• Journal of KIISE
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• v.42 no.2
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• pp.190-202
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• 2015

Much research has been conducted on location-based intelligent personal assistants that can understand a user's intention by learning the user's route model and then inferring the user's destinations and routes using data of GPS and other sensors in a smartphone. The intelligence of the location-based personal assistant is contingent on the accuracy and efficiency of the real-time predictions of the user's intended destinations and routes by processing movement information based on uncertain sensor data. We propose a robust particle filter based on Dynamic Bayesian Network model to infer the user's routes. The proposed robust particle filter includes a particle generator to supplement the incorrect and incomplete sensor information, an efficient switching function and an weight function to reduce the computation complexity as well as a resampler to enhance the accuracy of the particles. The proposed method improves the accuracy and efficiency of determining a user's routes and destinations.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.94-99
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• 2015

With the recent machine learning paradigm of using nonparametric Bayesian statistics and statistical inference based on random sampling, the Dirichlet distribution finds many uses in a variety of graphical models. It is a multivariate generalization of the gamma distribution and is defined on a continuous (K-1)-simplex. This paper presents a sampling method for a Dirichlet distribution for the problem of dividing an integer X into a sequence of K integers which sum to X. The target samples in our problem are all positive integer vectors when multiplied by a given X. They must be sampled from the correspondingly gridded simplex. In this paper we develop a Markov Chain Monte Carlo (MCMC) proposal distribution for the neighborhood grid points on the simplex and then present the complete algorithm based on the Metropolis-Hastings algorithm. The proposed algorithm can be used for the Markov model, HMM, and Semi-Markov model for accurate state-duration modeling. It can also be used for the Gamma-Dirichlet HMM to model q the global-local duration distributions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.230-245
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• 2005

In this paper, we develop a gesture-based input device equipped with accelerometers and gyroscopes. The sensors measure the inertial measurements, i.e., accelerations and angular velocities produced by the movement of the system when a user is inputting gestures on a plane surface or in a 3D space. The gyroscope measurements are integrated to give orientation of the device and consequently used to compensate the accelerations. The compensated accelerations are doubly integrated to yield the position of the device. With this approach, a user's gesture input trajectories can be recovered without any external sensors. Three versions of motion tracking algorithms are provided to cope with wide spectrum of applications. Then, a Bayesian network based recognition system processes the recovered trajectories to identify the gesture class. Experimental results convincingly show the feasibility and effectiveness of the proposed gesture input device. In order to show practical use of the proposed input method, we implemented a prototype system, which is a gesture-based remote controller (Magic Wand).

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.86-99
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• 2011

Due to the uncertainty of intention recognition for behaviors of users, the intention is differently recognized according to the situation for the same behavior by the same user, the accuracy of user intention recognition by minimizing the uncertainty is able to be improved. This paper suggests a novel ontology-based method to recognize user intentions, and able to minimize the uncertainties that are the obstacles against the precise recognition of user intention. This approach creates ontology for user intention, makes a hierarchy and relationship among user intentions by using RuleML as well as Dynamic Bayesian Network, and improves the accuracy of user intention recognition by using the defined RuleML as well as the gathered sensor data such as temperature, humidity, vision, and auditory. To evaluate the performance of robot proactive planning mechanism, we developed a simulator, carried out some experiments to measure the accuracy of user intention recognition for all possible situations, and analyzed and detailed described the results. The result of our experiments represented relatively high level the accuracy of user intention recognition. On the other hand, the result of experiments tells us the fact that the actions including the uncertainty get in the way the precise user intention recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.89-90
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo all over the world. This paper conducts researches about a classification method which can distinguish anomalous propagation echo in the radar data using naive Bayes classifier and unique attributes of the echo such as reflectivity, altitude, and so on. It is confirmed that the fine classification results are derived by verifying the suggested naive Bayes classifier using actual appearance cases of the echo.

• Earthquakes and Structures
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• v.8 no.4
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• pp.859-887
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• 2015

Structural health monitoring (SHM) has gained in popularity in recent years since it can assess the performance and condition of instrumented structures in real time and provide valuable information to the asset's manager and owner. Operational modal analysis plays an important role in SHM and it involves the determination of natural frequencies, damping ratios and mode shapes of a constructed structure based on measured dynamic data. This paper presents the operational modal analysis and seismic response characterization of the Tsing Ma Suspension Bridge of 2,160 m long subjected to different earthquake events. Three kinds of events, i.e., short-distance, middle-distance and long-distance earthquakes are taken into account. A fast Bayesian modal identification method is used to carry out the operational modal analysis. The modal properties of the bridge are identified and compared by use of the field monitoring data acquired before and after the earthquake for each type of the events. Research emphasis is given on identifying the predominant modes of the seismic responses in the deck during short-distance, middle-distance and long-distance earthquakes, respectively, and characterizing the response pattern of various structural portions (deck, towers, main cables, etc.) under different types of earthquakes. Since the bridge is over 2,000 m long, the seismic wave would arrive at the tower/anchorage basements of the two side spans at different time instants. The behaviors of structural dynamic responses on the Tsing Yi side span and on the Ma Wan side span under each type of the earthquake events are compared. The results obtained from this study would be beneficial to the seismic design of future long-span bridges to be built around Hong Kong (e.g., the Hong Kong-Zhuhai-Macau Bridge).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.354-361
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• 2011

Increasing interest and technological advances in smart home has led to active research on context-awareness service and prediction algorithms such as Bayesian Networks, Tree-Dimensional Structures and Genetic prediction algorithms. Context-awareness service presents that providing automatic customized service regarding individual user's pattern surely helps users improve the quality of life. However, it is difficult to implement context-awareness service because the problems are that handling coincidence with context information and exceptional cases have to consider. To overcome this problem, we proposes an Intelligent Sequential Matching Algorithm(ISMA), models context-awareness service using Timed Petri-net(TPN) which is petri-net to have time factor. The example scenario illustrates the effectiveness of the Timed Petri-net model and our proposed algorithm improves average 4~6% than traditional in the accuracy and reliability of prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Korean Journal of Remote Sensing
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• v.21 no.6
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• pp.493-503
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• 2005

In this study, the weight of evidence(WofE) technique based on GIS was applied to spatially estimate the groundwater yield characteristics at the Pocheon area In Gyunggi-do. The groundwater preservation depends on many hydro-geologic factors that include hydrologic data, land-use data, topographic data, geological map and other natural materials collected at the site, even with man-made things. All these data can be digitally processed and managed by GIS database. In the applied technique of WofE, the prior probabilities were estimated as the factors that affect the yield on lineament, geology, drainage pattern or river system density, landuse and soil. We calculated the value of the weight values, W+ and W-, of each factor and estimated the contrast value of it. Results by the groundwater yield characteristic computation using this scheme were presented feasibility map in the form of the posterior probability to the consideration of in-situ samples. It is concluded that this technique is regarded as one of the effective techniques for the feasibility mapping related to the estimation of groundwater-bearing potential zones and its spatial pattern.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.87-87
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• 2017

최근 이상기후로 인해 국내 가뭄피해가 증가하고 있는 추세이며, 미래 가뭄의 심도 및 지속시간은 증가할 것으로 예측되고 있다. 특히 우리나라는 용수공급의 56.5%를 댐에 의존하여 댐 유역의 가뭄은 생 공 농업용수 공급제한 등의 광범위한 피해를 발생시킬 수 있다. 다만 가뭄은 홍수와 달리 진행속도가 비교적 느리기 때문에 사전에 정확한 댐 유입량 예측이 가능하다면, 용수공급량 조정을 통해 피해를 최소화할 수 있다. 국내에서는 댐 유입량 예측에 ESP (Ensemble Streamflow Prediction) 기법을 활용하고 있으며, ESP 기법은 과거 기상자료를 기반으로 미래를 예측하기 때문에 기상자료, 초기수문조건, 매개변수 등에 불확실성을 가지고 있다. 본 연구에서는 베이지안 이론을 이용하여 댐 예측유입량의 정확도 향상기법을 개발하고 예측성을 평가하고자 하며, 강우유출모델은 ABCD를 활용하였다. 대상유역은 국내의 대표 다목적댐인 충주댐 유역을 선정하였으며, 기상자료는 기상청, 국토교통부 및 한국수자원공사의 지점자료를 수집하였다. 예측성 평가기법으로는 도시적 분석방법인 시계열 분석, 통계적 분석방법인 Skill Score (SS)를 활용하였다. 시계열 분석 결과 ESP 댐 예측유입량(ESP)은 매년 월별 전망값의 큰 차이가 없었으며, 다우년 및 과우년의 예측성이 떨어지는 것으로 나타났다. 베이지안 기반의 댐 예측유입량(BAYES-ESP)는 ESP의 과소모의하는 경향을 보정하였으며, 다우년에 예측성이 향상되었다. 월별 평균 댐 관측유입량과 ESP, BAYES-ESP의 SS 비교분석 결과 ESP는 유입량 값이 적은 1, 2, 3월에 SS가 양의 값을 가졌으며, 이외의 월에는 음의 값으로 나타났다. BAYES-ESP는 ESP와 관측값이 비교적 선형관계를 나타내는 1, 2, 3월에 ESP의 예측성을 개선시키는 것으로 나타났다. ESP 기법은 강수량의 월별, 계절별 변동성이 큰 우리나라에 적용하기에는 예측성의 한계가 있었으며, 이를 개선한 BAYES-ESP 기법은 댐 유입량 예측 연구에 가치가 있는 것으로 판단된다.