• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.329-339
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• 2022

Small cells, particularly femtocells, are regarded a promising solution for limited resources required to handle the increasing data demand. They usually boost wireless network capacity. While widespread usage of femtocells increases network gain, it also raises several challenges. Interference is one of such concerns. Interference management is also seen as a main obstacle in the adoption of two-tier networks. For example, placing femtocells in a traditional macrocell's geographic area. Interference comes in two forms: cross-tier and co-tier. There have been previous studies conducted on the topic of interference management. This study investigates the principle of categorization of interference management systems. Many methods exist in the literature to reduce or eliminate the impacts of co-tier, cross-tier, or a combination of the two forms of interference. Following are some of the ways provided to manage interference: FFR, Cognitive Femtocell and Cooperative Resource Scheduling, Beamforming Strategy, Transmission Power Control, and Clustering/Graph-Based. Approaches, which were proposed to solve the interference problem, had been presented for each category in this work.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.349-357
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• 2016

The era of the Internet of Things (IoT) is upon us. In this era, minimizing power consumption becomes a primary concern for system-on-chip designers. While traditional power minimization and dynamic power management (DPM) techniques have been heavily explored to improve the power efficiency of devices inside very large-scale integration (VLSI) platforms, there is one critical factor that is often overlooked, which is the power conversion efficiency of a power delivery network (PDN). This paper is a tutorial that focuses on the power conversion efficiency of the PDN, and introduces novel methods to improve it. Circuit-, architecture-, and system-level approaches are presented to optimize PDN designs, while case studies for three different VSLI platforms validate the efficacy of the introduced approaches.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.84-92
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• 2021

The research on waste power batteries in China in the past ten years reveals that the power battery recycling industry is enormous but marred with several challenges. A study of China's current power battery closed-loop supply chain revealed some issues in the power battery recycling industry, such as imperfect supply chain, small recycling scale, asymmetric information, and imperfect profit distribution mechanism. This paper uses the theory of corporate social responsibility and consumer choice to propose a closed-loop network of power batteries based on block chain technology and analyzes the existing closed-loop supply chain of power batteries. Consequently, this study provides a new idea for developing the power battery closed-loop supply chain by proposing the closed-loop network of power batteries based on blockchain technology.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.33-40
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• 2017

Internet of Vehicles (IoV) is a subset of Internet of Things (IoT) and it is an infrastructure for vehicles. Therefore, IoV consists of three main network including inter-vehicle network, intra-vehicle network, and vehicular mobile internet. IoV mainly used in urban traffic environment to provide network access for drivers, passengers and traffic management. Accordingly, many research works have focused on network technology. But, recent concerted efforts in academia and industry point to paradigm shift in IoV system. In this paper, we proposed a knowledge base for intelligence service in IoV. A detailed design and implementation of the proposed knowledged base is illustrated. We hope this work will show power of IoV as a disruptive technology.

• Journal of Integrative Natural Science
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• v.16 no.4
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• pp.139-145
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• 2023

Due to changes in the distribution system and increased demand for renewable energy, interest in technology to increase the limit capacity of distributed energy grid connection using grid flexible resources is also increasing. Recently, the distribution system system is changing due to the increase in distributed power from renewable energy, and as a result, problems with the limited capacity of the distribution system, such as waiting for renewable energy to connect and increased overload, are occurring. According to the power generation facility status report provided by the Korea Power Exchange, of the total power generation capacity of 134,020 MW as of 2021, power generation capacity through new and renewable energy facilities is 24,855 MW, accounting for approximately 19%, and among them, power generation through solar power accounts for a total portion of the total. It was analyzed that the proportion of solar power generation facilities was high, accounting for 75%. In the future, the proportion of new and renewable energy power generation facilities is expected to increase, and accordingly, an efficient operation plan for the distribution system is needed. Advanced country-type NWAs that can integrate the operation and management of load characteristics for each line of the distribution system, power distribution, regional characteristics, and economic feasibility of distributed power in order to improve distribution network use efficiency without expanding distribution facilities due to the expansion of renewable energy. An integrated operating system is needed. In this study, in order to improve the efficiency of distribution network use without expanding distribution facilities due to the expansion of renewable energy, we developed a method that can integrate the operation and management of load characteristics for each line of the distribution system, power distribution, regional characteristics, and economic feasibility of distributed power. We want to develop an integrated operation system for NWAs similar to that of advanced countries.

• Corrosion Science and Technology
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• v.12 no.2
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• pp.79-84
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• 2013

In general, pipelines at nuclear power plants are affected by various types of degradation mechanisms and may be ruptured after gradually thinning. FAC (Flow-Accelerated Corrosion) is typical aging mechanism affecting the secondary side piping system. In Korea nuclear power plants, CHECWORKS program have been used for management of wall thinning damages. However, sometimes, CHECWORKS program shows wrong results at the stage of NFA (Network Flow Analysis) in case of complex pipelines. This paper describes the calculation results of pressure drop in a complex pipeline and single line by using the CHECWORKS program and the analysis results are compared with those of engineering calculation results including errors between them.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.111-115
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• 2012

In this paper, a solar energy powered bicycle linked to a wireless sensor network (WSN) which monitors the transfer of solar energy to an electrical energy storage unit and an analysis of its effectiveness is proposed. In order to achieve this goal, a solar-powered bicycle with an attached ZigBee and a far-end wireless network supervisory system is setup. Experimental results prove that our prototype, solar energy powered bicycle, can achieve enough solar energy for charging a two lead-acid battery pack. As a result, the user, through use of a wireless network in the parking period can be kept aware of the data on the amount of immediate solar radiation, the degree of illumination, the ambient temperature, and electrical energy storage capacity information of the bicycle through an internet interface.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.57-81
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• 1993

This paper is concerned with analyzing the bankruptcy prediction power of three methods : Multivariate Discriminant Analysis (MDA), Inductive Learning, Neural Network, MDA has been famous for its effectiveness for predicting bankrupcy in accounting fields. However, it requires rigorous statistical assumptions, so that violating one of the assumptions may result in biased outputs. In this respect, we alternatively propose the use of two AI models for bankrupcy prediction-inductive learning and neural network. To compare the performance of those two AI models with that of MDA, we have performed massive experiments with a number of Korean bankrupt-cases. Experimental results show that AI models proposed in this study can yield more robust and generalizing bankrupcy prediction than the conventional MDA can do.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.122-126
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• 2015

Credit enhancement is absolutely essential for financing Independent Power Producer (IPP) projects in Asia particularly for countries whose sovereign credit rating is on non-investment grade and foreign investment is difficult to achieve. Due to nexus of agreements among varies parties in IPP project, it is hard to clearly visualize the roles of these agreements. Examples are: What credit enhancement factors are most influential to minimize the associated risks of IPP projects? Why are they powerful? What are their roles? Who are less powerful and what are the obstacles that causes them less powerful? A research is conducted to identify the credit enhancement factors for IPP projects in Asia. IPP professionals validated 27 out of 28 identified credit enhancement factors, and five factor groupings were made through factor analysis. Afterwards, network theory is applied to find the unanswered questions, which by graphical and mathematical representations show that the host government's credit enhancement, MDBs, ECAs and other parties' credit enhancement are prominent and of great importance to handle the associated risks of IPP projects in Asia

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.