• IEMEK Journal of Embedded Systems and Applications
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• 제18권6호
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• IE interfaces
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• 제3권1호
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• pp.13-20
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• 1990

The application of human-factor concept in developing a complex weapon system is important for system engineer to determine the system performance and reliability. This paper describes the evaluation procedure of human factors in the X-system wherein the evaluation result provides a better performance than the previous model in operatability and maintainability. The criteria used for the evaluation of a design adequacy of the X-system are based on the military human-factor standards.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제33권4호
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• pp.85-99
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• 2010

This study is to identify the present condition and level of human resource administration in domestic profit and non-profit organizations, and to investigate on the relation between human resource management and management performance in enterprise. This study is to make items related to the human resource management of American Malcolm Baldrige Award's level and management performance researching questions, investigates seven patterns of organs surveyed positively. The results are as follows: Firstly, the profit organs like manufacturing companies shows more positive than non-profit in comparison with human resource management of each organs. And a local government and public enterprise which is non-profit show to recognize human resource management is insufficient on th whole. Secondly, perception level of a medical institution, educational institution and service industry about aromaticity of human resource management by global standard shows to be positive, but the local government negative. Thirdly, the profit organ in the recognition of human resource management about details practice too, shows to be positive, while non-profit negative. Fourth, the result which compares and analyzes management performance between the similar industry overall show to recognize positive, but public enterprise negative about product, service performance and human resource management. Fifthly, the details practice of human resource administration shows to influence meaningly to all management performance. Therefore, all organizations will positively confront human resource management, make the circumstance of organization through systematic program, and promote the management performance of the organization.

• Journal of the Korea Society for Simulation
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• 제31권1호
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 대한산업공학회/한국경영과학회 1995년도 춘계공동학술대회논문집; 전남대학교; 28-29 Apr. 1995
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• pp.787-796
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• 1995

A man model is a useful design tool for the evaluation of man machine systems and products. An arm reach trajectory prediction for such a model will be specifically useful to present human activities and, consequently, could increase the accuracy and reality of the evaluation. In this study, a three-dimensional reach trajectory prediction model was developed using an inverse kinematics technique. The upper body was modeled as a four link open kinematic chain with seven degrees of freedom. The Resolved Motion Method used for the robot kinematics problem was used to predict the joint movements. The cost function of the perceived discomfort developed using the central composite design was also used as a performance function. This model predicts the posture by moving the joints to minimize the discomfort on the constraint of the end effector velocity directed to a target point. The results of the pairwise t-test showed that all the joint coordinates except the shoulder joint's showed statistically no differences at .alpha. = 0.01. The reach trajectory prediction model developed in this study was found to accurately simulate human arm reach trajectory and the model will help understand the human arm reach movement.

• Journal of the Semiconductor & Display Technology
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• 제18권1호
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• pp.97-101
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• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.246-252
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• 2005

Intelligent Space is a space where many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents, which provide human with services. To realize this, human and mobile robots have to approach each other as much as possible. Moreover, it is necessary for them to perform interactions naturally. It is desirable for a mobile robot to carry out human affinitive movement. In this research, a mobile robot is controlled by the Intelligent Space through its resources. The mobile robot is controlled to follow walking human as stably and precisely as possible. In order to follow a human, control law is derived from the assumption that a human and a mobile robot are connected with a virtual spring model. Input velocity to a mobile robot is generated on the basis of the elastic force from the virtual spring in this model. And its performance is verified by the computer simulation and the experiment.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.391-399
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• 2020

This study aims to analyze the relationship of e-HRM implementation to employee performance both directly and indirectly through the intervening of the Human Resource service Quality variable, both practically and theoretically. This study uses variance-based structural equation modeling (SEM) techniques with partial least square (PLS) statistical testing tools to test the direct relationship of e-HRM and the performance and relationship moderated by Human Resources service quality tested on 200 civil servants in five offices under the coordination of the Government of the South Sulawesi Province of Indonesia. The data collection model in this study uses an online survey. The data analysis stages through the explanatory concept consist of, first, the interpretation of the distribution of the average frequency of respondents' answers; second, outer-loading; third, determination of the validity and reliability; fourth, the coefficient of determination test and partial test; fifth, the GoF model; sixth, validity test; and seventh, hypothesis testing. This study explores four hypotheses in a comprehensive fashion; the results of this study show that all hypotheses have positive and significant effects both through direct and intervening relationships. Among the three direct relationships, the relationship of e-HRM variables on HR Service Quality is greatest and most dominant.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.150-161
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• 2018

We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.

• Journal of Korean Institute of Industrial Engineers
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• 제30권1호
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• pp.17-26
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• 2004

In this study, the usability evaluation of a menu-structure was performed using spreading activation model with respect to human's memory retrieval. Spreading Activation Model is effectively used to understand the process of information retrieval, so it can be used as a theoretical background for modeling of the process of human's information retrieval. For spreading activation test (SAT), subjects were presented with 67 pairs of menu titles, which consist of a menu title in the high level menu item and a menu title for the next lower level menu item, from Korea University's web site. For performance tests, three scenarios were developed with longer reaction times and ambiguous associations found in the SAT to reflect the existing problems of the website. As a result, the SAT was found to bean effective tool to enhance the website usability because the SAT could bea substitute for the performance test with a high correlation $({\rho}=0.735,\;{\alpha}=0.05)$. After remaining menu titles with slow reaction times and ambiguous associations found in SAT, the website usability was significantly improved with faster reaction times and less ambiguous associations proven with smaller number of web-page visits. Therefore, the SAT could be used as a methodology to design and evaluate the user-centered menu structure related to human's association structure.