• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.135-139
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• 2022

The current-voltage modeling plays an important role in characterizing photovoltaic systems. A solar cell has a nonlinear characteristic with various parameters influenced by the external environments such as the irradiance and the temperature. In order to accurately predict current-voltage characteristics at low irradiance, the artificial neural networks are applied to effectively quantify nonlinear behaviors. In this paper, a multi-layer perceptron scheme that can make accurate predictions is employed to learn complex formulas for large amounts of continuous data. The simulated results of artificial neural networks model show the accuracy improvement by using MATLAB/Simulink.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.1
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• pp.1-9
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• 2016

In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

• Journal of Multimedia Information System
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• v.1 no.2
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• pp.101-110
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• 2014

The purpose of this research is to extend Ad-hoc network system lifetime with the proposed routing protocol which has considered hop lifetimes of the nodes while guaranteeing QoS in the establishment process of Ad-hoc network communication paths. Based on another power aware routing system that proposed in the advanced research [1], we are proposing an alternative power aware routing system in which nodes' hop lifetimes are compared in order to extend the lifetime of an Ad-hoc network system and delay factors have been considered for the assurance of QoS. The research of the routing protocol in this paper, which aims to maximize the system survival time considering power consumption status during the path searching in MANET and pursues the mechanism that controls hop delays for the same reason, can be applied to the study of WSN. The study concerning such phenomena is essential so that the proposed protocol has been simulated and verified with NS-2 in Linux system focusing on the lifetimes of the hops of the nodes. Commercialization of smart devices and arrival of the ubiquitous age has brought about the world where all the people and things are connected with networks. Since the proposed power aware method and the hop delay control mechanism used to find the adequate communication paths in MANET which mainly uses batteries or in WSN, they can largely contribute to the lifetime extension of the network system by reducing power consumptions when utilized for the communications attempts among soldiers during military operation, disaster areas, temporary events or exhibitions, mobile phone shadow areas, home networks, in-between vehicle communications and sense networks, etc. This paper presents the definitions and some advantages regarding the proposed outing protocol that sustain and extend the lifetime of the networks.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.6
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• pp.47-53
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• 2008

This research focuses on the issue of seismic retrofit prioritization based on the Caltrans' highway network serving Los Angeles and Orange counties. Retrofit prioritization is one of most important problems in earthquake engineering, and it is a problem that most decision makers face in the process of resource allocation. This study demonstrates the methods of prioritized resource allocation in the process of retrofitting a regional highway network. For the criteria of a retrofit ranking, seismic vulnerability and the importance of network link are first introduced. Subsequently, link-based seismic retrofit cases are simulated, investigating the effects of the seismic retrofit in terms of seismic performance, such as driver's delay. In this study, probabilistic scenario earthquakes are used to perform a probabilistic seismic risk analysis. The results show that the retrofit prioritization can be differently defined and ranked depending on the stakeholders. This study provides general guidelines for prioritization strategy for the effective retrofitting of a highway network system.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.48-58
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• 2013

The stock market is constantly changing and sometimes a slump or a sudden rising in stocks happens without any special reason. So the stock market is recognized as a complex system and it is hard to predict the change on stock prices. In this paper we consider the stock market to a network consisting of stocks. We analyzed the dynamics of the Korean stock market network and evaluated the changing of the correlation between shares consisting of the time series data of 137 companies belong to KOSPI200. Our analysis shows that the stock prices tend to plummet when the correlation between stocks is very high. We propose a method for recommending the stock portfolio based on the analysis of the stock market network. To show the effectiveness of the recommended portfolio, we conducted the simulated stock investment and compared the recommended portfolio with the efficient portfolio proposed Markowitz. According to the experiment results, the rate of return of the portfolio is about 10.6% which is about 3.7% and 5.6% higher than the average rate of return of the efficient portfolio and KOSPI200 respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12B
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• pp.1656-1664
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• 2001

When a packet loss occurs in a communication network operating a TCP protocol, the TCP protocol regards it that the loss has resulted from network congestion. Then the TCP protocol performs congestion control. When it is applied to the wireless network having quite a high BER characteristics, the performance of TCP protocol is degraded very much. In this paper, we propose an Explicit Error Notification(EEN) algorithm to improve the performance of the wireless TCP When a packet loss occurs in the wireless network, the TCP receiver decodes the TCP segment sequence number and the address of the TCP sender and receiver, and then informs the TCP sender of the error in wireless network by sending a NACK. It is to distinguish packets in error from losses of network congestion. In this paper, the performance of the proposed EEN algorithm is analyzed and simulated. In fact, as more errors are corrected, the proposed algorithm shows a larger improvements in performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1043-1048
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• 2017

Since year 2016, in Korea, the police department started to use drones to patrol violated vehicles on the highway area. They monitor vehicle drivers who use side ways on the highway during traffic congested hours of the season, drunken drivers, or violent drivers. They use the 'Spot Mobility' method which floats the drones for 30 minute period. However, this method is inefficient since it requires manually charging batteries, gathering data, and operate drones with many numbers of policeman. Therefore, in this paper, for the efficient patrol in this purpose, I have suggested an effectively manageable network system consists of many drones as the wireless network nodes and with small numbers of policeman in a wide highway area. To accomplish this, the two topologies of effective drone patrol network systems are designed and simulated in OPNET simulator for performance evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.671-686
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• 2020

Due to the long training time and high training cost of traditional surgical training methods, the emerging virtual surgical training method has gradually replaced it as the mainstream. However, the virtual surgical system suffers from poor authenticity and high computational cost problems. For overcoming the deficiency of these problems, we propose an optimized model for the local compression deformation of soft tissue. This model uses a simulated annealing algorithm to optimize the parameters of the soft tissue model to improve the authenticity of the simulation. Meanwhile, although the soft tissue deformation is divided into local deformation region and non-deformation region, our proposed model only needs to calculate and update the deformation region, which can improve the simulation real-time performance. Besides, we define a compensation strategy for the "superelastic" effect which often occurs with the mass-spring model. To verify the validity of the model, we carry out a compression simulation experiment of abdomen and human foot and compare it with other models. The experimental results indicate the proposed model is realistic and effective in soft tissue compression simulation, and it outperforms other models in accuracy and real-time performance.