• Journal of Welding and Joining
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• 제29권4호
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• pp.61-66
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• 2011

The purpose of this study is to develop improved boom structures with reliable fatigue strength of weldment and lower production cost. For that purpose, multi-body dynamic analysis was performed to evaluate forces acting on arm & boom cylinders and joints of boom structure during operation of an excavator for three working postures, then stress analysis was made to investigate stress distribution around diaphragms at the bottom plate of boom structures which was known to be susceptible to fatigue failures of welded joints, and finally boom structures with optimum arrangement of diaphragms was proposed. This work mainly consists of the following two parts: part 1 focuses on multi-body dynamic analysis of excavators during operation and part 2 includes evaluations of fatigue strength of welded joints for modified boom structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.631-653
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• 2020

Sign language is a natural, visually oriented and non-verbal communication channel between people that facilitates communication through facial/bodily expressions, postures and a set of gestures. It is basically used for communication with people who are deaf or hard of hearing. In order to understand such communication quickly and accurately, the design of a successful sign language translation system is considered in this paper. The proposed system includes object detection and classification stages. Firstly, Single Shot Multi Box Detection (SSD) architecture is utilized for hand detection, then a deep learning structure based on the Inception v3 plus Support Vector Machine (SVM) that combines feature extraction and classification stages is proposed to constructively translate the detected hand gestures. A sign language fingerspelling dataset is used for the design of the proposed model. The obtained results and comparative analysis demonstrate the efficiency of using the proposed hybrid structure in sign language translation.

• 로봇학회논문지
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• 제17권4호
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• pp.463-476
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• 2022

In this paper, we propose a table tennis posture classification system using a cooperative robot to develop a table tennis robot that can be trained like a real game. The most ideal table tennis robot would be a robot with a high joint driving speed and a high degree of freedom. Therefore, in this paper, we intend to use a cooperative robot with sufficient degrees of freedom to develop a robot that can be trained like a real game. However, cooperative robots have the disadvantage of slow joint driving speed. These shortcomings are expected to be overcome through quick recognition. Therefore, in this paper, we try to quickly classify the opponent's posture to overcome the slow joint driving speed. To this end, learning about dynamic postures was conducted using image data as input, and finally, three classification models were created and comparative experiments and evaluations were performed on the designated dynamic postures. In conclusion, comparative experimental data demonstrate the highest classification accuracy and fastest classification speed in classification models using MLP (Multi-Layer Perceptron), and thus demonstrate the validity of the proposed algorithm.

• 한국산업융합학회 논문집
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• 제22권2호
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• pp.87-93
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• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• 정보처리학회논문지D
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• 제16D권1호
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• pp.11-26
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• 2009

심혈관계 질환의 진단 위해서 복합 진단 지표를 이용한 출현 패턴 기반의 분류 기법을 제안하였다. 복합 진단 지표 적용을 위해서 심박동변이도의 선형/비선형적 특징들을 세 가지 누운 자세에 대해 분석하였고 ST-segments로부터 4개의 진단 지표를 추출하였다. 이 논문에서는 질환진단을 위해서 필수 출현 패턴을 이용한 분류 모델을 제안하였다. 이 분류 기법은 환자 그룹의 질환 패턴들을 발견하며, 이러한 출현 패턴은 심혈관계 질환 환자들에서는 빈발하지만 정상인 그룹에서는 빈발하지 않는 패턴들이다. 제안된 분류 알고리즘의 평가를 위해서 120명의 협심증(AP: angina pectrois) 환자, 13명의 급성관상동맥증후군(ACS: acute coronary syndrome) 환자 그리고 128명의 정상인 데이터를 사용하였다. 실험 결과 복합 지표를 사용하였을 때, 세 그룹의 분류에 대한 정확도는 약 88.3%였다.

• 한국산업정보학회논문지
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• 제19권5호
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• pp.25-31
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• 2014

생체전기 임피던스 해석은 체 성분 측정에 있어 비침습적이며, 비용이 적게 들고 안전성과 재현성이 우수하여 체 성분의 변화를 평가하기 위해 널리 사용되고 있는 방법이다. 본 연구에서는 자세에 따른 생체임피던스와 혈압차이에 대해 연구하였으며, 피 실험자를 대상으로 생체전기 임피던스 측정시스템을 통한 저항 및 리액턴스의 실시간 측정 실험과 등가모델을 통한 모의실험 그리고 자세 변화에 대한 혈압 차이를 비교하는 실험을 실시하였다. 생체임피던스는 다중 주파수(1 kHz, 10 kHz, 20 kHz, 50 kHz, 70 kHz, 100 kHz)에서 4분간 측정하였다. 실험결과, 선 자세, 앉은 자세, 누운 자세 순으로 몸의 임피던스는 저항과 리액턴스의 변화로 인해 증가하는 것으로 나타났다. 특히, 누운 자세에서의 저항은 50 kHz에서 앉은 자세에서의 저항보다 평균 16.49% 높았으며, 누운 자세에서의 리액턴스는 5 kHz일 때에 앉은 자세보다 평균 26.05% 높았다. 혈압의 경우에는 다른 자세에 비해 선 자세에서의 평균 최고치($125.14{\pm}12.30$) 및 평균 최저치($75.57{\pm}10.31$) 혈압이 높게 나타났다. 본 연구의 생체임피던스 및 혈압 차이에 관한 연구는 급성 질환, 고도 비만, 신체 기형 등의 연구에 활용될 수 있을 것으로 사료된다.

• 한국전문물리치료학회지
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• 제15권1호
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• pp.30-37
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• 2008

This study was designed to examine the effects of temporary immobilization of the ankle and knee joints on standing in healthy young adults with the use of a postural control mechanism. The subjects were twenty-four college students (12 males and 12 females, aged between 20 and 28). A Biodex balance system SD 950-302 and its software were used to measure indirect balance parameters in standing. Each subject underwent postural stability tests in 4-different joint conditions: free joints, ankle immobilization only, knee immobilization only, and ankle and knee immobilization. In addition, the postural stability test was conducted once with the subject's eyes open and once with the eyes closed conditions. For data analysis of the postural stability tests, the overall stability index, antero-posterior stability index, and medio-lateral stability index were recorded. The overall stability index (p=.000) and medial-lateral index (p=.003) were significantly greater different conditions with eyes closed in postural stability. Therefore, the eyes closed condition is expected to be used as an effective postural stability training for treatment planning in patients with unstable postures. In addition, training based on the dynamic multi-segment model can improve postural stability and is available to therapeutic programs, helping people with unstable balance to reduce their risk of falling.

• 로봇학회논문지
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• 제14권3호
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.277-283
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• 2023

Vital signals provide essential information regarding the health status of individuals, thereby contributing to health management and medical research. Present monitoring methods, such as ECGs (Electrocardiograms) and smartwatches, demand proximity and fixed postures, which limit their applicability. To address this, Non-contact vital signal measurement methods, such as CW (Continuous-Wave) radar, have emerged as a solution. However, unwanted signal components and a stepwise processing approach lead to errors and limitations in heart rate detection. To overcome these issues, this study introduces an integrated neural network approach that combines noise removal, demodulation, and dominant-frequency detection into a unified process. The neural network employed for signal processing in this research adopts a MLP (Multi-Layer Perceptron) architecture, which analyzes the in-phase and quadrature signals collected within a specified time window, using two distinct input layers. The training of the neural network utilizes CW radar signals and reference heart rates obtained from the ECG. In the experimental evaluation, networks trained on different datasets were compared, and their performance was assessed based on loss and frequency accuracy. The proposed methodology exhibits substantial potential for achieving precise vital signals through non-contact measurements, effectively mitigating the limitations of existing methodologies.