• 한국정보통신설비학회:학술대회논문집
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• 2003.08a
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• pp.25-28
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• 2003

본 논문은 Ka 밴드(26.5GHz-40GHz)를 사용한 양방향 멀티미디어 시스템의 설계에 대해서 다루고 있다. 우선 무궁화 3호 위성을 이용한 시범사업의 구성 및 운영과정에 대해서 개괄하고, 시범운영에서 발견된 문제점을 소개한다. Ka 밴드 양방향 위성 시스템은 MF-TDMA(Multi-Frequency Time Division Multiple Access) 방식을 사용하여 최대 전송속도 2Mbps 이상이고, QOS(Quality of Service) 제공 등 다양한 기능을 갖추고 있으나 Ka 주파수 특성상 그 안정성에 있어서 어려움을 갖고 있다. 강우감쇄 및 전송 지연 등의 현상들을 소개하고 향후 위성 양방향 시스템의 구성과 응용 서비스를 구현할 때 고려해야 할 사항들에 대해 고찰해 보고, 해결방안을 제시하였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1306-1314
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• 2015

This paper deals with a switching method of a three-phase interleaved bidirectional DC-DC converter to obtain high efficiency in wide load range. The proposed soft-switching method provides ZVS and ZCS at turn-on, and ZVS at turn-off of the switch as well as considerably reduced conduction loss in light load. Simulation and experiment are carried out with a bidirectional DC-DC converter having the power rating of 3 [kW], and those results show the validity of the proposed switching method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.7-12
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• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.398-405
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• 2004

A human-friendly interactive system that is based on the harmonious symbiotic coexistence of humans and robots is explored. Based on the interactive technology paradigm, a robotic cane is proposed for blind or visually impaired pedestrians to navigate safely and quickly through obstacles and other hazards. Robotic aids, such as robotic canes, require cooperation between humans and robots. Various methods for implementing the appropriate cooperative recognition, planning, and acting, have been investigated. The issues discussed include the interaction between humans and robots, design issues of an interactive robotic cane, and behavior arbitration methodologies for navigation planning.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.9
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• pp.135-142
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• 2009

In the present study replication of microstructured surfaces by microinjection molding was carried out. For a fabrication of mold inserts, nickel microstructures having various characteristic dimensions were fabricated by nickel electroforming onto Si mother microstructures. In addition, reverse nickel microstructures based on the electroformed nickel microstructures were successfully realized by electroforming with passivation process. The fabricated nickel microstructures were used as mold inserts for a replication of microstructured surfaces by microinjection molding. Microinjection molding experiment was carried out under three different processing conditions, which revealed effects of a packing stage and mold wall temperature. The microinjection-molded microstructured surfaces were characterized by using an atomic force microscope (AFM). It was found that mold wall temperature could enhance replication quality resulting in the precise microstructured surfaces.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.480-486
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• 2007

A gait diagnosis supporting system is necessary to evaluate the characteristics of abnormal gait of a patient in a systematic and efficient manner. The present study developed a gait diagnosis supporting system which compares abnormal gait of a patient with a reference gait data and presents abnormal gait characteristics in an organized form. Three types of diagnosis modules were developed for the spatio-temporal, kinematic and kinetic gait parameters, and a gait data for Korean normal adults was used for the reference data of the system. The system was applied to evaluate the gait pattern of three arthritis patients and the abnormal gait characteristics of them could be easily identified with a systematic and graphical presentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.529-546
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• 2010

Greedy forwarding is the key mechanism of geographic routing and is one of the protocols used most commonly in wireless sensor networks. Greedy forwarding uses 1-hop local information to forward packets to the destination and does not have to maintain the routing table, and thus it takes small overhead and has excellent scalability. However, the signal intensity reduces exponentially with the distance in realistic wireless sensor network, and greedy forwarding consumes a lot of energy, since it forwards the packets to the neighbor node closest to the destination. Previous proposed greedy forwarding protocols are the sender-based greedy forwarding that a sender selects a neighbor node to forward packets as the forwarding node and hence they cannot guarantee energy efficient forwarding in unpredictable wireless environment. In this paper, we propose the receiver-based greedy forwarding called RGF where one of the neighbor nodes that received the packet forwards it by itself. In RGF, sender selects several energy efficient nodes as candidate forwarding nodes and decides forwarding priority of them in order to prevent unnecessary transmissions. The simulation results show that RGF improves delivery rate up to maximum 66.8% and energy efficiency, 60.9% compared with existing sender-based greedy forwarding.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.6 s.336
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• pp.41-52
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• 2005

In wireless networks there are various errors, caused by multi-path fading and interference between devices which lower the network Performance. Especially, performance of IEEE 802.IS.3 High-Rate WPAN (Wireless Personal Area Network) which is operated in ISM unlicensed frequency band is easily affected by channel errors. In this paper, we propose a scheduling algorithm which takes channel errors into consideration in scheduling asynchronous data traffic. The proposed scheduling algorithm can allocate CTA(Channel Time Allocation) proportionally in accordance with the requested channel time of each device. It also prevents waste of channel time by allocating CTA of the channel-error devices to other channel-error free devices. After recovering from the channel error, the devices are compensated as much as they conceded during channel error status. Simulation results show that the proposed scheduling algorithm is superior to the existing SRPT(Shortest Remain Processing Time) and RR(Round Robin) in throughput and fairness aspects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.85-95
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• 2019

Recently, studies on the fourth industrial revolution have been actively conducted in the areas of government, business, and academia. Corporate business models that utilize the major agendas of the fourth industrial revolution such as robots, artificial intelligence, Internet of things (IoT), and block chains have been created, and various changes have occurred in not only business, education, and living environments but also in international relations. In this study, we looked at changes in social contribution activities from the perspective of a company facing impacts of the fourth industrial revolution. This study examines the definition and activities of corporate social contribution and how we can contribute to society through corporate activities. 'AT Educom', LG Uplus 'Social Contribution through IoT', KT's anti-infectious disease prevention platform and cases of Intel using IoT. In addition, we have presented what we need to do in the future to promote corporate social contribution activities that will make more meaningful impacts on how corporate social contribution activities will change according to technology development. The first, measuring the performance of corporate social contribution activities needs a standardized methodology and social contribution activities through platform business and ICT should be actively pursued. Lastly, social contribution activities between companies and sectors will increase.