• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.389-395
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• 2022

A Mobile Sensor Network is widely used in real time applications. A critical need in Mobile Sensor Network is to achieve energy efficiency during routing as the sensor nodes have scarce energy resource. The nodes' mobility in MWSN poses a challenge to design an energy efficient routing protocol. Clustering helps to achieve energy efficiency by reducing the organization complexity overhead of the network which is proportional to the number of nodes in the network. This paper proposes"EERA: Energy Efficient Routing Algorithm for Mobile Sensor Network" is divided into five phases. 1, Cluster Formation 2.Cluster head and Transmission head selection 3.Path Establishment / Route discovery and 4,Data Transmission. Experimental Analysis has been done and is found that the proposed method performs better than the existing method with respect to four parameters.

• Journal of Biosystems Engineering
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• v.37 no.4
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• pp.271-278
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• 2012

Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.419-425
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• 2020

DC distribution system has been evaluated as an excellent one in comparison with existing AC distribution network because it needs fewer power conversion stages and the full capacity of the equipment can be used without consideration for power factor. Recently, research and development on the implementation of DC distribution networks have been progressed globally based on the rapid advancement in power-electronics technology, and the technological developments from the viewpoint of infrastructure are also in progress. However, to configure a distribution network which is a distribution line for DC, more accurate and rapid introduction of analysis technology is needed for the monitoring, control and operation of the system, which ensure the system run flexible and efficiently. However, in case of a bipolar DC distribution network, there are two buses acting as slack buses, so the Jacobian matrix cannot be configured. Without solving this problem, DC distribution network cannot be operated when the network is unbalanced. Therefore, this paper presented a comprehensive method of analysis with consideration of operating elements which are directly connected between neutral electric potential caused by the unbalanced of load in DC distribution network with bipolar structure.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.17-22
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• 2019

Network attack analysis and visualization methods using network traffic session data detect network anomalies by visualizing the sender's and receiver's IP addresses and the relationship between them. The traffic flow is a critical feature in detecting anomalies, but simply visualizing the source and destination IP addresses symmetrically from up-down or left-right would become a problematic factor for the analysis. Also, there is a risk of losing timely security situation when designing a visualization interface without considering the temporal characteristics of time-series traffic sessions. In this paper, we propose a visualization interface and analysis method that visualizes time-series traffic data by using the radial axis, divide IP addresses into network and host portions which then projects on the cylindrical coordinate system that could effectively monitor network attacks. The proposed method has the advantage of intuitively recognizing network attacks and identifying attack activity over time.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.641-654
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• 2014

A new computer code, named CORONA (Core Reliable Optimization and thermo-fluid Network Analysis), was developed for the core thermo-fluid analysis of a prismatic gas cooled reactor. The CORONA code is targeted for whole-core thermo-fluid analysis of a prismatic gas cooled reactor, with fast computation and reasonable accuracy. In order to achieve this target, the development of CORONA focused on (1) an efficient numerical method, (2) efficient grid generation, and (3) parallel computation. The key idea for the efficient numerical method of CORONA is to solve a three-dimensional solid heat conduction equation combined with one-dimensional fluid flow network equations. The typical difficulties in generating computational grids for a whole core analysis were overcome by using a basic unit cell concept. A fast calculation was finally achieved by a block-wise parallel computation method. The objective of the present paper is to summarize the motivation and strategy, numerical approaches, verification and validation, parallel computation, and perspective of the CORONA code.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.462-475
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• 2022

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.300-306
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• 2022

The theoretical analysis and synthesis of prospects for the development of distance learning in Ukraine, the main topical problems of distance education in Ukraine are considered, the main factors that hinder the introduction of distance learning are analyzed, to pay attention to the need to increase the level of computer literacy among Ukrainian educators and the formation of modern methodology of distance learning, in particular, a single, systematic, national approach of organization, coordination and control in this area. Research methods: analytical method, method of structural and functional analysis, phenomenological method, content analysis method, philosophical reflection method, sociological methods (questionnaire, interview).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.65-70
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• 2019

The purpose of this study is to understand the college students' needs for software curriculum which based on surveys from educational satisfaction of the software lecture evaluation, as well as to find out the improvement plan by applying the text content analysis method. The research method used the text content analysis program to calculate the frequency of words occurrence, key words selection, co-occurrence frequency of key words, and analyzed the text center and network analysis by using the network analysis program. As a result of this research, the decent points of the software education network are mentioned with 'lecturer' is the most frequently occurrence after then with 'kindness', 'student', 'explanation', 'coding'. The network analysis of the shortage points has been the most mention of 'lecture', 'wish to', 'student', 'lecturer', 'assignment', 'coding', 'difficult', and 'announcement' which are mentioned together. The comprehensive network analysis of both good and shortage points has compared among key words, we can figure out difference among the key words: for example, 'group activity or task', 'assignment', 'difficulty on level of lecture', and 'thinking about lecturer'. Also, from this difference, we can provide that the lack of proper role of individual staff at group activities, difficult and excessive tasks, awareness of the difficulty and necessity of software education, lack of instructor's teaching method and feedback. Therefore, it is necessary to examine not only how the grouping of software education (activities) and giving assignments (or tasks), but also how carried out group activities and tasks and monitored about the contents of lectures, teaching methods, the ratio of practice and design thinking.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.123-140
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• 2009

Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.2
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• pp.49-56
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• 2003

As semi-solid forging (SSF) is compared with conventional casting such as gravity die-casting and squeeze casting, the product without inner defects can be obtained from semi-solid forming and globular microstructure as well. Generally, SSF consists of reheating, forging, and ejecting processes. In the reheating process, the materials are heated up to the temperature between the solidus and liquidus line at which the materials exists in the form of liquid-solid mixture. The process variables such as reheating time, reheating temperature, reheating holding time, and induction heating power has large effect on the quality of the reheated billets. It is difficult to consider all the variables at the same time for predicting the quality. In this paper, Taguchi method, regression analysis and neural network were applied to analyze the relationship between processing conditions and solid fraction. A356 alloy was used for the present study, and the learning data were extracted from the reheating experiments. Results by neural network were in good agreement with those by experiment. Polynominal regression analysis was formulated using the test data from neural network. Optimum processing condition was calculated to minimize the grain size and solid fraction standard deviation or to maximize the specimen temperature average. Discussion is given about reheating process of row material and results are presented with regard to accurate process variables fur proper solid fraction, specimen temperature and grain size.