• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권3호
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.294-301
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• 2022

COVID-19 is a crisis with numerous casualties. The World Health Organization (WHO) has declared the use of masks as an essential safety measure during the COVID-19 pandemic. Therefore, whether or not to wear a mask is an important issue when entering and exiting public places and institutions. However, this makes face recognition a very difficult task because certain parts of the face are hidden. As a result, face identification and identity verification in the access system became difficult. In this paper, we propose a system that can detect masked face using transfer learning of Yolov5s and recognize the user using transfer learning of Facenet. Transfer learning preforms by changing the learning rate, epoch, and batch size, their results are evaluated, and the best model is selected as representative model. It has been confirmed that the proposed model is good at detecting masked face and masked face recognition.

• 한국게임학회 논문지
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• 제12권2호
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• pp.63-74
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• 2012

본 논문은 최근에 국내 게임시장에 큰 반향을 일으키고 있는 오픈마켓과 셧다운제의 제도화 과정을 통해 시장에 미치는 영향과 그 의미를 살펴본다. 제도는 사회적 환경과 인식이 반영된 결과물이라는 점에서 오픈마켓과 셧다운제를 이해하는데 핵심 요소이다. 이에 오픈마켓과 셧다 운제의 제도가 추진되는 사회적 환경과 인식의 영향과 그 결과의 방향성을 조망한다. 오픈마켓은 스마트폰의 도입에 따라 이전과 다른 모바일콘텐츠 유통체계의 변화에 대한 기존 국지성 제도와의 합리적이고 미래지향적인 접근을 통해 시장 활성화에 기여했다. 한편 셧다운제는 게임이용과정에서 나타난 문제적 현상에 대한 환경적 구조적 진단 보다는 현상적 형태에 집중함으로써 제도화의 의미가 퇴색하고 오히려 시장에서 부작용을 부추기는 결과를 가져오고 있다. 이러한 상반된 결과의 방향성을 통해 제도화의 중요성을 지적하였고, 오픈마켓과 셧다운제도의 실효성과 제도 및 시장성에 대하여 고찰하였다.

• Journal of Information Processing Systems
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• 제9권2호
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• pp.333-348
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• 2013

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately $91.33{\pm}0.67%$ for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

• 한국지능시스템학회논문지
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• 제19권6호
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• pp.755-759
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• 2009

인간의 감정을 인식하는 기술은 인간-로봇 상호작용 분야의 중요한 연구주제 중 하나이다. 특히, 화자독립 감정인식은 음성감정인식의 상용화를 위해 꼭 필요한 중요한 이슈이다. 일반적으로, 화자독립 감정인식 시스템은 화자종속 시스템과 비교하여 감정특징 값들의 화자 그리고 성별에 따른 변화로 인하여 낮은 인식률을 보인다. 따라서 본 논문에서는 신뢰도 평가방법을 이용한 감정인식결과의 거절 방법을 사용하여 화자독립 감정인식 시스템을 일관되고 정확하게 구현할 수 있는 방법을 제시한다. 또한, 제안된 방법과 기존 방법의 비교를 통하여 제안된 방법의 효율성 및 가능성을 검증한다.

• 한국중재학회지:중재연구
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• 제23권1호
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• pp.1-23
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• 2013

When a court recognizes an arbitral award, it acknowledges that the award is valid and binding, and thereby gives it a set of effects similar to those of a court's judgment, among which res judicata is the most important. The res judicata effect of an arbitral award generally forbids parties to an action from subsequently litigating claims that were raised in a prior arbitration. In common law countries, res judicata may also preclude re-adjudication of issues raised and decided in a prior arbitration. The Korean Arbitration Act acknowledges the rights of parties to an arbitral award to seek not only an enforcement judgment but also a recognition judgment on an arbitral award. Therefore, the question arises whether or not the winning party in an arbitration must acquire a recognition judgment on the arbitral award in order to enjoy the effects of a recognized award. However, according to the case law and generally accepted views, an arbitral award is automatically recognized without any additional procedure, as long as it satisfies the requirements for recognition. Therefore, in order to resolve this question, it is desirable to eliminate the statutory clause that stipulates the right to seek recognition judgment.

• 한국환경교육학회지:환경교육
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• 제20권1호
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• pp.90-105
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• 2007

The purpose of this study is to find the influence of environmental issue analysis instruction on the environmental decision making ability for grade 5 elementary school students. The study was done through pre and post testing control group structure. The object of this study is grade 5 of I elementary school students which were divided into 35 student test group and 54 student control group. Through studying references, the selection standard of appropriate environment issue and the environmental issue analysis instructing objective. Conducted the environment issue instructing based on the selected environment issue and instructing objective. The classes were held in total of 6 sessions in the chapters related to class objective and class content within the curriculum. The pre and post testing was done using environment decision making ability test sheet which was reconstructed by myself and the results were analyzed by t-test. As a result of comparing pre and post testing the students in test group showed significant results in the processes of problem recognition, evaluation of alternatives, behave planing (p<.001). As a result of comparing the differences of environment decision making ability of pre and post test of test group and control group, it showed significant results in the process of evaluation of alternatives(p<.00l). The environment issue analysis class has positive influence on the environment decision making abilities of the students but since the outcome of environment decision making ability is lower, there is a need for long term environment education plan and further studies to find whether the environment issues within the textbook is appropriate in the elementary student level, useful school aspect and the influence of environment issue analysis class on the change of values for individuals.

• 한국항공운항학회지
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• 제31권3호
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• pp.119-132
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• 2023

In recent years, the competition for employment among college students has become more intense. It is also the time when strong personal beliefs and will to develop careers are required for successful employment through stable major study. Therefore, in this study, we tried to find out the effect on major attitude and employment efficacy according to the level of aviation safety perception, which is an important issue in the aviation industry. For analysis, survey is conducted targeting college students majoring in aviation service who are enrolled in universities in the metropolitan area and Chungcheong area. To verify the hypotheses of the study, demographic characteristics are identified based on questionnaires, reliability and validity of measurement items are verified, and structural equation model analysis is performed to verify the hypotheses. The analysis results are as follows. First, it is found that safety knowledge and safety consciousness, which are sub-factors of aviation safety perception of college students majoring in aviation service, have a positive (+) effect on subject recognition, learning process recognition, and career recognition of major recognition. Second, subject recognition, learning process recognition, and career recognition, which are sub-factors of major recognition, are found to have a positive effect on major commitment. Third, it is found that major commitment have a positive (+) effect on employment efficacy. Based on the research results, practical support plans and strategies for effective major study and successful employment are presented.

• 대한임베디드공학회논문지
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• 제9권6호
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• pp.345-351
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• 2014

This paper presents pedestrian recognition to improve performance for vehicle safety system or surveillance system. Pedestrian detection method using HOG (Histograms of Oriented Gradients) has showed 90% recognition rate. But if someone takes a picture in the rain, the image may be distorted by rain streaks and recognition rate goes down by 62%. To solve this problem, we applied image decomposition method using MCA (Morphological Component Analysis). In this case, rain removal method improves recognition rate from 62% to 70%. However, it is difficult to apply conventional image decomposition method using MCA on vehicle safety system or surveillance system as conventional method is too slow for real-time system. To alleviate this issue, we propose a rain removal method by using low-pass filter and DCT (Discrete Cosine Transform). The DCT helps separate the image into rain components. The image is removed rain components by Butterworth filtering. Experimental results show that our method achieved 90% of recognition rate. In addition, the proposed method had accelerated processing time to 17.8ms which is acceptable for real-time system.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.