• Journal of Internet of Things and Convergence
• /
• v.8 no.4
• /
• pp.1-7
• /
• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1123-1146
• /
• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

• Journal of Information Technology Applications and Management
• /
• v.17 no.3
• /
• pp.57-69
• /
• 2010

Many organizations have had deep interests in studies concerning leadership and in academic areas, in not only management but also psychology. Until now, leadership has been accentuated by managers or team leaders especially. Recently, however, the concept of self-leadership directing one's own activities through self-control or self-management is being focused on practices and in academia. This study is to investigate the influence between self-leadership strategies and learning performance in IT classes mediated by attitude of attendance focused on the social science students in a university. Research results can give us direction of task-taking attitudes in firms or learning attitudes in teaching organizations and implications to human resource managers who are in charge of improving learning performance or productivity.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.4
• /
• pp.35-41
• /
• 2012

Recently, universities have difficulties in operating the normal curriculum because fresher's basic academic ability is declined. It causes campus misfits so managing students is also not an easy matter. The education system that focuses only on college entrance exams is one of the reasons why this phenomenon occurred. Activity with self-directed Learning Community to know learning level themselves and execute systematic studying habit is essential for improving this problem. This activity can help students understanding and having interest in class and be motivated to study. But it had burdened tutors with submitting activity report in written form. In this paper, we suggest the Mobile Based Activity Report Submission System which can be the solution of the problem that the Self-directed Learning Community System has. This system reduces the emotional burden to write the reports and manages them efficiently.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.6 no.4
• /
• pp.193-198
• /
• 2010

Smart phone systems such as Android and iPhone are spreading into the next generation's computing and the prediction that cell phone (especially smart phone) penetration rate will be higher than that of PC in the near future is predominant. In this paper, we designed and materialized an item pool system which has self directed learning function that enables learning with smart phone (the PC in the hand). Users can choose the way of studying and do the offered estimation depending on their levels with smart phone. It is materialized the studying environment which can be done immediately anytime and anywhere.

• Management Science and Financial Engineering
• /
• v.16 no.1
• /
• pp.81-117
• /
• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.935-936
• /
• 2006

In this paper, we have applied a real-time face processor includes detection, recognition, and learning to a intelligent store management service robot. We use the Haar classifier and adaboost learning algorithm for face detection. For face recognition and learning, a PCA algorithm and a SVDD algorithm is used. We have developed a store management service robot and applied these algorithms to verify the performance.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.04a
• /
• pp.607-610
• /
• 2002

최근 5년간 e-Loaming에 대한 중요성과 웹 기반 학습의 활용성은 대부분의 기업에서 LMS(Learning Management System)의 형태로 도입을 하고 있다. 또한, 현재는 학습관리와 컨텐츠의 관리영역을 통합하고 학습 컨텐츠의 객체화를 통한 재사용성과 관리 측면을 극대화하는 노력이 이루어지고 있다. e-Learning을 활용하는 80%이상의 기업이 표준적인 메타데이터와 리파지토리를 기반으로하는 LCMS(Leaning Content Management System)형태로 전환하는 시점에서 LCMS 관린 연구가 요구된다. 본 연구에서는 학습객체를 통한 코스의 개발과 관리 배포를 위한 LCMS를 재사용 가능한 실행 모듈인 컴포넌트 기반으로 구성하고자 한다. 학습 컨텐츠 관리시스템에서의 주요 기능을 계층적으로 체계화하며, LCMS를 위한 컴포넌트 참조 아키텍처를 정의함으로써 개발의 용이성과 시간, 비용의 효율성을 보장한다. 또한, 재사용 및 공유가능한 학습객체를 통한 코스 개발로 학습 컨텐츠의 중복을 피하고 학습과정 개발의 시간 효율성을 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.10
• /
• pp.3771-3788
• /
• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.2
• /
• pp.93-100
• /
• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.