• ETRI Journal
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• 제40권4호
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• pp.499-510
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• 2018

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

• 유통과학연구
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• 제20권11호
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• pp.121-129
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• 2022

Purpose: Artificial intelligence is widely used, particularly in the popular neural network theory called Deep learning. The improvement of computing speed and capability expedited the progress of Deep learning applications. The application of Deep learning in education has various effects and possibilities in creating and managing educational content and services that can replace human cognitive activity. Among Deep learning, Deep fake technology is used to combine and synchronize human faces with voices. This paper will show how to develop e-Learning content videos using those technologies and open-source tools. Research design, data, and methodology: This paper proposes 4 step development process, which is presented step by step on the Google Collab environment with source codes. This technology can produce various video styles. The advantage of this technology is that the characters of the video can be extended to any historical figures, celebrities, or even movie heroes producing immersive videos. Results: Prototypes for each case are also designed, developed, presented, and shared on YouTube for each specific case development. Conclusions: The method and process of creating e-learning video contents from the image, video, and audio files using Deep fake open-source technology was successfully implemented.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.134-138
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• 2023

The field of digital forensics requires good theoretical and practical knowledge, so practitioners should have an in-depth understanding and knowledge of both theory and practical as they need to take decisions which impacts human lives. With the demand and advancements in the realm of digital forensics, many universities around the globe are offering digital forensics programs, but there is a huge gap between the skills acquired by the student's and the market needs. This research work explores the problems faced by digital forensics programs, and provides solution to overcome the gap between the skills acquired by the student's and the market needs using Activity led learning pedagogy for digital forensics programs.

• 전기전자학회논문지
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• 제24권2호
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• pp.523-528
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• 2020

스마트 모바일 장치의 확산은 인간의 일상 행동 분석을 보다 일반적이고 간단하게 만들었다. 행동 분석은 이미 본인 인증, 감시, 건강 관리 등 많은 분야에서 사용 중이고 그 유용성이 증명되었다. 본 논문에서는 스마트폰의 가속도 센서 신호를 사용하여 효율적이고 정확하게 행동 인식을 수행하는 합성곱 신경망(모델 A)과 순환 신경망까지 적용한(모델 B) 심층 신경망 모델을 제시한다. 모델 A는 batch normalization과 같은 단순한 기법만 적용해도 이전의 결과보다 더 작은 모델로 더 높은 성능을 달성할 수 있다는 것을 보인다. 모델 B는 시계열 데이터 모델링에 주로 사용되는 LSTM 레이어를 추가하여 예측 정확도를 더욱 높일 수 있음을 보인다. 이 모델은 29명의 피실험자를 대상으로 수집한 벤치마크 데이트 세트에서 종합 예측 정확도 97.16%(모델 A), 99.50%(모델 B)를 달성했다.

• 한국심리학회지 : 문화 및 사회문제
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• 제14권1호_spc
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• pp.415-425
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• 2008

There are many social issues that should be solved through activity in the local community, such as community development, social service, environmental protection and disaster prevention. Despite a large number of activities, they are not always effective. In this investigation, we examine some alternative approaches to disaster prevention in local communities based on Japanese research and practices. Activity theory (Engestr öm, 1987) was adopted as a theoretical viewpoint. Implications for community education, which is another important issue in the community, are also discussed.

• 한국생활과학회지
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• 제19권1호
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• pp.63-74
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• 2010

The purpose of the study was to examine the actual conditions of caregiver-infant ratios, group-room activity areas, evaluations of infant programs and caregiver-infant interactions based on structural and process indicators which are major factors of infant care. The subjects were 20 caregivers and 91 infants from 14 infant classes of 13 day care centers in Daejeon. An actual survey was conducted on caregiver-infant ratios and group-room activity areas, and teaching-learning plans for infants and daily schedules were gathered for the evaluation of infant programs. The caregiver-infant interactions were observed every one minute for a total of 20 minutes using Lee Wan Jeong's "Evaluation Measure of Caregiver-infant Interactions"(1999). The results of this study were as follows: First, caregiver-infant ratios ranged from 2.5 to 7 infants per caregiver, resulting in the difference of the number of infants. Second, the 14 classes for one-year-old infants were arranged in three different ways; 5 classrooms with distinctive activity areas, 2 without any divided areas and 7 containing a mix of partial activity areas. Third, in teaching-learning plans for infants, there were a large number of topics related to seasonal features and experiences while the fewest were about basic life habits. Fourth, in the caregiver-infant interactions, caregivers used more positive interactions and linguistic modeling than sensitive responses to infants and social interactions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권3호
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• pp.219-234
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• 2009

This paper presents a novel approach for extracting simultaneously human daily activity patterns and discovering the temporal relations of these activity patterns. It is necessary to resolve the services conflict and to satisfy a user who wants to use multiple services. To extract the simultaneous activity patterns, context has been collected from physical sensors and electronic devices. In addition, a context model is organized by the proposed incremental statistical method to determine conflicts and to infer user intentions through analyzing the daily human activity patterns. The context model is represented by the sets of the simultaneous activity patterns and the temporal relations between the sets. To evaluate the method, experiments are carried out on a test-bed called the Ubiquitous Smart Space. Furthermore, the user-intention simulator based on the simultaneous activity patterns and the temporal relations from the results of the inferred intention is demonstrated.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.359-366
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• 2013

Although many research efforts have been conducted to address the effect of crew members' work skills (e.g., technical and planning skills) on work performance (e.g., work duration and quality) in construction projects, the relationship between skill and performance has generated a great deal of controversy in the field of management (Inkpen and Crossan 1995). This controversy can lead to under- or over-estimations of the overall project schedule, and can make it difficult for project managers to implement appropriate managerial policies for enhancing project performance. To address this issue, the following aspects need to be considered: (a) work performances are determined not only by individual-level work skill but also by the group-level work skill affected by work team members, each member's role, and any working behavior pattern; (b) work planning has significant effects on to what extent work skill enhances performance; and (c) different types of activities in construction require different types of work, skill, and team composition. This research, therefore, develops a system dynamics (SD) model to analyze the effects of both individual-and group-level (i.e., multi-level) skill on performances by utilizing the advantages of SD in capturing a feedback process and state changes, especially in human factors (e.g., attitude, ability, and behavior). The model incorporates: (a) a multi-level skill evolution and relevant behavior development mechanism within a work group; (b) the interaction among work planning, a crew's skill-learning, skill manifestation, and performances; and (c) the different work characteristics of each activity. This model can be utilized to implement appropriate work planning (e.g., work scope and work schedule) and crew management policies (e.g., work team composition and decision of each worker's role) with an awareness of crew's skill and work performance. Understanding the different characteristics of each activity can also support project managers in applying strategic work planning and crew management for a corresponding activity, which may enhance each activity's performance, as well as the overall project performance.

• 대한임베디드공학회논문지
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• 제14권5호
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제13권3호
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• pp.217-233
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• 2009

The purpose of this study is to describe how dynamic geometry systems can be useful in proof activity; teaching sequences based on the use of dynamic geometry systems and to analyze the possible roles of dynamic geometry systems in both teaching and learning of proof. And also dynamic geometry environments can generate powerful interplay between empirical explorations and formal proofs. The point of this study was to show that how using dynamic geometry software can provide an opportunity to link between empirical and deductive reasoning, and how such software can be utilized to gain insight into a deductive argument.