• Journal of Korean Society of Water and Wastewater
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• v.19 no.3
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• pp.279-287
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• 2005

In this study, I suggest an effective operation of waterwork facilities in large cities and a scientific method for utilizing water in water distribution systems. To achieve this goal, my simulation were carried out on data from Kwangju City using Pipenet '98, a pipe-network program. From this simulation, I examine the possibilities of application the system in large cities, comparing data measured at 33 hydraulic pressure monitoring places from waterwork enterprises. The result is coincident with that of waterwork enterprises, with about a 12.5% average error rate and $0.32kg/cm^2$ average deviation. The method and program I use here can be helpful in cities where there is a need to extend the waterwork facilities, or where there is a need to suspend the water supply, and/or there is an accident. The simulation shows how to expand waterwork facilities effectively, how to prevent accidents, and how to estimate the hydraulic pressure even in the areas without monitoring places.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.1
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• pp.55-59
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• 2010

Most applications for solar-powered wireless sensor networks are usually deployed in remote areas without a continuous connection to the external networks and a regular maintenance by an administrator. In this case, sensory data has to be stored in the network as much as possible until it is uploaded by the data mule. For this purpose, a balanced data distribution over the network should be performed, and this can be achieved efficiently by taking the amount of available energy and storage into account, in the system layer of each node. In this paper, we introduce a simple but very efficient data distribution algorithm, by which each solar-powered node utilizes the harvested energy and the storage space maximally. This scheme running on each node determines the amount of energy which can be used for a data distribution as well as the amount of data which should be transferred to each neighbor, by using the local information of energy and storage status.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.975-980
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• 2020

To protect wireless sensor networks (WSNs), various key distribution and management schemes have been proposed. However, most of them conducted simulations and experiments for performance evaluation by considering only the two-dimensional (2D) environments. In this paper, we investigate the effect of real-world three-dimensional (3D) topographic features on the key pre-distribution schemes for WSNs. For this purpose, we analyze and compare the performance of three pairwise key pre-distribution schemes in 2D and 3D environments: full pairwise (FP), random pairwise (RP), and full and random pairwise (FRP) schemes. For the experiments, we employ a network simulator NS-3 and 3D graphic tools such as Blender and Unity. As a result, we confirm that there was a difference in the performance of each scheme according to the actual 3D terrain and that the location-based FRP that considers deployment errors, has the highest efficiency in many aspects.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.712-718
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• 2013

With the development of industry and the improvement of living standards, better quality in power electric service is required more than ever before. This paper deals with the optimal algorithms for voltage regulation in the case where Distributed Storage and Generation (DSG) systems are operated in distribution systems. It is very difficult to handle the interconnection issues for proper voltage managements, because the randomness of the load variations and the irregular operation of DSG should be considered. This paper proposes the optimal on-line real time voltage regulation methods in power distribution systems interconnected with the DSG systems. In order to deliver suitable voltage to as many customers as possible, the optimal sending voltage should be decided by the effective voltage regulation method by using artificial neural networks to consider the rapid load variation and random operation characteristics of DSG systems. The simulation results from a case study show that the proposed method can be a practical tool for the voltage regulation in distribution systems including many DSG systems.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.408-416
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• 2005

Quality of service (QoS) provision is an important and indispensable function for multi-service wireless networks. In this paper, we present a new scheduling/admission control frame­work, including an efficient rate-guaranteed opportunistic scheduling (ROS) scheme and a coordinated admission control (ROS­CAC) policy to support statistic QoS guarantee in multi-service wireless networks. Based on our proposed mathematical model, we derive the probability distribution function (PDF) of queue length under ROS and deduce the packet loss rate (PLR) for individual flows. The new admission control policy makes admission decision for a new incoming flow to ensure that the PLR requirements of all flows (including the new flow) are satisfied. The numerical results based on ns-2 simulations demonstrate the effectiveness of the new joint packet scheduling/admission control framework.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.8
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• pp.898-903
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• 2004

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. For treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And also these parameter is applied to classify PD sources by neural networks. Neural Networks has good recognition rate for three PD sources.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.6 no.1
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• pp.42-47
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• 1988

In this study bias estimation method was applied to the free leveling networks adjustment by the concepts of free leveling networks. Optimum bias coefficients were determined by analizing the distribution of height errors with regard to bias coefficients. The object of this study lies in suggesting the utilities of free leveling networks adjustment, comparing one fixed-point and two fixed-points leveling networks adjustment.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.223-231
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• 2021

The study aimed to investigate the impact of social networks safety (SNS) on the marketing information quality (MIQ) during the COVID-19 pandemic in Saudi Arabia. The study examines the statistical differences in social networks safety SNS and marketing information quality MIQ according to the demographics such as age, sex, income, and education. For this study purpose, information security and privacy are two components of social networks safety. The research materials are website resources, regular books, journals, and articles. The population includes all Saudi users of social networks. The figures show that active users of the social network reached 25 Million in 2020. The snowball method was used and sample size is 500 respondents and the questionnaire is the tool for the data collection. The Structural Equation Modelling SEM technique is used. Convergent Validity, Discriminate Validity, and Multicollinearity are the main assumptions of structural equation modeling SEM. The findings show the high positive impact of SNS networks safety on MIQ and the statistical differences in such variables refer to education. Finally, the study presents a set of future suggestions to enhance the safety of social networks in Saudi Arabia.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9B
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• pp.832-844
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• 2008

Distributed localization algorithms are necessary for large-scale wireless sensor network applications. In this paper, we introduce an efficient node distribution based localization algorithm that emphasizes simple refinement and low system load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighbor nodes for sensors, update its position estimate by minimizing a local cost function and then passes this update to the neighbor nodes. The update process considers a distribution of nodes for large-scale networks which have same density in a unit area for optimizing the system performance. Neighbor nodes are selected within a range which provides the smallest received signal strength error based on the real experiments. MATLAB simulation showed that the proposed algorithm is more accurate than trilateration and les complex than multidimensional scaling. The implementation on MicaZ using TinyOS-2.x confirmed the practicality of the proposed algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.311-316
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• 2019

Classification problem is to predict the class to which an input data belongs. One of the most popular methods to do this is training a machine learning algorithm using the given dataset. In this case, the dataset should have a well-balanced class distribution for the best performance. However, when the dataset has an imbalanced class distribution, its classification performance could be very poor. To overcome this problem, we propose an over-sampling scheme that balances the number of data by using Conditional Generative Adversarial Networks (CGAN). CGAN is a generative model developed from Generative Adversarial Networks (GAN), which can learn data characteristics and generate data that is similar to real data. Therefore, CGAN can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Experiments using actual collected data show that the over-sampling technique using CGAN is effective and that it is superior to existing over-sampling techniques.