• Earthquakes and Structures
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• v.11 no.3
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• pp.445-459
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• 2016

Quantitative estimation of seismic response of various structural systems at the collapse limit state is one of the most significant objectives in Performance-Based Earthquake Engineering (PBEE). Assessing the effects of uncertainties, due to variability in ground motion characteristics and random nature of earthquakes, on nonlinear structural response is a pivotal issue regarding collapse safety prediction. Incremental Dynamic Analysis (IDA) and fragility curves are utilized to estimate demand parameters and seismic performance levels of structures. Since producing these curves based on a large number of nonlinear dynamic analyses would be time-consuming, selection of appropriate earthquake ground motion records resulting in reliable responses with sufficient accuracy seems to be quite essential. The aim of this research study is to propose a methodology to assess the seismic behavior of reinforced concrete frames at collapse limit state via accurate estimation of seismic fragility curves for different Engineering Demand Parameters (EDPs) by using a limited number of ground motion records. Research results demonstrate that accurate estimating of structural collapse capacity is feasible through applying the proposed method offering an appropriate suite of limited ground motion records.

• Journal of Biosystems Engineering
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• v.23 no.2
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• pp.157-166
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• 1998

In this study. a traveling control system was developed to transfer a machine without an operator in the working zone. The dimension of the system was modelized to design and construct smaller than that of real configuration of a greenhouse. For this system, the fixed path type was used to detect exact position during operating a manless machine. and the X-Y table actuator type to escape a unique path, which had the disadvantage in a fixed path type environment. Based on the results of this research the following conclusions were made : 1. This system used two screws to move toward horizontal direction, and a Plate to reach at any points in the working zone. 2. The software combined the functions of path selection and motor operation to control into one program. The path selection program was a menu driven program written in Visual Basic, and the motor operation program was written in Borland C++ for actuating motors. 3. The path-select mode of the program was used by selecting the desired paths, and the user path-create mode by selecting a random path in the path-selection program. 4. The system proved to be a reliable system for operating a manless machine, since accuracy and precision to reach the positions were less than 1%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.75-82
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• 2006

본 연구에서 조합 최적화(Combinatorial Optimization) 이론에 바탕을 두고 있는 네스티드 분할(Nested Partition, 이하 NP) 방법을 이용한 최적화 기탄 요소선택 방법(Feature Selection)을 제안한다. 이 새로운 방법은 좋은 요소 부분집합을 찾는 휴리스틱 탐색 절차를 채용하고 있으며 데이터의 인스턴스(Instances 또는 Records)의 무작위 추출(Random Sampling)을 이용하여 이 요소선택 방법의 처리시간 관점에서의 성능을 항상 시키고자 한다. 이 새로운 접근 방법은 처리시간 향상을 위해 2단계 샘플링 방법을 채용하여 근접 최적해로의 수렴(Convergence)을 보장하는 샘플 사이즈를 결정한다. 이는 앨고리듬이 유한한 시간내에 끝이날 때 최종 요소 부분집합 해의 질(Qualtiy)에 관한 정확한 설명을 할 수 있는 이론적인 배경을 제시한다. 중요 결과를 예시하기 위해서 다양한 형태의 다섯 개의 데이터 셋을 이용하였으며 다섯 번의 반복 실험을 통한 실험 결과가 제시되며, 이 새로운 접근 방법이 기존의 단순 네스티드 분할 방법 기반의 요소선택 방법보다 처리시간 관점에서 더욱 효율적임을 보여준다.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.11
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• pp.471-474
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• 2013

In this paper, we propose a distributed parent peer selection method to construct an efficient peer-to-peer(P2P) network topology by considering the capacity of a peer and the hop distance from a data source to the peer. To achieve this goal, we propose a method to combine the two performance metrics to calculate the probability that a peer becomes a parent peer. Using the probability, we propose a method to select a parent peer stochastically by making use of the state information of the neighboring peers that each peer maintains. Through simulation studies, we show that the proposed method drives high capacity peers to support more children peers and makes the diameter of the P2P network shorter than the other methods.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.167-174
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• 2022

In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.9-15
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• 2022

This paper proposes a coordinated multi-point communication (CoMP) method with channel selection to improve performance of a macro user equipment (MUE) in a dense small-cell network environment in a building located within coverage of a macro base station (MBS). In the proposed CoMP method, in order to improve the performance of the MUE located in the building, A small-cell base station (SBS) selects a channel with lower interference to the neighboring MUE and transmits appropriate signals to the MUE requiring CoMP. Simulation results show that the proposed CoMP method improves the performance of the MUE by up to 164% and 51%, respectivley, compared to a random channel allocation based traditional SBS network and CoMP method.

• ETRI Journal
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• v.45 no.3
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• Journal of Microbiology
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• v.37 no.2
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• pp.97-101
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• 1999

Random hexapeptide library on the surface of filamentous bacteriophage was constructed using the SurfZAP vector. The size of the library was approximately 105. The peptide insert was flanked by two cysteines to constrain the peptide structure with a disulfide bond. This library was screened for the topoisomerase II binding peptide. Dramatic enrichment of the fusion phage over the VCS M13 helper phage was demonstrated by bio-panning affinity selection.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.199-207
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• 2010

Decision analysis has becomes an important technique for decision making in the face of uncertainty. It is characterized by enumerating all the available courses of action, identifying the payoffs for all possible outcomes, and quantifying the subjective probabilities for the all possible random events. When the data are available, decision analysis becomes a powerful tool for determining an optimal course of action. In this paper, we use the analytic hierarchy process in weights calculating. For the purpose of making optimal decision, the data of three different smart phones models are used.