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Dong Keun Kim
Department of Intelligent Engineering Information for Human, Sangmyung University, Seoul 03016, Korea

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Journal article
Published: 14 August 2020 in Electronics
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The development of Distributed Energy Resources (DERs) is essential in accordance with the mandatory greenhouse gas (GHG) emission reduction policies, resulting in many DERs being integrated into the power system. Currently, South Korea is also focusing on increasing the penetration of renewable energy sources (RES) and EV by 2030 to reduce GHGs. However, indiscriminate DER development can give a negative impact on the operation of existing power systems. The existing power system operation is optimized for the hourly net load pattern, but the integration of DERs changes it. In addition, since ToU (Time-of-Use) tariff and Demand Response (DR) programs are very sensitive to changes in the net load curve, it is essential to predict the hourly net load pattern accurately for the modification of pricing and demand response programs in the future. However, a long-term demand forecast in South Korea provides only the total amount of annual load (TWh) and the expected peak load level (GW) in summer and winter seasons until 2030. In this study, we use the annual photovoltaic (PV) installed capacity, PV generation, and the number of EV based on the target values for 2030 in South Korea to predict the change in hourly net load curve by year and season. In addition, to predict the EV charging load curve based on Monte Carlo simulation, the EV users’ charging method, charging start time, and State-of-Charge (SoC) were considered. Finally, we analyze the change in hourly net load curve due to the integration of PV and EV to determine the amplification of the duck curve and peak load time by year and season, and present the risks caused by indiscriminate DERs development.

ACS Style

Chi-Yeon Kim; Chae-Rin Kim; Dong-Keun Kim; Soo-Hwan Cho. Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs. Electronics 2020, 9, 1310 .

AMA Style

Chi-Yeon Kim, Chae-Rin Kim, Dong-Keun Kim, Soo-Hwan Cho. Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs. Electronics. 2020; 9 (8):1310.

Chicago/Turabian Style

Chi-Yeon Kim; Chae-Rin Kim; Dong-Keun Kim; Soo-Hwan Cho. 2020. "Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs." Electronics 9, no. 8: 1310.

Journal article
Published: 09 March 2020 in Mobile Information Systems
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Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

ACS Style

Daegyu Choe; Eunjeong Choi; Dong Keun Kim. The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning. Mobile Information Systems 2020, 2020, 1 -13.

AMA Style

Daegyu Choe, Eunjeong Choi, Dong Keun Kim. The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning. Mobile Information Systems. 2020; 2020 ():1-13.

Chicago/Turabian Style

Daegyu Choe; Eunjeong Choi; Dong Keun Kim. 2020. "The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning." Mobile Information Systems 2020, no. : 1-13.

Journal article
Published: 06 February 2020 in Sensors
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This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

ACS Style

Seungjun Oh; Jun-Young Lee; Dong Keun Kim. The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals. Sensors 2020, 20, 866 .

AMA Style

Seungjun Oh, Jun-Young Lee, Dong Keun Kim. The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals. Sensors. 2020; 20 (3):866.

Chicago/Turabian Style

Seungjun Oh; Jun-Young Lee; Dong Keun Kim. 2020. "The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals." Sensors 20, no. 3: 866.

Journal article
Published: 04 February 2020 in Sustainability
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The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

ACS Style

Eunjeong Choi; Soohwan Cho; Dong Keun Kim. Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability. Sustainability 2020, 12, 1109 .

AMA Style

Eunjeong Choi, Soohwan Cho, Dong Keun Kim. Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability. Sustainability. 2020; 12 (3):1109.

Chicago/Turabian Style

Eunjeong Choi; Soohwan Cho; Dong Keun Kim. 2020. "Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability." Sustainability 12, no. 3: 1109.

Research article
Published: 05 May 2019 in Mobile Information Systems
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A real-time mobile content player was developed that can recognize and reflect emotions in real time using a smartphone. To determine effective awareness, a photoplethysmogram (PPG), which is a biological signal, was measured to recognize emotional changes in users presented with content intended to induce an emotional response. To avoid the need for a separate sensor to measure the PPG, PPG signals were extracted from the red (R) values of images acquired by the rear camera of a smartphone. To reflect an emotion, the saturation (S) and brightness (V) levels, which are related to the ambience of a content, are changed to reflect the emotional changes of the user within the content itself in real time. Arousal- and relaxation-inducing scenarios were conducted to validate the effectiveness. The samplet-test results show that the average peak-to-peak interval (PPI), which is the time interval between the peaks of PPG signals, was significantly low when viewing the content under the arousal-inducing scenario as compared to when watching regular content, and it was determined that the emotion of the user was led to a state of arousal. Ten university students (five males and five females) participated in the experiment. The users had no cardiac disease and were asked not to drink or smoke before the experiment. The average PPI was significantly higher when the content was viewed in the relaxation-inducing scenario compared to regular content, and it was determined that the emotion of the user was induced to a state of relaxation. The designed emotional content player was confirmed to be an interactive system, in which the video content and user concurrently affect each other through the system.

ACS Style

Haena Lee; Dong Keun Kim. Real-Time Mobile Emotional Content Player Using Smartphone Camera-Based PPG Measurement. Mobile Information Systems 2019, 2019, 1 -12.

AMA Style

Haena Lee, Dong Keun Kim. Real-Time Mobile Emotional Content Player Using Smartphone Camera-Based PPG Measurement. Mobile Information Systems. 2019; 2019 ():1-12.

Chicago/Turabian Style

Haena Lee; Dong Keun Kim. 2019. "Real-Time Mobile Emotional Content Player Using Smartphone Camera-Based PPG Measurement." Mobile Information Systems 2019, no. : 1-12.

Journal article
Published: 01 January 2018 in Healthcare Informatics Research
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Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

ACS Style

Eun Jeong Choi; Dong Keun Kim. Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management. Healthcare Informatics Research 2018, 24, 309 -316.

AMA Style

Eun Jeong Choi, Dong Keun Kim. Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management. Healthcare Informatics Research. 2018; 24 (4):309-316.

Chicago/Turabian Style

Eun Jeong Choi; Dong Keun Kim. 2018. "Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management." Healthcare Informatics Research 24, no. 4: 309-316.

Journal article
Published: 01 January 2017 in Healthcare Informatics Research
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The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems.

ACS Style

Se-Hui Song; Dong Keun Kim. Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring. Healthcare Informatics Research 2017, 23, 285 -292.

AMA Style

Se-Hui Song, Dong Keun Kim. Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring. Healthcare Informatics Research. 2017; 23 (4):285-292.

Chicago/Turabian Style

Se-Hui Song; Dong Keun Kim. 2017. "Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring." Healthcare Informatics Research 23, no. 4: 285-292.

Journal article
Published: 31 January 2014 in International Journal of Multimedia and Ubiquitous Engineering
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ACS Style

Kyoung Shin Park; YongJoo Cho; Dong Keun Kim. A Framework for the Creating, Expressing and Sharing of User’s Emotion. International Journal of Multimedia and Ubiquitous Engineering 2014, 9, 425 -442.

AMA Style

Kyoung Shin Park, YongJoo Cho, Dong Keun Kim. A Framework for the Creating, Expressing and Sharing of User’s Emotion. International Journal of Multimedia and Ubiquitous Engineering. 2014; 9 (1):425-442.

Chicago/Turabian Style

Kyoung Shin Park; YongJoo Cho; Dong Keun Kim. 2014. "A Framework for the Creating, Expressing and Sharing of User’s Emotion." International Journal of Multimedia and Ubiquitous Engineering 9, no. 1: 425-442.

Conference paper
Published: 01 January 2013 in Transactions on Petri Nets and Other Models of Concurrency XV
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This paper presents the design and implementation of emotional digital picture frame system, which is designed for a group of users to share their emotions via photographs with their own emotional expressions. This system detects user emotions using physiological sensor signals in real-time and changes audio-visual elements of photographs dynamically in response to the user’s emotional state. This system allows user emotions to be shared with other users in remote locations. Also, it provides the emotional rule authoring tool to enable users to create their own expression for audio-visual element to fit their emotion. In particular, the rendering elements of a photograph can appear differently when another user’s emotion is received.

ACS Style

Kyoung Shin Park; YongJoo Cho; Minyoung Kim; Ki-Young Seo; Dongkeun Kim. Emotion Sharing with the Emotional Digital Picture Frame. Transactions on Petri Nets and Other Models of Concurrency XV 2013, 339 -345.

AMA Style

Kyoung Shin Park, YongJoo Cho, Minyoung Kim, Ki-Young Seo, Dongkeun Kim. Emotion Sharing with the Emotional Digital Picture Frame. Transactions on Petri Nets and Other Models of Concurrency XV. 2013; ():339-345.

Chicago/Turabian Style

Kyoung Shin Park; YongJoo Cho; Minyoung Kim; Ki-Young Seo; Dongkeun Kim. 2013. "Emotion Sharing with the Emotional Digital Picture Frame." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 339-345.

Conference paper
Published: 01 January 2013 in Transactions on Petri Nets and Other Models of Concurrency XV
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Social emotions are emotion that can be induced from human social relationships when people are interacting with others. In this study, we are aim to analyze a brain function connectivityin terms of different relations of social emotions. The brain function connectivity can be used to observe the neural responses with features of EEG coherences during a cognitive process.In this study, the EEG coherence is measured according to different social emotion evocations. The auditory and visual stimulus for inducing social emotions was presented to participants during 20.5 sec (±3.1 sec). The participants were asked to imagine and explain about similar emotion experience after watching each video clips. The measured EEG coherencewas grouped into two different social emotion categories;the information sharing relation and emotion sharing relation, and compared with the results of subjective evaluation and independent T-test.The information sharing relation was related with the brain connectivity oftherighttemporo-occipitalposition associated with a language memory. The emotion sharing relation was related with the brain connectivity of the left fronto-right parietal position associated with a visual information processing area.

ACS Style

Jonghwa Kim; Dongkeun Kim; Sangmin Ann; Sangin Park; Mincheol Whang. Brain Function Connectivity Analysis for Recognizing Different Relation of Social Emotion in Virtual Reality. Transactions on Petri Nets and Other Models of Concurrency XV 2013, 441 -447.

AMA Style

Jonghwa Kim, Dongkeun Kim, Sangmin Ann, Sangin Park, Mincheol Whang. Brain Function Connectivity Analysis for Recognizing Different Relation of Social Emotion in Virtual Reality. Transactions on Petri Nets and Other Models of Concurrency XV. 2013; ():441-447.

Chicago/Turabian Style

Jonghwa Kim; Dongkeun Kim; Sangmin Ann; Sangin Park; Mincheol Whang. 2013. "Brain Function Connectivity Analysis for Recognizing Different Relation of Social Emotion in Virtual Reality." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 441-447.

Journal article
Published: 30 July 2010 in Journal of the Korea Institute of Information and Communication Engineering
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ACS Style

So-Young Park; Dong-Keun Kim; Min-Cheol Whang. Maximum Entropy-based Emotion Recognition Model using Individual Average Difference. Journal of the Korea Institute of Information and Communication Engineering 2010, 14, 1557 -1564.

AMA Style

So-Young Park, Dong-Keun Kim, Min-Cheol Whang. Maximum Entropy-based Emotion Recognition Model using Individual Average Difference. Journal of the Korea Institute of Information and Communication Engineering. 2010; 14 (7):1557-1564.

Chicago/Turabian Style

So-Young Park; Dong-Keun Kim; Min-Cheol Whang. 2010. "Maximum Entropy-based Emotion Recognition Model using Individual Average Difference." Journal of the Korea Institute of Information and Communication Engineering 14, no. 7: 1557-1564.

Journal article
Published: 30 July 2010 in Journal of the Korea Institute of Information and Communication Engineering
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ACS Style

Min-Young Kim; Dong-Keun Kim; Yong-Joo Cho. A Study on the Development of the Interactive Emotional Contents Player Platform. Journal of the Korea Institute of Information and Communication Engineering 2010, 14, 1572 -1580.

AMA Style

Min-Young Kim, Dong-Keun Kim, Yong-Joo Cho. A Study on the Development of the Interactive Emotional Contents Player Platform. Journal of the Korea Institute of Information and Communication Engineering. 2010; 14 (7):1572-1580.

Chicago/Turabian Style

Min-Young Kim; Dong-Keun Kim; Yong-Joo Cho. 2010. "A Study on the Development of the Interactive Emotional Contents Player Platform." Journal of the Korea Institute of Information and Communication Engineering 14, no. 7: 1572-1580.

Journal article
Published: 30 April 2010 in Journal of the Ergonomics Society of Korea
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ACS Style

Jin-Cheol Woo; Min-Cheol Whang; Jong-Wha Kim; Chi-Joong Kim; Yong-Woo Kim; Ji-Hye Kim; Dong-Keun Kim. The Research on Prediction of Attentive Hand Movement using EEG Coherence. Journal of the Ergonomics Society of Korea 2010, 29, 189 -196.

AMA Style

Jin-Cheol Woo, Min-Cheol Whang, Jong-Wha Kim, Chi-Joong Kim, Yong-Woo Kim, Ji-Hye Kim, Dong-Keun Kim. The Research on Prediction of Attentive Hand Movement using EEG Coherence. Journal of the Ergonomics Society of Korea. 2010; 29 (2):189-196.

Chicago/Turabian Style

Jin-Cheol Woo; Min-Cheol Whang; Jong-Wha Kim; Chi-Joong Kim; Yong-Woo Kim; Ji-Hye Kim; Dong-Keun Kim. 2010. "The Research on Prediction of Attentive Hand Movement using EEG Coherence." Journal of the Ergonomics Society of Korea 29, no. 2: 189-196.