• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.190-191
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• 2010

본 논문은 전기자동차 (Electric Vehicles, EVs) 및 플러그인 하이브리드 자동차 (Plug-In Hybrid Electric Vehicles, PHEVs)용 리튬 이온 (Li-Ion) 배터리 충전을 위한 3.3 kW급 차량 탑재형 (On-Board) 충전기 하드웨어의 설계 및 제작에 대하여 기술한다. 차량 실장 특성을 고려하여 부하직렬공진형 dc-dc 컨버터를 적용하고, 80-130kHz의 고주파 스위칭 및 ZVS (Sero-Voltage Switching) 기법을 통해 수동소자의 크기를 최적화하여 5.84L, 5.8kg의 저부피, 경량을 달성한다. 전자부하를 대상으로 정전류 (Continuous Current, CC) 및 정전압 (Continuous Voltage, CV) 제어를 수행하여 93%의 고효율 획득 및 성능을 검증한다.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.89-97
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• 2021

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.90-102
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• 2010

Flash memory has been widely used as an important storage device for consumer electronics. For the flash memory-based storage systems, FTL (Flash Translation Layer) is used to handle the mapping between a logical page address and a physical page address. Especially, log buffer-based FTLs provide a good performance with small-sized mapping information. In designing the log buffer-based FTL, one important factor is to determine the mapping structure between data blocks and log blocks, called associativity. While previous works use static associativity fixed at the design time, we propose a new log block mapping scheme which adjusts associativity based on the run-time workload. Our proposed scheme improves the I/O performance about 5~16% compared to the static scheme by adjusting the associativity to provide the best performance.

• Journal of KIISE
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• v.42 no.6
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• pp.723-733
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• 2015

The OTT service that provides multimedia video has spread over the Internet for terminals with a variety of resolutions. The terminals are in communication via a networks such as 3G, LTE, VDSL, ADSL. The service of the network has been increased for a variety of terminals giving rise to the need for a new way of encoding multimedia is increasing. SVC is an encoding technique optimized for OTT services. We proposed an efficient multimedia management system for the SVC encoded multimedia data. The I/O trace was generated using a zipf distribution, and were comparatively evaluated for performance with the existing system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.36-43
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• 1999

This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1782-1791
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• 2017

In this paper, an optimal design of hybrid magnetic levitation(Maglev) system using intelligent optimization algorithms is proposed. The proposed maglev system adopts hybrid suspension system with permanent-magnet(PM) and electro magnet(EM) to reduce the suspension power loss and the teaching-learning based optimization(TLBO) that can overcome the drawbacks of conventional intelligent optimization algorithm is used. To obtain the mathematical model of hybrid suspension system, the magnetic equivalent circuit including leakage fluxes are used. Also, design restrictions such as cross section areas of PM and EM, the maximum length of PM, magnetic force are considered to choose the optimal parameters by intelligent optimization algorithm. To meet desired suspension power and lower power loss, the multi object function is proposed. To verify the proposed object function and intelligent optimization algorithms, we analyze the performance using the mean value and standard error of 10 simulation results. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Annual Conference of KIPS
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• 2009.04a
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• pp.643-646
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• 2009

최근에 IPTV(Internet Protocol TV) 셋톱박스의 보조기억장치의 성능과 저전력을 위한 연구가 많이 수행 되고 있다. IPTV를 위한 셋톱박스의 구성품인 하드디스크는 멀티미디어 데이터를 저장하고 저장된 데이터를 재생한다. 하지만 하드디스크는 기계적인 특성으로 인하여 전력 소모 문제 및 성능 저하 문제 등이 있다. 본 논문에서는 IPTV 환경에서 하드디스크와 플래시 메모리를 혼합한 하이브리드 저장 시스템을 구성 하여 멀티미디어 데이터의 n-프레임을 플래시 메모리로 선반입 하는 새로운 방법을 제안한다. 이 방법을 통해 하드디스크의 대기시간을 줄이고 전력 사용을 최적화 할 수 있다. 실험을 통해 제안한 방법이 기존 방법과 비교하여 20.69%의 평균응답시간을 개선하고 전력소모를 28.14% 감소시킴을 확인 하였다.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.35-47
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• 2005

This paper is to review a literature concerning Bus Transit Route Network Design(BTRNDP), to describe a future study direction for a systematic application for the BTRNDP. Since a bus transit uses a fixed route, schedule, stop, therefore an approach methodology is different from that of auto network design problem. An approach methodology for BTRNDP is classified by 8 categories: manual & guideline, market analysis, system analytic model. heuristic model. hybrid model. experienced-based model. simulation-based model. mathematical optimization model. In most previous BTRNDP, objective function is to minimize user and operator costs, and constraints on the total operator cost, fleet size and service frequency are common to several previous approach. Transit trip assignment mostly use multi-path trip assignment. Since the search for optimal solution from a large search space of BTRNDP made up by all possible solutions, the mixed combinatorial problem are usually NP-hard. Therefore, previous researches for the BTRNDP use a sequential design process, which is composed of several design steps as follows: the generation of a candidate route set, the route analysis and evaluation process, the selection process of a optimal route set Future study will focus on a development of detailed OD trip table based on bus stop, systematic transit route network evaluation model. updated transit trip assignment technique and advanced solution search algorithm for BTRNDP.