• Journal of Multimedia Information System
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• v.8 no.1
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• pp.31-34
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• 2021

The classical HMM is defined by a parameter triple �� = (��, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order. This paper proposes a new stationary parameter e = (e1, e2, …, eN) where N is the number of states and et = P(|xt = i, y) for describing how an input pattern y ends in state xt = i at time t followed by nothing. It is often said that all is well that ends well. We argue here that all should end well. The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization. The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors. Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1062-1072
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• 2006

Recently, new intelligent service frameworks, such as ubiquitous computing are proposed. So, the necessity of adaptive agent system has been increased. In this paper, we propose an intelligent service agent to help that ubiquitous computing system offer user suitable service in ubiquitous computing environment. In order to offer user suitable uT-service, an intelligent service agent mediates the gap between the context information in uT-service system, and user preference is reflected in it. Therefore, we focus on following three components; the first is suitable multi agent framework-agent communication analysis and applicable method of inference engine, the second is uT-ontologies to describe various context information-context information sharing between agents and context information understanding between agents, the third is learning method of user profile to apply in uT-service system. This approach enables us to build adaptive uT-service system to offer suitable service according to user preference.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.322-324
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• 2006

유비쿼터스 컴퓨팅은 사물에 컴퓨터 기능이 내장되어 언제, 어디에서나, 어느 장치로도 편리하게 주변 환경으로부터 서비스를 사용할 수 있게 하는 정보기술의 패러다임이다. 이를 위해선 상황인지가 전제되어야 하는데 여기서 상황인지라 함은 시스템의 다양한 센서 정보를 바탕으로 스스로 상황(Context)을 인지하는 것으로서, 그 정의에 있어서, 아직 논란이 많으나 지능형 서비스를 위한 중요한 개념이다. 본 논문에서는 센서로부터 바로 생성된 Raw Context 정보를 Low-Level Context라고 하고 이를 복합(Fusion)하여 이미 알려진 High-Level Context로 분류하는 논리적 연산을 추론에 기반한 상황인지로서 정의한다. 이때 센서 정보를 통해 특정한 상황정보를 추론하기 위한 모델을 설계하여 상황인지 미들웨어에 적용시켜 보고 그에 따른 효율적인 구조를 기술한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.911-913
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• 2013

The user's context awareness is important to the reliability of sensors data. The sensor data is constantly change to external temp, internal& external environment and vibration. This noise environment is affecting that the data collected information from sensors. Of course this method of digital filter and inference algorithm specifically request for the use of ripple noise and action inference. In this paper, experiment was a comparison of the KF(Kalman Filter) and WMAF(Weight Moving Average Filter) for noise decrease and distortion prevention according to user behavior. And, we compared the EWDF(Extended Weight Dual Filter) with several filer. In an experiment, in contrast to other filter, the proposed filter is robust in a noise-environment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.42-50
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• 2010

It is necessary to implementation of system contain intelligent decision making algorithm considering marine feature because existing vessel USN system is simply monitoring obtained data from vessel USN. In this paper, we designed inference system using case based reasoning method and implemented knowledge base that case for fire and demage of digital marine vessel. We used K-Nearest Neighbor algorithm for recommend best similar case and input 3.000 EA by case for fire and demage context case base. As a result, we obtained about 82.5% average accuracy for fire case and about 80.1% average accuracy for demage case. We implemented digital marine vessel monitoring system using inference result.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.65-74
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• 2011

With the rapid development of cites, urban facilities have rapidly been increasing, and the task of managing them have also become complicated more and more. In this paper, we propose and design an intelligent context awareness system that effectively operates underground facilities in u-City. The suggested system consists of three major steps. The information acquisition step receives alarm information of sensors from the integrated platform. The analysis and inference step analyzes the alarm data and related information and infers the reason why the alarm happens. The information transmission step sends the final results of context awareness to the platform and other related modules. We produced some design products such as data flow diagram, function diagram, etc. We expect that the proposed context awareness system will be embedded in the integrated platform and used for an active management strategy of urban facilities.

• Journal of IKEEE
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• v.11 no.4
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• pp.279-282
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• 2007

In this paper, we introduce the general way for producing context information to support context-aware applications. It can fetch raw data from the service environments, translate it to reasonable context information, and provide to multiple applications. It is designed originally for the ubiquitous computing middleware and based on the ontology processing model. Automated service applications can use this system as the form of libraries or of web services for deciding its semantic cause of action.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.174-190
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• 2010

In ubiquitous computing, invisible devices and software are connected to one another to provide convenient services to users [1][2]. Users hope to obtain a personalized service which is composed of customized devices among sharable devices in a ubiquitous smart space (which is called USS in this paper). However, the situations of each user are different and user preferences also are various. Although users request the same service in the same USS, the most suitable devices for composing the service are different for each user. For these user requirements, this paper proposes a device recommender system which infers and recommends customized devices for composing a user required service. The objective of this paper is the development of the systems for recommending devices through context-aware inference in peer-to-peer environments. For this goal, this paper considers the context and user preference. Also I implement a prototype system and test performance on the real ubiquitous mobile object (UMO).

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.144-152
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• 2009

To provide intelligent service in mobile environment, it needs to estimate user's intention or requirement, through analyzing context information of end-users such as preference or behavior patterns. In this paper, we infer context information from uncertain log stored in mobile device. And we propose the inference method of end-user's behavior to match context information with service, and the proposed method is based on context-tree. We adopt bayesian probabilistic method to infer uncertain context information effectively, and the context-tree is constructed to utilize non-numerical context which is hard to handled with mathematical method. And we verify utility of proposed method by appling the method to intelligent phone book service.

• Genomics & Informatics
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• v.10 no.4
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• pp.266-270
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• 2012

Grammatical inference methods are expected to find grammatical structures hidden in biological sequences. One hopes that studies of grammar serve as an appropriate tool for theory formation. Thus, we have developed JSequitur for automatically generating the grammatical structure of biological sequences in an inference framework of string compression algorithms. Our original motivation was to find any grammatical traits of several cancer genes that can be detected by string compression algorithms. Through this research, we could not find any meaningful unique traits of the cancer genes yet, but we could observe some interesting traits in regards to the relationship among gene length, similarity of sequences, the patterns of the generated grammar, and compression rate.