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Prof. Hwamin Lee
Department of Computer Software & Engineering, Soonchunhyang University, South Korea

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Research Keywords & Expertise

0 Air Quality
0 Big Data
0 Cloud Computing
0 Deep Learning
0 Machine Learning

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Cloud Computing
Deep Learning
Particulate Matter
Air Quality
Big Data
Machine Learning

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Short Biography

HwaMin Lee is a professor in the Department of Computer Software Engineering at Soonchunhyang University. She received her B.S., M.S. and Ph.D. degrees in Computer Science Education from Korea University in Seoul, Korea in 2000, 2002, and 2006, respectively. Her research interests include Deep Learning, IoT, Cloud computing, Mobile computing and Wellness. E-mail : [email protected] Publicatoin List : https://scholar.google.com/citations?hl=ko&user=VdRe1pUAAAAJ&view_op=list_works&sortby=pubdate Homepage : http://oslab.sch.ac.kr

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Journal article
Published: 13 July 2021 in Diagnostics
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Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

ACS Style

Minsu Chae; Sangwook Han; Hyowook Gil; Namjun Cho; Hwamin Lee. Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning. Diagnostics 2021, 11, 1255 .

AMA Style

Minsu Chae, Sangwook Han, Hyowook Gil, Namjun Cho, Hwamin Lee. Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning. Diagnostics. 2021; 11 (7):1255.

Chicago/Turabian Style

Minsu Chae; Sangwook Han; Hyowook Gil; Namjun Cho; Hwamin Lee. 2021. "Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning." Diagnostics 11, no. 7: 1255.

Journal article
Published: 25 January 2021 in Journal of Clinical Medicine
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Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3,302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients’ ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice.

ACS Style

Samel Park; Min Hong; Hwamin Lee; Nam-Jun Cho; Eun-Young Lee; Won-Young Lee; Eun-Jung Rhee; Hyo-Wook Gil. New Model for Predicting the Presence of Coronary Artery Calcification. Journal of Clinical Medicine 2021, 10, 457 .

AMA Style

Samel Park, Min Hong, Hwamin Lee, Nam-Jun Cho, Eun-Young Lee, Won-Young Lee, Eun-Jung Rhee, Hyo-Wook Gil. New Model for Predicting the Presence of Coronary Artery Calcification. Journal of Clinical Medicine. 2021; 10 (3):457.

Chicago/Turabian Style

Samel Park; Min Hong; Hwamin Lee; Nam-Jun Cho; Eun-Young Lee; Won-Young Lee; Eun-Jung Rhee; Hyo-Wook Gil. 2021. "New Model for Predicting the Presence of Coronary Artery Calcification." Journal of Clinical Medicine 10, no. 3: 457.

Journal article
Published: 23 January 2021 in Toxics
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We investigated clinical impacts of various acid-base approaches (physiologic, base excess (BE)-based, and physicochemical) on mortality in patients with acute pesticide intoxication and mutual intercorrelated effects using principal component analysis (PCA). This retrospective study included patients admitted from January 2015 to December 2019 because of pesticide intoxication. We compared parameters assessing the acid-base status between two groups, survivors and non-survivors. Associations between parameters and 30-days mortality were investigated. A total of 797 patients were analyzed. In non-survivors, pH, bicarbonate concentration (HCO3−), total concentration of carbon dioxide (tCO2), BE, and effective strong ion difference (SIDe) were lower and apparent strong ion difference (SIDa), strong ion gap (SIG), total concentration of weak acids, and corrected anion gap (corAG) were higher than in survivors. In the multivariable logistic analysis, BE, corAG, SIDa, and SIDe were associated with mortality. PCA identified four principal components related to mortality. SIDe, HCO3−, tCO2, BE, SIG, and corAG were loaded to principal component 1 (PC1), referred as total buffer bases to receive and handle generated acids. PC1 was an important factor in predicting mortality irrespective of the pesticide category. PC3, loaded mainly with pCO2, suggested respiratory components of the acid-base system. PC3 was associated with 30-days mortality, especially in organophosphate or carbamate poisoning. Our study showed that acid-base abnormalities were associated with mortality in patients with acute pesticide poisoning. We reduced these variables into four PCs, resembling the physicochemical approach, revealed that PCs representing total buffer bases and respiratory components played an important role in acute pesticide poisoning.

ACS Style

Hyo-Wook Gil; Min Hong; Hwamin Lee; Nam-Jun Cho; Eun-Young Lee; Samel Park. Impact of Acid-Base Status on Mortality in Patients with Acute Pesticide Poisoning. Toxics 2021, 9, 22 .

AMA Style

Hyo-Wook Gil, Min Hong, Hwamin Lee, Nam-Jun Cho, Eun-Young Lee, Samel Park. Impact of Acid-Base Status on Mortality in Patients with Acute Pesticide Poisoning. Toxics. 2021; 9 (2):22.

Chicago/Turabian Style

Hyo-Wook Gil; Min Hong; Hwamin Lee; Nam-Jun Cho; Eun-Young Lee; Samel Park. 2021. "Impact of Acid-Base Status on Mortality in Patients with Acute Pesticide Poisoning." Toxics 9, no. 2: 22.

Conference paper
Published: 05 January 2021 in Lecture Notes in Electrical Engineering
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With the spread of smart devices, the use of big data, and the proliferation of the Internet of Things, virtualization technology for cloud servers have become important worldwide. Also, research has been conducted to efficiently manage the resources of hosts in VMs. Container-based virtualization has less performance degradation than VMs because there is no emulation for the operating system. Using the Docker API is slow to measure. In this paper, we implement the resource measurement module of Job nodes and design middleware that supports auto-scaling in auto-scaling module and Docker-based multi-host environment.

ACS Style

Minsu Chae; Sangwook Han; Hwa Min Lee. Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment. Lecture Notes in Electrical Engineering 2021, 301 -307.

AMA Style

Minsu Chae, Sangwook Han, Hwa Min Lee. Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment. Lecture Notes in Electrical Engineering. 2021; ():301-307.

Chicago/Turabian Style

Minsu Chae; Sangwook Han; Hwa Min Lee. 2021. "Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment." Lecture Notes in Electrical Engineering , no. : 301-307.

Journal article
Published: 15 July 2020 in Electronics
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Particulate matter (PM) has become a problem worldwide, with many deleterious health effects such as worsened asthma, affected lungs, and various toxin-induced cancers. The International Agency for Research on Cancer (IARC) under the World Health Organization (WHO) has designated PM as a group 1 carcinogen. Although Korea Environment Corporation forecasts the status of outdoor PM four times a day, whichever is higher among PM10 and PM2.5. Korea Environment Corporation forecasts for the stages of PM. It remains difficult to predict the value of PM when going out. We correlate air quality and solar terms, address format, and weather data, and PM in the Republic of Korea. We analyzed the correlation between address format, air quality data, and weather data, and PM. We evaluated performance according to the sequence length and batch size and found the best outcome with a sequence length of 7 days, and a batch size of 96. We performed PM prediction using the Long Short-Term Recurrent Unit (LSTM), the Convolutional Neural Network (CNN), and the Gated Recurrent Unit (GRU) models. The CNN model suffered the limitation of only predicting from the training data, not from the test data. The LSTM and GRU models generated similar prediction results. We confirmed that the LSTM model has higher accuracy than the other two models.

ACS Style

Minsu Chae; Sangwook Han; Hwamin Lee. Outdoor Particulate Matter Correlation Analysis and Prediction Based Deep Learning in the Republic of Korea. Electronics 2020, 9, 1146 .

AMA Style

Minsu Chae, Sangwook Han, Hwamin Lee. Outdoor Particulate Matter Correlation Analysis and Prediction Based Deep Learning in the Republic of Korea. Electronics. 2020; 9 (7):1146.

Chicago/Turabian Style

Minsu Chae; Sangwook Han; Hwamin Lee. 2020. "Outdoor Particulate Matter Correlation Analysis and Prediction Based Deep Learning in the Republic of Korea." Electronics 9, no. 7: 1146.

Journal article
Published: 31 March 2020 in Atmosphere
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Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.

ACS Style

Guang Yang; Hwamin Lee; Giyeol Lee. A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere 2020, 11, 348 .

AMA Style

Guang Yang, Hwamin Lee, Giyeol Lee. A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere. 2020; 11 (4):348.

Chicago/Turabian Style

Guang Yang; Hwamin Lee; Giyeol Lee. 2020. "A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea." Atmosphere 11, no. 4: 348.

Journal article
Published: 24 March 2020 in Sustainability
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Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

ACS Style

Thanongsak Xayasouk; Hwamin Lee; Giyeol Lee. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability 2020, 12, 2570 .

AMA Style

Thanongsak Xayasouk, Hwamin Lee, Giyeol Lee. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability. 2020; 12 (6):2570.

Chicago/Turabian Style

Thanongsak Xayasouk; Hwamin Lee; Giyeol Lee. 2020. "Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models." Sustainability 12, no. 6: 2570.

Conference paper
Published: 04 December 2019 in Lecture Notes in Electrical Engineering
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APIs provided by the Docker is executed through the container engine. Thus, it is a reality that the speed of creation and deletion of containers or requests for information is very slow. The load is even worse when many containers sending API requests at once. In this paper, we propose a method to apply module related to resource management in a container to reduce the load on API request generated in serverless environment. And we propose a new framework that manages resources of several containers or multiple Docker serves.

ACS Style

Sangwook Han; Minsu Chae; Hwamin Lee. Serverless Framework for Efficient Resource Management in Docker Environment. Lecture Notes in Electrical Engineering 2019, 189 -194.

AMA Style

Sangwook Han, Minsu Chae, Hwamin Lee. Serverless Framework for Efficient Resource Management in Docker Environment. Lecture Notes in Electrical Engineering. 2019; ():189-194.

Chicago/Turabian Style

Sangwook Han; Minsu Chae; Hwamin Lee. 2019. "Serverless Framework for Efficient Resource Management in Docker Environment." Lecture Notes in Electrical Engineering , no. : 189-194.

Conference paper
Published: 01 July 2019 in 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN)
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Previous studies on arrhythmia were used to diagnose the abnormally fast, slow, or irregular heart rhythm through ECG (Electrocardiogram), which is one of the biological signals. ECG has the form of P-QRS-T wave, and many studies have been done to extract the features of QRS-complex and R-R interval. However, in the conventional method, the P-QRS-T wave must be accurately detected, and the feature value is extracted through the P-QRS-T wave. If an error occurs in the peak detection or feature extraction process, the accuracy becomes very low. Therefore, in this paper, we implement a system that can perform PVC (Premature Ventricular Contraction) and PAC (Premature Atrial Contraction) classification by using P-QRS-T peak value without feature extraction process using deep neural network. The parameters were updated for PVC and PAC classification in the learning process using P-QRS-T peak without feature value. As a result of the performance evaluation, we could confirm higher accuracy than the previous studies and omit the process of feature extraction, and the time required for the preprocessing process to construct the input data set is relatively reduced.

ACS Style

Eunkwang Jeon; Minsu Chae; Sangwook Han; Hwamin Lee. Arrhythmia Classification System Using Deep Neural Network. 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) 2019, 111 -114.

AMA Style

Eunkwang Jeon, Minsu Chae, Sangwook Han, Hwamin Lee. Arrhythmia Classification System Using Deep Neural Network. 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN). 2019; ():111-114.

Chicago/Turabian Style

Eunkwang Jeon; Minsu Chae; Sangwook Han; Hwamin Lee. 2019. "Arrhythmia Classification System Using Deep Neural Network." 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) , no. : 111-114.

Original research
Published: 20 June 2019 in Journal of Ambient Intelligence and Humanized Computing
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Recently, the cloud computing platform has come to be widely used to analyze large amounts of data collected in real-time from SNS or IoT sensors. In order to analyze big data, a large number of VMs are created in the cloud server, and that many PMs are needed to handle it. When VMs are allocated to PMs in cloud computing, each VM is allocated by a VM scheduling algorithm. However, existing scheduling algorithms waste substantial PM resources due to the low density of VM. This waste of resources dramatically reduces the energy efficiency of the entire cloud server. Therefore, minimizing idle PMs by increasing the density of VMs allocated to PMs is critical for VM scheduling. In this paper, a VM relocation method is suggested to improve the energy efficiency by increasing the density of VMs using the Knapsack algorithm. In addition, it is possible through the proposed method to achieve efficient VM relocation in a short period by improving the Knapsack algorithm. Therefore, we proposed the effective resource management method of cloud cluster for big data analysis.

ACS Style

Sangwook Han; Se Dong Min; Hwamin Lee. Energy efficient VM scheduling for big data processing in cloud computing environments. Journal of Ambient Intelligence and Humanized Computing 2019, 1 -10.

AMA Style

Sangwook Han, Se Dong Min, Hwamin Lee. Energy efficient VM scheduling for big data processing in cloud computing environments. Journal of Ambient Intelligence and Humanized Computing. 2019; ():1-10.

Chicago/Turabian Style

Sangwook Han; Se Dong Min; Hwamin Lee. 2019. "Energy efficient VM scheduling for big data processing in cloud computing environments." Journal of Ambient Intelligence and Humanized Computing , no. : 1-10.

Conference paper
Published: 19 June 2018 in Air Pollution XXVI
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Welcome to the WIT Press eLibrary - the home of the Transactions of the Wessex Institute collection, providing on-line access to papers presented at the Institute's prestigious international conferences and from its State-of-the-Art in Science & Engineering publications.

ACS Style

Thanongsak Xayasouk; Hwamin Lee. AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING. Air Pollution XXVI 2018, 1 .

AMA Style

Thanongsak Xayasouk, Hwamin Lee. AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING. Air Pollution XXVI. 2018; ():1.

Chicago/Turabian Style

Thanongsak Xayasouk; Hwamin Lee. 2018. "AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING." Air Pollution XXVI , no. : 1.

Article
Published: 04 May 2018 in The Journal of Supercomputing
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With the rapid development of sensors, wireless communication, and cloud computing, information technology today focuses on service environments created by the Internet of Things (IoT). IoT technologies have become widely used in various contexts including smart homes, building management, surveillance services, and smart farms. Some IoT applications such as Siri are popular in everyday life. IoT requires communication and interaction between various devices and services. To solve the various complex problems associated with IoT services, earlier research focused on IoT service platforms such as gateways and mobile edge computing services. However, the similarities and reusabilities of IoT services have received little attention. In this paper, we develop an IoT service classification and clustering system. We classify the operation of an IoT service into four steps that differ in their characteristics. Based on this classification, we extend the classic EM (expectation–maximization) algorithm to cluster IoT services in terms of their similarities. To validate our proposed classification and clustering system, we divide over 100 commercial IoT services into five clusters, showing that such services are well clustered by similarity and purpose.

ACS Style

Daewon Lee; Hwamin Lee. IoT service classification and clustering for integration of IoT service platforms. The Journal of Supercomputing 2018, 74, 6859 -6875.

AMA Style

Daewon Lee, Hwamin Lee. IoT service classification and clustering for integration of IoT service platforms. The Journal of Supercomputing. 2018; 74 (12):6859-6875.

Chicago/Turabian Style

Daewon Lee; Hwamin Lee. 2018. "IoT service classification and clustering for integration of IoT service platforms." The Journal of Supercomputing 74, no. 12: 6859-6875.

Journal article
Published: 28 February 2018 in KSII Transactions on Internet and Information Systems
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ACS Style

Eunkwang Jeon; Bong-Keun Jung; Yunyoung Nam; Hwamin Lee. Classification of Premature Ventricular Contraction using Error Back-Propagation. KSII Transactions on Internet and Information Systems 2018, 12, 988 -1001.

AMA Style

Eunkwang Jeon, Bong-Keun Jung, Yunyoung Nam, Hwamin Lee. Classification of Premature Ventricular Contraction using Error Back-Propagation. KSII Transactions on Internet and Information Systems. 2018; 12 (2):988-1001.

Chicago/Turabian Style

Eunkwang Jeon; Bong-Keun Jung; Yunyoung Nam; Hwamin Lee. 2018. "Classification of Premature Ventricular Contraction using Error Back-Propagation." KSII Transactions on Internet and Information Systems 12, no. 2: 988-1001.

Article
Published: 20 December 2017 in Cluster Computing
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Virtualization is a foundational element of cloud computing. Since cloud computing is slower than a native system, this study analyzes ways to improve performance. We compared the performance of Docker and Kernel-based virtual machine (KVM). KVM uses full virtualization, including \(\times \)86 hardware virtualization extensions. Docker is a solution provided by isolation in userspace instead of creating a virtual machine. The performance of KVM and Docker was compared in three ways. These comparisons show that Docker is faster than KVM.

ACS Style

Minsu Chae; Hwamin Lee; Kiyeol Lee. A performance comparison of linux containers and virtual machines using Docker and KVM. Cluster Computing 2017, 22, 1765 -1775.

AMA Style

Minsu Chae, Hwamin Lee, Kiyeol Lee. A performance comparison of linux containers and virtual machines using Docker and KVM. Cluster Computing. 2017; 22 (S1):1765-1775.

Chicago/Turabian Style

Minsu Chae; Hwamin Lee; Kiyeol Lee. 2017. "A performance comparison of linux containers and virtual machines using Docker and KVM." Cluster Computing 22, no. S1: 1765-1775.

Original research
Published: 12 July 2017 in Journal of Ambient Intelligence and Humanized Computing
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The health of concrete structures is important because collapse of these structures can cause damage for humans. This paper presents an analytical model for calculation of crack widths in structural concrete members using Internet of Things. The model is mathematically derived from the actual bond stress-slip relationship between the reinforcing steel and the surrounding concrete. The relationships summarized in CEB-FIP Model Code 1990 and Eurocode 2 are used in this study together with the numerical analysis result of a linear slip distribution along the interface at the stabilized cracking stage. With these, the actual strains of the steel and the concrete are integrated respectively along the embedment length between the adjacent cracks to obtain the difference in the axial elongation. This model is applied to the test results available in literature. The predicted values are shown to be in agreement with the experimentally measured data.

ACS Style

Kiyeol Lee; Hwamin Lee. Numerical analysis and modeling for crack width calculation using IoT in reinforced concrete members. Journal of Ambient Intelligence and Humanized Computing 2017, 9, 1119 -1130.

AMA Style

Kiyeol Lee, Hwamin Lee. Numerical analysis and modeling for crack width calculation using IoT in reinforced concrete members. Journal of Ambient Intelligence and Humanized Computing. 2017; 9 (4):1119-1130.

Chicago/Turabian Style

Kiyeol Lee; Hwamin Lee. 2017. "Numerical analysis and modeling for crack width calculation using IoT in reinforced concrete members." Journal of Ambient Intelligence and Humanized Computing 9, no. 4: 1119-1130.

Conference paper
Published: 14 May 2017 in Lecture Notes in Electrical Engineering
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ACS Style

Sangwook Han; Minsoo Chae; Hwamin Lee. VM Relocation Method for Increase the Resource Utilization in Cloud Computing. Lecture Notes in Electrical Engineering 2017, 263 -268.

AMA Style

Sangwook Han, Minsoo Chae, Hwamin Lee. VM Relocation Method for Increase the Resource Utilization in Cloud Computing. Lecture Notes in Electrical Engineering. 2017; ():263-268.

Chicago/Turabian Style

Sangwook Han; Minsoo Chae; Hwamin Lee. 2017. "VM Relocation Method for Increase the Resource Utilization in Cloud Computing." Lecture Notes in Electrical Engineering , no. : 263-268.

Methodologies and application
Published: 21 March 2017 in Soft Computing
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In this paper, we present an effective and efficient Byzantine-resilient dual membership management technique in cloud environments, in which nodes are prone to churn and the network topology is not fully connected. Our method is based on unstructured message communication model, namely a gossip protocol that is able to handle the dynamic behavior of nodes properly in the system. We argue that due to the presence of malicious Byzantine nodes, existing membership management mechanisms are not suitable for preserving uniformity of random sampling. Therefore, we propose a new membership management mechanism using gossip with social membership information. The proposed membership management scheme maintains not only neighbor nodes in a social graph, but also Byzantine nodes in a local data structure. The results show that our dual membership management effectively deals with Byzantine nodes, requiring only n\(\ge \) 2f + 1, where n is the number of nodes and f is the number of Byzantine nodes in the system. The message complexity is reduced from O(n\(^{2}\)) to O(n) with our proposed algorithm compared to broadcast-based algorithms.

ACS Style

Jongbeom Lim; Kwang-Sik Chung; Hwamin Lee; Kangbin Yim; Heonchang Yu. Byzantine-resilient dual gossip membership management in clouds. Soft Computing 2017, 22, 3011 -3022.

AMA Style

Jongbeom Lim, Kwang-Sik Chung, Hwamin Lee, Kangbin Yim, Heonchang Yu. Byzantine-resilient dual gossip membership management in clouds. Soft Computing. 2017; 22 (9):3011-3022.

Chicago/Turabian Style

Jongbeom Lim; Kwang-Sik Chung; Hwamin Lee; Kangbin Yim; Heonchang Yu. 2017. "Byzantine-resilient dual gossip membership management in clouds." Soft Computing 22, no. 9: 3011-3022.

Journal article
Published: 01 September 2016 in Advanced Science Letters
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Many products already exist which can transmit some information using OBD through network communication. However, OBD products released in the market only for providing the information regarding driving and vehicle diagnostic services. In this paper, we developed a method which provides the alarm service to the drivers when their driving behaviors are not in the good condition. This proposed method sends those information using vibration motor or multiple LED on the basis of the data they received from the vehicles. It can be divided into two part: transmission part which collects vehicle data received from OBD and receiver part which informs drivers of their driving habits using vehicle data. The result of drivers’ driving habit can be installed on vehicle’s steering wheel, so that drivers’ bad driving habits can be improved. In addition, IoT product can prevent possible car accidents and can reduce the vehicle breakdown rate. Moreover, IoT products can impact on a better gas millage.

ACS Style

Sangwook Han; Eunkwang Jeon; Jungyeon Seo; Hwamin Lee. Development of Driver Safety Assist System Using OBD Data Analysis. Advanced Science Letters 2016, 22, 2292 -2295.

AMA Style

Sangwook Han, Eunkwang Jeon, Jungyeon Seo, Hwamin Lee. Development of Driver Safety Assist System Using OBD Data Analysis. Advanced Science Letters. 2016; 22 (9):2292-2295.

Chicago/Turabian Style

Sangwook Han; Eunkwang Jeon; Jungyeon Seo; Hwamin Lee. 2016. "Development of Driver Safety Assist System Using OBD Data Analysis." Advanced Science Letters 22, no. 9: 2292-2295.

Journal article
Published: 29 June 2016 in The Journal of Supercomputing
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As vehicular networking has recently been developed and commercialized, vehicular cloud computing has received much attention in various research areas, such as intelligent transportation systems and vehicular ad hoc networks. An efficient intersection traffic control using vehicular cloud computing is one of the key research topics in intelligent transportation systems. To efficiently deal with intersection traffic control via vehicle-to-vehicle communications, we design a distributed mutual exclusion algorithm that does not rely on broadcast, which introduces communication overheads; instead, our algorithm use point-to-point messages sent between the vehicles to keep network traffic load lower. In our algorithmic design, to pass an intersection, the lead vehicle on a lane must get permissions from a subset of other vehicles and its following vehicles on the same lane can follow the lead vehicle without permissions unlike the previous research. To evaluate the performance of our distributed mutual exclusion algorithm, we conduct extensive experiments. The results show that our algorithmic design is both effective and efficient.

ACS Style

Jongbeom Lim; Young Sik Jeong; Doo-Soon Park; Hwamin Lee. An efficient distributed mutual exclusion algorithm for intersection traffic control. The Journal of Supercomputing 2016, 74, 1090 -1107.

AMA Style

Jongbeom Lim, Young Sik Jeong, Doo-Soon Park, Hwamin Lee. An efficient distributed mutual exclusion algorithm for intersection traffic control. The Journal of Supercomputing. 2016; 74 (3):1090-1107.

Chicago/Turabian Style

Jongbeom Lim; Young Sik Jeong; Doo-Soon Park; Hwamin Lee. 2016. "An efficient distributed mutual exclusion algorithm for intersection traffic control." The Journal of Supercomputing 74, no. 3: 1090-1107.

Journal article
Published: 14 June 2016 in Sustainability
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The depletion of natural resources in the last century now threatens our planet and the life of future generations. For the sake of sustainable development, this paper pioneers an interesting and practical problem of dense substructure (i.e., maximal cliques) mining in a fuzzy graph where the edges are weighted by the degree of membership. For parameter 0 ≤λ≤ 1 (also called fuzzy cut in fuzzy logic), a newly defined concept λ-maximal clique is introduced in a fuzzy graph. In order to detect the λ-maximal cliques from a fuzzy graph, an efficient mining algorithm based on Fuzzy Formal Concept Analysis (FFCA) is proposed. Extensive experimental evaluations are conducted for demonstrating the feasibility of the algorithm. In addition, a novel recommendation service based on an λ-maximal clique is provided for illustrating the sustainable usability of the problem addressed.

ACS Style

Fei Hao; Doo-Soon Park; Shuai Li; Hwa Min Lee. Mining λ-Maximal Cliques from a Fuzzy Graph. Sustainability 2016, 8, 553 .

AMA Style

Fei Hao, Doo-Soon Park, Shuai Li, Hwa Min Lee. Mining λ-Maximal Cliques from a Fuzzy Graph. Sustainability. 2016; 8 (6):553.

Chicago/Turabian Style

Fei Hao; Doo-Soon Park; Shuai Li; Hwa Min Lee. 2016. "Mining λ-Maximal Cliques from a Fuzzy Graph." Sustainability 8, no. 6: 553.