effective management of stochastic characteristics of renewable power generations is vital for ensuring the stable and secure operation of power systems. This paper addresses the task of optimizing the chance-constrained voltage-stability-constrained optimal power flow (CC-VSC-OPF) problem, which is hindered by the implicit voltage stability index and intractable chance constraints Leveraging… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.14527v3-abstract-full').style.display = 'inline'; document.getElementById('2306.14527v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.14527v3-abstract-full" style="display: none;"> The effective management of stochastic characteristics of renewable power generations is vital for ensuring the stable and secure operation of power systems. This paper addresses the task of optimizing the chance-constrained voltage-stability-constrained optimal power flow (CC-VSC-OPF) problem, which is hindered by the implicit voltage stability index and intractable chance constraints Leveraging a neural network (NN)-based surrogate model, the stability constraint is explicitly formulated and directly integrated into the model. To perform uncertainty propagation without relying on presumptions or complicated transformations, an advanced data-driven method known as adaptive polynomial chaos expansion (APCE) is developed. To extend the scalability of the proposed algorithm, a partial least squares (PLS)-NN framework is designed, which enables the establishment of a parsimonious surrogate model and efficient computation of large-scale Hessian matrices. In addition, a dimensionally decomposed APCE (DD-APCE) is proposed to alleviate the "curse of dimensionality" by restricting the interaction order among random variables. Finally, the above techniques are merged into an iterative scheme to update the operation point. Simulation results reveal the cost-effective performances of the proposed method in several test systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.14527v3-abstract-full').style.display = 'none'; document.getElementById('2306.14527v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.12129">arXiv:2301.12129</a> <span> [<a href="https://arxiv.org/pdf/2301.12129">pdf</a>, <a href="https://arxiv.org/format/2301.12129">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Decentralized Energy Market Integrating Carbon Allowance Trade and Uncertainty Balance in Energy Communities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+Y">Yuanxi Wu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zhi Wu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Z">Zheng Xu</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+S">Shu Zheng</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Q">Qirun Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.12129v1-abstract-short" style="display: inline;"> With the sustained attention on carbon neutrality, the personal carbon trading (PCT) scheme has been embraced as an auspicious paradigm for scaling down carbon emissions. To facilitate the simultaneous clearance of energy and carbon allowance inside the energy community while hedging against uncertainty, a joint trading framework is proposed in this article. The energy trading is implemented in a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.12129v1-abstract-full').style.display = 'inline'; document.getElementById('2301.12129v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.12129v1-abstract-full" style="display: none;"> With the sustained attention on carbon neutrality, the personal carbon trading (PCT) scheme has been embraced as an auspicious paradigm for scaling down carbon emissions. To facilitate the simultaneous clearance of energy and carbon allowance inside the energy community while hedging against uncertainty, a joint trading framework is proposed in this article. The energy trading is implemented in a peer-to-peer (P2P) manner without the intervention of a central operator, and the uncertainty trading is materialized through procuring reserve of conventional generators and flexibility of users. Under the PCT scheme, carbon allowance is transacted via a sharing mechanism. Possible excessive carbon emissions due to uncertainty balance are tackled by obliging renewable agents to procure sufficient carbon allowances, following the consumption responsibility principle. A two-stage iterative method consisting of tightening McCormick envelope and alternating direction method of multipliers (ADMM) is devised to transform the model into a mixed-integer second-order cone program (MISOCP) and to allow for a fully decentralized market-clearing procedure. Numerical results have validated the effectiveness of the proposed market model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.12129v1-abstract-full').style.display = 'none'; document.getElementById('2301.12129v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.08894">arXiv:2208.08894</a> <span> [<a href="https://arxiv.org/pdf/2208.08894">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> EEG Machine Learning for Analysis of Mild Traumatic Brain Injury: A survey </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gu%2C+W">Weiqing Gu</a>, <a href="/search/eess?searchtype=author&query=Chang%2C+R">Ryan Chang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+B">Bohan Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.08894v1-abstract-short" style="display: inline;"> Mild Traumatic Brain Injury (mTBI) is a common brain injury and affects a diverse group of people: soldiers, constructors, athletes, drivers, children, elders, and nearly everyone. Thus, having a well-established, fast, cheap, and accurate classification method is crucial for the well-being of people around the globe. Luckily, using Machine Learning (ML) on electroencephalography (EEG) data shows… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.08894v1-abstract-full').style.display = 'inline'; document.getElementById('2208.08894v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.08894v1-abstract-full" style="display: none;"> Mild Traumatic Brain Injury (mTBI) is a common brain injury and affects a diverse group of people: soldiers, constructors, athletes, drivers, children, elders, and nearly everyone. Thus, having a well-established, fast, cheap, and accurate classification method is crucial for the well-being of people around the globe. Luckily, using Machine Learning (ML) on electroencephalography (EEG) data shows promising results. This survey analyzed the most cutting-edge articles from 2017 to the present. The articles were searched from the Google Scholar database and went through an elimination process based on our criteria. We reviewed, summarized, and compared the fourteen most cutting-edge machine learning research papers for predicting and classifying mTBI in terms of 1) EEG data types, 2) data preprocessing methods, 3) machine learning feature representations, 4) feature extraction methods, and 5) machine learning classifiers and predictions. The most common EEG data type was human resting-state EEG, with most studies using filters to clean the data. The power spectral, especially alpha and theta power, was the most prevalent feature. The other non-power spectral features, such as entropy, also show their great potential. The Fourier transform is the most common feature extraction method while using neural networks as automatic feature extraction generally returns a high accuracy result. Lastly, Support Vector Machine (SVM) was our survey's most common ML classifier due to its lower computational complexity and solid mathematical theoretical basis. The purpose of this study was to collect and explore a sparsely populated sector of ML, and we hope that our survey has shined some light on the inherent trends, advantages, disadvantages, and preferences of the current state of machine learning-based EEG analysis for mTBI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.08894v1-abstract-full').style.display = 'none'; document.getElementById('2208.08894v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.07331">arXiv:2112.07331</a> <span> [<a href="https://arxiv.org/pdf/2112.07331">pdf</a>, <a href="https://arxiv.org/format/2112.07331">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Non-iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yu%2C+R">Ruizhi Yu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.07331v3-abstract-short" style="display: inline;"> Quasi-dynamic energy flow calculation is an indispensable tool for the heat and electricity integrated energy system (HE-IES) analysis. One solves the nonlinear partial differential algebraic equations to obtain thermal, hydraulic and electric variations. However, mainstream iteration solvers face the challenges of inefficiency and bad robustness. For one thing, the frequent update and factorizati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.07331v3-abstract-full').style.display = 'inline'; document.getElementById('2112.07331v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.07331v3-abstract-full" style="display: none;"> Quasi-dynamic energy flow calculation is an indispensable tool for the heat and electricity integrated energy system (HE-IES) analysis. One solves the nonlinear partial differential algebraic equations to obtain thermal, hydraulic and electric variations. However, mainstream iteration solvers face the challenges of inefficiency and bad robustness. For one thing, the frequent update and factorization of Jacobian matrices utilize high CPU time. For another, the per-step iteration numbers grow exponentially as the system loading level creeps up. This paper presents a novel non-iterative algorithm for the quasi-dynamic energy flow calculation. The kernel of the proposed algorithm is to transform these nonlinear equations into linear recursive ones, by solving which, we obtain explicit closed-form solutions of unknown variables. In each step, the proposed algorithm requires only one matrix factorization and fixed times of arithmetic operations regardless of the loading levels, so that it achieves small and consistent per-step time costs. A semi-discrete scheme is used in PDE solution to avoid dissipative and dispersive errors that are often overlooked in previous literature. To ensure convergence, we also propose to control the temporal step sizes adaptively by estimating the simulation errors. Case studies showed that the proposed method manifested efficient and robust time performance compared with the iterative algorithms, and meanwhile preserved high accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.07331v3-abstract-full').style.display = 'none'; document.getElementById('2112.07331v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.09986">arXiv:2111.09986</a> <span> [<a href="https://arxiv.org/pdf/2111.09986">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Boost Distribution System Restoration with Emergency Communication Vehicles Considering Cyber-Physical Interdependence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ye%2C+Z">Zhigang Ye</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+C">Chen Chen</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+R">Ruihuan Liu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+K">Kai Wu</a>, <a href="/search/eess?searchtype=author&query=Bie%2C+Z">Zhaohong Bie</a>, <a href="/search/eess?searchtype=author&query=Lou%2C+G">Guannan Lou</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Wei Gu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+Y">Yubo Yuan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.09986v2-abstract-short" style="display: inline;"> Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events. However, most of the existing studies assume the communication infrastructures are intact for distribution automation, which is unrealistic. Motivated by the applications of the emergency communication vehicles (ECVs) in quickly setting up wireless commu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.09986v2-abstract-full').style.display = 'inline'; document.getElementById('2111.09986v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.09986v2-abstract-full" style="display: none;"> Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events. However, most of the existing studies assume the communication infrastructures are intact for distribution automation, which is unrealistic. Motivated by the applications of the emergency communication vehicles (ECVs) in quickly setting up wireless communication networks after disasters, in this paper, we propose an integrated distribution system restoration (DSR) framework and optimization models, which can coordinate the repair crews, the distribution system (physical sectors), and the emergency communication (cyber sectors) to pick up unserved power loads as quickly as possible. Case studies validated the effectiveness of the proposed models and proved the benefit of considering ECVs and cyber-physical interdependencies in DSR. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.09986v2-abstract-full').style.display = 'none'; document.getElementById('2111.09986v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.00667">arXiv:2106.00667</a> <span> [<a href="https://arxiv.org/pdf/2106.00667">pdf</a>, <a href="https://arxiv.org/format/2106.00667">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3479722.3480994">10.1145/3479722.3480994 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> SoK: Oracles from the Ground Truth to Market Manipulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Eskandari%2C+S">Shayan Eskandari</a>, <a href="/search/eess?searchtype=author&query=Salehi%2C+M">Mehdi Salehi</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W+C">Wanyun Catherine Gu</a>, <a href="/search/eess?searchtype=author&query=Clark%2C+J">Jeremy Clark</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.00667v2-abstract-short" style="display: inline;"> One fundamental limitation of blockchain-based smart contracts is that they execute in a closed environment. Thus, they only have access to data and functionality that is already on the blockchain, or is fed into the blockchain. Any interactions with the real world need to be mediated by a bridge service, which is called an oracle. As decentralized applications mature, oracles are playing an incre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.00667v2-abstract-full').style.display = 'inline'; document.getElementById('2106.00667v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.00667v2-abstract-full" style="display: none;"> One fundamental limitation of blockchain-based smart contracts is that they execute in a closed environment. Thus, they only have access to data and functionality that is already on the blockchain, or is fed into the blockchain. Any interactions with the real world need to be mediated by a bridge service, which is called an oracle. As decentralized applications mature, oracles are playing an increasingly prominent role. With their evolution comes more attacks, necessitating greater attention to their trust model. In this systemization of knowledge paper (SoK), we dissect the design alternatives for oracles, showcase attacks, and discuss attack mitigation strategies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.00667v2-abstract-full').style.display = 'none'; document.getElementById('2106.00667v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 3rd ACM Conference on Advances in Financial Technologies (AFT '21), September 26--28, 2021, Arlington, VA, USA </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.02885">arXiv:2102.02885</a> <span> [<a href="https://arxiv.org/pdf/2102.02885">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jiasong Chen</a>, <a href="/search/eess?searchtype=author&query=Qian%2C+L">Linchen Qian</a>, <a href="/search/eess?searchtype=author&query=Urakov%2C+T">Timur Urakov</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Weiyong Gu</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+L">Liang Liang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.02885v1-abstract-short" style="display: inline;"> Machine learning technologies using deep neural networks (DNNs), especially convolutional neural networks (CNNs), have made automated, accurate, and fast medical image analysis a reality for many applications, and some DNN-based medical image analysis systems have even been FDA-cleared. Despite the progress, challenges remain to build DNNs as reliable as human expert doctors. It is known that DNN… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.02885v1-abstract-full').style.display = 'inline'; document.getElementById('2102.02885v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.02885v1-abstract-full" style="display: none;"> Machine learning technologies using deep neural networks (DNNs), especially convolutional neural networks (CNNs), have made automated, accurate, and fast medical image analysis a reality for many applications, and some DNN-based medical image analysis systems have even been FDA-cleared. Despite the progress, challenges remain to build DNNs as reliable as human expert doctors. It is known that DNN classifiers may not be robust to noises: by adding a small amount of noise to an input image, a DNN classifier may make a wrong classification of the noisy image (i.e., in-distribution adversarial sample), whereas it makes the right classification of the clean image. Another issue is caused by out-of-distribution samples that are not similar to any sample in the training set. Given such a sample as input, the output of a DNN will become meaningless. In this study, we investigated the in-distribution (IND) and out-of-distribution (OOD) adversarial robustness of a representative CNN for lumbar disk shape reconstruction from spine MR images. To study the relationship between dataset size and robustness to IND adversarial attacks, we used a data augmentation method to create training sets with different levels of shape variations. We utilized the PGD-based algorithm for IND adversarial attacks and extended it for OOD adversarial attacks to generate OOD adversarial samples for model testing. The results show that IND adversarial training can improve the CNN robustness to IND adversarial attacks, and larger training datasets may lead to higher IND robustness. However, it is still a challenge to defend against OOD adversarial attacks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.02885v1-abstract-full').style.display = 'none'; document.getElementById('2102.02885v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published at SPIE Medical Imaging: Image Processing 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.06149">arXiv:1910.06149</a> <span> [<a href="https://arxiv.org/pdf/1910.06149">pdf</a>, <a href="https://arxiv.org/format/1910.06149">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Accelerometer-Based Gait Segmentation: Simultaneously User and Adversary Identification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ding%2C+Y">Yujia Ding</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+W">Weiqing Gu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1910.06149v1-abstract-short" style="display: inline;"> In this paper, we introduce a new gait segmentation method based on accelerometer data and develop a new distance function between two time series, showing novel and effectiveness in simultaneously identifying user and adversary. Comparing with the normally used Neural Network methods, our approaches use geometric features to extract walking cycles more precisely and employ a new similarity metric… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.06149v1-abstract-full').style.display = 'inline'; document.getElementById('1910.06149v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.06149v1-abstract-full" style="display: none;"> In this paper, we introduce a new gait segmentation method based on accelerometer data and develop a new distance function between two time series, showing novel and effectiveness in simultaneously identifying user and adversary. Comparing with the normally used Neural Network methods, our approaches use geometric features to extract walking cycles more precisely and employ a new similarity metric to conduct user-adversary identification. This new technology for simultaneously identify user and adversary contributes to cybersecurity beyond user-only identification. In particular, the new technology is being applied to cell phone recorded walking data and performs an accuracy of $98.79\%$ for 6 classes classification (user-adversary identification) and $99.06\%$ for binary classification (user only identification). In addition to walking signal, our approach works on walking up, walking down and mixed walking signals. This technology is feasible for both large and small data set, overcoming the current challenges facing to Neural Networks such as tuning large number of hyper-parameters for large data sets and lacking of training data for small data sets. In addition, the new distance function developed here can be applied in any signal analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.06149v1-abstract-full').style.display = 'none'; document.getElementById('1910.06149v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 62-07 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> 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