• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.5
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• pp.416-425
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• 2013

A hybrid PN code phase acquisition system based on a least-mean-square adaptive filter, interpreted as a channel estimator is proposed and analyzed for direct-sequence spread-spectrum systems under frequency selective Rayleigh fading channels. Closed form expressions are derived for the filter tap weights and detection/false alarm probabilities. Compared to previously proposed systems, the proposed system achieves smaller mean acquisition times, is more robust to the operating signal-to-noise ratio and allows for multiplication free tap weight updates.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.2
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• pp.200-206
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• 2014

This paper describes magnetically levitated vehicle with hybrid magnets, which have been studied by the authors in place of streetcars or conveyance system. An experimental vehicle of 20kg was magnetically levitated by using a small number of dry-cell batteries, which consisted of 10 Ni-MH cells of 1900mAh in series. The magnets were activated sequentially, because the internal resistance of the batteries suppressed the maximum current. The vehicle was kept levitating for about 2 hours and was stable against disturbance due to instantaneous external force. In this paper, dynamic characteristics of the magnetically levitated vehicle using a small number of dry cell batteries are presented.

• Journal of Chest Surgery
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• v.47 no.4
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• pp.327-332
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• 2014

Single-port video-assisted thoracic surgery (VATS) has slowly established itself as an alternate surgical approach for the treatment of an increasingly wide range of thoracic conditions. The potential benefits of fewer surgical incisions, better cosmesis, and less postoperative pain and paraesthesia have led to the technique's popularity worldwide. The limited single small incision through which the surgeon has to operate poses challenges that are slowly being addressed by improvements in instrument design. Of note, instruments and video-camera systems that are narrower and angulated have made single-port VATS major lung resection easier to perform and learn. In the future, we may see the development of subcostal or embryonic natural orifice translumenal endoscopic surgery access, evolution in anaesthesia strategies, and cross-discipline imaging-assisted lesion localization for single-port VATS procedures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.735-748
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.757-763
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• 2002

A newly designed haptic interface enables an operator to control a remote robot precisely. It transmits position information to the remote robot and feeds back the interaction force from it. A control algorithm of haptic interface has been studied to improve the robustness and stability to uncertain dynamic environments with a proposed contact dynamic model that incorporates human hand dynamics. A simplified hybrid parallel robot dynamic model fur a 6 DOF haptic device was proposed to from a real time control system, which does not include nonlinear components. LQC/LTR scheme was adopted in this paper for the compensation of un-modeled dynamics. The recovery of the farce from the remote robot at the haptic interface was demonstrated through the experiments.

• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.103-109
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• 1996

The GTD-MM hybrid technique, which is used for analyzing elctromagnetic scattering problems of infinite scatterer or scatterers having no analytic solutions, requires knowledges about the diffracted current by the scatterers. And the technique determines the extent of moment method analysis area and the position of GTD matching points empirically. But these parameters give considerable effects to the accuracy of the solution. So we examine the problems and try to seek some practical guidance.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.90-94
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• 2020

Artificial intelligence (AI) is software that learns large amounts of data and provides the desired results for certain patterns. In other words, learning a large amount of data is very important, and the role of memory in terms of computing systems is important. Massive data means wider bandwidth, and the design of the memory system that can provide it becomes even more important. Providing wide bandwidth in AI systems is also related to power consumption. AlphaGo, for example, consumes 170 kW of power using 1202 CPUs and 176 GPUs. Since more than 50% of the consumption of memory is usually used by system chips, a lot of investment is being made in memory technology for AI chips. MRAM, PRAM, ReRAM and Hybrid RAM are mainly studied. This study presents various memory technologies that are being studied in artificial intelligence chip design. Especially, MRAM and PRAM are commerciallized for the next generation memory. They have two significant advantages that are ultra low power consumption and nearly zero leakage power. This paper describes a comparative analysis of the four representative new memory technologies.

• International Journal of Automotive Technology
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• v.3 no.4
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• pp.147-155
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• 2002

Mechatronic subsystems are more and more developed in automotive industries. To enhance the local controls performances, a cooperative control between ABS and Suspension systems is proposed. The respective controls are first designed separately with their dedicated models. Then a hybrid hierarchical architecture is developed. The advantage of this architecture is discussed through vehicle performance with simulation results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.185-188
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• 2007

본 논문은 하이브리드용 자동차의 리튬 이온 전지의 사이클 라이프에 따른 용량의 감소를 예측하고 잔량을 예측하기 위한 지능형 스마트 모듈의 설계를 제안한다. 리튬 이온 전지는 충 방전 횟수에 따라 전하를 담을 수 있는 용량이 감소하고, 방전 전압이 비선형이므로 정확한 잔량 예측이 어렵다. 따라서, 지능형 스마트 모듈은 전압과 전류, 온도의 측정을 위한 데이터 수집 장치를 제작하고 퍼지 로직을 이용한 잔량 측정 알고리즘을 통해 정확도가 높은 리튬 이온 전지의 잔량을 예측하고, 충 방전 실험 값과 퍼지 로직을 이용한 결과 값의 비교를 통해 그 효용성을 보인다.