• 한국음향학회지
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• 제29권4호
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• pp.286-293
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• 2010

기존 데이터 압축 방식에는 크게 resolution-constrained quantization (RCQ) 방식과entropy-constrained quantization (ECQ) 방식이 있다. RCQ 방식은 고정 비트율 전송을 가능하게 하지만 셀 크기의 변화에 따른 이상 신호왜곡이 발생하며, ECQ 방식은 셀 크기가 고정된 대신에 이상 비트율 할당 문제가 발생한다. 본 논문에서는 기존 RCQ 방식의 대표적인 학습기법인 generalized Lloyd algorithm (GLA)을 개선한 cell-size constrained vector quantization (CCVQ) 방식을 제안한다. CCVQ 알고리즘은 셀 크기에 따라 유동적으로 패널티 척도를 주는 방식으로 기존의 RCQ와 ECQ 사이의 soft-decision을 가능하게 한다. 제안 알고리즘을 사용할 경우 기존의 GLA에 비해 약간의 평균왜곡 증가는 발생하나 이상 신호왜곡을 줄일 수 있다.

• 스마트미디어저널
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• 제6권3호
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• pp.89-94
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• 2017

기존의 지오펜스는 사용자의 관심 지역에 대한 가상의 영역을 설정하면, 설정된 영역에 대한 진/출입 상황인식 서비스만 제공한다. 최근 차세대 기술로서 주목받고 있는 커넥티드 카(Connected Cars)에 지오펜스 기술을 적용하면 추가적인 인프라 구축 비용 없이 응용 서비스가 가능하지만, 이를 위해선 기존 지오펜스의 기능을 확대하여 개선해야 한다. 본 논문에서는 커넥티드 카 기술을 지원하기 위해서 사전 연구된 모바일 3차원 지오펜스 시스템을 기반으로, 차량과 차량에 적용된 지오펜스 영역이 상황인식에 따라 반경이 변경되는 가변적인 모바일 지오펜스를 제안하고 구현한다. 제안된 가변적 모바일 지오펜스는 가변요인 분석에 의해 가변 결정 알고리즘을 적용한 후, 자신과 주변의 상황인식에 따라 지오펜스의 영역이 변경되는 것을 실험으로 나타낸다. 향후에는 본 논문에서 제안된 가변적인 지오펜스가 커넥티드 카 기술뿐만 아니라 안전하고 효율적인 차량 운행을 돕는 V2X(Vehicle to Everything)의 응용 기술로 활용될 것으로 전망한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권3호
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• pp.1093-1104
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• 2014

The High Efficiency Video Coding (HEVC) is a new video coding standard that can provide much better compression efficiency than its predecessor H.264/AVC. However, it is computationally more intensive due to the use of flexible quadtree coding unit structure and more choices of prediction modes. In this paper, a fast intraframe coding scheme is proposed for HEVC. Firstly, a fast bottom-up pruning algorithm is designed to skip the mode decision process or reduce the candidate modes at larger block size coding unit. Then, a low complexity rough mode decision process is adopted to choose a small candidate set, followed by early DC and Planar mode decision and mode filtering to further reduce the number of candidate modes. The proposed method is evaluated by the HEVC reference software HM8.2. Averaging over 5 classes of HEVC test sequences, 41.39% encoding time saving is achieved with only 0.77% bitrate increase.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.271-274
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• 2001

Recently, most vehicles has the Automatic transmission system as their transmission system. The automatic transmission system operates with fixed shift patterns. In the opposite of manual operation, it is easy and convenient for driving. Though these merit, the system can not evaluate the driver's intension because of usage of fixed shift pattern, To consider driver's intension, we must consider both the driving intensity of driver and the status of vehicle. In this paper, we developed flexible automatic transmission system by using the proposed moduled neural networks which can learn the status of the vehicle and driver's intensity As a result, we compare the transmission system using fixed shift pattern and the proposed transmission system and show the good performance in the change of shift position.

• 산업경영시스템학회지
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• 제20권43호
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• pp.249-263
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• 1997

This paper addresses the parts route selection and economic design in flexible manufactuirng system (FMS). Parts are processed through several stage workstations according to operation sequences. The machine of each workstation can do multiple operation functions. And the operation stage of a part can be processed in several workstations, which are non-identical in functional performance. The objective of this paper is to determine the processing routes of parts, number of machine at each workstation, number of vehicle and makespan time. Two models are suggested. One is assumed that the operation stage of parts can be processed at the only one among several available workstations. Other is assumed that the operation stage of parts is allowed to be processed at several workstations. Parts are transported by automated guided vehicles (AGVs). The decision criteria is to minimize the sum of processing cost, travel cost, setup cost and overhead cost. The formulation of models is represented. A solution algorithm is suggested, and a numerical example is shown.

• 산업경영시스템학회지
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• 제21권46호
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• pp.19-32
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• 1998

This paper addresses the processing route of parts and economic design in flexible manufacturing systems (FMSs) employing AGVs for Transport. Parts are processed through several workstations according to operation sequences. The machine of each workstation can do multiple operation functions. The operation stage of a part can be processed in several workstations, which are non-identical in functional performance. The objective of this paper is to determine the processing route of parts, number of machines at each workstation, number of vehicles. The model is assumed that the operation stage of parts can be processed at the only one among several available workstations. Parts are transported by automated guided vehicle system(AGVS). The decision criteria is to minimize the sum of processing cost, travel cost, operating cost. A model formulation is represented. A solution algorithm is suggested by using mathematical programming and simulation technique, and a numerical example is shown.

• 전자공학회논문지CI
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• 제47권1호
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• pp.104-115
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• 2010

유비쿼터스 기술의 보편화에 따라 유비쿼터스 환경의 보안 취약성을 해결하기 위한 보안기술의 연구가 주목받고 있다. 그러나 현재의 대다수 보안 시스템은 고정된 규칙을 기반으로 하는 것으로서, 유비쿼터스 기반 사용자의 다양한 상황에 제대로 대응하지 못하는 문제점이 있다. 또한 기존의 상황인식 보안 연구는 ACL (Access Control List) 혹은 RBAC (Role-Based Access Control) 계열의 연구가 많이 수행되고 있으나 보안정책의 관리에 대한 오버헤드가 크고, 또한 예상하지 못한 상황에 대한 대응이 어렵다는 문제점을 보이고 있다. 이에 본 논문에서는 퍼지 알고리즘과 MAUT를 이용하여 다양한 상황을 인식하고 적절한 보안기능을 제공하는 상황인식 보안 서비스를 제안한다.

• 지능정보연구
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• 제2권2호
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• pp.69-83
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• 1996

The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.209-213
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• 2021

Data mining is the application of specific algorithms for extracting patterns from data and KDD is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams. Data mining can be used for decision making in educational system. But educational institution does not use any knowledge discovery process approach on these data; this knowledge can be used to increase the quality of education. The problem was happening in the educational management system, but to make education system more flexible and discover knowledge from it huge data, we will use data mining techniques to solve problem.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
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• pp.256-259
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• 2002

Recently, most vehicles has the Automatic transmission system as their transmission system. The automatic transmission system operates with fixed shift patterns. In the opposite of manual operation, it is easy and convenient for driving. Though these merit, the system can not evaluate the driver's intension because of usage of firmed shift pattern. Especially, when the load has declination the AT system must operate for engine break effect. Namely, if the vehicle drives on the load of decrease, the acceleration of the vehicle goes to high then. At that time, the shift goes to down position the vehicle has some negative acceleration with the resistance of engine. To consider driver's intension in this case, we must consider both the driving intensity of driver and the status of load. In this paper, we developed flexible automatic transmission system by using the proposed moduled neural networks which can learn the status of the load and driver's intensity As a result, we compare the transmission system using firmed shift pattern and the proposed transmission system and show the good performance in the change of shift position.