• 한국멀티미디어학회논문지
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• 제6권1호
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• pp.89-96
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• 2003

본 논문은 카오스 이론의 프랙탈 차원과 수정된 에농 어트랙터를 이용하여 인쇄체 숫자를 인식하는 새로운 방법을 제안한다. 먼저 숫자 영상으로부터 망 특징 투영 특징, 교차거리 특징을 1차 구한 후, 이 특징들을 시계열 데이터로 변환한다. 그리고 본 논문에서 제안한 수정된 에농 시스템을 이용하여 프랙탈 차원을 나타내는 자연 척도 및 정보 비트값을 구한다. 마지막으로 표준패턴 데이터베이스와 비교하여, 최소 거리값을 이용하여 숫자 인식을 행한다. 실험 결과 10가지 숫자에 대하여 100%의 분류율을 나타내었고, 또한 실제 문서를 대상으로 실험한 결과 90%의 인식률과 초당 26자의 인식속도를 보임으로써 제안된 방법의 유효성을 보였다.

• 대한간호
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• 제35권4호
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• pp.64-74
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• 1996

In general. social cognition for a disabled person seemed that he was limited aspects of emotion and psychology. Thus he was rejected. avoided. worthless and not accepted. People who have been raised in an ethnic collectivity often acquire from that experience not only basic conceps and attitudes toward health and illness but also fundamental styles of interpersonal behavior and concerns about the world. The effects of this enculuration carryover into health- care situation and also become an important influence on personal activities devoted to health maintenance and disease prevention. Our Korean culture is a state of tradition Confucianism. respects his honor and external feature. Therefore recognition of a disabled person is more specipic. This study uses Walker and Avant's process of concept analysis. The concep of recognition of disabilty can be defined as follows : Recognition of disability is a person's conscious process of sensation. perception. memory and thought and is constructed from value. attitude. emotion and expierince which is dynamics. and in everyday life is feeling that basic activity is not free and occurs interaction of envionment. Attributes of disability recognition are defined as 1) It is feeling that basic activity of his daily life is not free in everyday life. 2) It is a person's conscious process of sensation. perception. memory and thought. 3) It occurs interaction of enviornment. 4) It is constructed from value. attitude. emotion and experience. 5) it is dynamics ( changing but not stasis). Nurse is always suppoted and pushed him. She plans institutional and situational surroundings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권11호
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• 한국중재학회지:중재연구
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• 제32권3호
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• pp.47-70
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• 2022

This paper studies legal regulation of the public policy exception in the Russian Federation and domestic judicial practice on the issue. It reviews current legislation and analyzes a number of recent court cases where an arbitral award rendered by a foreign arbitration body was refused recognition and enforcement based on public policy violation. By doing so, it contributes to the knowledge on the concept of public policy in the Russian legal system and how public policy can affect the process of recognition and enforcement of foreign arbitral awards on its territory. The review of court cases demonstrates different aspects of how the public policy exception can be applied by Russian arbitrazh courts. Such decisions can provide a clearer picture of the kinds of situation that can lead to invoking the public policy clause by the court. Also, it is of practical value as persons preparing to file a claim or to be a defendant in a Russian court can be required to present existing court decisions in support of their claim or defence.

• 한국인터넷방송통신학회논문지
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• 제21권2호
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• pp.55-60
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• 2021

스마트 팩토리화가 진행됨에 따라 자동화 설비 및 로봇의 활용이 늘어나고 있다. 또한 IT 기술의 발달로 음성인식을 활용한 시스템의 활용도도 올라가고 있다. 음성인식 기술은 스마트홈과 각종 IoT 기술에서 두각을 나타내고 있는 기술이지만 공장의 특수성으로 공장에 적용되기 힘든 상황에 있다. 따라서 본 연구에서는 제조 현장의 상황을 고려한 음성인식 기술을 활용하여 산업용 다관절 로봇을 제어하는 방법을 설계하였다. 모바일을 통해 로봇 조작을 위한 음성명령을입력 받은 후 네트워크 프로토콜 변환 및 명령어 변환 과정을 거쳐 로봇을 제어할 수 있음을 확인하였다.

• 한국식생활문화학회지
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• 제22권1호
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• pp.58-69
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• 2007

The objective of this study was to analyze the actual situation of ethics management in foodservice industry and its employees’ recognition on corporate ethics management. Self-administrated questionnaires were completed by 342 employees, and the data were analyzed by frequency, chi-square, t-test, and one-way ANOVA. The results showed that the employees consider the corporate ethics management to be very important and it has been much more improved in foodservice industry. However, they are still skeptical about the continuous and consistent practice of ethics management. In addition, the survey revealed that the ethics management was regarded to be critically important to improve the value and the culture of the corporation. It also showed that the recognition of the improvement of ethics management in foodservice industry has been affected by the work environment of the whole society too. The result concluded that the taking the initiative by CEO is the most important factor for introducing the ethics management, while the propagation of ethics management requires the volition of the employees inside the corporation.

• 한국전자통신학회논문지
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• 제11권8호
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• pp.759-766
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• 2016

본 논문은 비정상 상황 시 발생하는 음원에 대해 주변 환경 음에 강인한 음원 구간을 검출하여, 구간내의 신호를 이용한 음원 인식 과 위치 추적 시스템 설계에 관한 연구이다. 강인한 음원 구간 검출은 수신되는 오디오 신호로부터 단 구간 가중 평균 델타 에너지를 계산하여, 저역 통과 필터에 입력 후, 출력되는 결과 값들의 비교를 통해 배경음에 강인한 구간을 정의 하며, 음원 인식은 검출된 구간 내 데이터로부터 종래의 인식 방법인 HMM(: Hidden Markov Model)을 이용해, 음원 인식 정보를 생성하여 학습 및 인식을 한다. 이는 주변 배경음이 포함된 음원 신호에 대해 기존 신호의 에너지를 이용해 구간을 검출 후, HMM을 통한 인식에 비해 3.94% 상향된 인식률을 보인다. 또한 인식 결과를 바탕으로 구간내의 신호간의 TDOA(: Time Delay of Arrival)를 이용한 위치 파악은 실제 발생 위치와의 각도와 97.44%일치함을 보인다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제34권1호
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• pp.70-78
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• 2007

얼굴 인식 시스템의 성능은 조명 변화로 인하여 발생하는 개인내 (intra-person) 차이가 개인간 (inter-person)의 차이보다 클 수 있기 때문에 조명 변화에 많은 영향을 받는다. 본 연구에서는 이러한 문제를 해결하기 위해서 대칭형 bilinear 모델을 이용하여 조명 요소와 신원 요소를 분리하는 방법을 제안한다. Bilinear 모델로 조명 요소와 신원 요소를 얻기 위한 translation 과정은 반복적 역행렬을 구하는 것이 요구되는데 입력 데이타에 따라 수렴하지 않는 경우가 발생할 수 있다. 이러한 문제를 완화하기 위해서 ridge regression 모델과 bilinear 모델을 결합한 ridge regressive bilinear 모델을 제안하였다. 제안된 모델은 조명 요소와 신원 요소의 분산을 적절히 줄여줌으로서 bilinear 모델에 안정성을 제공하며, 인식에 더 많은 고차원 요소 정보를 이용하게 함으로써 인식 성능을 높여 준다. 실험 결과에서 제안한 ridge regressive bilinear 모델이 bilinear 모델, 고유얼굴(eigenface) 방법, Quotient image 보다 좋은 인식 성능을 보여줌을 확인 할 수 있다.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.