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Among many dangerous situations, the number of cases of violence has been growing recently. However, there is currently no research to recognize conditions such as assault. Therefore, this paper presents a VR (Violence-Recognition) model for recognition activity using LSTM. The VR model develops algorithms that can detect dangerous situations through processing and analysis of sensing data. Also, to improve accuracy by using the FFT algorithm for processing digital signals in combination with LSTM.
Svetlana Kim; Hyejeong Nam; Hyunho Park; Yong-Tae Lee; Yongik Yoon. Activity-Recognition Model for Violence Behavior Using LSTM. Lecture Notes in Electrical Engineering 2021, 529 -535.
AMA StyleSvetlana Kim, Hyejeong Nam, Hyunho Park, Yong-Tae Lee, Yongik Yoon. Activity-Recognition Model for Violence Behavior Using LSTM. Lecture Notes in Electrical Engineering. 2021; ():529-535.
Chicago/Turabian StyleSvetlana Kim; Hyejeong Nam; Hyunho Park; Yong-Tae Lee; Yongik Yoon. 2021. "Activity-Recognition Model for Violence Behavior Using LSTM." Lecture Notes in Electrical Engineering , no. : 529-535.
With the development of the Internet of Things (IoT), the amount of data is growing and becoming more diverse. There are several problems when transferring data to the cloud, such as limitations on network bandwidth and latency. That has generated considerable interest in the study of edge computing, which processes and analyzes data near the network terminals where data is causing. The edge computing can extract insight data from a large number of data and provide fast essential services through simple analysis. The edge computing has a real-time advantage, but also has disadvantages, such as limited edge node capacity. The edge node for edge computing causes overload and delays in completing the task. In this paper, we proposes an efficient offloading model through collaboration between edge nodes for the prevention of overload and response to potential danger quickly in emergencies. In the proposed offloading model, the functions of edge computing are divided into data-centric and task-centric offloading. The offloading model can reduce the edge node overload based on a centralized, inefficient distribution and trade-off occurring in the edge node. That is the leading cause of edge node overload. So, this paper shows a collaborative offloading model in edge computing that guarantees real-time and prevention overload prevention based on data-centric offloading and task-centric offloading. Also, we present an intelligent offloading model based on several scenarios of forest fire ignition.
Jieun Kang; Svetlana Kim; Jaeho Kim; NakMyoung Sung; Yongik Yoon. Dynamic Offloading Model for Distributed Collaboration in Edge Computing: A Use Case on Forest Fires Management. Applied Sciences 2020, 10, 2334 .
AMA StyleJieun Kang, Svetlana Kim, Jaeho Kim, NakMyoung Sung, Yongik Yoon. Dynamic Offloading Model for Distributed Collaboration in Edge Computing: A Use Case on Forest Fires Management. Applied Sciences. 2020; 10 (7):2334.
Chicago/Turabian StyleJieun Kang; Svetlana Kim; Jaeho Kim; NakMyoung Sung; Yongik Yoon. 2020. "Dynamic Offloading Model for Distributed Collaboration in Edge Computing: A Use Case on Forest Fires Management." Applied Sciences 10, no. 7: 2334.
In recent years, driver’s drowsiness is one of the main causes of traffic accidents, which can result in severe physical injury and serious economic loss. Fatigue of the driver is an important factor in road accidents, and fatigue detection has a significant influence on traffic safety. This article describes a drowsiness detection approach based on the combination of various multi-sensors. The present study proposed a method to detect the driver’s drowsiness that combines features of electrocardiography (ECG) and environmental factors, such as vehicle temperature and humidity, to improve detection performance. The activity of the autonomic nervous system which can be measured in heart rate variability (HRV) signals obtained from surface ECG, indicates changes during stress, extreme fatigue, and episodes of drowsiness. The combination of the multi-sensors feature of drowsiness is significant factors in determining the driver’s fatigue state and can use this information to transportation drowsy driving control center if necessary.
Svetlana Kim; Hyunho Park; Yong-Tae Lee; Yongik Yoon. Detecting Driver Drowsiness Based Fusion Multi-sensors Method. Lecture Notes in Electrical Engineering 2019, 459 -464.
AMA StyleSvetlana Kim, Hyunho Park, Yong-Tae Lee, Yongik Yoon. Detecting Driver Drowsiness Based Fusion Multi-sensors Method. Lecture Notes in Electrical Engineering. 2019; ():459-464.
Chicago/Turabian StyleSvetlana Kim; Hyunho Park; Yong-Tae Lee; Yongik Yoon. 2019. "Detecting Driver Drowsiness Based Fusion Multi-sensors Method." Lecture Notes in Electrical Engineering , no. : 459-464.
As the vast amount of data in social Internet of Things (IoT) environments considering interactions between IoT and people is accumulated and processed through cloud and big data technologies, the services that utilize them are applied in various fields. The trust between IoT devices and their data is recognized as the core of IoT ecosystem creation and growth. Connection with suspicious IoT devices may pose a risk to services and system operation. Therefore, it is essential to analyze and manage trust information for devices, services, and people, as well as to provide the trust information to the other devices or users that need it. This paper presents a trust information management framework which contains a generic IoT reference model with trust capabilities to achieve the goal of converged trust information management. Additionally, a trust information management platform (TIMP) consisting of trust agents, trust information brokers, and trust information management systems has been proposed, which aims to provide trustworthy and safe interactions among people, virtual objects, and physical things. Implementing and deploying a TIMP enables a trustworthy ecosystem to be built while activating social IoT businesses by reducing transaction costs, as well as by eliminating the uncertainties in the use of social IoT services and data transactions.
Tai-Won Um; Eunhee Lee; Gyu Myoung Lee; Yongik Yoon; Lee; Um; Yoon. Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments. Sensors 2019, 19, 4707 .
AMA StyleTai-Won Um, Eunhee Lee, Gyu Myoung Lee, Yongik Yoon, Lee, Um, Yoon. Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments. Sensors. 2019; 19 (21):4707.
Chicago/Turabian StyleTai-Won Um; Eunhee Lee; Gyu Myoung Lee; Yongik Yoon; Lee; Um; Yoon. 2019. "Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments." Sensors 19, no. 21: 4707.
South Korea invests a budget of trillions in national R&D projects every year, and has achieved excellent performance doing so each year. However, since the projects are planned, evaluated, and managed by different departments and institutions, duplicate planning and submission leads to insufficient sharing of research results. Currently, the National Technology Information Service (NTIS) inspects project duplication based on keywords, which leads to duplicate planning among departments and closed management of research results. Since the NTIS builds in centralized systems, the inspection systems supports one-way management for duplication checking and information sharing. Therefore, we propose a new platform, called the Trusted Information Project Platform (TIP-Platform), for easily checking for project duplication, sharing research results, and updating research results. TIP-Platform adopts a new concept for user authority setting, the distributed ledger structure, transaction structure, and service. For the adaption, the TIP-Platform uses blockchain technology that performs recording and management via blocks by distributing the right to record and managing transactions. This platform makes it easy for anyone to view and use project-related information such as research results and duplication review. In this paper, we describe how the TIP-Platform can achieve excellent research results through information sharing of a project. This platform needs to be based on trust, because it shares information and continually updates information.
Eunhee Lee; Yongik Yoon. Trusted information project platform based on blockchain for sharing strategy. Journal of Ambient Intelligence and Humanized Computing 2019, 1 -11.
AMA StyleEunhee Lee, Yongik Yoon. Trusted information project platform based on blockchain for sharing strategy. Journal of Ambient Intelligence and Humanized Computing. 2019; ():1-11.
Chicago/Turabian StyleEunhee Lee; Yongik Yoon. 2019. "Trusted information project platform based on blockchain for sharing strategy." Journal of Ambient Intelligence and Humanized Computing , no. : 1-11.
The challenge that journalism is facing these days in the Internet mobile environment is greater than ever before. Journalism is losing its revenue structure to platform operators favoring a certain markets, and also the trust of its readers in light of fake news and infected news. To alleviate this situation, we propose a blockchain technology that is applicable to journalism in order to achieve decentralization as a reasonable alternative. The journalism model based on hybrid blockchain aims to achieve the following: the delivery of articles with sharing value, what we call proof of sharing; the distribution of roles of personalized agenda settings; and finally, the use of agora to collect public opinions. With all these, we attempt to resolve the issues with current journalism with our proposed model based on blockchain.
Byeowool Kim; Yongik Yoon. Journalism Model Based on Blockchain with Sharing Space. Symmetry 2018, 11, 19 .
AMA StyleByeowool Kim, Yongik Yoon. Journalism Model Based on Blockchain with Sharing Space. Symmetry. 2018; 11 (1):19.
Chicago/Turabian StyleByeowool Kim; Yongik Yoon. 2018. "Journalism Model Based on Blockchain with Sharing Space." Symmetry 11, no. 1: 19.
The advances in multiple types of sensing technology, wireless communication, and context-aware services increase interest in the design and development of pedestrian behavior for hazard detection. This paper focuses on research of the hybrid sensing fusion approach that identifies behavior activities and provides behavior-aware alerts for safety to pedestrians. Hybrid sensing techniques use to integrate data gathered from several sensors and increase the reliability of the algorithm for identifying various activities. The main purpose of this paper is to present the overview of hybrid sensing and behavior-aware to apply for the pedestrian hazard detection.
Svetlana Kim; Yongik Yoon. Hybrid Sensing and Behavior-Aware in Pedestrian Hazard Detection. Lecture Notes in Electrical Engineering 2017, 1114 -1120.
AMA StyleSvetlana Kim, Yongik Yoon. Hybrid Sensing and Behavior-Aware in Pedestrian Hazard Detection. Lecture Notes in Electrical Engineering. 2017; ():1114-1120.
Chicago/Turabian StyleSvetlana Kim; Yongik Yoon. 2017. "Hybrid Sensing and Behavior-Aware in Pedestrian Hazard Detection." Lecture Notes in Electrical Engineering , no. : 1114-1120.
Ambient Intelligence refers to environments that consisting of smart sensor devices that can sense and respond to the existence of people. Through context awareness, ambient intelligence may deliver accurate detection of a user’s situation, predict future events, and support real-time decision making that requires intelligent analysis of large amounts of context data gathered from various sensing devices. This paper presents a context awareness framework called AC (awareness-cognition) for ambient intelligence that also solves problems pertaining to predictions by discovering personalized knowledge through combining multiple contexts.
Svetlana Kim; Yong-Ik Yoon. Ambient intelligence middleware architecture based on awareness-cognition framework. Journal of Ambient Intelligence and Humanized Computing 2017, 9, 1131 -1139.
AMA StyleSvetlana Kim, Yong-Ik Yoon. Ambient intelligence middleware architecture based on awareness-cognition framework. Journal of Ambient Intelligence and Humanized Computing. 2017; 9 (4):1131-1139.
Chicago/Turabian StyleSvetlana Kim; Yong-Ik Yoon. 2017. "Ambient intelligence middleware architecture based on awareness-cognition framework." Journal of Ambient Intelligence and Humanized Computing 9, no. 4: 1131-1139.
Svetlana Kim; Yong-Ik Yoon. A Model of Energy-Awareness Predictor to Improve the Energy Efficiency. Lecture Notes in Electrical Engineering 2017, 656 -662.
AMA StyleSvetlana Kim, Yong-Ik Yoon. A Model of Energy-Awareness Predictor to Improve the Energy Efficiency. Lecture Notes in Electrical Engineering. 2017; ():656-662.
Chicago/Turabian StyleSvetlana Kim; Yong-Ik Yoon. 2017. "A Model of Energy-Awareness Predictor to Improve the Energy Efficiency." Lecture Notes in Electrical Engineering , no. : 656-662.
Context information can be an important factor of user behavior modeling and various context recognition recommendations. However, state-of-the-art context modeling methods cannot deal with contexts of other dimensions such as those of users and items and cannot extract special semantics. On the other hand, some tasks for predicting multidimensional relationships can be used to recommend context recognition, but there is a problem with the generation recommendations based on a variety of context information. In this paper, we propose MRTensorCube, which is a large-scale data cube calculation based on distributed parallel computing using MapReduce computation framework and supports efficient context recognition. The basic idea of MRTensorCube is the reduction of continuous data combined partial filter and slice when calculating using a four-way algorithm. From the experimental results, it is clear that MRTensor is superior to all other algorithms.
Svetlana Kim; Suan Lee; Jinho Kim; Yong-Ik Yoon. MRTensorCube: tensor factorization with data reduction for context-aware recommendations. The Journal of Supercomputing 2017, 1 -11.
AMA StyleSvetlana Kim, Suan Lee, Jinho Kim, Yong-Ik Yoon. MRTensorCube: tensor factorization with data reduction for context-aware recommendations. The Journal of Supercomputing. 2017; ():1-11.
Chicago/Turabian StyleSvetlana Kim; Suan Lee; Jinho Kim; Yong-Ik Yoon. 2017. "MRTensorCube: tensor factorization with data reduction for context-aware recommendations." The Journal of Supercomputing , no. : 1-11.
This paper is focused on hedonic model study for retargeting advertising Based Internet of Things using useful information. Many research related to the existing Internet of things, relatively not many study for effective advertising model based Internet of Things. So, this paper is designed more information, fun, interactive advertising model based on Internet of Things. Therefore, result of this paper show that implication to produce advertising based on Internet of Things provides a practical guide.
Bo-Ram Kim; Man-Soo Chung; Yong-Ik Yoon. Hedonic Model Study for Retargeting Advertising Based on Space-Centered Internet of Things. Lecture Notes in Electrical Engineering 2016, 705 -711.
AMA StyleBo-Ram Kim, Man-Soo Chung, Yong-Ik Yoon. Hedonic Model Study for Retargeting Advertising Based on Space-Centered Internet of Things. Lecture Notes in Electrical Engineering. 2016; ():705-711.
Chicago/Turabian StyleBo-Ram Kim; Man-Soo Chung; Yong-Ik Yoon. 2016. "Hedonic Model Study for Retargeting Advertising Based on Space-Centered Internet of Things." Lecture Notes in Electrical Engineering , no. : 705-711.
Recently, CCTV is being applied to prevent crimes. It senses the level of danger as being searching criminal records, a wanted one’s montage and so on through mainly Facial recognition. However, it needs additional judging to provide against emergencies, because it cannot predict every criminal situation. In the cause of it, a computer is being fed three-dimensional coordinates from CCTV into a device, and catches not only motion of body or arms but also pattern of hand that were grasped by ConvexHull. And then it predicts suspicious behaviors via judging the movements of an object. Furthermore, to add information about surroundings and location, preventing crimes with more exact judging is the aim on this research.
Ji-Hyen Choi; Jong-Won Choe; Yong-Ik Yoon. Prediction Method for Suspicious Behavior Based on Omni-View Model. Lecture Notes in Electrical Engineering 2016, 607 -612.
AMA StyleJi-Hyen Choi, Jong-Won Choe, Yong-Ik Yoon. Prediction Method for Suspicious Behavior Based on Omni-View Model. Lecture Notes in Electrical Engineering. 2016; ():607-612.
Chicago/Turabian StyleJi-Hyen Choi; Jong-Won Choe; Yong-Ik Yoon. 2016. "Prediction Method for Suspicious Behavior Based on Omni-View Model." Lecture Notes in Electrical Engineering , no. : 607-612.
In this paper, we measure human physiological changes from different body parts to quantify human mental stress level by using multimodal bio-sensors. By integrating these physiological responses, we generate bio-index and rule for the prediction of mental status, such as tension, normal, and relax. We also develop an inspection service middleware for analyzing health parameters such as electroencephalography (EEG), electrocardiography (ECG), oxygen saturation (SpO2), blood pressure (BP), and respiration rate (RR). In this service middleware, we use the multi-level assessment model for mental stress level that consists of three steps as follows; classification, reasoning, and decision making. The classification of datasets from bio-sensors is enabled by fuzzy logic and SVM algorithm. The reasoning uses the decision-tree model and random forest algorithm to classify the mental stress level from the health parameters. Finally, we propose a prediction model to make a decision for the wellness contents by using Expectation Maximization (EM).
Yuchae Jung; Yong Ik Yoon. Multi-level assessment model for wellness service based on human mental stress level. Multimedia Tools and Applications 2016, 76, 11305 -11317.
AMA StyleYuchae Jung, Yong Ik Yoon. Multi-level assessment model for wellness service based on human mental stress level. Multimedia Tools and Applications. 2016; 76 (9):11305-11317.
Chicago/Turabian StyleYuchae Jung; Yong Ik Yoon. 2016. "Multi-level assessment model for wellness service based on human mental stress level." Multimedia Tools and Applications 76, no. 9: 11305-11317.
Study on abnormal behavior prediction models using flexible multi-level regression Abnormal behavior;analysis;association;behavior prediction;probability model;situation awareness; In the recently, violent crime and accidental crime has been generated continuously. Consequently, people anxiety has been heightened. The Closed Circuit Television (CCTV) has been used to ensure the security and evidence for the crimes. However, the video captured from CCTV has being used in the post-processing to apply to the evidence. In this paper, we propose a flexible multi-level models for estimating whether dangerous behavior and the environment and context for pedestrians. The situation analysis builds the knowledge for the pedestrians tracking. Finally, the decision step decides and notifies the threat situation when the behavior observed object is determined to abnormal behavior. Thereby, tracking the behavior of objects in a multi-region, it can be seen that the risk of the object behavior. It can be predicted by the behavior prediction of crime.
Yu Jin Jung; Yong Ik Yoon. Study on abnormal behavior prediction models using flexible multi-level regression. Journal of the Korean Data And Information Science Society 2016, 27, 1 -8.
AMA StyleYu Jin Jung, Yong Ik Yoon. Study on abnormal behavior prediction models using flexible multi-level regression. Journal of the Korean Data And Information Science Society. 2016; 27 (1):1-8.
Chicago/Turabian StyleYu Jin Jung; Yong Ik Yoon. 2016. "Study on abnormal behavior prediction models using flexible multi-level regression." Journal of the Korean Data And Information Science Society 27, no. 1: 1-8.
The high incidence of heinous crime is increasing to use of CCTV. However, CCTV has been used to obtain evidence rather than crime prevention. Also it shows a weak effect about preventing crime. To solve the weak effort, we propose a Flexible Multi-level Regression (FMR) model that should estimate a dangerous situation for the pedestrian. The FMR model is tracking the behavior of between pedestrians from multiple CCTV that are located in different locations. The FMR has a prediction logic that should estimate an abnormal situation to analyze the possibility of crime by using the Regression and Apriori algorithm. The FMR model can be usefully used to prevent the crime because of an immediate response and rapid situation assessment.
Yu-Jin Jung; Yong-Ik Yoon. Flexible Multi-level Regression Model for Prediction of Pedestrian Abnormal Behavior. Lecture Notes in Electrical Engineering 2016, 137 -143.
AMA StyleYu-Jin Jung, Yong-Ik Yoon. Flexible Multi-level Regression Model for Prediction of Pedestrian Abnormal Behavior. Lecture Notes in Electrical Engineering. 2016; ():137-143.
Chicago/Turabian StyleYu-Jin Jung; Yong-Ik Yoon. 2016. "Flexible Multi-level Regression Model for Prediction of Pedestrian Abnormal Behavior." Lecture Notes in Electrical Engineering , no. : 137-143.
A success progress of pose estimation approaches motivates the activity recognition used in CCTV-based surveillance systems. In this paper, a method is proposed for recognizing interactive activities between two human objects. Based on articulated joint coordinates obtained from a pose estimation algorithm, the distance and direction feature are extracted from objects to describe both the spatial and temporal relation. The multiclass Support Vector Machine is finally employed for activity classification task. Compared with existing methods using the public interaction dataset, the proposed method outperforms in overall classification accuracy.
Thien Huynh-The; Dinh-Mao Bui; Sungyoung Lee; Yongik Yoon. Interactive Activity Recognition Using Articulated-Pose Features on Spatio-Temporal Relation. Lecture Notes in Electrical Engineering 2015, 345 -351.
AMA StyleThien Huynh-The, Dinh-Mao Bui, Sungyoung Lee, Yongik Yoon. Interactive Activity Recognition Using Articulated-Pose Features on Spatio-Temporal Relation. Lecture Notes in Electrical Engineering. 2015; ():345-351.
Chicago/Turabian StyleThien Huynh-The; Dinh-Mao Bui; Sungyoung Lee; Yongik Yoon. 2015. "Interactive Activity Recognition Using Articulated-Pose Features on Spatio-Temporal Relation." Lecture Notes in Electrical Engineering , no. : 345-351.
To deal with inference and reasoning problems, Gaussian process has been considered as a promising tool due to the robustness and flexibility features. Especially, solving the regression and classification, Gaussian process coupling with Bayesian learning is one of the most appropriate supervised learning approaches in terms of accuracy and tractability. Because of these features, it is reasonable to engage Gaussian process for energy saving purpose. In this paper, the research focuses on analyzing the capability of Gaussian process, implementing it to predict CPU utilization, which is used as a factor to predict the status of computing node. Subsequently, a migration mechanism is applied so as to migrate the system-level processes between CPU cores and turn off the idle ones in order to save the energy while still maintaining the performance.
Dinh-Mao Bui; Thien Huynh-The; Yongik Yoon; Sungik Jun; Sungyoung Lee. EAP: Energy-Awareness Predictor in Multicore CPU. Lecture Notes in Electrical Engineering 2015, 361 -366.
AMA StyleDinh-Mao Bui, Thien Huynh-The, Yongik Yoon, Sungik Jun, Sungyoung Lee. EAP: Energy-Awareness Predictor in Multicore CPU. Lecture Notes in Electrical Engineering. 2015; ():361-366.
Chicago/Turabian StyleDinh-Mao Bui; Thien Huynh-The; Yongik Yoon; Sungik Jun; Sungyoung Lee. 2015. "EAP: Energy-Awareness Predictor in Multicore CPU." Lecture Notes in Electrical Engineering , no. : 361-366.
CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road surveillance. The proposed system combines the pachinko allocation model (PAM) and support vector machine (SVM) for a hierarchical representation and identification of traffic behavior. A background subtraction technique using Gaussian mixture models (GMMs) and an object tracking mechanism based on Kalman filters are utilized to firstly construct the object trajectories. Then, the sparse features comprising the locations and directions of the moving objects are modeled by PAM into traffic topics, namely activities and behaviors. As a key innovation, PAM captures not only the correlation among the activities, but also among the behaviors based on the arbitrary directed acyclic graph (DAG). The SVM classifier is then utilized on top to train and recognize the traffic activity and behavior. The proposed model shows more flexibility and greater expressive power than the commonly-used latent Dirichlet allocation (LDA) approach, leading to a higher recognition accuracy in the behavior classification.
Thien Huynh-The; Oresti Banos; Ba-Vui Le; Dinh-Mao Bui; Yongik Yoon; Sungyoung Lee. Traffic Behavior Recognition Using the Pachinko Allocation Model. Sensors 2015, 15, 16040 -16059.
AMA StyleThien Huynh-The, Oresti Banos, Ba-Vui Le, Dinh-Mao Bui, Yongik Yoon, Sungyoung Lee. Traffic Behavior Recognition Using the Pachinko Allocation Model. Sensors. 2015; 15 (7):16040-16059.
Chicago/Turabian StyleThien Huynh-The; Oresti Banos; Ba-Vui Le; Dinh-Mao Bui; Yongik Yoon; Sungyoung Lee. 2015. "Traffic Behavior Recognition Using the Pachinko Allocation Model." Sensors 15, no. 7: 16040-16059.
For the past ten years, Gaussian process has become increasingly popular for modeling numerous inferences and reasoning solutions due to the robustness and dynamic features. Particularly concerning regression and classification data, the combination of Gaussian process and Bayesian learning is considered to be one of the most appropriate supervised learning approaches in terms of accuracy and tractability. However, due to the high complexity in computation and data storage, Gaussian process performs poorly when processing large input dataset. Because of the limitation, this method is ill-equipped to deal with the large-scale system that requires reasonable precision and fast reaction rate. To improve the drawback, our research focuses on a comprehensive analysis of Gaussian process performance issues, highlighting ways to drastically reduce the complexity of hyper-parameter learning and training phases, which could be applicable in predicting the CPU utilization in the demonstrated application. In fact, the purpose of this application is to save the energy by distributively engaging the Gaussian process regression to monitor and predict the status of each computing node. Subsequently, a migration mechanism is applied to migrate the system-level processes between multi-core and turn off the idle one in order to reduce the power consumption while still maintaining the overall performance.
Dinh-Mao Bui; Huu-Quoc Nguyen; Yongik Yoon; Sungik Jun; Muhammad Amin; Sungyoung Lee. Gaussian process for predicting CPU utilization and its application to energy efficiency. Applied Intelligence 2015, 43, 874 -891.
AMA StyleDinh-Mao Bui, Huu-Quoc Nguyen, Yongik Yoon, Sungik Jun, Muhammad Amin, Sungyoung Lee. Gaussian process for predicting CPU utilization and its application to energy efficiency. Applied Intelligence. 2015; 43 (4):874-891.
Chicago/Turabian StyleDinh-Mao Bui; Huu-Quoc Nguyen; Yongik Yoon; Sungik Jun; Muhammad Amin; Sungyoung Lee. 2015. "Gaussian process for predicting CPU utilization and its application to energy efficiency." Applied Intelligence 43, no. 4: 874-891.
The Closed Circuit Television (CCTV) systems have been used at large scale for tracking and getting popularity with every passing day. The most common goal of CCTV system is prevention of crime and disorder by tracking the objects. In the future, the efficiently usage of CCTV is the protection of crime based the tracking system for predication about abnormal situations. In this paper, we propose a tracking model for prevention of crime by using Kalman Filter. The tracking model uses some extracted characteristic of objects and relationships among objects from CCTVs on the street. So, this paper studies the tracking model that consists of three steps as follows; object assessment, situation assessment, and risk assessment.
Yong-Ik Yoon; Jee-Ae Chun. Tracking Model for Abnormal Behavior from Multiple Network CCTV Using the Kalman Filter. Lecture Notes in Electrical Engineering 2015, 330, 933 -939.
AMA StyleYong-Ik Yoon, Jee-Ae Chun. Tracking Model for Abnormal Behavior from Multiple Network CCTV Using the Kalman Filter. Lecture Notes in Electrical Engineering. 2015; 330 ():933-939.
Chicago/Turabian StyleYong-Ik Yoon; Jee-Ae Chun. 2015. "Tracking Model for Abnormal Behavior from Multiple Network CCTV Using the Kalman Filter." Lecture Notes in Electrical Engineering 330, no. : 933-939.