• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 봄 학술발표회 논문집
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• pp.187-194
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• 1995

Traditional genetic algorithms(GA) have mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its targe computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of read code GA are developed to use continuous design variables directly. The results of real code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As results of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the rent code GA developed here can be used for the general optimization problem.

• Journal of the Korean Statistical Society
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• 제29권4호
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• Journal of Electrical Engineering and Technology
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• 제10권5호
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• pp.2142-2148
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• 2015

This paper proposes novel real-time footstep planning and following methods for the navigation of humanoid robots. A footstep command is defined by a walking direction and step lengths for footstep planning. The walking direction is determined by a uni-vector field navigation method, and the allowable yawing range caused by hardware limitation is considered. The lateral step length is determined to avoid collisions between the two legs while walking. The sagittal step length is modified by a binary search algorithm when collision occurs between the robot body and obstacles in a narrow space. If the robot body still collides with obstacles despite the modification of the sagittal step length, the lateral step length is shifted at the next footstep. For footstep following, a walking pattern generator based on a 3-D linear inverted pendulum model is utilized, which can generate modifiable walking patterns using the zero-moment point variation scheme. Therefore, it enables a humanoid robot to follow the footstep command planned for each footstep. The effectiveness of the proposed method is verified through simulation and experiment.

• 한국경영과학회지
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• 제28권4호
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• pp.191-198
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• 2003

The generalized knapsack problem or gknap is the combinatorial optimization problem of optimizing a nonnegative linear function over the integral hull of the intersection of a polynomially separable 0-1 polytope and a knapsack constraint. The knapsack, the restricted shortest path, and the constrained spanning tree problem are a partial list of gknap. More interesting1y, all the problem that are known to have a fully polynomial approximation scheme, or FPTAS are gknap. We establish some necessary and sufficient conditions for a gknap to admit an FPTAS. To do so, we recapture the standard scaling and approximate binary search techniques in the framework of gknap. This also enables us to find a weaker sufficient condition than the strong NP-hardness that a gknap does not have an FPTAS. Finally, we apply the conditions to explore the fully polynomial approximability of the constrained spanning problem whose fully polynomial approximability is still open.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1237-1240
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• 1996

This paper proposes a heuristic algorithm for performing the line balancing of PCB assembly fine including multiple surface mounters efficiently. We consider a PCB assembly line including the multiple surface mounters arranged serially as a target system. We assume that the number of heads of surface mounters can be changed. Also, the conveyor is assumed to move at a constant speed and have no buffer. Considering the minimum number of machines required for the desired production rate is a discrete nonincreasing function which is inversely proportional to the cycle time, we propose an optimization algorithm for line balancing by using the binary search method. Also we propose an head-changing algorithm. The algorithms are validated through the computer simulation.

• 한국통신학회논문지
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• 제24권10A호
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• pp.1507-1516
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• 1999

본 논문에서는 전력증폭기 선형화를 위한 Cartesian feedback 방식의 궤환 경로에서 발생하는 DC offset과 이득 및 위상 불일치를 자동적으로 보상하는 개선된 방식을 제안한다. Cartesian feedback에 의한 비선형 전력증폭기 왜곡성분의 감쇠 정도는 시스템 루프의 이득, 대역폭, 시간지연에 의해 결정된다고 알려져 있다. 그러나 궤환 경로 각 소자에서 발생하는 DC offset과 이득 및 위상의 불일치로 인하여 송신기의 출력신호에 원하지 않는 반송파 성분과 이미지 신호가 발생하여 궤환보상의 효과가 반감되는 결과를 초래한다. 본 논문에서는 디지털 신호처리 시스템 구조에서 이진 검색 (binary search) 알고리즘을 이용하여 궤환 경로에서 발생하는 DC offset과 이득 및 위상 불일치를 자동적으로 보상하는 방식을 제안하고 컴퓨터 모의실험을 통하여 제안된 방식의 성능을 분석한다. 모의실험에서 고려된 방식에 비하여 동일한 정도의 DC offset과 이득 및 위상 불일치의 보상에 걸리는 시간을 평균적으로 40% 단축할 수 있었다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2012년도 제46차 하계학술발표논문집 20권2호
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• pp.335-338
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• 2012

교통카드는 1996년 충전방식의 선불카드가 처음 사용되었고, 후불방식의 교통카드는 1998년 6월부터 도입되어 함께 사용되었다. 교통카드 사용할 수 있기 위해서는 사용자의 사용여부 및 각종 신상정보의 변경에 따라 결제방식이 변경되는 것에 대해 카드거래 승인시스템에 적절한 반영이 필요하다. 이를 위해 기존 서울교통시스템에서는 메모리 주소를 이용한 카드거래승인시스템을 이용하고 있으며 본 연구에서는 임베디드 교통카드단말기에서 사용 가능한 오프라인 카드거래 승인 알고리즘을 개발하는 것을 목표로 한다. 본 논문에서는 카드 사용정보를 압축 저장하는 방식을 제안하고 있으며, 사용자 할인정보는 한 카드당 2bit의 공간을 차지하도록 설계 했다. 또한 검색알고리즘으로 이진탐색을 사용하여 기존에 비해 검색 속도가 향상 되었다.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2005년도 하계학술대회
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• pp.285-289
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• 2005

In recent years, RFID is widely used in industrial applications including factory, material flow, logistics and defense areas. In this paper, we developed a RFID baseband system with ASK modulation and convolutional channel code. A commercial ASK RF module is used its frequency range in $350{\sim}351$MHz and power is 10mW and the convolution code is constraint length k=3 and rate R=1/2 The performance is measured implemented the binary search algorithm as anti-collision method and we show the wave shapes whit collision occurrence.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.599-602
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• 2010

인터넷 검색 사이트는 사용자들이 검색한 단어들의 순위를 매기는 실시간 검색 순위 서비스를 제공하는데 검색되는 단어들의 순위를 매기기 위해서는 각 단어들의 분포도를 알 수 있는 어절 빈도 계산을 수행해야 한다. 어절 빈도는 BST(Binary Search Tree)를 수행하여 계산할 수 있는데, 사용자에 의하여 검색되는 단어들은 길이와 그 형태가 다양하여 빈도 계산시에 BST 의 깊이가 깊어져서 계산 시간이 오래 걸리게 된다. 본 논문에서는 문자 해시를 이용하여 깊이가 깊은 BST 의 탐색 속도를 개선하는 알고리즘을 제안하였다. 이 방법으로 빈도 계산 속도를 비교하였을 때 문자 해시의 범위에 의해 1KB 의 추가적인 기억공간의 사용하여 9.3%의 성능 개선 효과가 있었고, 해시 공간을 10KB 추가로 사용할 때는 24.3%, 236KB 일 때는 40.6%로의 효율로 BST 의 빈도 계산 속도를 향상 시킬 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.