• Nuclear Engineering and Technology
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• v.37 no.2
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• pp.151-158
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• 2005

Advanced nuclear power plants are generally large complex systems automated by computers. Whenever a rare plant emergency occurs the plant operators must cope with the emergency under severe mental stress without committing any fatal errors. Furthermore, The operators must train to improve and maintain their ability to cope with every conceivable situation, though it is almost impossible to be fully prepared for an infinite variety of situations. In view of the limited capability of operators in emergency situations, there has been a new approach to preventing the human error caused by improper human-machine interaction. The new approach has been triggered by the introduction of advanced information systems that help operators recognize and counteract plant emergencies. In this paper, the adverse effect of automation in human-machine systems is explained. The discussion then focuses on how to configure a joint human-machine system for ideal human-machine interaction. Finally, there is a new proposal on how to organize technologies that recognize the different states of such a joint human-machine system.

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.444-451
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• 2005

The analysis of human arm motion during steering maneuver is carried out for investigation of man-machine interface of driver and steering system Each arm is modeled as interconnection of upper arm, lower arm, and hand by rotational joints that can properly represents permissible joint motion, and both arms are connected to a steering wheel through spring and damper at the contact points. The joint motion law during steering motion is determined through the measurement of each arm movement, and subsequent inverse kinematic analysis. Combining the joint motion law and inverse dynamic analysis, joint stiffness of arm is estimated. Arm dynamic analysis model for steering maneuver is setup, and is validated through the comparison with experimentally measured data, which shows relatively good agreement. To demonstrate the usefulness of the arm model, it is applied to study the effect of steering column angle on the steering motion.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.265-272
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• 2001

The human arm is modeled by three rigid bodies(the upper arm, the forearm and the hand)with seven degree of freedom(three in the shoulder, two in the elbow and two in the wrist). The objective of this work is to present a method to determine the three-dimensional kinematics of the human elbow joint using a magnetic tracking device. Euler angle were used to determine the elbow flexion-extension, and the pronation-supination. The elbow motion for the various driving conditions is measured through the driving test using a simulator. Discomfort levels of elbow joint motions were obtained as discomfort functions, which were based on subjects' perceived discomfort level estimated by magnitude estimation. The results showed that the discomfort posture of elbow joint motions occurred in the driving motion.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.233-239
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• 2003

Three dimensional joint motion data were obtained using X-ray and precise magnetic sensors. Six metal markers were inserted on the femur and the tibia to set the coordinate system. Two magnetic position sensors were used to record motion data and these positions were transformed into the knee motion. The quadriceps muscle was extended in an automatic manner by an extraction machine. Results of the knee joint motion were the same as the clinical data. The proposed method is found to be reasonable in describing the knee motion so that these motion data can be used to simulate the normal knee joint.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.929-934
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• 2003

Three dimensional joint motion data were obtained using precise magnetic sensors and X-ray. Six metal markers were inserted on the femur and the tibia to set the coordinate system. Two magnetic position sensors were used to record motion data and these positions were transformed into the knee motion. The quadriceps muscle was extended in an automatic manner by an extraction machine. Results of the knee motion were the same as the clinical data. The proposed method is found to be reasonable in describing the knee motion so that these motion data can be used to simulate the normal knee joint.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.38-42
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• 2002

Conditions are discussed for operating a dissymmetric human master-small (or micro) slave system in best (large position gain-small velocity gain) conditions allowing higher operator dexterity when real effects (joint compliance, link flexion delay and transmission distortion) are taken into account. It is shown that position PD feedback law advantage for ideal case no longer holds, and that more complicated feedback law depending on real effects has to be implemented with adapted transmission line. Drawback is slowdown of master slave interaction, suggesting to use more advanced predictive methods for the master and more intelligent control law for the slave.

• Proceedings of the KIEE Conference
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• 2008.10c
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• pp.6-8
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• 2008

Electrical machineries have been developed as following with various and high technical application in these days. Especially the robot is integrated system including mechanical structure, electronic control, and electrical technology. The robot system is not compact and has not natural motion like human, although the technology of robot has been developing continuously. The spherical wheel motor is useful electric machine for using robot joint as operation of 3-degrees of freedom. In this paper, a permanent magnet spherical wheel motor is introduced and performance characteristics are analyzed for improving of operation stability.

• ETRI Journal
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• v.42 no.1
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• pp.78-89
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• 2020

Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.