• Journal of Communications and Networks
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• v.11 no.4
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• pp.406-415
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• 2009

In this work, we consider performance modeling of a wireless sensor network with a time division multiple access (TDMA) media access protocol with slot reuse. It is assumed that all the nodes are peers of each other and they have two modes of operation, active and sleep modes. We model the sensor network as a Jackson network with unreliable nodes with on and off states. Active and sleep modes of sensor nodes are modeled with on and off states of unreliable nodes. We determine the joint distribution of the sensor node queue lengths in the network. From this result, we derive the probability distribution of the number of active nodes and blocking probability of node activation. Then, we present the mean packet delay, average sleep period of a node and the network throughput. We present numerical results as well as simulation results to verify the analysis. Finally, we discuss how the derived results may be used in the design of sensor networks.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.198-205
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• 2003

This paper proposes the adaptive relaying of protective devices applied in the neighboring distribution feeders for reliable and efficient operations of a wind farm interconnected with distribution networks by dedicated lines. A wind farm connected to an electric power network is one of the greatest alternative energy sources. However, the wind turbine generators are influenced by abnormal grid conditions such as disturbances occurring in the neighboring distribution feeders as well as the dedicated power. Particularly, in cases of a fault happening in the neighboring distribution feeders, a wind farm might be accelerated until protective devices clear the fault. Therefore, the delayed operation time of protective devices for satisfying the coordination might overly expose the interconnected wind turbine generators to the fault and cause damage to them. This paper describes the proper delayed operation time of protective relay satisfying the coordination of the distribution networks as well as reducing damage on the interconnected wind farm. The simulation results for the Hoenggye substation model composed of five feeders and one dedicated line using PSCAD/EMTDC showed that the proper delayed time of protective devices reflecting the fault condition and the power output of the wind farm could improve the operational reliability, efficiency, and stability of the wind farm.

• Journal of Distribution Science
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• v.21 no.10
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• pp.63-75
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• 2023

Purpose: This research delves into the relationship between self-leadership and procrastination behavior in banking distribution networks. Based on the self-determination theory, the study explores how organizational commitment mediates the relationship between these variables. Moreover, the research considers the vital role that intrinsic motivation plays in enhancing and reinforcing these connections. Research design and methodology: Using data from 384 bank employees and partial least squares structural equation modeling, the research found evidence to support the theory. This methodological approach enabled the investigation to uncover the intricate links between self-leadership, procrastination behavior, organizational commitment, and intrinsic motivation. Results: The findings strongly support the hypotheses, indicating a negative association between self-leadership and procrastination behavior at the workplace; conversely, a positive correlation was found between self-leadership and organizational commitment. The discovery further strengthens the results that intrinsic motivation amplifies the positive relationship between self-leadership and organizational commitment. Conclusions: This research underscores the importance of cultivating a culture of self-leadership among banking distribution network employees. By doing so, procrastination can be substantially reduced, enhancing both productivity and overall performance. The study's insights are particularly valuable for organizational leaders in the banking sector, as they provide actionable pathways to foster a more committed, motivated, and efficient workforce.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.3
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• pp.1-12
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• 2001

In this paper, we investigate and analyze weight distribution of feed forward two-layer neural networks with a hidden layer in order to understand and improve time-consuming training process of neural networks. Generally, when a new problem is presented, neural networks have to be trained again without any benefit from the previous training process. In order to address this problem, training process is viewed as finding a solution point in the weight space and the distribution of solution points is analyzed. Then we propose to initialize neural networks using the information of the distribution of the solution points. Experimental results show that the proposed initialization using the weight distribution provides a better performance than the conventional one.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1999.12a
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• pp.21-25
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• 1999

Planning support systems(PSS) add more advanced spatial analysis functions than Geographic information systems(GIS) and intertemporal functions to the functions of spatial decision support systems(SDSS). This paper reports the continuing development of a PSS providing a framework that facilitates urban planners and civil engineers in conducting coherent deliberations about planning, design and operation & maintenance(O&M) of water-distribution networks for urban growth management. The PSS using dynamic optimization model, modeling-to-generate-alternatives, value engineering(VE) and life-cycle cost(LCC) can generate network alternatives in consideration of initial cost and O&H cost. Users can define alternatives by the direct manipulation of networks or by the manipulation of parameters in the models. The water-distribution network analysis model evaluates the performance of the user-defined alternatives. The PSS can be extended to include the functions of generating sewer network alternatives, combining water-distribution and sewer networks, eventually the function of planning, design and O&H of housing sites. Capacity expansion by the dynamic water-distribution network optimization model using MINLP includes three advantages over capacity expansion using optimal control theory(Kim and Hopkins 1996): 1) finds expansion alternatives including future capacity expansion times, sizes, locations, and pipe types of a water-distribution network provided, 2) has the capabilities to do the capacity expansion of each link spatially and intertemporally, and 3) requires less interaction between models. The modeling using MINLP is limited in addressing the relationship between cost, price, and demand, which the optimal control approach can consider. Strictly speaking, the construction and O&M costs of water-distribution networks influence the price charged for the served water, which in turn influence the. This limitation can be justified in rather small area because price per unit water in the area must be same as that of neighboring area, i.e., the price is determined administratively. Planners and engineers can put emphasis on capacity expansion without consideration of the relationship between cost, price, and demand.

• Journal of Distribution Science
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• v.14 no.9
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• pp.31-45
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• 2016

Purpose - This study develops a simulation model that looks at the dynamics between social capital and business performance in strategic networks to understand their behaviors in relation to each other, and to suggest dynamic relationship strategies. Research design, data, and methodology - Based on existing literature, this study identifies the complex causal loop diagram on social capital and business performance in strategic networks, and converts them into a simulation model for observing how the changes in business environment and relationship dependency affect social capital and business performance. Results - The simulation results showed that, first, the formation in social capital and business performance of networks with low relationship dependency was less affected by the changes in business environment. Second, the formation in social capital and business performance of networks with high relationship dependency was negatively impacted by the changes in business environment. In other words, higher relationship dependency strengthened the impact of changes in business environment on business performance. Conclusions - Thus, this study confirmed that in strategic networks, the changes in business environment and the degree of relationship dependency dynamically affect business performance, and that relationship dependency mediates the degree in which changes in the business environment affect business performance. The results of the simulations were further verified through actual business cases.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.236-245
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• 2007

Wireless sensor networks consist of numerous nodes equipped with small-sized and limited calculation capacities and storage space as well as low-capacity batteries. Therefore, the key issue is to reduce energy consumption of sensor nodes in sensor network environment. To reduce energy consumption of sensor nodes, consideration must be given to decreasing frequency of messages transmitted by nodes. Also, considering network application, security of sensor networks is also considered important. Therefore, this study proposes a key distribution scheme in dynamic clustering model. The dynamic clustering model used for this scheme is very effective in extending life span of wireless sensor networks. The proposed scheme provides improved security compared to the existing key distribution scheme by applying grid-based key distribution scheme and allocating polynomial s hare to the nodes forming a cluster. Also, comparison was made with the previously proposed grid-based, location-based and cluster-based key distribution schemes to illustrate the advantages of the proposed scheme.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.274-284
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• 2016

The increasing penetration of distributed energy resources on the distribution networks have brought a number of technical impacts where voltage and thermal variations have been identified as the dominant effects. Active network management in distribution networks aims to integrate distributed energy resources with flexible network management so that distributed energy resources are organized to make better use of existing capacity and infrastructure. This paper propose active solutions which aims to solve the voltage and thermal issues in a distributed manner utilizing a collaborative approach. The proposed algorithms have been fully tested on a distribution network with distributed generation units.

• Transactions on Electrical and Electronic Materials
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• v.18 no.6
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• pp.350-358
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• 2017

Recently, owing to increased interest in low-carbon energy supplies, renewable energy sources such as photovoltaics and wind turbines in distribution networks have received considerable attention for generating clean and unlimited energy. The presence of energy storage systems (ESSs) in the promising field of active distribution networks (ADNs) would have direct impact on power system problems such as encountered in probabilistic distributed generation (DG) model studies. Hence, the optimal procedure is offered herein, in which the simultaneous placement of an ESS, photovoltaic-based DG, and wind turbine-based DG in an ADN is taken into account. The main goal of this paper is to maximize the net present value of the loss reduction benefit by considering the price of electricity for each load state. The proposed framework consists of a scenario tree method for covering the existing uncertainties in the distribution network's load demand as well as DG. The collected results verify the considerable effect of concurrent installation of probabilistic DG models and an ESS in defining the optimum site of DG and the ESS and they demonstrate that the optimum operation of an ESS in the ADN is consequently related to the highest value of the loss reduction benefit in long-term planning as well. The results obtained are encouraging.