• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.115-122
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• 2005

W These days, Ubiquitous is coming a more popular tool in mass communications and in advanced countries ubiquitous computing is commercialized. In Korea, we have studied the business models of the ubiquitous computing generation as well. Ubiquitous computing is the environment in which humans and computers coexist and properly work together. I would like to present a service infrastructure for a more efficient context-awareness than computing home networking system in this monograph. In the mean time, they have focused the context-awareness system on identifying users in the designated space and recognition. But I would like to focus on the middleware system structure for more efficient recognition.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1252-1261
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• 2009

One notable challenge in video searching and summarizing is extracting semantic from video contents and annotating context for video contents. Video semantic or context could be obtained by two methods to extract objects and contexts between objects from video. However, the method that use just to extracts objects do not express enough semantic for shot or scene as it does not describe relation and interaction between objects. To be more effective, after extracting some objects, context like relation and interaction between objects needs to be extracted from conversation situation. This paper is a study for how to detect speaker and how to compose context for talking to annotate conversation context. For this, based on this study, we proposed the methods that characters are recognized through face recognition technology, speaker is detected through mouth motion, conversation context is extracted using the rule that is composed of speaker existing, the number of characters and subtitles existing and, finally, scene context is changed to xml file and saved.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.21-29
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• 2011

U-health care services have been attracted to effectively solve some problems in promoting health and preparing aging society. Although the recent U-health care services have been developed to treat diseases, it requires environment information related to health for preventing fundamental diseases and for promoting health. In this study, a U-health environment service that reflects context recognition information is proposed. The proposed service draws environment information using local weather and healthcare information in users' residential areas. In the context recognition based U-health environment services, various services are provided to users not only health, living weather based menu, and exercise services but user location based warning messages for dangerous regions and remote emergency services. That is, based on such context recognition, some events that are to be occurred to users are detected and then it will provide proper services. Thus, it improves the satisfaction of U-health services and its service qualities.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1072-1075
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• 2005

This paper presents that TD method is applied to the human adaptive devices for smart home with context awareness (or recognition) technique. For smart home, the very important problem is how the appliances (or devices) can adapt to user. Since there are many humans to manage home appliances (or devices), managing the appliances automatically is difficult. Moreover, making the users be satisfied by the automatically managed devices is much more difficult. In order to do so, we can use several methods, fuzzy controller, neural network, reinforcement learning, etc. Though the some methods could be used, in this case (in dynamic environment), reinforcement learning is appropriate. Among some reinforcement learning methods, we select the Temporal Difference learning method as a core algorithm for adapting the devices to user. Since this paper assumes the environment is a smart home, we simply explained about the context awareness. Also, we treated with the TD method briefly and implement an example by VC++. Thereafter, we dealt with how the devices can be applied to this problem.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.44-50
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• 2017

To understand whether Falling, which is one of the causes of injuries, occurs, various behavior recognition research is proceeding. However, in most research recognize only the fact that Falling has occurred and provide the service. As well as the occurrence of the Falling, the risk varies greatly based on the type of Falling and the situation before and after the Falling. Therefore, when Falling occurs, it is necessary to infer the user's current situation and provide appropriate services. In this paper, we propose to base on Fog Computing and Cloud Computing to design Context-aware System using analysis of behavior data and process sensor data in real-time. This system solved the problem of increase latency and server overload due to large capacity sensor data.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.542-546
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• 2007

In the vision of ubiquitous computing environment, smart objects would communicate each other and provide many kinds of information about user and their surroundings in the home. This information enables smart objects to recognize context and to provide active and convenient services to the customers. However in most cases, context-aware services are available only with expert systems. In this paper, we present generalized activity recognition application in the smart home based on a naive Bayesian network(BN) and fuzzy clustering. We quantize continuous sensor data with fuzzy c-means clustering to simplify and reduce BN's conditional probability table size. And we apply mutual information to learn the BN structure efficiently. We show that this system can recognize user activities about 80% accuracy in the web based virtual smart home.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2839-2852
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• 2013

Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user's context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.3-8
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• 1999

Previous variable vocabulary speech recognition systems with context-independent acoustic modeling, could not represent the effect of neighboring phonemes. To solve this problem, we use allophone-based context-dependent acoustic model. This paper describes the method to improve acoustic model of the system effectively. Acoustic model is improved by using allophone clustering technique that uses entropy as a similarity measure and the optimal allophone model is generated by changing the number of allophones. We evaluate performance of the improved system by using Phonetically Optimized Words(POW) DB and PC commands(PC) DB. As a result, the allophone model composed of six hundreds allophones improved the recognition rate by 13% from the original context independent model m POW test DB.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3153-3158
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• 2014

Children in schoolzone accidents occur frequently in order to actively respond to the situation on the module for automated recognition research. By the vehicle penetration such like schoolzone, child object recognition, and GPS coordination information, the monitoring scenario can be constructed, and if an event occurs corresponding to strategic scenario so that suitable reaction can be provided to increase safety level to the schoolzone. In this paper, a GPS sensor and the image sensor and the monitoring server on the network based on the integration of context-aware methods have been studied. The image sensor section and the GPS section through analysis of the situation analysis and recognition of the object based on the scenario can actively cope with the situation according to the methods proposed.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.197-200
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• 2002

In this paper, we propose a phoneme duration modeling in a speech recognition system based on disicion tree state tying. We assume that phone duration has a Gamma distribution. In a training mode, we model mean and variance of each state duration in context-independent phone model based on decision tree state tying. In a recognition mode, we get mean and variance of each context-dependent phone duration form state duration information obtaind during training mode. We make a comparative study of the proposed meth with conventinal methods. Our method results in good performance compared with conventional methods.