• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.122-129
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• 2006

The context-awareness has become the one of core technologies and the indispensable function. for application services in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a music recommendation system. Our proposed system consists of such components as Intention Module, Mood Module and Recommendation Module. Among these modules, the Intention Module infers whether a user wants to listen to the music or not from the environmental context information. We built the Intention Module using data mining techniques such as decision tree, support vector machine and case-based reasoning. The results showed that the case-based reasoning model outperformed the other models and its accuracy was 84.1%.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.123-126
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• 2015

This paper described fault tolerance system running on distributed multimedia. We implemented the error manager service so that the users participated in distribute multimedia collaborative work may refer synchronized error objects as the same view to others. distributed multimedia environment are based on IP-USN(Internet Protocol - Ubiquitous Sensor Network) and M2M(Machine to machine). This is a system that is suitable for detecting, sharing and recovering software error in distribute multimedia CSCW(Computer Supportes Cooperated Work) environment. With error synchronization system, a group cooperating users can synchronize error applications.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.271-272
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• 2014

현재 디지털 시스템 설계가 필요한 모든 유한 상태머신을 설계에는 필수적 밀리 모델이나 무어 모델이 들어간다. 그러나 각각의 기기와 기능에 따라서 밀리 모델과 무어 모델 중 어느 모델이 디지털 논리회로 설계에 효율적인지 판단이 모호한 상황이다. 이를 위해 본 논문에서는 유한 상태머신의 하나인 벤딩머신을 대상으로 밀리 모델과 무어 모델을 사용하여 설계한 후, 설계의 복잡도와 구현 게이트 수를 구하여 각 모델의 효율성에 대해 비교 분석하고자 한다.

• Information and Communications Magazine
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• v.25 no.10
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• pp.18-24
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• 2008

RFID(Radio Frequency Identification) 기술은 사물에 전자 태그를 부착하여 무선 인터페이스를 통해 사물의 정보를 자동으로 취득 할 수 있는 기술이다. 물류의 재고관리나 자동화 처리를 위해 주로 사용되었으나 최근에는 Cellular phone, PDA등 휴대용기기 에 RFID리더가 탑재되어 기존의 응용 이외에 다양한 서비스를 만들어 내려는 시도들이 나타나고 있다[1],[2]. 이는 궁극적으로는 모든 사물에 컴퓨팅 및 통신 기능을 부여하여 언제, 어디서, 무엇이든 통신이 가능한 환경을 구현함으로써 이제까지 사람 중심(Person to Person)에서 사물 중심(Machine to Machine) 정보화 사회로의 새로운 변화 시도이며 RFID기술은 진정한 Ubiquitous세상이 만들어지기 위한 주요 기술로서 지속적으로 발전되고 적용되어야 할 기술임이 분명하다. 이러한 사회적인 요구에 만족하기 위해서 RFID 기술도 변화하고 있고 새로운 기술적 이슈들이 나타나게 되었다. 본고에서는 RFID 기술의 변화를 알아보고, 특히 UHF 대역 RFID 리더가 Cellular phone에 내장되기 위해 소형화 및 저전력화가 되면서 발생하는 문제와 이의 해결방안을 살펴보고자 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.608-615
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• 2006

Embedded System is one of important factor of ubiquitous computing, and it has various application areas. In this paper, we develop an educational contents for the education of young children using portable embedded machine; it shows embedded system can be applied to education area as well as to industry area. The system is similar to portable game machine, and it is easy to use everywhere. It also can download new contents from host computer or internet. The developed contents forms game and multimedia to derive children's interest.

• ETRI Journal
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• v.42 no.5
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• Journal of Digital Contents Society
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• v.7 no.2
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• pp.117-124
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• 2006

In ubiquitous computing environment, a user can access their personalized services at anytime, anywhere, through any possible mobile or fixed terminals in a secure way. If the user wants to move to a different location, they need to stop the video play at the current location and makes a new request at the new location. As well, the user needs to manually search for the last frame to be seen at the previous location. In this paper, we proposed an implementation of multimedia contents streaming service mobility by user's location tracking. User can play video seamlessly while moving from one machine to another along the user's moving trail.

• Korea Information Processing Society Review
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• v.11 no.6
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.