• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.149-161
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• 2008

As Programmable Logic Controllers(PLCs) are frequently used to implement real-time safety critical software, testing of PLC software is getting more important. We propose a structural testing technique on Function Block Diagram(FBD) which is one of the PLC programming languages. In order to test FBD networks, we define templates for function blocks including timer function blocks and propose an algorithm based on the templates to transform a unit FBD into a flowgraph. We generate test cases by applying existing testing techniques to the generated flowgraph. While the existing FBD testing technique do not consider infernal structure of FBD to generate test cases and can be applied only to FBD from which the specific intermediate model can be generated, this approach has advantages of systematic test case generation considering infernal structure of FBD and applicability to any FBD without regard to its intermediate format. Especially, the proposed method enables FBD networks including timer function blocks to be tested thoroughly. To demonstrate the effectiveness of the proposed method, we use trip logic of bistable processor of digital nuclear power plant protection systems which is being developed in Korea.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.649-658
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• 2003

We propose a hierarchical architecture of ART2 Network for performance improvement and fast pattern classification model using fitness selection. This hierarchical network creates coarse clusters as first ART2 network layer by unsupervised learning, then creates fine clusters of the each first layer as second network layer by supervised learning. First, it compares input pattern with each clusters of first layer and select candidate clusters by fitness measure. We design a optimized fitness function for pruning clusters by measuring relative distance ratio between a input pattern and clusters. This makes it possible to improve speed and accuracy. Next, it compares input pattern with each clusters connected with selected clusters and finds winner cluster. Finally it classifies the pattern by a label of the winner cluster. Results of our experiments show that the proposed method is more accurate and fast than other approaches.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.208-216
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• 2009

A number of estimation of distribution algorithms have been proposed that do not use explicitly crossover and mutation of traditional genetic algorithms, but estimate the distribution of population for more efficient search. But because it is not easy to discover higher-order correlations of variables, lower-order correlations are estimated most cases under various constraints. In this paper, we propose a new estimation of distribution algorithm that represents higher-order correlations of the data and finds global optimum more efficiently. The proposed algorithm represents the higher-order correlations among variables by building random hypergraph model composed of hyperedges consisting of variables which are expected to be correlated, and generates the next population by Bayesian sampling algorithm Experimental results show that the proposed algorithm can find global optimum and outperforms the simple genetic algorithm and BOA(Bayesian Optimization Algorithm) on decomposable functions with deceptive building blocks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3384-3389
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• 2009

There are two major problems with current National GAP system. One was false in traceability record because it was written or inputted by farmers or distributers and no other measures to check the accuracy was valid. The other was incapability of tracking back and recalling the contaminated agricultural products. For solving these matters, IT convergence model which combined information technology with agricultural experience is elaborated. In IT convergence model, video analytic system classifies every activity depending on the pre-programmed farming process and create the traceability data automatically. Also real time trace system based on USN would solve the problem of tracking back. This system transmits the present location and monitors data of agricultural products from farm to table at all times.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1072-1079
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• 2003

This study is purported for suggesting a new clustering algorithm that enables incremental categorization of numerous documents. The suggested algorithm adopts the natures of the spherical k-means algorithm, which clusters a mass amount of high-dimensional documents, and the fuzzy ART(adaptive resonance theory) neural network, which performs clustering incrementally. In short, the suggested algorithm is a combination of the spherical k-means vector space model and concept vector and fuzzy ART vigilance parameter. The new algorithm not only supports incremental clustering and automatically sets the appropriate number of clusters, but also solves the current problems of overfitting caused by outlier and noise. Additionally, concerning the objective function value, which measures the cluster's coherence that is used to evaluate the quality of produced clusters, tests on the CLASSIC3 data set showed that the newly suggested algorithm works better than the spherical k-means by 8.04% in average.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1219-1229
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• 1999

일반적으로 AND, OR, NOT과 같은 연산자를 사용하는 불리언 질의는 사용자의 검색의도를 정확하게 표현할 수 있기 때문에 검색 전문가들은 불리언 질의를 사용하여 높은 검색성능을 얻는다고 알려져 있지만, 일반 사용자는 자신이 원하는 정보를 불리언 형태로 표현하는데 익숙하지 않다. 본 논문에서는 검색성능의 향상과 사용자 편의성을 동시에 만족하기 위하여 사용자의 자연어 질의를 확장 불리언 질의로 자동 변환하는 방법론을 제안한다. 먼저 자연어 질의를 범주문법에 기반한 구문분석을 수행하여 구문트리를 생성하고 연산자 및 키워드 정보를 추출하여 구문트리를 간략화한다. 다음으로 간략화된 구문트리로부터 명사구를 합성하고 키워드들에 대한 가중치를 부여한 후 불리언 질의를 생성하여 검색을 수행한다. 또한 구문분석의 오류로 인한 검색성능 저하를 최소화하기 위하여 상위 N개 구문트리에 대해 각각 불리언 질의를 생성하여 검색하는 N-BEST average 방법을 제안하였다. 정보검색 실험용 데이타 모음인 KTSET2.0으로 실험한 결과 제안된 방법은 수동으로 추출한 불리언 질의보다 8% 더 우수한 성능을 보였고, 기존의 벡터공간 모델에 기반한 자연어질의 시스템에 비해 23% 성능향상을 보였다. Abstract There have been a considerable evidence that trained users can achieve a good search effectiveness through a boolean query because a structural boolean query containing operators such as AND, OR, and NOT can make a more accurate representation of user's information need. However, it is not easy for ordinary users to construct a boolean query using appropriate boolean operators. In this paper, we propose a boolean query formulation method that automatically transforms a user's natural language query into a extended boolean query for both effectiveness and user convenience. First, a user's natural language query is syntactically analyzed using KCCG(Korean Combinatory Categorial Grammar) parser and resulting syntactic trees are structurally simplified using a tree-simplifying mechanism in order to catch the logical relationships between keywords. Next, in a simplified tree, plausible noun phrases are identified and added into the same tree as new additional keywords. Finally, a simplified syntactic tree is automatically converted into a boolean query using some mapping rules and linguistic heuristics. We also propose an N-BEST average method that uses top N syntactic trees to compensate for bad effects of single incorrect top syntactic tree. In experiments using KTSET2.0, we showed that a proposed method outperformed a traditional vector space model by 23%, and surprisingly manually constructed boolean queries by 8%.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.318-326
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• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.663-673
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• 2005

An analysis model for the dynamics information of two-dimensional time-series patterns is described. In the proposed model, two novel transforms that visualize the dynamic characteristics are proposed. The first transform, referred to as speed equalization, reproduces a time-series pattern assuming a constant linear velocity to effectively model the temporal characteristics of the signing process. The second transform, referred to as velocity transform, maps the signal onto a horizontal vs. vertical velocity plane where the variation oi the velocities over time is represented as a visible shape. With the transforms, the dynamic characteristics in the original signing process are reflected in the shape of the transformed patterns. An analysis in the context of these shapes then naturally results in an effective analysis of the dynamic characteristics. The proposed transform technique is applied to an online signature verification problem for evaluation. Experimenting on a large signature database, the performance evaluated in EER(Equal Error Rate) was improved to 1.17$\%$ compared to 1.93$\%$ of the traditional signature verification algorithm in which no transformed patterns are utilized. In the case of skilled forgery experiments, the improvement was more outstanding; it was demonstrated that the parameter set extracted from the transformed patterns was more discriminative in rejecting forgeries

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.04a
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• pp.82-87
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• 1994

자동차 엔진의 주기적인 연소과정 동아네 생성된 힘에 의해 엔진의 크랭크 샤프트에 회전 불균일성이 나타난다. 이것은 엔진 플라이휘일 부분에 비틀림 변동토크를 발생시키고, 이 토크는 클러치를 통하여 변속기의 인풋기어(input gear)에 전달되어 변속기에 비틀림 진동을 일으키는 주요 원인이 된다. 공회전시 변속기에서 기어의 충돌은 주로 이 비틀림 변동토크에 의해 발생하며, 이 충돌은 차내 소음의 원인이 된다. 또한 엔진의 경량화 및 고출력화에 따른 회전수 변동의 증가는 비틀림 변동토크를 증가시켜 변속기에 커다란 진동을 초래한다. 시뮬레이션을 사용한 클러치 비틀림 기구의 적절한 특성치를 구하는 것은 클러치 설계에 효율적이고, 이미 여러 연구 결과들이 보고되었다. H.Arai은 2자유도 비선형 모델을 사용하여 클러치 접속시 발생하는 외란과 계의 안정성을 고려하여 치타음 저감을 위한 시뮬레이션을 수행하였고, S.Ohnuma은 비선형 2단 비틀림 특성을 가진 클러치 디스크의 설계에 대해서, 그리고, T.Fujimoto와 R.J.Comparin는 치타음의 발생구조와 특성을 고찰하고 비선형 비틀림 공진 저감에 의한 치타음 저감 기법에 대하여 연구하였다. 그리고, Wu Hui-Le는 자동차 동력전달계의 비틀림 진동 현상을 실험과 이론적인 계산을 통해 연구하였고, G.J.Fudala는 다자유도 모델을 이용하여 클러치의 비틀림 특성에 따라 주파수분석을 수행하여 치타음 저감 방법을 연구하였다. 또한, T.Sakai는 5자유도 모델을 이용하여 엔진 공회전시 발생하는 치타음에 대해 이론과 실험을 통해 해석하고, 엔진 회전수 변동, 클러치 특성, 변속기의 드래그(drag) 토크의 영향과 치타음 저감을 위한 개선된 클러치 특성을 제시하였다. 클러치는 동력을 전달 또는 차단하는 기능 뿐만 아니라 엔진이나 변속기에서 발생하는 소음이나 진동을 저감시키는 기능을 가지고 있다. 따라서 엔진 공회전시에 발생하는 치타음(rattle noise)이나 비틀림 진동을 저감시키는 방법으로는 여러가지가 있으나 클러치 디스크(clutch disc)의 비틀림 기구의 설계 인자들을 적절히 조절함으로써 변속기의 인풋기어에 전달되는 비틀림 진동을 저감시키는 방법이 일반적으로 수행되어지고 있다. 본 연구는 4 실린더 4 싸이클 1.5L 엔진을 장착한 경승용차의 실차실험을 통해 공회전시 엔진 플라이휘일과 인풋기어에서의 회전수 변동을 측정하고, 이 실험 데이타를 기초로 하여 엔진 토크 및 변속기에서의 드래그 토크를 계산하여 엔진-변속기 인풋기어의 반한정계 2자유도 진동모델과 비틀림 특성을 가진 클러치 디스크의 프리댐퍼 영역에 대해 시뮬레이션을 수행하여 클러치 비틀림 기구의 설계인자인 비틀림 강성, 히스테리시스 토크에 따른 비틀림 진동 저감 효과를 연구하고자 한다.