• 제목/요약/키워드: Probabilistic Search

검색결과 99건 처리시간 0.019초

SCORM에서 SCO의 클러스터링 기법 (A Method of Clustering for SCOs in the SCORM)

  • 윤홍원
    • 한국정보통신학회논문지
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    • 제10권12호
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    • pp.2230-2234
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    • 2006
  • SCORM에서 SCO는. 학습자가 검색하는 학습 단위가 된다. e-러닝 환경에서 학습자가 찾는 SCO를 신속하게 검색할 수 있는 저장 방법이 필요하다. 본 논문에서는 SCO의 클러스터링 방법을 수학적으로 정형화하여 정의하였다. 또한 SCO를 평가하는 기준을 제시하였고 각 SCO를 평가하는 절차를 나타내었다. 실험을 통하여 제안한 클러스터링 방법에 기반을 둔 검색이 기존의 검색 방법보다 성능이 우수함을 보였다.

퍼지 집합이론과 유전알고리즘을 이용한 일간 발전기 보수유지계획의 수립 (A Daily Scheduling of Generator Maintenance using Fuzzy Set Theory combined with Genetic Algorithm)

  • 오태곤;최재석;백웅기
    • 전기학회논문지
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    • 제60권7호
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    • pp.1314-1323
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    • 2011
  • The maintenance of generating units is implicitly related with power system reliability and has a tremendous bearing on the operation of the power system. A technique using a fuzzy search method which is based on fuzzy multi-criteria function has been proposed for GMS (generator maintenance scheduling) in order to consider multi-objective function. In this study, a new technique using combined fuzzy set theory and genetic algorithm(GA) is proposed for generator maintenance scheduling. The genetic algorithm(GA) is expected to make up for that fuzzy search method might search the local solution. The effectiveness of the proposed approach is demonstrated by the simulation results on a practical size test systems.

양자화 유전자알고리즘을 이용한 무기할당 (An Application of Quantum-inspired Genetic Algorithm for Weapon Target Assignment Problem)

  • 김정훈;김경택;최봉완;서재준
    • 산업경영시스템학회지
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    • 제40권4호
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    • pp.260-267
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    • 2017
  • Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

민감도가 고려된 알고리듬을 이용한 최적화 방법에 관한 연구 (A Study on the Optimization Method using the Genetic Algorithm with Sensitivity Analysis)

  • 이재관;신효철
    • 대한기계학회논문집A
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    • 제24권6호
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    • pp.1529-1539
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    • 2000
  • A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

커버곡 검색 정확도 향상을 위한 적합도 기반 크로마그램 쌍별 유사도 (A relevance-based pairwise chromagram similarity for improving cover song retrieval accuracy)

  • 서진수
    • 한국음향학회지
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    • 제43권2호
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    • pp.200-206
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    • 2024
  • 음악 유사도 계산은 음악 검색 서비스 구현에서 중요한 구성 요소 중 하나이다. 본 논문은 커버곡 검색 성능을 제고하기 위해서, 크로마그램 벡터 별로 커버곡 검색 적합도를 구하여 음악 유사도 계산 시 가중치로 활용한다. 커버곡 검색 적합도는 확률 적합도 모델을 이용하여 구한다. 커버곡 검색에 도움이 될 수 있는 분별도가 높은 벡터에 높은 가중치를 부여하고, 흔하게 존재하여 분별도가 떨어지는 벡터에 낮은 가중치를 부여하는 방식으로 음악 유사도 함수를 유도한다. 두 가지 커버곡 실험 데이터셋에서 성능 비교를 수행하여 제안한 음악 유사도 함수가 커버곡 검색 성능을 개선시킬 수 있음을 보였다.

Performance Analysis of Layer Pruning on Sphere Decoding in MIMO Systems

  • Karthikeyan, Madurakavi;Saraswady, D.
    • ETRI Journal
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    • 제36권4호
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    • pp.564-571
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    • 2014
  • Sphere decoding (SD) for multiple-input and multiple-output systems is a well-recognized approach for achieving near-maximum likelihood performance with reduced complexity. SD is a tree search process, whereby a large number of nodes can be searched in an effort to find an estimation of a transmitted symbol vector. In this paper, a simple and generalized approach called layer pruning is proposed to achieve complexity reduction in SD. Pruning a layer from a search process reduces the total number of nodes in a sphere search. The symbols corresponding to the pruned layer are obtained by adopting a QRM-MLD receiver. Simulation results show that the proposed method reduces the number of nodes to be searched for decoding the transmitted symbols by maintaining negligible performance loss. The proposed technique reduces the complexity by 35% to 42% in the low and medium signal-to-noise ratio regime. To demonstrate the potential of our method, we compare the results with another well-known method - namely, probabilistic tree pruning SD.

Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and Data Preprocessing Techniques

  • Huang, Wei;Oh, Sung-Kwun
    • Journal of Electrical Engineering and Technology
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    • 제12권2호
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    • pp.911-917
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    • 2017
  • There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

Learning Similarity with Probabilistic Latent Semantic Analysis for Image Retrieval

  • Li, Xiong;Lv, Qi;Huang, Wenting
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권4호
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    • pp.1424-1440
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    • 2015
  • It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

생리적 내재반응 및 얼굴표정 간 확률 관계 모델 기반의 감정인식 시스템에 관한 연구 (A Study on Emotion Recognition Systems based on the Probabilistic Relational Model Between Facial Expressions and Physiological Responses)

  • 고광은;심귀보
    • 제어로봇시스템학회논문지
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    • 제19권6호
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    • pp.513-519
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    • 2013
  • The current vision-based approaches for emotion recognition, such as facial expression analysis, have many technical limitations in real circumstances, and are not suitable for applications that use them solely in practical environments. In this paper, we propose an approach for emotion recognition by combining extrinsic representations and intrinsic activities among the natural responses of humans which are given specific imuli for inducing emotional states. The intrinsic activities can be used to compensate the uncertainty of extrinsic representations of emotional states. This combination is done by using PRMs (Probabilistic Relational Models) which are extent version of bayesian networks and are learned by greedy-search algorithms and expectation-maximization algorithms. Previous research of facial expression-related extrinsic emotion features and physiological signal-based intrinsic emotion features are combined into the attributes of the PRMs in the emotion recognition domain. The maximum likelihood estimation with the given dependency structure and estimated parameter set is used to classify the label of the target emotional states.

BtPDR: Bluetooth and PDR-Based Indoor Fusion Localization Using Smartphones

  • Yao, Yingbiao;Bao, Qiaojing;Han, Qi;Yao, Ruili;Xu, Xiaorong;Yan, Junrong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권8호
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    • pp.3657-3682
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    • 2018
  • This paper presents a Bluetooth and pedestrian dead reckoning (PDR)-based indoor fusion localization approach (BtPDR) using smartphones. A Bluetooth and PDR-based indoor fusion localization approach can localize the initial position of a smartphone with the received signal strength (RSS) of Bluetooth. While a smartphone is moving, BtPDR can track its position by fusing the localization results of PDR and Bluetooth RSS. In addition, BtPDR can adaptively modify the parameters of PDR. The contributions of BtPDR include: a Bluetooth RSS-based Probabilistic Voting (BRPV) localization mechanism, a probabilistic voting-based Bluetooth RSS and PDR fusion method, and a heuristic search approach for reducing the complexity of BRPV. The experiment results in a real scene show that the average positioning error is < 2m, which is considered adequate for indoor location-based service applications. Moreover, compared to the traditional PDR method, BtPDR improves the location accuracy by 42.6%, on average. Compared to state-of-the-art Wireless Local Area Network (WLAN) fingerprint + PDR-based fusion indoor localization approaches, BtPDR has better positioning accuracy and does not need the same offline workload as a fingerprint algorithm.