• Title, Summary, Keyword: Markov

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A Study of Image Target Tracking Using ITS in an Occluding Environment (표적이 일시적으로 가려지는 환경에서 ITS 기법을 이용한 영상 표적 추적 알고리듬 연구)

  • Kim, Yong;Song, Taek-Lyul
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.4
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    • pp.306-314
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    • 2013
  • Automatic tracking in cluttered environment requires the initiation and maintenance of tracks, and track existence probability of true track is kept by Markov Chain Two model of target existence propagation. Unlike Markov Chain One model for target existence propagation, Markov Chain Two model is made up three hypotheses about target existence event which are that the target exist and is detectable, the target exists and is non-detectable through occlusion, and the target does not exist and is non-detectable according to non-existing target. In this paper we present multi-scan single target tracking algorithm based on the target existence, which call the Integrated Track Splitting algorithm with Markov Chain Two model in imaging sensor.

Enhanced Markov-Difference Based Power Consumption Prediction for Smart Grids

  • Le, Yiwen;He, Jinghan
    • Journal of Electrical Engineering and Technology
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    • v.12 no.3
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    • pp.1053-1063
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    • 2017
  • Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

Quaternion Markov Splicing Detection for Color Images Based on Quaternion Discrete Cosine Transform

  • Wang, Jinwei;Liu, Renfeng;Wang, Hao;Wu, Bin;Shi, Yun-Qing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.7
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    • pp.2981-2996
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    • 2020
  • With the increasing amount of splicing images, many detection schemes of splicing images are proposed. In this paper, a splicing detection scheme for color image based on the quaternion discrete cosine transform (QDCT) is proposed. Firstly, the proposed quaternion Markov features are extracted in QDCT domain. Secondly, the proposed quaternion Markov features consist of global and local quaternion Markov, which utilize both magnitude and three phases to extract Markov features by using two different ways. In total, 2916-D features are extracted. Finally, the support vector machine (SVM) is used to detect the splicing images. In our experiments, the accuracy of the proposed scheme reaches 99.16% and 97.52% in CASIA TIDE v1.0 and CASIA TIDE v2.0, respectively, which exceeds that of the existing schemes.

An Automatic Summarization of Call-For-Paper Documents Using a 2-Phase hidden Markov Model (2단계 은닉 마코프 모델을 이용한 논문 모집 공고의 자동 요약)

  • Kim, Jeong-Hyun;Park, Seong-Bae;Lee, Sang-Jo;Park, Se-Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.2
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    • pp.243-250
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    • 2008
  • This paper proposes a system which extracts necessary information from call-for-paper (CFP) documents using a hidden Markov model (HMM). Even though a CFP does not follow a strict form, there is, in general, a relatively-fixed sequence of information within most CFPs. Therefore, a hiden Markov model is adopted to analyze CFPs which has an advantage of processing consecutive data. However, when CFPs are intuitively modeled with a hidden Markov model, a problem arises that the boundaries of the information are not recognized accurately. In order to solve this problem, this paper proposes a two-phrase hidden Markov model. In the first step, the P-HMM (Phrase hidden Markov model) which models a document with phrases recognizes CFP documents locally. Then, the D-HMM (Document hidden Markov model) grasps the overall structure and information flow of the document. The experiments over 400 CFP documents grathered on Web result in 0.49 of F-score. This performance implies 0.15 of F-measure improvement over the HMM which is intuitively modeled.

Weighted Markov Model for Recommending Personalized Broadcasting Contents (개인화된 방송 컨텐츠 추천을 위한 가중치 적용 Markov 모델)

  • Park, Sung-Joon;Hong, Jong-Kyu;Kang, Sang-Gil;Kim, Young-Kuk
    • Journal of KIISE:Computing Practices and Letters
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    • v.12 no.5
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    • pp.326-338
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    • 2006
  • In this paper, we propose the weighted Markov model for recommending the users' prefered contents in the environment with considering the users' transition of their content consumption mind according to the kind of contents providing in time. In general, TV viewers have an intention to consume again the preferred contents consumed in recent by them. In order to take into the consideration, we modify the preference transition matrix by providing weights to the consecutively consumed contents for recommending the users' preferred contents. We applied the proposed model to the recommendation of TV viewer's genre preference. The experimental result shows that our method is more efficient than the typical methods.

Prediction of the Urbanization Progress Using Factor Analysis and CA-Markov Technique (요인분석 및 CA-Markov기법을 이용한 미래의 도시화 진행 양상 예측기법 개발)

  • Park, Geun-Ae;Kim, Seong-Joon
    • Journal of The Korean Society of Agricultural Engineers
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    • v.49 no.6
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    • pp.105-114
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    • 2007
  • This study is to predict the spatial expansion of urban areas by applying CA(Cellular Automata)-Markov technique considering MCE(multi-criteria evaluation) and MOLA(multi-objective land allocation) of factor analysis. For the 10 administration districts$(3677.3km^2)$ including the whole Anseong-cheon watershed, the past six temporal land use data(1973, 1981, 1985, 1990, 1994, 2000) from Landsat satellite images were prepared. During this period, the urban area increased $233.71km^2$. Using the 36 indices composed of topological characteristics, population and land use change, the final factor map of MOLA was produced through 5 maps of MCE. Using 1990 and 1994 land use data, the 2000 predicted urban area of CA-Markov with factor map showed 0.06% improvement of absolute error comparing with that of CA-Markov without factor map. By the CA-Markov technique considering factor map, the 2030 and 2060 urban area increased $58.94km^2(0.78%)\;and\;60.14km^2(0.81%)$ respectively comparing with 2000 urban area$(313.19km^2)$. The 2030 and 2060 paddy area decreased $93.28km^2(2.54%)\;and\;93.65km^2(2.55%)$ respectively comparing with 2000 paddy area$(1383.23km^2)$.

Study on Demand Estimation of Agricultural Machinery by Using Logistic Curve Function and Markov Chain Model (로지스틱함수법 및 Markov 전이모형법을 이용한 농업기계의 수요예측에 관한 연구)

  • Yun Y. D.
    • Journal of Biosystems Engineering
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    • v.29 no.5
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    • pp.441-450
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    • 2004
  • This study was performed to estimate mid and long term demands of a tractor, a rice transplanter, a combine and a grain dryer by using logistic curve function and Markov chain model. Field survey was done to decide some parameters far logistic curve function and Markov chain model. Ceiling values of tractor and combine fer logistic curve function analysis were 209,280 and 85,607 respectively. Based on logistic curve function analysis, total number of tractors increased slightly during the period analysed. New demand for combine was found to be zero. Markov chain analysis was carried out with 2 scenarios. With the scenario 1(rice price $10\%$ down and current supporting policy by government), new demand for tractor was decreased gradually up to 700 unit in the year 2012. For combine, new demand was zero. Regardless of scenarios, the replacement demand was increased slightly after 2003. After then, the replacement demand is decreased after the certain time. Two analysis of logistic owe function and Markov chain model showed the similar trend in increase and decrease for total number of tractors and combines. However, the difference in numbers of tractors and combines between the results from 2 analysis got bigger as the time passed.

Accuracy evaluation of ZigBee's indoor localization algorithm (ZigBee 실내 위치 인식 알고리즘의 정확도 평가)

  • Noh, Angela Song-Ie;Lee, Woong-Jae
    • Journal of Internet Computing and Services
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    • v.11 no.1
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    • pp.27-33
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    • 2010
  • This paper applies Bayesian Markov inferred localization techniques for determining ZigBee mobile device's position. To evaluate its accuracy, we compare it with conventional technique, map-based localization. While the map-based localization technique referring to database of predefined locations and their RSSI data, the Bayesian Markov inferred localization is influenced by changes of time, direction and distance. All determinations are drawn from the estimation of Received Signal Strength (RSS) using ZigBee modules. Our results show the relationship between RSSI and distance in indoor ZigBee environment and higher localization accuracy of Bayesian Markov localization technique. We conclude that map-based localization is not suitable for flexible changes in indoors because of its predefined condition setup and lower accuracy comparing to distance-based Markov Chain inference localization system.

A study on Classification of Insider threat using Markov Chain Model

  • Kim, Dong-Wook;Hong, Sung-Sam;Han, Myung-Mook
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.4
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    • pp.1887-1898
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    • 2018
  • In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

Analysis of Korean Language by First Order Markov Source (한글의 First Order Markov Source에 의한 해석)

  • 한영렬;박종원
    • Proceedings of the Korean Institute of Communication Sciences Conference
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    • pp.51-55
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    • 1982
  • The analysis of Korean language by the first order markov source is carried out. The calculated entropy of the first order Markov source is also included. The results presented here are new data. The data can be useful in designing the keyboard pattern of terminal and the automatic discrimination of monosyllable in Korean language.

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