• Asia pacific journal of information systems
• /
• v.11 no.4
• /
• pp.63-79
• /
• 2001

In this study, we tried to find the factors that influenced appraisal and reward satisfaction in knowledge management, and to observe whether appraisal and reward satisfaction were related to employees' commitment to knowledge management. Analyzing valid 38 data in the organizational level, we found that only result validity and reward justness affected employee appraisal and reward satisfaction. Also, if was found that appraisal and reward satisfaction were related to employees' commitment to knowledge management. The implications of the findings and future research directions were discussed.

• Journal of Korea Society of Industrial Information Systems
• /
• v.5 no.3
• /
• pp.77-86
• /
• 2000

The main objectives of this dissertation is to propose a forth generation index C for the case where the target value T is not equal to the midpoint of the specification limits (i.e. asymmetric tolerances), and show that this index is more sensitive compared to the standard PCI's in detacting small shifts of the process mean from the target value. In conclusion, in this dissertation , a new methods for estimating a measure of process capability for non-normally distributed variable data is proposed using the percentage nonconforming.

• Journal of Korea Society of Industrial Information Systems
• /
• v.2 no.1
• /
• pp.53-72
• /
• 1997

소프트웨어 시스템 개발의 수명 주기에서 유지·보수 단계에 투입되는 시간과 비용은 상당한 비율에 달한다. 그 결과 거기에는 시스템을 개발할 대, 프로그래밍에 중점을 두었지만, 현재는 유지·보수에 중점을 두고 있는 실정이다. 따라서, 효과적인 소프트웨어의 창출을 위해서는 좀더 나은 유지·보수 기법을 도출할 필요가 있다. 또한 소프트웨어 중요성을 감안할 때 특히 소프트웨어에 대한 성능이 추가, 수정, 보완을 원활하게 할 수 있는 소프트웨어 유지·보수 기법의 적용이 절실하다 하겠다. 본 연구에서 생산성 측정요인(프로그램 크기, 프로그램 복잡도)에 따라서 어떠한 유지·보수 기법의 적용이 효과적인가를 실험을 통하여 도출해보았다.

• Journal of Korea Society of Industrial Information Systems
• /
• v.2 no.1
• /
• pp.1-17
• /
• 1997

Genetic algorithms have proved to be a versatile and effectvie approach for solving optimization problems. Nevertheless, there are many situations that the genetic algorithm does not perform particularly well, and so various methods of hybridization have been proposed. Thus, this paper develop a hybrid method and a precision search method around optimum in the gentic algorithm and the conventional optimization techniques in finding global or near optimum.

• Journal of Korea Society of Industrial Information Systems
• /
• v.2 no.1
• /
• pp.73-87
• /
• 1997

Multilayer neural network using a modified beackpropagation learning algorithm was introduced to achieve automatic identification of different types of aircraft in a variety of 3-D orientations. A 3-D shape of an aircraft can be described by a library of 2-D images corresponding to the projected views of an aircraft. From each 2-D binary aircraft image we extracted 2-D invariant (L, Φ) feature vector to be used for training neural network aircraft classifier. Simulations concerning the neural network classification rate was compared using nearest-neighbor classfier (NNC) which has been widely served as a performance benchmark. And we also introduced reliability measure of the designed neural network classifier.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.310-318
• /
• 2022

Job offers have become more widespread and it has become easier and faster to apply for jobs through electronic recruitment platforms. In order to increase the protection of the data that is attached to the recruitment platforms. In this research, a proposed model was created through the use of hybrid encryption, which is used through the following algorithms: AES,Twofish,. This proposed model proved the effectiveness of using hybrid encryption in protecting personal data.

• Asia pacific journal of information systems
• /
• v.34 no.1
• /
• pp.209-225
• /
• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.111-114
• /
• 2003

In this paper, an industrial electric endoscope is implemented. Most industrial endoscope that used in domestic are imported and the equipments are divided into several parts. So these endoscopes spend a lot of time, labor and inspection cost on inspection process. This research is accomplished to solve above mentioned weak points and to improve previous inspection systems into embedded systems.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1999.10a
• /
• pp.188-188
• /
• 1999

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.4
• /
• pp.237-250
• /
• 2015

This paper describes a hardware-oriented algorithm and its conceptual implementation in a real-time speed limit traffic sign detection system on an automotive-oriented field-programmable gate array (FPGA). It solves the training and color dependence problems found in other research, which saw reduced recognition accuracy under unlearned conditions when color has changed. The algorithm is applicable to various platforms, such as color or grayscale cameras, high-resolution (4K) or low-resolution (VGA) cameras, and high-end or low-end FPGAs. It is also robust under various conditions, such as daytime, night time, and on rainy nights, and is adaptable to various countries' speed limit traffic sign systems. The speed limit traffic sign candidates on each grayscale video frame are detected through two simple computational stages using global luminosity and local pixel direction. Pipeline implementation using results-sharing on overlap, application of a RAM-based shift register, and optimization of scan window sizes results in a small but high-performance implementation. The proposed system matches the processing speed requirement for a 60 fps system. The speed limit traffic sign recognition system achieves better than 98% accuracy in detection and recognition, even under difficult conditions such as rainy nights, and is implementable on the low-end, low-cost Xilinx Zynq automotive Z7020 FPGA.