• Journal of Power Electronics
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• 제13권5호
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• pp.798-805
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• 2013

This paper presents the control and implementation of the dual-stator-winding induction generator for variable frequency AC (VFAC) generating system. This generator has two sets of stator windings embedded into the stator slots. The power winding produces the VFAC power to feed the loads, and the control winding is connected to the static excitation controller to control the generator for output voltage regulation with speed and load variations. On the basis of the idea of power balance, an instantaneous slip frequency control (ISFC) strategy using the information of both the output voltage and the output power is used in this system. A series of experiments is carried out on a 15 kW prototype for verification. Results show that the system has good static and dynamic performance in a wide speed range, which demonstrates that the ISFC strategy is suitable for this system.

• 한국콘텐츠학회논문지
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• 제8권4호
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• pp.48-54
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• 2008

본 논문에서는 데이터 스트림 환경에서 윈도우 기반 연산자를 대상으로 메모리와 연산 비용의 상대적인 이해득실 관계를 분석한다. 이를 위하여 기본적인 연산자 네트워크 구성 요소를 식별하고, 윈도우 메모리의 재배치를 통한 메모리 소요량의 감소 효과와, 이로 인한 추가적인 연산 비용의 규모를 산정하는 비용 모델을 수립한다. 이러한 비용 모델을 통해 윈도우 메모리의 재배치의 효용성을 확인하고, 이러한 접근 방법을 데이터 스트림 질의의 실행 계획 개선을 위해 효과적으로 활용할 수 있는 방법을 모색한다. 이를 통해 데이터 스트림 환경에서 질의 처리 및 최적화의 적용 영역을 확장시키고, 윈도우 메모리 재배치를 통한 질의최적화를 위한 비용 산정 모델의 토대를 제공한다.

• 대한불안의학회지
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• 제3권2호
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• pp.69-76
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• 2007

Anorexia nervosa is a physical and psychosocial disorder that occurs most frequently in adolescent girls and young adult women. A decade ago, anorexia nervosa was rare outside of the developed western countries. However, it is now becoming a common clinical problem among young women in Korea. It is not enough to merely focus on relieving patients from the symptoms of "not eating," which is a practice that has been adopted by some forms of hospital care. The evidence base to guide treatment is limited. Nevertheless, there is the hope that a better understanding of the factors that play a role in the initiation and maintenance of disordered eating behaviors may be lead to more sophisticated treatments. This review aims to look beyond the overt "not eating" phenotype of anorexia nervosa and considers eating disorder endophenotypes based on Treasure's model. The first part of the review sets the basis for a framework of potential eating disorder endophenotypes. A description of the evidence of disordered eating behaviors as well as the clinical and psychopathological features associated with the central control of appetite follow. Finally, we describe how endophenotypes can be translated into treatments.

• 인터넷정보학회논문지
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• 제8권3호
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• pp.109-118
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• 2007

본 논문에서는 퍼지규칙 기반 시스템에서 규칙 내에 포함된 불완전한 속성을 제거하여 보다 간략화 된 규칙으로도 분류할 수 있는 방법을 제안하였다. 제안한 방법에서는 규칙 내에 포함된 불완전한 속성을 제거하기 위해 러프집합을 이용하였고 보다 명확한 분류를 위해 출력부 소속함수의 적합도가 최대인 속성들을 추출하였다. 또한 모의실험에서는 제안된 방법의 타당성을 검증하기 위해 rice taste data를 기반으로 규칙 감축 전 퍼지 max-product 결과와 규칙 감축 후 퍼지 max-product 결과를 비교하였다. 그 결과, 규칙 감축 전 max-product 결과와 규칙 감축 후 max-product 결과가 정확히 일치함을 볼 수 있었고, 보다 객관적인 검증을 위해 비퍼지화 된 실수 구간을 비교하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

• 한국화재소방학회논문지
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• 제6권1호
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• pp.90-98
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• 1992

Over the last 100 years since the introduction of electricity, the nation has faced ever increasing demand for electricity as consequence of the rapid economic growth. The expanded consumption ratio for electricity naturally increased the possibility for electricity related accident mainly iii the form of electrically ignited fire and human injuries from electric shock. Under such circumstances, the presented study sets a focus on analysing the causes of the electrically related fire accidents happened in the nation over the last 10 years(in the 80's) to provide a scientific basis for identifying the cause of electric fires. Identification of the cause of fire ignited electrically may be approached either by studying accident related electrical properties or by investigating power instruments at the place of the accient. In the present paper, the former approach is taken especially on investigating the consequences of over current induced by short circuiting of high power instruments which is reported as the primary cause electricity related fire accidents. In order to provide reliability of the identification method, microscopic photograph's are taken for the cross sections of the electrical materials(fuse, wire, plug socket and plug) after being exposed to over current and heated by external means respectively. The results are consequently compared and analysed.

• 전기학회논문지
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• 제65권6호
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• pp.1060-1069
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• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

• 한국조리학회지
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• 제13권3호
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• pp.220-238
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• 2007

This study investigates the importance of customers who use hotel restaurants on the basis of literature and actual data, establishes positioning strategies to stimulate hotel restaurants amidst an intensely competitive market, and sets up marketing strategies that can be applied to hotel restaurant business from the analysis results. Determinant factors for hotel restaurants were service quality, food, atmosphere and cleanness, brand and reputation, the attitude and appearance of attendants, and variety of menu, in the order of importance. As for the analysis results for satisfaction, the higher the customers regarded on the attitude and appearance of attendants and the food of the restaurant, the higher the overall satisfaction, the intention of revisiting, and the intention of recommendation of the customers became. Therefore, the marketing and promotion staffs of hotel restaurants should search for the ways to meet these needs of customers as much as possible, and identify the usage inclinations and satisfaction level of customers when carrying out marketing activities and establishing customer relationship marketing strategies.

• Preventive Nutrition and Food Science
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• 제13권4호
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• pp.348-353
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• 2008

Aerobic bacterial growth on Korean pan.fried meat patties as a primary quality deterioration factor was modeled as a function of temperature to estimate microbial spoilage on a real.time basis under dynamic storage conditions. Bacteria counts in the stretch.wrapped foods held at constant temperatures of 0, 5, 10 and $15^{\circ}C$ were measured throughout storage. The bootstrapping method was applied to generate many resampled data sets of mean microbial counts, which were then used to estimate the parameters of the microbial growth model of Baranyi & Roberts in the form of differential equations. The temperature functions of the primary model parameters were set up with confidence limits. Incorporating the temperature dependent parameters into the differential equations of bacterial growth could produce predictions closely representing the experimental data under constant and fluctuating temperature conditions.

• 한국대기환경학회지
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• 제23권5호
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• pp.526-538
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• 2007

Since air quality monitoring data sets are important base for developing of air quality management strategies including policy making and policy performance assessment, the environmental protection authorities need to organize and operate monitoring network properly. Air quality monitoring network of Busan, consisting of 18 stations, was allocated under unscientific and irrational principles. Thus the current state of air quality monitoring networks was reassessed the effect and appropriateness of monitoring objectives such as population protection and sources surveillance. In the process of the reassessment, a GIS-based decision support system was constructed and used to simulate air quality over complex terrain and to conduct optimization analysis for air quality monitoring network with multi-objective. The maximization of protection capability for population appears to be the most effective and principal objective among various objectives. The relocation of current monitoring stations through optimization analysis of multi-objective appears to be better than the network building for maximization of population protection capability. The decision support system developed in this study on the basis of GIS-based database appear to be useful for the environmental protection authorities to plan and manage air quality monitoring network over complex terrain.