• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.198-201
• /
• 2001

Recently the wireless-internet has been spreading extensively. People are spending a large part of their time gaining access to information using a mobile device. With the rapid growth of on-line Electronic Commerce, the use of mobile devices creates a new paradigm that provides users with location-independent real time service. Although this new paradigm does have some advantages, limited process speed, low bandwidth, the low battery capacity of mobile devices, and a high rate of wireless network errors causes many overhead expenses during service time with the server. In this paper, we suggest an autonomous service delivery system, which provides mobile agent capability to users that cannot maintain a connection. We have developed the system based on java mobile agent technology. Using this system, we can provide more effective service to users when the user is sending requirements for service through a mobile device that has limited resources. Furthermore we can manage the contact server dynamically when new services are added.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.733-736
• /
• 1996

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bankruptcy prediciton on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediciton. Therefore, we have decided to focus on textiles and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Korean Management Science Review
• /
• v.14 no.2
• /
• pp.91-111
• /
• 1997

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Smart Structures and Systems
• /
• v.3 no.3
• /
• pp.299-320
• /
• 2007

Smart sensors densely distributed over structures can provide rich information for structural monitoring using their onboard wireless communication and computational capabilities. However, issues such as time synchronization error, data loss, and dealing with large amounts of harvested data have limited the implementation of full-fledged systems. Limited network resources (e.g. battery power, storage space, bandwidth, etc.) make these issues quite challenging. This paper first investigates the effects of time synchronization error and data loss, aiming to clarify requirements on synchronization accuracy and communication reliability in SHM applications. Coordinated computing is then examined as a way to manage large amounts of data.

• Journal of the Korea Society of Computer and Information
• /
• v.3 no.3
• /
• pp.99-106
• /
• 1998

This Paper proposes a novel fractal image method for image communication to shorten time to take on fractal encoding by using limited search area method and scaling method First, the original image is contracted respectively by half and by quarter with the scaling method. And then, the corresponding domain block of the quarter-sized image which is most similar with one range block of the half-sized image is searched within the limited area in order to reduce the encoding time extremely As the result of the evaluation, the proposed algorithm provided much shorter encoding time and better compression ratio with a little degradation of the decoded image quality than Jacquin's method.

• Journal of the Korea Society of Computer and Information
• /
• v.4 no.2
• /
• pp.135-140
• /
• 1999

This paper describes a affine transform coding which is reducing long time to take on image encoding by using scaling method and limited search area technique. For evaluating its performance, the proposed algorithm compare with Jacquin's method using traditional affine transform coding methods. Simulation results, the proposed algorithm considerably reduces encoding time by using scaling method and limited search area method. Also, the proposed algorithm provides much shorter encoding time with a little degradation of the decoded image quality than Jacquin's method.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.14 no.2
• /
• pp.91-91
• /
• 1989

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Journal of the Korea Society of Computer and Information
• /
• v.5 no.3
• /
• pp.64-69
• /
• 2000

This paper proposes a novel fractal coding method for fast encoding and high compression to shorten time to take on fractal encoding by using limited search area. First. the original image is contracted respectively by half and by quarter with the scaling method and bit-plane method. And then, the corresponding domain block of the quarter-sized image which is most similar with one range block of the half-sized image is searched within the limited area in order to reduce the encoding time extremely. As the result of the evaluation, the proposed algorithm provided much shorter encoding time and better compression ratio with a little degradation of the decoded image qualify than Jacquin's method.

• Journal of the Korean Society of Industry Convergence
• /
• v.21 no.6
• /
• pp.439-444
• /
• 2018

The performance improvement in the high speed region for the sensorless based synchronous machine drive is discussed in the paper. Conventional dynamic overmodulation method in the vector controlled AC driver requires some calculation of maximum amplitude of the applying voltage vector to limit its amplitude, which leads to increase the calculation time of microprocessor. For low performance microprocessor, this might be impossible to complete the control loop within limited control time. Thus, to reduce the calculation time, the constantly limited amplitude for applying voltage vector is tried in this paper to drive sensorless based synchronous motor. Certainly, there exists some errors in amplitude and phase angle between inverter voltage and calculating voltage in the sensorless algorithm. But, this errors are too small to prevent the high speed sensorless operation within overmodulation region. The validities of the proposed method is proved by the experimental results.

• Journal of Korea Society of Industrial Information Systems
• /
• v.10 no.2
• /
• pp.21-25
• /
• 2005

Human performance of system control under excessive mental-workload may differ from stable situation. In this study, design guidelines of secondary control system were introduced to enhance performance of safety control system. Under urgent situation, the first performance criterion is not a reaction time but safe control reaction that prevents system disaster. Therefore it is important to find out the facts that are mainly related system safety. Experimental results show performance of primary task didn't reflect whole system influence within a limited short reaction time. In this situation, the secondary task is more sensitive to system influence that varied with some factors of urgent status. Therefore, when a system proceeds to abnormal and unsafe status, and even more the reaction time is limited within a very short time to control the system, the estimation of human performance is more sensitive using secondary task performance then primary task performance. Those results mean it is required to develop various secondary tasks to design safety control systems preventing disaster, And also require many studies of estimation methods human performances especially when system status varies dangerous and/or unsafe situation.