• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1405-1411
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• 2022

Power management is still an important issue in embedded environments as hardware advances like high-performance processors. Power management methods such as DVFS control CPU frequencies in an adaptive manner for efficient power management in polling-based I/O programs such as network communication. This paper presents the problems of the existing power management method and proposes a new power management method. Through this, it is possible to reduce electric consumption by increasing the polling cycle in situations where the frequency of data reception is low, and on the contrary, in situations where data reception is frequent, it can operate at the maximum frequency without performance degradation. After implementing this as a code layer on the embedded board and observing it through Atmel's Power Debugger, the proposed method showed a performance improvement of up to 30% in energy consumption compared to the existing power management method.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.87-97
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• 2023

Charger infrastructure facilities are designed and installed based on a constant power supply. Initially designed charging facilities support charging of rapidly growing electric vehicles on a limited power supply basis. In addition, current commercial vehicles can only be fully charged, and are supported by the rapid equalization charging method. However, commercial vehicles operate according to a set schedule, so flexible charging is essential. In this paper, we propose a power operation method with more than 20% efficiency improvement by using a fixed schedule-based charging scheduling and power distribution technique of a commercial bus based on the same amount of power in accordance with the rapid growth and increase of electric vehicles.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.61-71
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• 2009

Vehicle license plate is the only way to check the registrated information of a vehicle. Many works have been devoted to the vision system of recognizing the license plate, which has been widely used to control an illegal parking. However, it is difficult to correctly segment characters on the license plate since an illumination is affected by a weather change and a neighboring obstacles. This paper proposes a robust method of segmenting the character of the license plate on irregular illumination condition. The proposed method enhance the contrast of license plate images using the Chi-Square probability density function. For segmenting characters on the license plate, binary images with the high quality are gained by applying the adaptive threshold. Preprocessing and labeling algorithm are used to eliminate noises existing during the whole segmentation process. Finally, profiling method is applied to segment characters on license plate from binary images.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2945-2965
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• 2023

The laser inertial measurement unit is a precision device widely used in rocket navigation system and other equipment, and its quality is directly related to navigation accuracy. In the quality evaluation of laser inertial measurement unit, there is inevitably uncertainty in the index input information. First, the input numerical information is in interval form. Second, the index input grade and the quality evaluation result grade are given according to different national standards. So, it is a key step to transform the interval information input by the index into the data form consistent with the evaluation result grade. In the case of uncertain input, this paper puts forward a method based on probability distribution to solve the problem of asymmetry between the reference grade given by the index and the evaluation result grade when evaluating the quality of laser inertial measurement unit. By mapping the numerical relationship between the designated reference level and the evaluation reference level of the index information under different distributions, the index evidence symmetrical with the evaluation reference level is given. After the uncertain input information is transformed into evidence of interval degree distribution by this method, the information fusion of interval degree distribution evidence is carried out by interval evidential reasoning algorithm, and the evaluation result is obtained by projection covariance matrix adaptive evolution strategy optimization. Taking a five-meter redundant laser inertial measurement unit as an example, the applicability and effectiveness of this method are verified.

• Steel and Composite Structures
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• v.51 no.1
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• pp.25-41
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• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.139-148
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• 1999

오늘날 기술집약적인 벤처기업들에 대한 관심이 집중되고 있다. 소수의 진취적인 벤처기업들이 기술개발 및 신상품 개발 등 두드러진 활약을 보이고 있기 때문이다. 그러나 실제 이 벤처기업의 성공 가능성은 그렇게 높지 않다. 특히 벤처기업 환경이 아직 미약한 국내의 경우 위험부담이 훨씬 더 크다. 이러한 벤처기업 환경에서 투자대상 벤처기업을 선정하는 것은 매우 전략적인 의사결정이다. 일반적으로 일반 벤처투자가들은 관심이 있는 산업에 해당하는 기업의 사업계획서와 기초적인 관련 정보를 토대로 투자여부를 결정한다. 그렇지만 실제로는 이와 같은 분석에 필수적으로 요구되는 정보가 불확실할 뿐만 아니라 기술분야에 대한 전문적 지식도 부족하기 때문에 투자 여부를 결정하는 것은 매우 복잡하고 어려운 문제이다. 그러므로 투자대상 벤처기업의 선정을 효과적으로 지원해주는 체계적인 접근이 필요하다. 특히 벤처 사업과 관련된 기술 동향 및 수준 등에 관련된 전문 지식과 경험이 체계적으로 제공되어야 하고 또한 벤처 투자가의 개인적 경험과 판단이 평가 프로세스에 직접적으로 반영될 수 있어야 한다. 이에 본 연구에서는 전문가의 지식과 경험을 체계화하고 투자가의 개인적 판단을 효과적으로 수용할 수 있는 전문가시스템의 접근방법을 제시하고자 한다. 투자대상 벤처기업의 선정을 위한 전문가시스템을 구축하기 위해 본 연구에서는 다양한 정보수집 과정을 거쳤다. 우선 벤처 투자와 관련된 기존 문헌을 심층 분석하였으며 아울러 벤처 투자 업계에서 활약중인 전문 벤처캐피탈리스트들과의 수차례 인터뷰를 통해 벤처기업 평가의 주요 요인과 의사결정 과정을 파악할 수 있었다. 이러한 과정을 통하여 본 연구에서는 벤처 투자의 90%를 차지하는 정보통신분야에 속한 기법 중에서 투자대상 벤처기업의 선정을 위한 전문가시스템을 구축중이다.의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and in

• Smart Structures and Systems
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• v.20 no.1
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• pp.61-74
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• 2017

One of the most reliable and simplest tools for structural vibration control in civil engineering is Tuned Mass Damper, TMD. Provided that the frequency and damping parameters of these dampers are tuned appropriately, they can reduce the vibrations of the structure through their generated inertia forces, as they vibrate continuously. To achieve the optimal parameters of TMD, many different methods have been provided so far. In old approaches, some formulas have been offered based on simplifying models and their applied loadings while novel procedures need to model structures completely in order to obtain TMD parameters. In this paper, with regard to the nonlinear decision-making of fuzzy systems and their enough ability to cope with different unreliability, a method is proposed. Furthermore, by taking advantage of both old and new methods a fuzzy system is designed to be operational and reduce uncertainties related to models and applied loads. To design fuzzy system, it is required to gain data on structures and optimum parameters of TMDs corresponding to these structures. This information is obtained through modeling MDOF systems with various numbers of stories subjected to far and near field earthquakes. The design of the fuzzy systems is performed by three methods: look-up table, the data space grid-partitioning, and clustering. After that, rule weights of Mamdani fuzzy system using the look-up table are optimized through genetic algorithm and rule weights of Sugeno fuzzy system designed based on grid-partitioning methods and clustering data are optimized through ANFIS (Adaptive Neuro-Fuzzy Inference System). By comparing these methods, it is observed that the fuzzy system technique based on data clustering has an efficient function to predict the optimal parameters of TMDs. In this method, average of errors in estimating frequency and damping ratio is close to zero. Also, standard deviation of frequency errors and damping ratio errors decrease by 78% and 4.1% respectively in comparison with the look-up table method. While, this reductions compared to the grid partitioning method are 2.2% and 1.8% respectively. In this research, TMD parameters are estimated for a 15-degree of freedom structure based on designed fuzzy system and are compared to parameters obtained from the genetic algorithm and empirical relations. The progress up to 1.9% and 2% under far-field earthquakes and 0.4% and 2.2% under near-field earthquakes is obtained in decreasing respectively roof maximum displacement and its RMS ratio through fuzzy system method compared to those obtained by empirical relations.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.633-641
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• 2015

Several complex processes that are employed in industries, such as shipping, power plants, and the petrochemical industry, involve time-varying behavior as well as strong nonlinear behavior during operation. The fixed-parameter proportional-integral-derivative (PID) controllers have difficulty in dealing with control problems that occur in such processes. In this paper, we propose a method of designing a nonlinear PID controller for industrial processes that exhibit a large number of nonlinearities and time-varying behavior. The gains of the nonlinear PID controller are characterized by a simple nonlinear function of the error and/or error rate depending on the process set-point and output. We tune the user-defined parameters using a genetic algorithm by minimizing the integral of time absolute error (ITAE) index. We verify the effectiveness of the proposed method by performing a comparison of the proposed method and two other nonlinear and adaptive methods that are employed for reference tracking, disturbance-rejection performances, and robustness to parameter changes on a continuously stirred tank reactor.