• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1307-1312
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• 2007

This paper proposes an optimal design scheme to reduce the noise of the engine cooling fan by adapting Kriging with two meta-heuristic techniques. An engineering model has been developed for the prediction of the noise spectrum of the engine cooling fan. The noise of the fan is expressed as the discrete frequency noise peaks at the BPF and its harmonics and line spectrum at the broad band by noise generation mechanisms. The object of this paper is to find the Optimal Design for Noise Reduction of the Engine Cooling Fan. We firstly show a comparison of the measured and calculated noise spectra of the fan for the validation of the noise prediction program. Orthogonal array is applied as design of experiments because it is suitable for Kriging. With these simulated data, we can estimate a correlation parameter of Kriging by solving the nonlinear problem with genetic algorithm and find an optimal level for the noise reduction of the cooling fan by optimizing Kriging estimates with simulated annealing. We notice that this optimal design scheme gives noticeable results. Therefore, an optimal design for the cooling fan is proposed by reducing the noise of its system.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.132-144
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• 2020

Cloud computing is an emerging technology based on the concept of enabling data access from anywhere, at any time, from any platform. The exponential growth of cloud users has resulted in the emergence of multiple issues, such as the workload imbalance between the virtual machines (VMs) of data centers in a cloud environment greatly impacting its overall performance. Our axis of research is the load balancing of a data center's VMs. It aims at reducing the degree of a load's imbalance between those VMs so that a better resource utilization will be provided, thus ensuring a greater quality of service. Our article focuses on two phases to balance the workload between the VMs. The first step will be the determination of the threshold of each VM before it can be considered overloaded. The second step will be a task allocation to the VMs by relying on an improved and faster version of the meta-heuristic "simulated annealing (SA)". We mainly focused on the acceptance probability of the SA, as, by modifying the content of the acceptance probability, we could ensure that the SA was able to offer a smart task distribution between the VMs in fewer loops than a classical usage of the SA.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.379-384
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• 2011

The scope of the problems that could be solved by monitoring and the improvement of the recognition time is directly correlated to the performance of the management function of the business process. However, the current monitoring process of business activities decides whether to apply warnings or not by assuming a fixed environment and showing expressions based on the design rules. Also, warnings are applied by carrying out the measuring process when the event attribute values are inserted at every point. Therefore, there is a limit for distinguishing the range of occurrence and the level of severity in regard to the new external problems occurring in a complicated environment. Such problems cannot be ed. Also, since it is difficult to expand the range of problems which can be possibly evaluated, it is impossible to evaluate any unexpected situation which could occur in the execution period. In this paper, a process-evaluating model based on the goal scenario is suggested to provide constant services through the current monitoring process in regard to the service demands of the new scenario which occurs outside. The new demands based on the outside situation are analyzed according to the goal scenario for the process activities. Also, by using the meta-heuristic algorithm, a similar process model is found and identified by combining similarity and interrelationship. The process can be stopped in advance or adjusted to the wanted direction.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.388-388
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• 2019

하도 홍수추적의 방법은 크게 수리학적 방법과 수문학적 방법으로 구분할 수 있다. 수리학적 홍수추적 방법은 정확하지만 대량의 자료가 필요하고 시간이 오래 걸린다. 이와 반대로 수문학적 홍수추적 방법은 정확성은 떨어지지만 소량의 자료만 있으면 되고 시간이 적게 걸린다. 여러 수문학적 홍수추적에 관한 연구들이 있으며 대표적으로 Muskingum 방법이 있다. Muskingum 방법 중 Linear Muskingum Model(LMM)은 방정식의 구조적 한계 때문에 정확한 홍수추적이 어려웠고, 이를 개선하기위하여 Nonlinear Muskingum Model(NLMM), Nonlinear Muskingum Model Incorporation Lateral Flow(NLMM-L) 및 Advanced Nonlinear Muskingum Model Incorporating Lateral Flow(ANLMM-L)이 제안되었다. 본 연구는 수문학적 홍수추적 중 Muskingum 방법의 결과 차이가 어떤 요인으로 인해 발생하는지 검토하였다. 최적화 알고리즘으로 화음탐색법(Harmony Search, HS)을 사용하였으며 LMM, NLMM, NLMM-L 및 ANLMM-L의 매개변수를 산정하였다. 각 방법에 적용 시 HS의 매개변수에 변화를 주어 민감도 분석을 실시하였으며, 분석을 위한 홍수자료는 The Willson Flood data (1947)를 선택하였다. 오차비교방법은 Sum of Squares(SSQ), Root Mean Square Errors(RMSE), Nash-Sutcliffe Efficiency(NSE)를 비교하였다. 비교 결과 알고리즘의 성능에 의한 차이보다 홍수추적 방법의 차이가 더 영향이 큰 것으로 나타났다.

• Structural Engineering and Mechanics
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• v.47 no.1
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• pp.119-129
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• 2013

Nonlinear Static Procedures (NSPs) have been developed as a practical tool to estimate the seismic demand of structures. Several researches have accomplished to minimize errors of NSPs, namely pushover procedures, in the Nonlinear Time History Analysis (NTHA), as the most exact method. The most important issue in a typical pushover procedure is the pattern and technique of loading which are extracted based on structural dynamic fundamentals. In this paper, the coefficients of modal force combination is focused involving a meta-heuristic optimization algorithm to find the optimum load pattern which results in a response with minimum amount of errors in comparison to the NTHA counterpart. Other parameters of the problem are based on the FEMA recommendations for pushover analysis of building structures. The proposed approach is implemented on a high-rise 20 storey concrete moment resisting frame under three earthquake records. In order to demonstrate the effectiveness and robustness of the studied procedure the results are presented beside other well-known pushover methods such as MPA and the FEMA procedures, and the results show the efficiency of the proposed load patterns.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.597-609
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• 2023

One of the applicable methods for the stabilization of soil walls is the nailing system which consists of tensile struts. The stability and safety of soil nail wall systems are influenced by the geometrical parameters of the nailing system. Generally, the determination of nailing parameters in order to achieve optimal performance of the nailing system for the safety of soil walls is defined in the framework of optimization problems. Also, according to the various uncertainty in the mechanical parameters of soil structures, it is necessary to evaluate the reliability of the system as a probabilistic problem. In this paper, the optimal design of the nailing system is carried out in deterministic and probabilistic cases using meta-heuristic and reliability-based design optimization methods. The colliding body optimization algorithm and first-order reliability method are used for optimization and reliability analysis problems, respectively. The objective function is defined based on the total cost of nails and safety factors and reliability index are selected as constraints. The mechanical properties of the nailing system are selected as design variables and the mechanical properties of the soil are selected as random variables. The results show that the reliability of the optimally designed soil nail system is very sensitive to uncertainty in soil mechanical parameters. Also, the design results are affected by uncertainties in soil mechanical parameters due to the values of safety factors. Reliability-based design optimization results show that a nailing system can be designed for the expected level of reliability and failure probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.1-7
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• 2018

Feature selection, one of data preprocessing techniques, is one of major research areas in many applications dealing with large dataset. It has been used in pattern recognition, machine learning and data mining, and is now widely applied in a variety of fields such as text classification, image retrieval, intrusion detection and genome analysis. The proposed method is based on a genetic algorithm which is one of meta-heuristic algorithms. There are two methods of finding feature subsets: a filter method and a wrapper method. In this study, we use a wrapper method, which evaluates feature subsets using a real classifier, to find an optimal feature subset. The training dataset used in the experiment has a severe class imbalance and it is difficult to improve classification performance for rare classes. After preprocessing the training dataset with SMOTE, we select features and evaluate them with various machine learning algorithms.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.23-29
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• 2012

Existing pedestrian and evacuation models generally seek to find locally optimal solutions for the shortest or the least time paths to exits from individual locations considering pedestrian's characteristics (eg. speed, direction, sex, age, weight and size). These models are not designed to produce globally optimal solutions that reduce the total evacuation time of the entire pedestrians in a building when all of them evacuate at the same time. In this study, we suggest a globally optimal model for indoor pedestrian evacuation to minimize the total evacuation time of occupants in a building considering different distributions of them. We used the genetic algorithm, one of meta-heuristic techniques because minimizing the total evacuation time can not be easily solved by polynomial expressions. We found near-optimal evacuation path and time by expressing varying pedestrians distributions using chromosomes and repeatedly filtering solutions. In order to express and experiment our suggested algorithm, we used CA(cellular automata)-based simulator and applied to different indoor distributions and presented the results.