• Advances in Computational Design
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• v.7 no.1
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• pp.57-68
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• 2022

A simulation study of the distribution of magnetic flux induced by a U-shaped electromagnet into a two-layer massive object with variations in the depth and properties of the surface layer has been carried out. It has been established that the hardened surface layer "pushes" the magnetic flux into the bulk of the magnetized object and the magnetic flux penetration depth monotonically increases with increasing thickness of the hardened layer. A change in the thickness and magnetic properties of the surface layer leads to a redistribution of magnetic fluxes passing between the poles of the electromagnet along with the layer and the bulk of the steel object. In this case, the change in the layer thickness significantly affects the magnitude of the tangential component of the field on the surface of the object in the interpolar space, and the change in the properties of the layer affects the magnitude of the magnetic flux in the magnetic "transducer-object" circuit. This difference in magnetic parameters can be used for selective testing of the surface hardening quality. It has been shown that the hardened layer pushes the magnetic flux into the depth of the magnetized object. The nominal depth of penetration of the flow monotonically increases with an increase in the thickness of the hardened layer.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.667-674
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• 2014

One of the largest problems for tokamak devices such as Kazakhstan Tokamak for Material Testing (KTM) is the operation scenarios' development and execution. Operation scenarios may be varied often, so a convenient hardware and software solution is required for scenario management and execution. Dozens of diagnostic and control subsystems with numerous configuration settings may be used in an experiment, so it is required to automate the subsystem configuration process to coordinate changes of the related settings and to prevent errors. Most of the diagnostic and control subsystems software at KTM was unified using an extra software layer, describing the hardware abstraction interface. The experiment sequence was described using a command language. The whole infrastructure was brought together by a universal communication protocol supporting various media, including Ethernet and serial links. The operation sequence execution infrastructure was used at KTM to carry out plasma experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.553-556
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• 2022

이상지질혈증의 발병에 대한 조기 진단 및 관리하는 것은 중요한 문제이다. 이상지질혈증의 진단은 혈액계측 정보 중에서 네 가지 LDL, HDL, TG, 그리고 TC를 이용하여 진단하며, 이상지질혈증 관리를 위해서는 LDL을 추정하는 것이 중요하다. 본 논문에서는 나이, 성별, 그리고 BMI와 같은 신체계측 정보를 학습하여 LDL-콜레스테롤을 예측하기 위한 준지도학습(Semi-supervised learning) 기반 기계학습 방법을 제안한다. 제안 방법은 얕은 학습(Shallow Learning)기반의 MLP(Multi-Layer Perceptron)을 이용하고, 이상지질혈증 진단인자간의 상관관계를 고려하여 신체계측 정보로 예측된 HDL, TG, 그리고 TC을 이용하여 일반적인 기계학습을 이용한 예측방법의 정확도를 개선한다. 즉, 제안방법은 신체계측 정보를 이용하여 혈액계측 정보의 LDL, HDL, TG, 그리고 TC을 각각 예측하고, 신체계측에 혈액계측의 예측 정보를 추가하여 학습한 준지도학습 기반 얕은 네트워크를 설계한다. 실험결과, HDL, TG, 그리고 TC의 혈액예측 정보를 이용한 준지도학습 기반 LDL 예측 정확도는 71.4%로 신체계측 정보만을 이용한 예측 방법의 67.0% 보다 약 4.4% 개선할 수 있음을 확인한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05b
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• pp.1405-1408
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• 2003

유선망에 최적화되도록 진화해온 TCP 는 GPRS, CDMA2000-1X 등의 무선망에서는 제 성능을 발휘하지 못한다는 것은 이미 널리 알려진 사실이며, 이를 극복하기 위한 방법들이 상당수 제안되었다. 하지만 제안된 연구들의 대부분은 이미 3G 서비스의 링크 계층에서 상당부분 해결된 무선 링크에서의 손실을 혼잡으로 오해하는 TCP 의 문제점을 해결하기 위한 것이다. 본 논문에서는 무선 링크에서의 손실보다는 무선 링크로 인해 야기되는 길고 변화가 심한 전송시간으로 인해 발생하는 TCP 의 문제점을 제시하고, 이를 해결하기 위한 방법을 제안한다. 특히, 본 연구에서는 시뮬레이션이 아닌 실제 CDMA2000-1X 망에서 단말기를 통해 측정된 자료를 기반으로 문제점을 도출하여 기존에 제안된 연구들과 차별화 한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1913-1920
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• 2017

Software Defined Networking creates totally new concept of networking and its applications which is based on separating the application and control layer from the networking infrastructure as a result it yields new opportunities in improving the network security and making it more automated in robust way, one of these applications is Denial of Service attack mitigation but due to the dynamic nature of Denial of Service attack it would require dynamic response which can mitigate the attack with the minimum false positive. In this paper we will propose a new mitigation Framework for DDoS attacks using Software Defined Networking technology to protect online services e.g. websites, DNS and email services against DoS and DDoS attacks.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.835-844
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• 2008

ZigBee devices are limited in resources especially on power and computational capacity but also require real-time operation at MAC layer. Therefore, it is important to take those requirement into consideration of system software design. In this paper, we proposed a compact system software design to support simultaneously ZigBee and IEEE802.15.4. The design strictly respects the resource and real-time constraints while being optimized for specific functions of both Zigbee and IEEE802.15.4. Various evaluations are done to show significant metrics of our design.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.709-710
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• 2019

제안 시스템은 모바일 사용자를 공공 인터넷 환경에서 사용 유형별로 분류 및 관리 가능한 서비스를 제공한다. 비콘을 이용하여 시스템 사용자의 위치와 서비스공간의 근접성을 확인한다. 또한 사용자의 서비스 사용 형태 및 성향에 따라 서비스 제공 장소에서 네트워크에 연결된 사물 인터넷 기기 및 기타 기기에 대한 접근 권한을 미리 정의하고 이를 제공할 수 있다. 본 연구는 이러한 시스템을 제안하고 구현된 결과를 바탕으로 시스템의 기능을 분석한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.881-882
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• 2020

최근 딥 러닝 (deep learning) 기술의 큰 발전으로 기존 기계 학습 분야의 기술들이 성공적으로 해결하지 못하던 많은 문제들을 해결할 수 있게 되었다. 이러한 딥 러닝의 학습 과정은 매우 많은 연산을 요구하기에 다수의 노드들로 모델을 학습하는 분산 학습 (distributed training) 기술이 연구되었다. 대표적인 분산 학습 기법으로 파라미터 서버 기반의 분산 학습 기법들이 있으며, 이 기법들은 파라미터 서버 노드가 학습의 병목이 될 수 있다는 한계를 갖는다. 본 논문에서는 이러한 파라미터 서버 병목 문제를 해결하는 파라미터 샤딩 기법에 대해 소개하고, 각 기법 별 학습 성능을 비교하고 그 결과를 분석하였다.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.603-611
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• 2001

This paper proposes a novel fast layer-by-layer algorithm that has better generalization capability. In the proposed algorithm, the weights of the hidden layer are updated by the target vector of the hidden layer obtained by least squares method. The proposed algorithm improves the learning speed that can occur due to the small magnitude of the gradient vector in the hidden layer. This algorithm was tested in a handwritten digits recognition problem. The learning speed of the proposed algorithm was faster than those of error back propagation algorithm and modified error function algorithm, and similar to those of Ooyen's method and layer-by-layer algorithm. Moreover, the simulation results showed that the proposed algorithm had the best generalization capability among them regardless of the number of hidden nodes. The proposed algorithm has the advantages of the learning speed of layer-by-layer algorithm and the generalization capability of error back propagation algorithm and modified error function algorithm.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.207-216
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• 2000

Due to recent growing interest in autonomous software agents and their potential application in areas such as electronic commerce, the autonomous negotiation become more important. Evidence from both theoretical analysis and observations of human interactions suggests that if decision makers have prior information on opponents and furthermore learn the behaviors of other agents from interaction, the overall payoff would increase. We propose a new methodology for a strategy finding process using data mining in autonomous negotiation system ; ANSIA (Autonomous Negotiation System using Intelligent Agent). ANSIA is a strategy based negotiation system. The framework of ANSIA is composed of following component layers : 1) search agent layer, 2) data mining agent layer and 3) negotiation agent layer. In the data mining agent layer, that plays a key role as a system engine, extracts strategy from the historic negotiation is extracted by competitive learning in neural network. In negotiation agent layer, we propose the autonomous negotiation process model that enables to estimate the strategy of opponent and achieve interactive settlement of negotiation. ANISIA is motivated by providing a computational framework for negotiation and by defining a strategy finding model with an autonomous negotiation process.