• 한국경영과학회지
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• 제39권3호
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• pp.23-40
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• 2014

This paper measures the operational efficiency of domestic online game companies and analyze its trends and patterns by using data envelopment analysis (DEA). DEA is a non-parametric approach to measuring the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs. 14 online game companies are selected as DMUs and three inputs (number of employees, capital and asset) and three outputs (sales, operating profit and net profit) are selected as DEA variables. First, the output-oriented BCC model and super-efficiency model are employed to measure the static operational efficiency of the online game companies from 2003 to 2012. We also conduct the dynamic analysis with DEA window model to capture the trends of their operational efficiency influenced by internal and external environmental changes. The results are expected to provide fruitful implications for strategic decision making of online game companies and policy making for the online game industry.

• 한국정보통신학회논문지
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• 제12권8호
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• pp.1384-1390
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• 2008

TCP는 유선망을 위하여 설계되었기 때문에, 유선망과 다른 특성을 가진 무선망에서 서비스되는 경우 성능이 크게 저하된다. 무선 Ad-hoc네트워크에서 혼잡윈도우(congestion window)값은 TCP의 성능에 크게 영향을 준다. 네트워크 상황에 따라 적절한 크기의 혼잡 윈도우 값을 설정함으로서 TCP성능을 향상시킬 수 있다. 본 논문에서는 무선Ad-hoc 네트워크에서 TCP 성능을 향상시키는 새로운 TCP 알고리즘을 제안한다. 제안된 알고리즘은 TCP 수신측에서 최적의 윈도우 크기를 측정하고, ACK 패킷의 윈도우 필드를 사용하여 CWL(Congestion Window Limit)를 최적 값에 세팅하는 최대 혼잡윈도우 조정 기법이다. 무선 Ad-hoc네트워크의 다양한 환경에서 컴퓨터 시뮬레이션을 수행한 결과 제안된 알고리즘이 기존 TCP new reno 프로토콜보다 전송률 및 패킷 손실에서 성능을 크게 향상하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.408-410
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• 2003

In this paper, an improved gradient direction assisted linking algorithm is proposed. This algorithm begins with initial seeds satisfying some local criteria. Then it will search along the direction provided by the initial point. A window will be generated in the gradient direction of the current point. Instead of the conventional method which only considers the value of the local salient structure, an improved mathematical model is proposed to describe the desired linear features. This model not only considers the value of the salient structure but also the direction of it. Furthermore, the linking problem under this model can be efficiently solved by dynamic programming method. This algorithm is tested for linear features detection in IKONOS images. The result demonstrates this algorithm is quite promising.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.438-441
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• 2004

Image binarization using a global threshold value [3] performs at high speed, but usually results in undesired binary images when the source images are of poor quality. In such cases, adaptive local thresholding algorithms [1][2][3] are used to obtain better results, and the algorithm proposed by A.E.Savekis which chooses local threshold using fore­ground and background clustering [1] is one of the best thresholding algorithms. However, this algorithm runs slowly due to its re-computing threshold value of each central pixel in a local window MxM. In this paper, we present a dynamic programming approach for the step of calculating local threshold value that reduces many redundant computations and improves the execution speed significantly. Experiments show that our proposal improvement runs more ten times faster than the original algorithm.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1021-1024
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• 1993

System identification is a major component for a control system. In biosystems, which is nonlinear and dynamic, precise identification would be very helpful for implementing a control system. It is difficult to precisely identify such non-linear systems. The measurable data on products from 2,3-butanediol fermentation could not be included in a process model based on kinetic approach. Meanwhile, a predictive capability is required in developing a control system. A neural network (NN) dynamic identifier with a by/(1＋ t ) transfer function was therefore designed being able to predict this fermentation. This modified inverse NN identifier differs from traditional models in which it is not only able to see but also able to predict the system. A moving window, with a dimension of 11 and a fixed data size of seven, was properly designed. One-step ahead identification/prediction by an 11-3-1 BPNN is demonstrated. Even under process fault, this neural network is still able to perform several-step ahead prediction.

• 산업경영시스템학회지
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• 제38권4호
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• pp.11-21
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• 2015

In order to achieve success in ground operations, searching for moving targets is one of critical factors. Usually, the system of searching for adversary ground moving targets has complex properties which includes target's moving characteristics, camouflage level, terrain, weather, available search time window, distance between target and searcher, moving speed, target's tactics, etc. The purpose of this paper is to present a practical quantitative method for effectively searching for infiltrated moving targets considering aforementioned complex properties. Based upon search theories, this paper consists of two parts. One is infiltration route analysis, through terrain and mobility analysis. The other is building dynamic probability maps through Monte Carlo simulation to determine the prioritized searching area for moving targets. This study primarily considers ground moving targets' moving pattern. These move by foot and because terrain has a great effect on the target's movement, they generally travel along a constrained path. With the ideas based on the terrain's effect, this study deliberately performed terrain and mobility analysis and built a constrained path. In addition, dynamic probability maps taking terrain condition and a target's moving speed into consideration is proposed. This analysis is considerably distinct from other existing studies using supposed transition probability for searching moving targets. A case study is performed to validate the effectiveness and usefulness of our methodology. Also, this study suggests that the proposed approach can be used for searching for infiltrated ground moving target within critical time window. The proposed method could be used not only to assist a searcher's mission planning, but also to support the tactical commander's timely decision making ability and ensure the operations' success.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.1062-1065
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• 2002

We have evaluated the ability of a time-delayed artificial neural network (TDANN) to predict muscle forces using only eletromyographic(EMG) signals. To achieve this goal, tendon forces and EMG signals were measured simultaneously in the gastrocnemius muscle of a dog while walking on a motor-driven treadmill. Direct measurements of tendon forces were performed using an implantable force transducer and EMG signals were recorded using surface electrodes. Under dynamic conditions, the relationship between muscle force and EMG signal is nonlinear and time-dependent. Thus, we adopted EMG amplitude estimation with adaptive smoothing window length. This approach improved the prediction ability of muscle force in the TDANN training. The experimental results indicated that dynamic tendon forces from EMG signals could be predicted using the TDANN, in vivo.

• 한국정밀공학회지
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• 제14권8호
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• pp.40-48
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• 1997

In this study, an analysis and design for hydraulic control system was developed. By using this system, the operator is able to simulate dynamic performance of the system without possessing special knowledge of software or control engineering. A graphical user interface was adopted in the system and all speration for simulation can be done by using window facilities on the display. The electro-hydraulic servo system is simulated to present the performances of the program and compared with the result of Matlab and experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• 전자공학회논문지B
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• 제30B권11호
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• pp.84-92
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• 1993

Calibration of Engine models is a painstaking process but very important for successful application to automotive industry problems. A combined heuristic and machine learning approach has therefore been adopted to improve the efficiency of model calibration. We developed an intelligent calibration program called ICALIB. It has been used on a daily basis for engine model applications, and has reduced the time required for model calibrations from many hours to a few minutes on average. In this paper, we describe the heuristic control strategies employed in ICALIB such as a hill-climbing search based on a state distance estimation function, incremental problem solution refinement by using a dynamic tolerance window, and calibration target parameter ordering for guiding the search. In addition, we present the application of amachine learning program called GID3*for automatic acquisition of heuristic rules for ordering target parameters.