Search results for: Jeongmin Kim

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Kim</title> <meta name="description" content="Search results for: Jeongmin Kim"> <meta name="keywords" content="Jeongmin Kim"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Jeongmin Kim"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Jeongmin Kim</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Prediction-Based Midterm Operation Planning for Energy Management of Exhibition Hall</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Doseong%20Eom">Doseong Eom</a>, <a href="https://publications.waset.org/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Building%20energy%20management" title="Building energy management">Building energy management</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=simulation-based%20optimization" title=" simulation-based optimization"> simulation-based optimization</a>, <a href="https://publications.waset.org/search?q=operation%20planning." title=" operation planning."> operation planning.</a> </p> <a href="https://publications.waset.org/10007235/prediction-based-midterm-operation-planning-for-energy-management-of-exhibition-hall" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007235/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007235/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007235/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007235/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007235/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007235/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007235/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007235/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007235/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007235/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007235.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">990</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Seyoung%20Kim">Seyoung Kim</a>, <a href="https://publications.waset.org/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (<em>k</em>-NN) as predictive models is that it does not require any explicit model building. Instead, <em>k</em>-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up <em>k</em>-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different <em>k</em>-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Big%20data" title="Big data">Big data</a>, <a href="https://publications.waset.org/search?q=k-NN" title=" k-NN"> k-NN</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=traffic%20speed%20prediction." title=" traffic speed prediction."> traffic speed prediction.</a> </p> <a href="https://publications.waset.org/10006804/comparison-of-different-k-nn-models-for-speed-prediction-in-an-urban-traffic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006804/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006804/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006804/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006804/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006804/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006804/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006804/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006804/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006804/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006804/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006804.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1377</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Performance Comparison of Situation-Aware Models for Activating Robot Vacuum Cleaner in a Smart Home</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Seongcheol%20Kwon">Seongcheol Kwon</a>, <a href="https://publications.waset.org/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Situation-awareness" title="Situation-awareness">Situation-awareness</a>, <a href="https://publications.waset.org/search?q=Smart%20home" title=" Smart home"> Smart home</a>, <a href="https://publications.waset.org/search?q=IoT" title=" IoT"> IoT</a>, <a href="https://publications.waset.org/search?q=Machine%20learning" title=" Machine learning"> Machine learning</a>, <a href="https://publications.waset.org/search?q=Classifier." title=" Classifier."> Classifier.</a> </p> <a href="https://publications.waset.org/10003665/performance-comparison-of-situation-aware-models-for-activating-robot-vacuum-cleaner-in-a-smart-home" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003665/apa" target="_blank" rel="nofollow" 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Search results for: Jeongmin Kim