• Journal of the Korean Society for Railway
• /
• v.13 no.3
• /
• pp.317-326
• /
• 2010

The purpose of this study is to depict statistically and exploratively the interrelationship among learning orientation, environmental turbulence, and performance in the railway public organizations. To attain this purpose, a questionnaire was provided based on literature survey, and 616 respondent's data received was tested for its validity and reliability. And then, simple and vertical regression analysis were performed. The main findings are as follows: First, learning orientation has a relatively positive effects on performance. Second, environmental turbulence shows a partial moderating effects on the relationship between learning orientation and performance. These findings suggest that learning orientation do exist as an important organizational cultural dimension that contributes to the acquisition of sustainable competitive advantage, and some restrictive factors of public organizations seem to inevitably disrupt the intrinsic necessity of learning in the railway organizations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.7
• /
• pp.584-588
• /
• 2002

The real-time characteristic of the adaptive leaning control algorithms is validated based on the applied results of the hydraulic servo system that has very strong a non-linearity. The evolutionary strategy automatically adjusts the search regions with natural competition among many individuals. The error that is generated from the dynamic system is applied to the mutation equation. Competitive individuals are reduced with automatic adjustments of the search region in accordance with the error. In this paper, the individual parents and offspring can be reduced in order to apply evolutionary algorithms in real-time. The feasibility of the newly proposed algorithm was demonstrated through the real-time test.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.10B
• /
• pp.1946-1952
• /
• 1999

A new image compression method to preserve edge characteristics in reconstructed images using an unsupervised learning neural is proposed in this paper. By the unsupervised competitive learning which generalizes previously proposed Centroid Neural Network(CNN) algorithm with the geometric characteristics of edge area and statistical characteristics of image data, more codevectors are allocated in the edge areas to provide the more accurate edges in reconstructed image. Experimental results show that the proposed method gives improved edge in reconstructed images when compared with SOM, Modified SOM and M/R-CNN.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.2
• /
• pp.187-196
• /
• 1996

This paper introduces the concept of fixed weights and proposes an algorithm for classification by adding this concept to vector space separation method in LVQ. The proposed algorithm is based on competitive learning. It uses fixed weightsfor generality and fast adaptation efficient radius for new weight creation, and L1 distance for fast calcualtion. It can be applied to many fields requiring adaptive learning with the support of generality, real-tiem processing and sufficient training effect using smaller data set. Recognition rate of over 98% for the train set and 94% for the test set was obtained by applying the suggested algorithm to on-line handwritten recognition.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.12
• /
• pp.1581-1590
• /
• 1995

Recently, advanced video coding techniques using segmentation technique have been actively researched as candidates for video coding of MPEG-4 standard. The conventional segmentation techniques are unsuitable for real-time process because they have sequential structure. In this paper, we propose a new image segmentation technique using competitive learning neural network for vector quantization. The proposed segmentation procedure consist of prefiltering, primary and secondary segmentation, and a small region ellimination process. Primary segmentation segments input image in detail. Secondary segmentation merges similar region using a repetitive FSCL(Frequency sensitive competive learning) neural network. In this process, it is possible to segment an image from high resolution to low resolution by adjusting the number of repetition. Finally, small regions are merged into adjacent regions. Experimental results show that the procedure described yields reconstructed images of reasonably acceptable quality at bit rates of 0. 25 - 0.3 bit/pel.

• Proceedings of the KIEE Conference
• /
• 2001.07a
• /
• pp.386-388
• /
• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• Advances in robotics research
• /
• v.2 no.1
• /
• pp.13-31
• /
• 2018

This paper presents the performance of Teaching-Learning-Based Optimization (TLBO) algorithm for optimum static balancing of a robot manipulator. Static balancing of robot manipulator is an important aspect of the overall robot performance and the most demanding process in any robot system to match the need for the production requirements. The average force on the gripper in the working area is considered as an objective function. Length of the links, angle between them and stiffness of springs are considered as the design variables. Three robot manipulator configurations are optimized. The results show the better or competitive performance of the TLBO algorithm over the other optimization algorithms considered by the previous researchers.

• Journal of Computing Science and Engineering
• /
• v.1 no.2
• /
• pp.162-176
• /
• 2007

Machine Learning (ML) has become very popular within Data Mining (KDD) and Artificial Intelligence (AI) research and their applications. In the ML and KDD contexts, two main approaches can be used for inducing a Bayesian Network (BN) from data, namely, Conditional Independence (CI) and the Heuristic Search (HS). When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at increasing the computational efficiency. In this paper a new CI based approach to induce BCs from data is proposed and two algorithms are presented. Such approach is based on the Markov Blanket concept in order to impose some constraints and optimize the traditional PC learning algorithm. Experiments performed with the ALARM, as well as other six UCI and three artificial domains revealed that the proposed approach tends to execute fewer comparison tests than the traditional PC. The experiments also show that the proposed algorithms produce competitive classification rates when compared with both, PC and Naive Bayes.

• Asia pacific journal of information systems
• /
• v.25 no.1
• /
• pp.61-81
• /
• 2015

Investment in research and development (R&D) is critical in the information technology (IT) firms, where newer and better technology is a quintessential goal that directly affects innovation and competitive advantage. This study investigates how R&D investment influences firm performance and value, and how the effect of R&D investment differs between IT hardware and software firms. We also analyze the relationship between firm age and R&D investment in order to identify learning effects on continuous R&D investment. The empirical investigation in this study, based on longitudinal archival data from 2001 to 2010, found a significant effect of R&D investment on firm performance in IT firms. Further, this study demonstrates causal relationship between firm age, and verifies that learning effects are present in R&D investment. Moreover, the results are found to differ between IT hardware and IT software firms.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.19-27
• /
• 2021

Fireworks Algorithm (FWA) is a new heuristic swarm intelligent algorithm inspired by the natural phenomenon of the fireworks explosion. Though it is an effective algorithm for solving optimization problems, FWA has a slow convergence rate and less information sharing between individuals. In this paper, we improve the FWA. Firstly, explosion operator and explosion amplitude are analyzed in detail. The coefficient of explosion amplitude and explosion operator change dynamically with iteration to balance the exploitation and exploration. The convergence performance of FWA is improved. Secondly, differential evolution and commensal learning (CDE) significantly increase the information sharing between individuals, and the diversity of fireworks is enhanced. Comprehensive experiment and comparison with CDE, FWA, and VACUFWA for the 13 benchmark functions show that the improved algorithm was highly competitive.