• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.55-60
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• 2022

Acceleration of the pace of life, increasing the amount of information, the emergence of "clip way of thinking" as a phenomenon has led to the problem of choosing forms of presentation of educational materials to students. One of the ways to solve this problem is to use the means of visualization of information flow, forasmuch as the thinking of modern youth is more effective in perceiving visual images than verbal means. The purpose of the research is to prove the effectiveness of the use of visualization in the process of teaching Ukrainian as a foreign language to students with clip way of thinking. The following methods have been used, namely: analysis, synthesis, comparison, systematization and generalization of scientific literature; testing and surveys; pedagogical experiment; quantitative and qualitative analysis of data, interpretation and generalization of the research results. The essence of visualization means has been revealed; the expediency of their use in the methodology of teaching foreign students the Ukrainian language has been substantiated. It has been proven that the role of Ukrainian teachers lies in taking into account all new trends in teaching, integrating computer perception of information by foreign students into teaching technology and using cognitive visualization in order to intensify the learning process.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.219-225
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• 2022

Recently, the popularization of personal mobility(PM) has made it possible to see many electric scooters. The energy source of personal transportation used by the general public and the disabled can be seen as environmentally friendly as electricity. Personal transportation means are divided into small electric vehicles because they use electric energy, and they are being treated as new models by automobile manufacturers in each country and spurring development. On the other hand, personal transportation means may cause various types of traffic accidents as they travel between roads and a human walk. In order to prevent such accidents, it is judged that the enactment of laws on the establishment of specifications for electric scooters, which are personal transportation means, and the method of restricting road operation should be given priority. The electric scooter is basically different from the conventional vehicle. The steering shaft of the steering system applied to the electric scooter one to two is possible. 1 to 2 the front-wheel under the steering column is used. It is classified according to the number of wheel installed at the electric scooter is the vehicle body into 2 wheel - electric scooter, and 3 wheel - electric scooter and 4 wheel - electric scooter. In this study, we propose a steering shaft design model that can be applied to an electric scooter, one of personal transportation means. A design model for 1-shaft steering and 2-shafts steering that can be applied to electric scooters is proposed. In addition, we have produced the prototypes for the commercialization of the proposed models, and reviewed the pros and cons of the manufactured prototypes and models.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1021-1024
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• 2009

현재 사용되고 있는 통행시간 분류방법은 하나의 통행시간을 대푯값으로 가지고 있다. 이에 문제점은 고속도로 특성으로 규정 속도 이상의 속도로 주행하는 차량, 규정 속도 및 휴게소 이용차량, 운전자의 운전 습성, 통행 목적, 피로의 정도, 운전자 성향과 도로상황에 따라 통행시간이 다르게 나타나는 점이다. TCS(Toll Collection System) 자료는 고속도로의 다양한 특성이 포함되어 있으며, 대상 구간의 거리가 멀수록 목적지에 도달하는 통행시간의 분산이 커지는 특성 또한 보인다. 따라서 이를 처리하기 위한 효율적인 통행시간 분류, 구간대표통행시간 추출 알고리즘이 필요하다. 기존의 방법은 전체 통행차량의 통행시간을 감안한 방법으로 통행시간 예측시 정확성이 저하된다. 본 연구에서는 TCS 자료를 Fuzzy c-means 알고리즘을 이용하여 일일 고속도로 통행시간의 시간별 주행특성을 고려한 대푯 값을 추출하는 알고리즘을 제안하였다. 실제 서울-청주 구간을 운행한 TCS 자료를 가지고 실시한 실험으로, 주행특성 및 도로상황을 고려한 Fuzzy c-means를 이용한 통행시간 분류방법과 기존의 통행시간 분류 방법을 통한 통행시간을 PIFAB를 사용 TCS 자료의 실제 통행시간과 경로통행시간을 비교 평가하였다. 평가한 결과 본 연구에서 제안하는 Fuzzy c-means기법은 기존 방법인 MAD기법보다 75%, 신뢰구간(95%) 추출법 대비 81%의 정확성을 제고하였다.

• Journal of Creative Information Culture
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• v.4 no.3
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• pp.221-230
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• 2018

Collaborative filtering technique is a major method of recommender systems and has been successfully implemented and serviced in real commercial online systems. However, this technique has several inherent drawbacks, such as data sparsity, cold-start, and scalability problem. Clustering-based collaborative filtering has been studied in order to handle scalability problem. This study suggests a collaborative filtering system which utilizes genetic algorithms to improve shortcomings of K-means algorithm, one of the widely used clustering techniques. Moreover, different from the previous studies that have targeted for optimized clustering results, the proposed method targets the optimization of performance of the collaborative filtering system using the clustering results, which practically can enhance the system performance.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.20-27
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• 2022

In this paper, we propose to improve the designation examination criteria of agencies that provide personal identity proofing based on the resident registration number (RRN), a 13-digit number uniquely assigned by the government to identify Korean citizens. In online commerce, etc., the personal identity proofing agency (PIPA) is a place where online users can prove their personal identity by presenting an alternative means instead of their RRN. The designation examination criteria for PIPAs established in 2012 is a revision of the relevant current laws, and there is a problem in applying the designation examination for alternative means of RRN as the current examination standard. Therefore, in this paper, we propose a method to make the current examination criteria applicable to the newly designated examination of the personal identity proofing service provider based on the current RRN alternative method. According to the current designation examination criteria, only those who satisfy all the examination criteria are designated as the PIPA. However, in reality, it is not in line with the purpose of regulatory reform to require that all examination criteria be satisfied. In the proposed method, it is proposed to apply the standard score system for designation of PIPAs, to make the law current, to secure legal compliance, and to establish a new examination standard to provide a new alternative means of personal identity proofing service. By applying the proposed method to the PIPA designation examination, various alternative means of RRN can be utilized in the online commerce service market.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.189-198
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• 2023

The phenomenon of climate change is deteriorating which increased heatwaves, typhoons and heavy snowfalls in recent years. Followed by the 25^th United nations framework convention on climate change(COP25), the world countries have achieved a consensus on achieving carbon neutrality. City plays a crucial role in achieving carbon mitigation as well as economic development. Considering economic and environmental factors, we selected 63 cities in South Korea to analyze carbon emission situation by Elbow method and K-means clustering algorithm. The results reflected that cities in South Korea can be categorized into 6 clusters, which are technology-intensive cities, light-manufacturing intensive cities, central-innovation intensive cities, heavy-manufacturing intensive cities, service-intensive cities, rural and household-intensive cities. Specific suggestions are provided to improve city-level carbon mitigation development.

• Asia pacific journal of information systems
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• v.30 no.2
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• pp.284-307
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• 2020

Customer engagement has been the main objective of brand companies in their marketing through social networking sites (SNS). Facebook is the most popular platform for SNS marketing, especially for companies that try to engage with their customers by providing various values through their brand pages (i.e., brand communities). The management of brand pages, therefore, becomes "the means" by which to achieve the result ("the end") of SNS marketing, and visitors to the brand pages (i.e., brand communities) then come to have a favorable attitude toward the brand. Based on a "means-ends" framework, this study examines the development of engagement between customers and brands in terms of brand page engagement as the means objective and brand attitude as the ends objective in the context of Facebook. This study further examines the antecedents and consequences of brand page engagement based on the customer value theory with two-stage data collection. This study contributes to the literature by explaining the roles and effects of brand page engagement in SNS marketing. This study further provides guidance to SNS providers and practitioners on SNS marketing strategies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.416-426
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• 2003

In this paper we propose a new method for choosing the initial values of Expectation-Maximization(EM) algorithm that has been used in various applications for clustering. Conventionally, the initial values were chosen randomly, which sometimes yields undesired local convergence. Later, K-means clustering method was employed to choose better initial values, which is currently widely used. However the method using K-means still has the same problem of converging to local points. In order to resolve this problem, a new method of initializing values for the EM process. The proposed method not only strengthens the characteristics of EM such that the number of iteration is reduced in great amount but also removes the possibility of falling into local convergence.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.355-359
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• 2014

In this paper, we proposed the new fuzzy pattern classifier which combines several fuzzy models with simple consequent parts hierarchically. The basic component of the proposed fuzzy pattern classifier with hierarchical structure is a fuzzy model with simple consequent part so that the complexity of the proposed fuzzy pattern classifier is not high. In order to analyze and divide the input space, we use Fuzzy C-Means clustering algorithm. In addition, we exploit Conditional Fuzzy C-Means clustering algorithm to analyze the sub space which is divided by Fuzzy C-Means clustering algorithm. At each clustered region, we apply a fuzzy model with simple consequent part and build the fuzzy pattern classifier with hierarchical structure. Because of the hierarchical structure of the proposed pattern classifier, the data distribution of the input space can be analyzed in the macroscopic point of view and the microscopic point of view. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.