• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.65-70
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• 2020

CNN's technologies that represent emotional detection include primitive CNN algorithms, deployment normalization, and drop-off. We present the methods and data of the three experiments in this paper. The training database and the test database are set up differently. The first experiment is to extract emotions using Batch Normalization, which complemented the shortcomings of distribution. The second experiment is to extract emotions using Dropout, which is used for rapid computation. The third experiment uses CNN using convolution and maxpooling. All three results show a low detection rate, To supplement these problems, We will develop a deep learning algorithm using feature extraction method specialized in image processing field.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 춘계학술발표대회
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• pp.33-36
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• 2014

최근 스마트폰이나 태블릿 PC 등의 모바일 디바이스가 상용화 되어감에 따라 그 안에서 핵심적인 처리기능을 담당하는 프로세서의 코어 수가 점차적으로 늘어나고 있다. 많은 수의 코어를 효율적으로 사용하기 위해 여러 가지 메커니즘이 구현되어 있으나, 단일 프로세스를 순차적으로 실행하는 경우 여전히 성능에서의 한계가 존재한다. 병렬화 되어 있지 않은 프로세스의 경우, Amdahl's Law[1]에 따르면 순차적으로 실행을 할 수 밖에 없는 부분이 존재하고, 이 부분은 하나의 코어에서만 실행되기 때문에 많은 연산 자원들이 낭비되는 현상이 발생한다. 본 논문은 다중 코어 환경에서 이러한 잉여자원을 효과적으로 사용하기 위해 Back-end Fusion 이라는 구조를 제안하여 프로세서의 성능 향상을 위한 연구를 진행하였다. Back-end Fusion 이란, 연산 처리를 담당하는 back-end 부분(execution unit, writeback 단계 등)을 필요에 따라 코어 간에 동적으로 재구성하여 성능을 향상시키는 메커니즘이다. 이 재구성된 프로세서의 back-end 를 효율적으로 사용하기 위해, 종속성과 로드 밸런스 등을 고려한 인스트럭션 분배 알고리즘을 함께 제안한다. Intel 사의 x86 Instruction Set Architecture(ISA)를 기반으로 한 시뮬레이터를 이용하여 Back-end Fusion 프로세서의 성능을 측정 해 본 결과 기존의 단일 코어 프로세서에 비해 평균 32.2%의 성능 향상을 확인할 수 있었다.

• ETRI Journal
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• 제27권6호
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• pp.713-724
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• 2005

This paper focuses on the problem of key-frames coding and proposes a new promising approach based on the use of fractals. The summary, made of a set of key-frames selected from a full-length video sequence, is coded by using a 3D fractal scheme. This allows the video presentation tool to expand the video sequence in a "natural" way by using the property of the fractals to reproduce the signal at several resolutions. This feature represents an important novelty of this work with respect to the alternative approaches, which mainly focus on the compression ratio without taking into account the presentation aspect of the video summary. In devising the coding scheme, we have taken care of the computational complexity inherent in fractal coding. Accordingly, the key-frames are first wavelet transformed, and the fractal coding is then applied to each subband to reduce the search range. Experimental results show the effectiveness of the proposed approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.797-799
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• 1999

In this paper, we design the Multi-FNN(Fuzzy-Neural Networks) using HCM Method. The proposed Multi-FNN uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. Also, We use HCM(Hard C-Means) method of clustering technique for improvement of output performance from pre-processing of input data. The parameters such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. We use the training and testing data set to obtain a balance between the approximation and the generalization of our model. Several numerical examples are used to evaluate the performance of the our model. From the results, we can obtain higher accuracy and feasibility than any other works presented previously.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.204-210
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• 2019

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.981-984
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• 1993

During the past decade, several specific hardwares for fast fuzzy inference have been developed. Most of them are dedicated to a specific inference method and thus cannot support other inference methods. In this paper, we present a hardware architecture called KAFA(KAist Fuzzy Accelerator) which provides various fuzzy inference methods and fuzzy set operators. The architecture has SIMD structure, which consists of two parts; system control/interface unit(Main Controller) and arithmetic units(FPEs). Using the parallel processing technology, the KAFA has the high performance for fuzzy information processing. The speed of the KAFA holds promise for the development of the new fuzzy application systems.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.266-269
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• 2002

This paper explores a fundamental study of acquiring the users' KANSEI information regarding the recognition of shape models. Since there are many differences such as background differences and knowledge differences among users, they will produce different evaluations based on their KANSEI even when an identical shape model is presented. Cluster analysis is proved to be available for catching a group tendency and for constructing a mapping relation between a description of the shape model and the HANSEl database. In order to investigate an analogical relation and a mutual influence in our consciousness, first, we made a questionnaire that asked subjects to represent images having different colors and shape cones by using 4 pairs of adjectives (KANSEI words). Next, based on the cluster analysis of the questionnaire using a fuzzy set theory, we proposed a hypothesis showing how the analogical relation and the mutual influence work in our mind while viewing the shape models. Furthermore, how the properties of KANSEI depend on their descriptions was also investigated by virtue of the cluster analysis. This work will be valuable to construct a personal KANSEI database regarding the Shape Model Processing System.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.1185-1188
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• 2005

Recently, ubiquitous computing and sensor networks are making a rapid development. These technology can enable a new way of biomedical signal processing and healthcare. that is, they can improve care giving by a more flexible acquisition of relevant vital sign data, and by providing more convenience for patients. In this paper, we realize the biomedical sensor networks by applying IEEE 802.15.4/Zigbee networks to some various biomedical sensing unit. For address this, we developed minimized zigbee module and set-up procedure using PDA. The main advantages that we achieve are interference-free operation of different body sensor networks in the vicinity, as well as intuitive usage by the nontechnical personnel.

• 한국전자통신학회논문지
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• 제10권12호
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• pp.1345-1354
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• 2015

본 논문은 정보통신용 표준 부호인(정보 교환용 부호계 $KS{\times}1001$ : 2004)에 정의되어 있는 한글낱자 부호집합에 대하여, Date link 계층에서 AMI/HDB-3 스크램블링 측면에서 효율적인 데이터 전송을 위한 새로운 부호집합 체계를 제시하였다. 기존 부호집합 체계와 상호비교를 위하여 ($4{\times}4$) 비트 원천부호화 규칙과 한글 빈도통계 (국립국어원)를 적용한 결과 약44 %의 데이터 전송 효율이 개선시킬 수 있음을 나타났다.

• 전자공학회논문지S
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• 제36S권9호
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• pp.103-112
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• 1999

본 논문에서는 차량 항법영 음성 인식을 위한 화자 독립 단독음 인식 시스템을 범용 DSP를 사용하여 구현하였으며, 잡음 처리 기술로 SNR 정규화와 RAS를 결합한 방법을 제안하여 인식 시스템의 성능을 개선시켰다. 인식 알고리즘으로서 반연속 HMM을 사용하였으며, TMS320C31을 이용하여 구현하였다. 실험에서 사용된 인식 단어는 차량 항법 시스템을 위한 명령어 69단어이며, 구현된 인식 시스템은 자동차 환경에서 녹음된 음성 데이터에 의한 인식 결과와 하드웨어 구현에 따르는 제약 조건을 동시에 고려하여 구현되었다. 주행 중에 녹음된 데이터에 대한 컴퓨터 시뮬레이션 상에서 특징 벡터 중 MFCC-CMS를 이용하고, 잡음 처리 방법으로 SNR 정규화와 스펙트럼 차감법을 결합하여 실험한 경우 최고 93.62%의 인식 성능을 보였으며, 89.93%의 인식률을 갖는 기존 방법보다 3.69%의 인식 성능 향상을 가져왔다. 제안된 잡음 처리 방법은 자동차 안에서의 SNR이 5dB이하에서 좋은 인식 성능을 보이는 것으로 나타났다.