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Dr. Hoon Ko
Chosun University

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Communication
Published: 21 June 2021 in Sensors
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The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

ACS Style

Hoon Ko; Kwangcheol Rim; Isabel Praça. Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System. Sensors 2021, 21, 4237 .

AMA Style

Hoon Ko, Kwangcheol Rim, Isabel Praça. Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System. Sensors. 2021; 21 (12):4237.

Chicago/Turabian Style

Hoon Ko; Kwangcheol Rim; Isabel Praça. 2021. "Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System." Sensors 21, no. 12: 4237.

Journal article
Published: 09 June 2021 in ACM Transactions on Internet Technology
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The present study aimed to use the proposed system to measure and analyze brain waves of users to allow intelligent upper limb rehabilitation and to optimize the system using a genetic algorithm. The study used EPOC Neuroheadset for Emotiv with EEG electrodes attached as a non-invasive method for measuring brain waves. The brain waves were measured according to the EEG 10-20 standard electrode layout, which allows measurement of signals from each spot where electrodes are attached based on EEG characteristics. The measured data were added in a database. In the intelligent neuro-fuzzy model, wave transform was used for extracting brain wave characteristics according to user intentions and to eliminate noise from the signals in an effort to increase reliability. Moreover, to construct the option rules of the neuro-fuzzy system, FCM technique and optimal cluster evaluation method were used. Furthermore, the asymmetric Gaussian membership function was used to improve performance, whereas SD and WF divided into left and right sides were used to express the chromosomes. Optimal EEG electrode locations were found, and comparative analysis was performed on the differences based on membership function, number of clusters, and number of learning generations, learning algorithm, and wavelet settings. The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation exercise in everyday living according to user intentions, which is expected to help improve the user's willingness to participate in rehabilitation and his or her quality of life.

ACS Style

Tae-Yeun Kim; Sung-Hwan Kim; Hoon Ko. Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System. ACM Transactions on Internet Technology 2021, 21, 1 -17.

AMA Style

Tae-Yeun Kim, Sung-Hwan Kim, Hoon Ko. Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System. ACM Transactions on Internet Technology. 2021; 21 (3):1-17.

Chicago/Turabian Style

Tae-Yeun Kim; Sung-Hwan Kim; Hoon Ko. 2021. "Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System." ACM Transactions on Internet Technology 21, no. 3: 1-17.

Journal article
Published: 12 March 2021 in Sensors
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Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

ACS Style

Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim; Ho-Da Kim. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors 2021, 21, 1997 .

AMA Style

Tae-Yeun Kim, Hoon Ko, Sung-Hwan Kim, Ho-Da Kim. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering. Sensors. 2021; 21 (6):1997.

Chicago/Turabian Style

Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim; Ho-Da Kim. 2021. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering." Sensors 21, no. 6: 1997.

Journal article
Published: 03 March 2021 in Electronics
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Biogas is a significant renewable fuel derived by sources of biological origin. One of today’s research issues is the effect of biofuels on engine efficiency. The experiments on the engine are complicated, time consuming and expensive. Furthermore, the evaluation cannot be carried out beyond the permissible limit. The purpose of this research is to build an artificial neural network successfully for dual fuel diesel engine with a view to overcoming experimental difficulties. Authors used engine load, bio-gas flow rate and n-butanol concentration as input parameters to forecast target variables in this analysis, i.e., smoke, brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbon (HC), nitrous-oxide (NOx). Estimated values and results of experiments were compared. The error analysis showed that the built model has quite accurately predicted the experimental results. This has been described by the value of Coefficient of determination (R2), which varies between 0.8493 and 0.9863 with the value of normalized mean square error (NMSE) between 0.0071 and 0.1182. The potency of the Nash-Sutcliffe coefficient of efficiency (NSCE) ranges from 0.821 to 0.8898 for BTE, HC, NOx and Smoke. This research has effectively emulated the on-board efficiency, emission, and combustion features of a dual-fuel biogas diesel engine taking the Swish activation mechanism in artificial neural network (ANN) model.

ACS Style

Vinay Arora; Sunil Mahla; Rohan Leekha; Amit Dhir; Kyungroul Lee; Hoon Ko. Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine. Electronics 2021, 10, 584 .

AMA Style

Vinay Arora, Sunil Mahla, Rohan Leekha, Amit Dhir, Kyungroul Lee, Hoon Ko. Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine. Electronics. 2021; 10 (5):584.

Chicago/Turabian Style

Vinay Arora; Sunil Mahla; Rohan Leekha; Amit Dhir; Kyungroul Lee; Hoon Ko. 2021. "Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine." Electronics 10, no. 5: 584.

Journal article
Published: 03 February 2021 in Sustainability
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This study proposes a Secure Energy Trading Model design based on a Blockchain is an attempt to overcome the weak security and instability of the current energy trading system. The focal point of the design lies in the user-security features of the model, such as user authentication and identification, and the blockchain that every transaction goes through. The user-security feature provides a safer system for peer-to-peer energy trade, and the blockchain technology ensures the reliability of the trading system. Furthermore, the Secure Energy Trading Model supports a decentralized data control mechanism as a future measure for handling vast amounts of data created by IoT.

ACS Style

Hoon Ko; Isabel Praca. Design of a Secure Energy Trading Model Based on a Blockchain. Sustainability 2021, 13, 1634 .

AMA Style

Hoon Ko, Isabel Praca. Design of a Secure Energy Trading Model Based on a Blockchain. Sustainability. 2021; 13 (4):1634.

Chicago/Turabian Style

Hoon Ko; Isabel Praca. 2021. "Design of a Secure Energy Trading Model Based on a Blockchain." Sustainability 13, no. 4: 1634.

Research article
Published: 02 February 2021 in Concurrency and Computation: Practice and Experience
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In the past, the emergency responses to disasters such as fire outbreak accidents, accidents that require first aid were slow and not optimal. With human intellect, it was impractical to analyze vast amounts of data regarding the continuity of the numerous environmental changes and the correlation there may be with emergency responses based on past experiences with similar situations. Today, artificial intelligence is presented as a powerful tool to various organizations. Many have already made various attempts to apply this technology as an advisor for emergency response. This research expands on the practicality and effectiveness of utilizing AI as an advisory platform for disaster response based on the big‐data, and also it designs an AI advisor platform for disaster response with big data‐based algorithms. Finally AI advisor function are defined as part of the AI advisor platform, the voice recognition function, natural language processing function, big data coordination function.

ACS Style

Minho Lee; Libor Mesicek; Kitae Bae; Hoon Ko. AI advisor platform for disaster response based on big data. Concurrency and Computation: Practice and Experience 2021, e6215 .

AMA Style

Minho Lee, Libor Mesicek, Kitae Bae, Hoon Ko. AI advisor platform for disaster response based on big data. Concurrency and Computation: Practice and Experience. 2021; ():e6215.

Chicago/Turabian Style

Minho Lee; Libor Mesicek; Kitae Bae; Hoon Ko. 2021. "AI advisor platform for disaster response based on big data." Concurrency and Computation: Practice and Experience , no. : e6215.

Journal article
Published: 12 December 2020 in Sensors
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Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.

ACS Style

Gyu Ho Choi; Hoon Ko; Witold Pedrycz; Amit Kumar Singh; Sung Bum Pan. Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics. Sensors 2020, 20, 7130 .

AMA Style

Gyu Ho Choi, Hoon Ko, Witold Pedrycz, Amit Kumar Singh, Sung Bum Pan. Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics. Sensors. 2020; 20 (24):7130.

Chicago/Turabian Style

Gyu Ho Choi; Hoon Ko; Witold Pedrycz; Amit Kumar Singh; Sung Bum Pan. 2020. "Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics." Sensors 20, no. 24: 7130.

Conference paper
Published: 01 October 2020 in Proceedings of the 2nd International Conference on Data Engineering and Communication Technology
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We demonstrate a security threat of mouse data by differentiating the real mouse data from the dummy mouse data by deriving features to have high accuracy based on data science. Features appearing between the mouse coordinates input by the user are analyzed, and the feature is defined as a feature for machine learning models to derive a method of improving the accuracy. As a result, we found a feature where the distance between coordinates is concentrated in a specific range. When the distance is used as a feature, we verified that the mouse data is stolen more accurately.

ACS Style

Kyungroul Lee; Hoon Ko; HyoungJu Kim; Sun-Young Lee; Junho Choi. Practical Vulnerability Analysis of Mouse Data According to Offensive Security Based on Machine Learning. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology 2020, 504 -510.

AMA Style

Kyungroul Lee, Hoon Ko, HyoungJu Kim, Sun-Young Lee, Junho Choi. Practical Vulnerability Analysis of Mouse Data According to Offensive Security Based on Machine Learning. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. 2020; ():504-510.

Chicago/Turabian Style

Kyungroul Lee; Hoon Ko; HyoungJu Kim; Sun-Young Lee; Junho Choi. 2020. "Practical Vulnerability Analysis of Mouse Data According to Offensive Security Based on Machine Learning." Proceedings of the 2nd International Conference on Data Engineering and Communication Technology , no. : 504-510.

Special issue paper
Published: 24 September 2020 in Concurrency and Computation: Practice and Experience
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Evaluation information generated by various users is processed using various requirements and data to make recommendations for solving the problems, and it analyzes satisfaction with the results. Despite people normally utilizes the processed information for decision making, not all information, however, brings positive outcomes to users. There are some users who perceived it negatively. In order to minimize the occurrence of such negative effects, the analysis of various user requirements is essential as well as diversifying user inputs for each requirement. Consequently, the results from individual inputs must be predicted. In the past, since the system relies on a single‐expert system, it is necessary to accept and process various limitations of recommendation and multiple requirements. Therefore, the results of the recommendation also have various problems. In order to solve this problem, this study applied an analytic hierarchy process to multiadvisor configuration. In the proposed system, one or multiple advisors are defined, and after analyzing the predefined requirements, the system accepts only the requirements that can be processed and calculates the individual recommendation results. A recommendation system was going to be studied by learning all situation.

ACS Style

Seong Wan Park; Libor Mesicek; Joohyun Shin; Kitae Bae; Kyungjin An; Hoon Ko. Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy‐analytic hierarchy process. Concurrency and Computation: Practice and Experience 2020, 1 .

AMA Style

Seong Wan Park, Libor Mesicek, Joohyun Shin, Kitae Bae, Kyungjin An, Hoon Ko. Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy‐analytic hierarchy process. Concurrency and Computation: Practice and Experience. 2020; ():1.

Chicago/Turabian Style

Seong Wan Park; Libor Mesicek; Joohyun Shin; Kitae Bae; Kyungjin An; Hoon Ko. 2020. "Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy‐analytic hierarchy process." Concurrency and Computation: Practice and Experience , no. : 1.

Journal article
Published: 18 June 2020 in Sensors
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This paper will present the authors’ own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors’ solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network.

ACS Style

Lidia Ogiela; Marek R. Ogiela; Hoon Ko. Intelligent Data Management and Security in Cloud Computing. Sensors 2020, 20, 3458 .

AMA Style

Lidia Ogiela, Marek R. Ogiela, Hoon Ko. Intelligent Data Management and Security in Cloud Computing. Sensors. 2020; 20 (12):3458.

Chicago/Turabian Style

Lidia Ogiela; Marek R. Ogiela; Hoon Ko. 2020. "Intelligent Data Management and Security in Cloud Computing." Sensors 20, no. 12: 3458.

Journal article
Published: 26 May 2020 in Information Processing & Management
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Transformative computing in the fourth-generation industrialization, receives all signals and all sequences from sensing devices under artificial intelligence in wireless networking. Then the system has to combine them and make a useful information for human. In an industrial building or in a home, many electronic devices would be using and they make various energy signals and sequences. The devices can find out the energy wastage in the absence of a smart energy management system to monitor the energy flow, and it causes a blackout. Once the energy flow is analysed, it is possible to realize the special-time or the rush-time, which will require a large amount of energy. Because the existing systems have no monitor to see the energy flow, a large amount of energy can be wasted. To distribute the energy efficiently, a smart energy management system should have the necessary special functions that can monitor the energy flow. Following the analysis result, the system can create a special strategy to plan energy distribution. In this study, the smart energy management system defines a special strategy based on the analysis result of the consumed energy by arranging more or less usage of energy. Moreover, the system can decrease the energy supply to idle devices and the connected extra devices by analysing how many IoT will be used in a service. This smart control system can detect human behaviour when they move and turn in activation automatically, so finally, the system can use the energy efficiently.

ACS Style

Hoon Ko; Jong Hyuk Kim; Kyungjin An; Libor Mesicek; Goreti Marreiros; Sung Bum Pan; Pankoo Kim. Smart home energy strategy based on human behaviour patterns for transformative computing. Information Processing & Management 2020, 57, 102256 .

AMA Style

Hoon Ko, Jong Hyuk Kim, Kyungjin An, Libor Mesicek, Goreti Marreiros, Sung Bum Pan, Pankoo Kim. Smart home energy strategy based on human behaviour patterns for transformative computing. Information Processing & Management. 2020; 57 (5):102256.

Chicago/Turabian Style

Hoon Ko; Jong Hyuk Kim; Kyungjin An; Libor Mesicek; Goreti Marreiros; Sung Bum Pan; Pankoo Kim. 2020. "Smart home energy strategy based on human behaviour patterns for transformative computing." Information Processing & Management 57, no. 5: 102256.

Research article
Published: 16 January 2020 in Journal of Advanced Transportation
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All persons in self-driving vehicle would like to receive each service. To do it, the system has to know the person’s state from emotion or stress, and to know the person’s state, it has to catch by analyzing the person’s bio-information. In this paper, we propose a system for inferring emotion using EEG, pulse, blood pressure (systolic and diastolic blood pressure) of user, and recommending color and music according to emotional state of user for a user service in self-driving vehicle. The proposed system is designed to classify the four emotional information (stability, relaxation, tension, and excitement) by using EEG data to infer and classify emotional state according to user’s stress. SVM algorithm was used to classify bio information according to stress index using brain wave data of the fuzzy control system, pulse, and blood pressure data. When 80% of data were learned according to the ratio of training data by using the SVM algorithm to classify the EEG, blood pressure, and pulse rate databased on the biometric emotion information, the highest performance of 86.1% was shown. The bio-information classification system based on the stress index proposed in this paper will help to study the interaction between human and computer (HCI) in the 4th Industrial Revolution by classifying emotional color and emotional sound according to the emotion of the user it is expected.

ACS Style

Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim. Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles. Journal of Advanced Transportation 2020, 2020, 1 -11.

AMA Style

Tae-Yeun Kim, Hoon Ko, Sung-Hwan Kim. Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles. Journal of Advanced Transportation. 2020; 2020 ():1-11.

Chicago/Turabian Style

Tae-Yeun Kim; Hoon Ko; Sung-Hwan Kim. 2020. "Data Analysis for Emotion Classification Based on Bio-Information in Self-Driving Vehicles." Journal of Advanced Transportation 2020, no. : 1-11.

Special issue paper
Published: 23 October 2019 in Concurrency and Computation: Practice and Experience
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Augmented‐reality (AR) devices allow physicians to incorporate data visualization into diagnostic and treatment procedures to improve work efficiency and safety and reduce cost. They are also used to enhance surgical training. In this study, we implemented an AR application for Botox injections using a face recognition algorithm based on deep learning, and we evaluated the recognition accuracy of this application using 27 participants. The accuracy was around 3 mm for all parts of the facial region. The method of increasing surgical efficiency with AR is accurate enough to be used for surgery and provides great potential for further development.

ACS Style

Hyojoon Kim; Sanghui Jeong; Jihyeon Seo; Inseok Park; Hoon Ko; Seong Yong Moon. Augmented reality for botulinum toxin injection. Concurrency and Computation: Practice and Experience 2019, 32, 1 .

AMA Style

Hyojoon Kim, Sanghui Jeong, Jihyeon Seo, Inseok Park, Hoon Ko, Seong Yong Moon. Augmented reality for botulinum toxin injection. Concurrency and Computation: Practice and Experience. 2019; 32 (18):1.

Chicago/Turabian Style

Hyojoon Kim; Sanghui Jeong; Jihyeon Seo; Inseok Park; Hoon Ko; Seong Yong Moon. 2019. "Augmented reality for botulinum toxin injection." Concurrency and Computation: Practice and Experience 32, no. 18: 1.

Journal article
Published: 18 September 2019 in IEEE Access
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There are many blogs that recommend places and foods and on the web. In addition, there are various fake news that provide false information. They both are written by a blogger; bloggers can write on any topic of their choice. Web visitors read these blogs and decide if place or food item is satisfactory. This implies that the decision is based on the blogger’s prejudice. This is not objective because all the decisions depend on the blogger’s disposition. Other visitors, who had followed the bloggerâĂŹs recommendation, may have disagree with the blogger. To avoid this conflict, all the words and sentences in the posts must be analyzed objectively. All entries such as direction, address, excessive compliments, and monophonic are analyzed. This study also analyzed the entries to see their correlation; finally, it can make the decision if a blog is trustable with an anomaly sign.

ACS Style

Hoon Ko; Libor Mesicek; Jong Youl Hong; Soon Sim Yeo; Sung Bum Pan; Pankoo Kim. Blog Reliability Analysis With Conflicting Interests of Contexts in the Extended Branch for Cyber-Security. IEEE Access 2019, 7, 143693 -143698.

AMA Style

Hoon Ko, Libor Mesicek, Jong Youl Hong, Soon Sim Yeo, Sung Bum Pan, Pankoo Kim. Blog Reliability Analysis With Conflicting Interests of Contexts in the Extended Branch for Cyber-Security. IEEE Access. 2019; 7 (99):143693-143698.

Chicago/Turabian Style

Hoon Ko; Libor Mesicek; Jong Youl Hong; Soon Sim Yeo; Sung Bum Pan; Pankoo Kim. 2019. "Blog Reliability Analysis With Conflicting Interests of Contexts in the Extended Branch for Cyber-Security." IEEE Access 7, no. 99: 143693-143698.

Editorial
Published: 01 September 2019 in Concurrency and Computation: Practice and Experience
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ACS Style

Hoon Ko; Goreti Marreiros. Smart media and application. Concurrency and Computation: Practice and Experience 2019, 33, 1 .

AMA Style

Hoon Ko, Goreti Marreiros. Smart media and application. Concurrency and Computation: Practice and Experience. 2019; 33 (2):1.

Chicago/Turabian Style

Hoon Ko; Goreti Marreiros. 2019. "Smart media and application." Concurrency and Computation: Practice and Experience 33, no. 2: 1.

Special issue paper
Published: 29 August 2019 in Concurrency and Computation: Practice and Experience
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Academic discussions on cultural intelligence (CQ) are now paying attention to their potential utilization from various angles. The field of study is expanded not only in business administration but also in psychology, education, tourism, communication, and arts. This is due to the widespread study of global communication competence in multicultural situations because of the deepening of globalization. In this paper, we try to find a way to utilize cultural intelligence model proposed by David Livermore. The aim is to develop education contents for the improvement of cultural intelligence of university students. The target is limited to university students and aims to develop education contents to enhance their cultural intelligence. The main purpose of the study was to measure and analyze the cultural intelligence of university students. For that, the level of cultural intelligence of Korean university freshmen was measured and analyzed. The individual level of the four areas constituting the cultural intelligence was identified, and the difference between the male and female was examined. At the same time, the differences in cultural intelligence were analyzed according to the duration of multicultural contact and experience in the case of foreign language lectures taught by foreigners. Finally, we analyzed how the correlation between the four areas that comprise cultural intelligence is occurring, and as a result, the content and results of this study are expected to be an important foundation for the direction of future development of education contents in universities.

ACS Style

Jong Youl Hong; Hoon Ko; Libor Mesicek; MoonBae Song. Cultural intelligence as education contents: Exploring the pedagogical aspects of effective functioning in higher education. Concurrency and Computation: Practice and Experience 2019, 33, 1 .

AMA Style

Jong Youl Hong, Hoon Ko, Libor Mesicek, MoonBae Song. Cultural intelligence as education contents: Exploring the pedagogical aspects of effective functioning in higher education. Concurrency and Computation: Practice and Experience. 2019; 33 (2):1.

Chicago/Turabian Style

Jong Youl Hong; Hoon Ko; Libor Mesicek; MoonBae Song. 2019. "Cultural intelligence as education contents: Exploring the pedagogical aspects of effective functioning in higher education." Concurrency and Computation: Practice and Experience 33, no. 2: 1.

Chapter
Published: 20 July 2019 in Handbook of Multimedia Information Security: Techniques and Applications
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Each person has unique bio-information such as: a face, a fingerprint, an iris, which are forms of static information and many systems have been trying to use them in their security systems, like a banking system. However, because they are just static information, which are never changing, they could be abused by replacing them with an attacker’s bio-information. To overcome this, dynamic bio-information, such as an Electrocardiogram (ECG), can be used in the next forms of security systems. One problem is that the dynamic bio-information is always different according to their state of health, evaluating time, moreover, their daily condition when they are evaluated. Therefore the security system can’t accept and pass with two different values. So, to use the ECG value in the security system, it tries to detect the ECG’s feature and tries to connect each relationship.

ACS Style

Hoon Ko; Libor Mesicek; Sung Bum Pan. ECG Security Challenges: Case Study on Change of ECG According to Time for User Identification. Handbook of Multimedia Information Security: Techniques and Applications 2019, 619 -628.

AMA Style

Hoon Ko, Libor Mesicek, Sung Bum Pan. ECG Security Challenges: Case Study on Change of ECG According to Time for User Identification. Handbook of Multimedia Information Security: Techniques and Applications. 2019; ():619-628.

Chicago/Turabian Style

Hoon Ko; Libor Mesicek; Sung Bum Pan. 2019. "ECG Security Challenges: Case Study on Change of ECG According to Time for User Identification." Handbook of Multimedia Information Security: Techniques and Applications , no. : 619-628.

Special issue paper
Published: 23 April 2019 in Concurrency and Computation: Practice and Experience
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Context awareness is a necessary technique for providing optimized services to users by recognizing their surrounding environment at a particular time. To provide context‐aware services, a context‐aware middleware is required to detect the changes surrounding the user as well as the processing procedure. However, when the context‐aware middleware is applied to wearable computing, the processing time increases in proportion to the increase in the number of context information due to the lack of processing capacity from the terminal devices. To access the terminal devices, a specific context representation with Resource Description Framework–based triplet is used. Since the triplet consists of three keyword elements that refer to the status of a given situation around the user, retrieving these elements takes O(n3) time complexity with a linear search. To overcome this problem, a hash‐based comparison method is suggested to minimize the response time. The suggested comparison method gives a better performance without searching every single keyword element among the triplet sets. In the experiment, we applied the suggested method to the context‐aware workflow middleware and demonstrated that the proposed method improves the processing time by at least 30% compared with the linear search by enhancing the comparison module in the middleware.

ACS Style

Yoosang Park; Jaehyung Ye; Jongsun Choi; Jaeyoung Choi; Hoon Ko. Extraction of abstracted sensory data to reduce the execution time of context‐aware services in wearable computing environments. Concurrency and Computation: Practice and Experience 2019, 32, 1 .

AMA Style

Yoosang Park, Jaehyung Ye, Jongsun Choi, Jaeyoung Choi, Hoon Ko. Extraction of abstracted sensory data to reduce the execution time of context‐aware services in wearable computing environments. Concurrency and Computation: Practice and Experience. 2019; 32 (18):1.

Chicago/Turabian Style

Yoosang Park; Jaehyung Ye; Jongsun Choi; Jaeyoung Choi; Hoon Ko. 2019. "Extraction of abstracted sensory data to reduce the execution time of context‐aware services in wearable computing environments." Concurrency and Computation: Practice and Experience 32, no. 18: 1.

Journal article
Published: 11 March 2019 in IEEE Access
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Although many security systems with biometric information have appeared, they only have been used the static bio-information, e.g., a fingerprint, ris, and so on. However, because these values are permanent, the attackers can modify and abuse that. To overcome this problem, many researchers would like to use dynamic bio-information, e.g., electrocardiograms (ECG), in security systems. In this case, a sensor in the system must measure the dynamic bio-information instead. The difficulty is that usually, the measured data is different whenever it measures. Therefore if the data is applied to existing algorithms, the results will not be matched and the user will be rejected to pass. This is because an unstable base point, which are Q and S values in the ECG, is used to calculate. To solve this, it suggests an adjusted (Q i * S i ) algorithm that defines a specific distance from the location of R-peak to obtain the Q i and S i values. The algorithm can use balanced input data to determine the features, thereby enabling a highly accurate dynamic biometric system.

ACS Style

Hoon Ko; Marek R. Ogiela; Lidia Ogiela; Libor Mesicek; Myoungwon Lee; Junho Choi; Pankoo Kim. ECG-Based Advanced Personal Identification Study With Adjusted (Q i * S i ). IEEE Access 2019, 7, 40078 -40084.

AMA Style

Hoon Ko, Marek R. Ogiela, Lidia Ogiela, Libor Mesicek, Myoungwon Lee, Junho Choi, Pankoo Kim. ECG-Based Advanced Personal Identification Study With Adjusted (Q i * S i ). IEEE Access. 2019; 7 ():40078-40084.

Chicago/Turabian Style

Hoon Ko; Marek R. Ogiela; Lidia Ogiela; Libor Mesicek; Myoungwon Lee; Junho Choi; Pankoo Kim. 2019. "ECG-Based Advanced Personal Identification Study With Adjusted (Q i * S i )." IEEE Access 7, no. : 40078-40084.

Special issue paper
Published: 13 February 2019 in Concurrency and Computation: Practice and Experience
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Each person has unique bio‐information, and this information rarely would be overlapped to other people. Because of this feature, many researchers have been working on a user's identification. However, the problem is that it is not certain if the feature is exactly matched at any time and in any place for the dynamic signal. It is very hard to match bio‐information whenever it is measured; however, all natural signals have an individual pattern. In this paper, it uses an ECG (electrocardiogram) as bio‐information, and it tries to find each pattern, which will be located within the threshold. With the pattern in the threshold, it detects the user's identification. To analyze the patterns, it analyzes them as measured for 120 seconds. Next, it divides them every 1‐2 seconds to 5 seconds. Then, it could recognize the users' identification with this study, and then, finally, the accuracy is 83.3618%.

ACS Style

Hoon Ko; Sung Bum Pan; Libor Měsíček. Personal identification study for touchable devices with ECG. Concurrency and Computation: Practice and Experience 2019, 32, 1 .

AMA Style

Hoon Ko, Sung Bum Pan, Libor Měsíček. Personal identification study for touchable devices with ECG. Concurrency and Computation: Practice and Experience. 2019; 32 (8):1.

Chicago/Turabian Style

Hoon Ko; Sung Bum Pan; Libor Měsíček. 2019. "Personal identification study for touchable devices with ECG." Concurrency and Computation: Practice and Experience 32, no. 8: 1.