• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 춘계학술대회 논문집
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• pp.75-80
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• 1999

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcome limitation of accuracy in the linear regression model or the engineering judgment model. And this model is compared with the engineering judgment model. It is not necessary complex process such like multi-regression analysis of the engineering judgment model. A fuzzy model does not need to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Like a regression model, this model can be applied to any machine, but it delivers greater accuracy and robustness.

• 산업공학
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• 제13권4호
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• pp.658-667
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• 2000

Seasonal variation of natural gas demand coupled with rigid and stable import pattern of gas represents the characteristic feature of the Korean Liquified Natural Gas(LNG) industry. This attribute has required a huge amount of investment for the construction of storage facility. Thus, to minimize the supply cost, it is legitimate to reduce storage requirement itself. In this study, we combine three alternative methods to deal with the storage requirement to minimize the supply cost. Those are (1) adding additional storage tanks, (2) inducing large firm customers, and (3) constructing gas-turbine self generation facilities. Methodologically, we employ the mixed integer program (MIP) to optimize the system. The model also consider demand and price-setting scheme in separate modules. From the results, it is shown that if alternatives are combined optimally, a number of storage tanks can be reduced substantially compared with the original capacity plan set by the industry authorities. We perform various sensitivity analyses to check the robustness of the results. The methodology presented in this study can be applied to the other physical network industry, such as hydraulics. The empirical results will shed some light on the rationalization of capacity planning of the Korean natural gas industry.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 B
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• pp.1111-1113
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• 2003

This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than a conventional PI speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PI controller and is proved robust about parameter variation through Matlab/Simulink simulation.

• 대한산업공학회지
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• 제37권3호
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• pp.180-190
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• 2011

Efficient energy consumption is a critical factor for deployment and operation of wireless sensor networks (WSNs). To achieve energy efficiency there have been several hierarchical routing protocols that organize sensors into clusters where one sensor is a cluster-head to forward messages received from its cluster-member sensors to the base station of the WSN. In this paper, we propose a self-organized clustering method for cluster-head selection and cluster based routing for a WSN. To select cluster-heads and organize clustermembers for each cluster, every sensor uses only local information and simple decision mechanisms which are aimed at configuring a self-organized system. By these self-organized interactions among sensors and selforganized selection of cluster-heads, the suggested method can form clusters for a WSN and decide routing paths energy efficiently. We compare our clustering method with a clustering method that is a well known routing protocol for the WSNs. In our computational experiments, we show that the energy consumptions and the lifetimes of our method are better than those of the compared method. The experiments also shows that the suggested method demonstrate properly some self-organized properties such as robustness and adaptability against uncertainty for WSN's.

• 한국정보처리학회논문지
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• 제3권3호
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• pp.581-590
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• 1996

본 논문에서는 복합적인 서비스를 동시에 제공하는 N-ISDN BRI용 멀티미디어통신 단말의 설계고려 사항과 그 시스템 구성 방식을 기술 하였다. 제한된 통신망 전송속도와 컴퓨터 하드웨어내에서 다양한 영상 서비스들을 복합 적으로 동시에 수용하기 위한 서비 스간 제어 및 미디어 처리방법을 제안하였다. 현재 시스템은 N-ISON 하에서 개인용 컴퓨터 기반의 탁상형 시스템으로 개발되었으며, 국내 N-ISDN에서의 충분한 현장 실험을 통해시스 템의 상호 환성 및 안정성을 확인하였다. 본 시스템은 영상회의 서비스를 기본으로 제공하 면서, 동시에 메시지 서비스와 검색 서비스들을 제공할 수 있으며, 아울러 화일 전송 및 고동 작업 환경을 제공한다.

• 대한의용생체공학회:의공학회지
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• 제27권5호
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• pp.291-299
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• 2006

A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5819-5840
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• 2018

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3258-3273
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• 2021

Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware.

• Current Optics and Photonics
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• 제4권6호
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• pp.509-515
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• 2020

A practical single photon source for fiber-based quantum information processing is still lacking. As a possible 1.55-㎛ quantum-dot single photon source, an InGaAsP/InP-air-aperture micropillar cavity is investigated in terms of fabrication tolerance. By properly modeling the processing uncertainty in layer thickness, layer diameter, surface roughness and the cavity shape distortion, the fabrication imperfection effects on the cavity quality are simulated using a finite-difference time-domain method. It turns out that, the cavity quality is not significantly changing with the processing precision, indicating the robustness against the imperfection of the fabrication processing. Under thickness error of ±2 nm, diameter uncertainty of ±2%, surface roughness of ±2.5 nm, and sidewall inclination of 0.5°, which are all readily available in current material and device fabrication techniques, the cavity quality remains good enough to form highly efficient and coherent 1.55-㎛ single photon sources. It is thus implied that a quantum dot contained InGaAsP/InP-air-aperture micropillar cavity is prospectively a practical candidate for single photon sources applied in a fiber-based quantum information network.

• Structural Engineering and Mechanics
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• 제77권4호
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.