• Journal of The Korean Association For Science Education
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• v.17 no.1
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• pp.75-83
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• 1997

The purpose of this study was to determine the strategies that middle school students used in solving problems concerning density and solubility. These were compared in the aspects of problem contexts for 42 students of varying logical reasoning ability, spatial ability, and learning approach. A coding scheme used consists of five categories: reading & organization, production, errors, evaluation, and strategy. Students' protocols were analyzed after intercoder agreement had been established to be .95. The results were as follows: 1. Students had more difficulties in reading and organizing the problems in everyday contexts than in scientific contexts. Students at the concrete-operational stage and / or surface approach were more likely to have difficulties in reading and organizing the problems than those at the formal-operational stage and / or deep approach. 2. Students tended to split up the solubility problems into sub-problems and to solve the density problem in everyday contexts in random manner. These were significantly correlated with the test scores concerning logical reasoning ability, spatial ability, and learning approach at the .1 level of significance. 3. Major errors in solving the density problems were to disregard the given information or generated and to use inappropriate information. Many errors in solving the solubility problems were found to be executive errors. The strategy to use the information given appropriately was positively related to students' logical reasoning ability, spatial ability, and learning approach. 4. More evaluation strategies were found in everyday contexts. Their strategies to grasp the meaning of answers and to check the math were significantly related to students' logical reasoning ability. 5. Students used the random trial-and-error strategy more than the systematic strategy and the systematic trial-and-error strategy, especially in everyday contexts. The strategies used by the students were significantly related to students' logical reasoning ability, spatial ability, and learning approach.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.180-188
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• 1995

A modified Kohonen's self-organizing feature map (SOFM) algorithm which has binary reinforcement function and a constant learning rate is proposed. In contrast to the time-varing adaptaion gain of the original Kohonen's SOFM algorithm, the proposed algorithm uses a constant adaptation gain, and adds a binary reinforcement function in order to compensate for the lowered learning ability of SOFM due to the constant learning rate. Since the proposed algorithm does not have the complicated multiplication, it's digital hardware implementation is much easier than that of the original SOFM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.2
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• pp.276-283
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• 1994

Using a neural network for vector quantization, we can expect to have better codebook design algorithm for its adaptive process. Also, the designed codebook puts the codewords in order by its self-organizing characteristics, which makes it possible to partially search the codebook for real time process. To exploit these features of the neural network, in this paper, we propose a new codebook design algorithm that modified the KSFM(Kohonen`s Self-organizing Feature Map) and then combines the K-means algorithm. Experimental results show the performance improvment and the ability of the partical seach of the codebook for the real time process.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of Gifted/Talented Education
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• v.21 no.4
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• pp.847-860
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• 2011

In this study, the instrument of mathematical thinking ability tests were considered, and the differences between gifted and regular high school students in the ability were investigated by the test. The instrument consists of 9 items, and verified its quality due to reliability. Participants were 353 regular and 252 gifted high school students from tenth grade. As a result, not only organizing ability of information but also ability of space perception and visualization and intuitive insight ability could be the characteristics of the mathematical giftedness.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1652-1662
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• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

• Archives of design research
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• v.4 no.1
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• pp.39-50
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• 1991

Recently, the society is being, developed physically and technically and the desire for the better equipped office environment has been required. Due to the change of recognition about the information-oriented office and the rapid progress ot the office automation many conventional office environment are changing to the new phase, the intelligent building. Unfortunately, because of the lack of the harmony between our business operating characteristics and the western office space planning, there are some severe problems in improving the business ability. To create the efficent office environment design, we have to understand the function of the western office space and our current phenomenon, appearing in the company. Thus, the goal of this study is to present the methodology for improving the office environmental design by analizing the following elements. - The concept of the office space - The historical frame of the office development - The consideration of the office characteristics - The Analysis of the business working Characteristics, organizing characteristics, and operating characteristics

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.5
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• pp.85-91
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• 1989

In this paper, we propose a neural net system for speech recognition, which is composed of two neural networks. Firstly the self-supervised BP(Back Propagation) network generates the distributed concept corresponding to the activity pattern in the hidden units. And then the self-organizing neural network forms a concept map which directly displays the similarity relations between concepts. By doing the above, the difficulty in learning the conventional BP network is solved and the weak side of BP falling into a pattern matcher is gone, while the strong point of generating the various internal representations is used. And we have obtained the concept map which is more orderly than the Kohonen's SOFM. The proposed neural net system needs not any special preprocessing and has a self-learning ability.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.179-187
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• 2010

The purpose of this study is to suggest the advancement of the competitive power of Korean electronics and automobile industries from a product-architectural point of view. There are two types for the characteristics of parts. They are the modular type and the integral type. The former is very independent between one subsystem and another, whereas the other is very dependent. The distinction of product architecture makes them different for the ability to organize developing products suited for the product manufacturing. The organizing ability is generally divided into the abilities of 'modulation integration' and 'selection assembly'. For the integral products such as cars, the integration ability is important between an enterprise and another, as well as between a part and another. However, the modular products such as a PC need the ability to select and effectively organize optimal parts or enterprises. The strategy raising product competitiveness is pursued by synthesizing 'design manufactu-turing strategy' for the product architecture and 'customer value strategy' for the customer values.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.57-67
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• 2022

This study aims to understand human resource management in the Library of the House of Representatives of the Republic of Indonesia (DPR RI) during the COVID-19 pandemic. This institution had to change its work procedures, including its management of human resources. The research uses a qualitative approach with a case study method. Data was collected through interviews, observations, and document analysis, carried out from October to December 2020. The findings show that working from home practices had the following impacts: reduced ability to concentrate while working; many forced changes to the work programme; and problems of miscommunication due to work instructions being conveyed through a WhatsApp group. In response to the situation, the planning function of management was used to suspend the recruitment of interns; the organizing function was divided up tasks into two or three working days a week; the actuating function was seen in the willingness of leadership to motivate staff and to optimize resources by providing training; and the controlling function was realized in an online presence and online reporting mechanisms. It was concluded that the implementation of human resource management in the library was achieved through flexibility and staff empowerment. This involved carrying out initiatives and controlling their effectiveness in response to whatever changes were required by the latest pronouncements from government. Obstacles were still encountered in the implementation of these changes, especially in relation to the organizing function of management, where some conflict was seen between the DPR RI librarians.