• 한국정밀공학회지
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• 제8권1호
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• pp.105-115
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• 1991

This paper describes an integrated CAD and CAPP system for prismatic parts of injection mold which generates a complete process plan automatically from CAD data of a part without human intervention. This system employs Auto CAD as a CAD model and GS-CAPP as an automatic process planning system for injection mold. The proposed CAD/CAPP system consists of three modules such as CAD data conversion module, manufacturing feature recognition module, and CAD/CAPP interface module. CAD data conversion module transforms design data of AutoCAD into three dimensional part data. Manufacturing feature recognition module extracts specific manufacturing features of a part using feature recognition rule base. Each feature can be recognized by combining geometry, position and size of the feature. CAD/CAPP interface module links manufacturing feature codes and other head data to automatic process planning system. The CAD/CAPP system can improve the efficiency of process planning activities and reduce the time required for process planning. This system can provide a basis for the development of part feature based design by analyzing manufacturing features.

• 한국항행학회논문지
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• 제25권4호
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• pp.299-304
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• 2021

기존의 사람 행동 인식 시스템은 웨어러블 센서, 카메라와 같은 장치를 통해 행동을 탐지하였다. 그러나, 이와 같은 방법들은 추가적인 장치와 비용이 요구되고, 특히 카메라 장치의 경우 사생활 침해 문제가 발생한다. 이미 설치되어 있는 WiFi 신호를 사용한다면 해당 문제를 해결할 수 있다는 장점이 있다. 본 논문에서는 WiFi 신호의 채널 상태 정보를 활용한 CNN 기반 사람 행동 인식 시스템을 제안하고, 가속 하드웨어 구조 설계 및 구현 결과를 제시한다. 해당 시스템은 실내 공간에서 학습 중 나타날 수 있는 네 가지 행동에 대해 정의하였고, 그에 대한 WiFi의 채널 상태 정보를 CNN으로 분류하여 평균 정확도는 91.86%를 보였다. 또한, 가속화를 위해 CNN 분류기에서 연산량이 가장 많은 완전 연결 계층에 대한 가속 하드웨어 구조 설계 결과를 제시하였다. FPGA 디바이스 상에서 성능 평가 결과, 기존 software 기반 시스템 대비 4.28배 빠른 연산 시간을 보임을 확인하였다.

• 한국생활과학회지
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• 제23권4호
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• pp.613-631
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• 2014

Personality education activities based on the roots of empathy program were designed and practiced in this study to investigate their effects on young children's empathic ability and emotional intelligence. The subjects of this research were 60 five years old of 2 classes in 'W' kindergarten in 'I' city, Jeonra Buk province. We randomly assigned 30 children of one class to the experimental group and 30 children of the other class to the controlled group. The personality education activities based on the roots of empathy program was by the researcher. The results of this study are summarized as follows. First, the personality education activities based on the roots of empathy program improved children's empathic ability. And that effects are shown in all sub-areas of empathic ability(sorrow/burden/joy/fear). Second, the personality education activities based on the roots of empathy program improved children's emotional intelligence. And that effects are shown in all sub-areas of emotional intelligence(recognition and expression of emotion/promotion of thinking by emotion/application of emotional knowledge/emotional reflective control).

• 한국멀티미디어학회논문지
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• 제8권6호
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• pp.768-775
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• 2005

For sign languages are main communication means among hearing-impaired people, there are communication difficulties between speaking-oriented people and sign-language-oriented people. Automated sign-language recognition may resolve these communication problems. In sign languages, finger spelling is used to spell names and words that are not listed in the dictionary. There have been research activities for gesture and posture recognition using glove-based devices. However, these devices are often expensive, cumbersome, and inadequate for recognizing elaborate finger spelling. Use of colored patches or gloves also cause uneasiness. In this paper, a vision-based finger spelling recognition system is introduced. In our method, captured hand region images were separated from the background using a skin detection algorithm assuming that there are no skin-colored objects in the background. Then, hand postures were recognized using a two-dimensional grid analysis method. Our recognition system is not sensitive to the size or the rotation of the input posture images. By optimizing the weights of the posture features using a genetic algorithm, our system achieved high accuracy that matches other systems using devices or colored gloves. We applied our posture recognition system for detecting Korean Sign Language, achieving better than $93\%$ accuracy.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.542-544
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• 2019

Changes in a person's health affect one's lifestyle and work activities. According to the World Health Organization (WHO), abnormal activity is growing faster in people aged 60 or more than any other age group in almost every country. This trend steadily continues and expected to increase further in the near future. Abnormal activity put these people at high risk of expected incidents since most of these people live alone. Human abnormal activity analysis is a challenging, useful and interesting problem among the researchers and its particularly crucial task in life and health care areas. In this paper, we discuss the problem of abnormal activities of old people lives alone at home. We propose Convolutional Neural Network (CNN) based model to detect the abnormal behaviors of elderlies by utilizing six simulated action data from daily life actions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.799-804
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• 1991

This paper is concerned with the artificial control of prosthetic devices using the electromyographic(EMG) activities of biceps and triceps in human subject during isometric contraction adjustments at the elbow. And it was analysised about recognition of EMG signals and dynamic characteristics at arm movements of human. For this study the error signal of autoregressive(AR) model were used to discriminate arm movement patterns of human. Interaction of dynamic characteristics (Position, Velocity, Acceleration) and EMG of biceps and triceps at arm movements of human was measured.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• 전기전자학회논문지
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• 제24권1호
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• pp.269-274
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• 2020

This paper is to study the exoskeleton robot for the walking of the elderly and the disabled. We developed and tested an Exoskeletal robot with two axes of freedom for joint motion. The EEG and EMG signals were used to move the joints of the Exoskeletal robot. By analyzing the EMG signal, the control signal was extracted and applied to the robot to facilitate the walking operation of the walking assistance robot. In addition, the brain-computer interface technology is applied to perform the operation of the robot using brain waves, spontaneous electrical activities recorded on the human scalp. These two signals were fused to study the walking recognition method of the supporting robot leg.

• 한국재난정보학회 논문집
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• 제11권4호
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• pp.475-486
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• 2015

본 연구에서는 직원들의 직위에 따른 기업보안 활동 중 산업기밀 유출 방지에 영향을 미치는 기업정보보호활동 통제요인을 분석하고자 하였다. 정부, 공공기관, 민간기업 종사자를 대상으로 내부 정보시스템에 대한 사용자 및 관리자 354 명을 정보보호활동 36문항, 산업기밀유출방지 10문항을 조사 하여 회귀분석을 실시하였다. 산업기밀 유출 통제 활동에 영향 미치는 기업정보보호활동인식으로 사원은 물리적 통제, 환경적 통제, 인적 통제, 소프트웨어통제를 대리는 환경적 통제, 하드웨어 통제, 과장은 하드웨어통제, 환경적 통제, 부장급 이상은 물리적 통제 순서로 보안통제활동 인식을 나타났다. 사원, 대리, 부장 직위이상에서는 기술적 통제 요인이 과장 직위에서만 시스템 통제 요인이 산업기밀유출 방지 통제활동에 가장 많은 영향을 미친다고 지목하였다.