• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.227-228
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• 2022

전통적인 HRA(Human Reliability Analysis)방법은 특정 애플리케이션 또는 산업을 염두에 두고 있으며. 또한 이러한 방법은 종종 복잡하며, 시간이 많이 걸리고 적용하는 데 비용이 많이 들며 직접 비교하기에는 적합하지 않다. 제안된 HFHM(Human Factors Hazard Model: 인적 요인 위험 모델)은 기검증되고 시간 테스트를 거친 FTA(Fault Tree Analysis:결함 트리 분석)및 ETA(Event Tree Analysis:이벤트 트리 분석)의 확률 분석 도구 및 새로 개발된 HEP(Human Error Probability:인적 오류 확률)예측 도구와 통합되고, 인간과 관련된 PSF(Performance Shaping Factors:성능 형성 요인)를 중심으로 새로운 접근 방식으로 개발되었다. 인간-시스템은 상호작용으로 인한 재난사고 가능성을 모델링하는 위험분석 접근법 HFHM은 다음과 같은 상용 소프트웨어 도구 내에서 예시되고 자동화된다. HFHM에서 생성된 데이터는 SE 분석가 및 설계에 대한 표준화된 가이드로 사용될 수 있다. 본 연구에서는 인적 위험을 예측하는 이 새로운 접근 방식을 통해, 전체 시스템에 대한 포괄적인 재난안전 분석을 가능하게 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.471-476
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• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.579-582
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• 1999

In this paper, we propose a human face detection algorithm using adaptive skin color model and neural networks. To attain robustness in the changes of illumination and variability of human skin color, we perform a color segmentation of input image by thresholding adaptively in modified hue-saturation color space (TSV). In order to distinguish faces from other segmented objects, we calculate invariant moments for each face candidate and use the multilayer perceptron neural network of backpropagation algorithm. The simulation results show superior performance for a variety of poses and relatively complex backgrounds, when compared to other existing algorithm.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.631-640
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• 2012

Purpose: This empirical study is focused on the relationship between innovation capability (R&D and Human Resource innovation) and performance in knowledge-based service firms. Methods: We build research model to test how each of innovation capability on technology and human resource is influenced on their financial and non-financial performance in the knowledge-based service industries. Based on the previous research, we hypothesized the factors are regarded innovation capabilities of the firms as the scale of R&D and human resources. Because this study is especially targeted to the performance of knowledge-based service firms. With the survey on 424 main knowledge-based service firms, the multi-regression analysis was performed. Results: The result showed that the scale of R&D and human resources capabilities are main factors for knowledge-based service firms' performance, which reflects the current industrial structure. Conclusion: This study empirically demonstrated that human resources are most important to the growth of knowledge-based service firms.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.21-28
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• 2019

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video.

• Journal of the Korean Society for Railway
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• v.11 no.1
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• pp.75-80
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• 2008

Human factors still play a significant part in many railway accidents. It is well blown that SPAD (Signal Passed at Danger) remains as the single most cause of railway accidents. In this study a stochastic model is developed to quantify SPAD human reliability. This model provides closed-form mathematical expressions into which multiple factors affecting the reliability of man-machine systems can be incorporated. Two basic elements are combined to form the framework for modeling: random signal occurrence and transient human performance characteristics.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3568-3584
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• 2016

In mobile opportunistic networks (MONs), human-carried mobile devices such as PDAs and smartphones, with the capability of short range wireless communications, could form various intermittent contacts due to the mobility of humans, and then could use the contact opportunity to communicate with each other. The dynamic changes of the network topology are closely related to the human mobility patterns. In this paper, we propose a social motivation-aware mobility model for MONs, which explains the basic laws of human mobility from the psychological point of view. We analyze and model social motivations of human mobility mainly in terms of expectancy value theory and affiliation motivation. Furthermore, we introduce a new concept of geographic functional cells, which not only incorporates the influence of geographical constraints on human mobility but also simplifies the complicated configuration of simulation areas. Lastly, we validate our model by simulating three real scenarios and comparing it with reality traces and other synthetic traces. The simulation results show that our model has a better match in the performance evaluation when applying social-based forwarding protocols like BUBBULE.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.499-507
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• 2006

Recently human gait has been considered as a useful biometric supporting high performance human identification systems. This paper proposes a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the Hidden Markov Model(HMM). Two types of gait models have been developed both with an endless cycle architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.

• Journal of the Ergonomics Society of Korea
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• v.27 no.1
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• pp.37-43
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• 2008

A lot of models have been developed for prevention of human errors. Nevertheless most of them failed to attract attention of industry which has been looking for an integrative model that can show practical countermeasures as well as causal factors of human errors. This research aimed to develop a comprehensive model that can mainly be applied to industrial fields. Therefore, in the model, it was tried to explain sequences of an operator's information process that might cause human errors on one hand, and life cycle stages of facilities involved when human errors occur on the other hand. This model was validated by using a typical accident case. With the comprehensive model presented in this research, one could follow up the sequence of human errors caused by operators, and errors made at the design stage which might cause accidents could be tracked. As a consequence, it is expected that much attention would be paid to preventing human errors in industrial fields since safety personnel can easily find out cause of human errors throughout life cycle stages of man-machine facilities if utilizing the suggested model.