检索结果-内蒙古大学图书馆

Optimal phasor measurement units placement to maintain network observability using a novel binary particle swarm optimization and fuzzy system

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第1期28卷 477-483页

作者： Abiri, Ebrahim Rashidi, Farzan Shiraz Univ Technol Elect & Elect Engn Dept Shiraz Iran

This paper presents a hybrid modified binary particle swarm optimization and fuzzy system for obtaining the optimal number of PMUs which makes the power system completely observable. Observability assessment is done by means of topological observability rules. Moreover a new rule is employed which can decrease the number of required PMUs for full network observability. In all of the investigations, the effect of zero-injection buses in the power system is considered. Developed PMU placement method is illustrated using IEEE standard systems to demonstrate the effectiveness of the proposed algorithm. The results show that the whole system can be observable with installing PMUs on less than 20% of system buses.

关键词： Phasor measurement units (PMUs) state estimation binary linear programming binary particle swarm optimization fuzzy systems power system observability

来源：评论

学校读者我要写书评

暂无评论

Identification of influential observations based on binary particle swarm optimization in the cox PH model

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2020年第3期49卷 567-590页

作者： Sancar, Nuriye Inan, Deniz Near East Univ Dept Math CY-99138 Nicosia Cyprus Marmara Univ Dept Stat Istanbul Turkey

Proper identification of influential observations should be an integral and significant part of the Cox modeling process. This is because the failure to identify influential observations may have negative effect on the estimates acquired from the process. Furthermore, in survival analysis, influential observations frequently propose that the model has imperfections, including poorly specified factor, non-proportional hazards, loss of important information and/or a variable that has been omitted. Nevertheless, many procedures have been developed for the identification of a single influential observation based on the leave-one-out method. However, the results from leave-one-out diagnostic techniques are often misleading as a result of swamping and masking problems in the presence of multiple influential observations in the dataset. In this paper, identification of the optimal set of influential observations problem has been considered as the combinatorial optimization problem and a new simultaneous approach for identification of the optimal set of influential observations is proposed based on binary particle swarm optimization (BPSO) approach in the Cox PH model. The performance of the proposed BPSO-based approach and conventional diagnostic techniques have been compared according to various evaluation criteria by simulation studies. The performance of the BPSO-based approach has been also demonstrated by the clinical real dataset.

关键词： Cox PH Model Influential Observations binary particle swarm optimization Likelihood Displacement Masking Effect Swamping Effect

来源：评论

学校读者我要写书评

暂无评论

Mahalanobis classification system (MCS) integrated with binary particle swarm optimization for robust quality classification of complex metallic turbine blades

引用

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 146卷

作者： Cheng, Liangliang Yaghoubi, Vahid Van Paepegem, Wim Kersemans, Mathias Univ Ghent Mech Mat & Struct MMS Technol Pk 46 B-9052 Zwijnaarde Belgium SIM Program M3 DETECT ION Technol Pk 48 B-9052 Zwijnaarde Belgium

Performing non-destructive testing on metallic components with very complex geometries, such as turbine blades, is very challenging. To inspect such components, powerful and robust non-destructive inspection protocols must be defined. Process Compensated Resonance Testing (PCRT) is a relatively novel approach that records a broadband vibrational fingerprint for each component and employs vibrational features such as resonant frequency, quality factor, and amplitude. These features are used in the Mahalanobis Taguchi System to classify the parts in terms of their quality, i.e. Good/Bad. In the present study, a two-stage MCS classification approach, coupled with binary particle swarm optimization, is proposed to optimize the process of selecting the most significant features and to search for the optimal decision boundary to discriminate healthy and unhealthy components. Further, the proposed MCS enables the features to be mapped into a higher dimensional Mahalanobis Distance space, thereby enhancing the performance of classification. An experimental case study on equiaxed Nickel alloy first-stage turbine blades, with very complex geometry and various damages, demonstrates the high classification accuracy and robustness of the developed MCS approach. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Mahalanobis taguchi system Mahalanobias classification system Mahalanobis distance Feature selection Classification binary particle swarm optimization Process compensated resonance testing Non-destructive testing Threshold determination

来源：评论

学校读者我要写书评

暂无评论

EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年 84卷

作者： Kouka, Najwa Fourati, Rahma Fdhila, Raja Siarry, Patrick Alimi, Adel M. Natl Engn Sch Sfax ENIS Univ Sfax Res Grp Intelligent Machines BP 1173 1173 Sfax 3038 Tunisia Univ Paris Est Lab Image Signaux & Syst Intelligents LISSI F-94400 Creteil France Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci Johannesburg South Africa

Electroencephalography (EEG) signals can demonstrate the activities of the human brain and recognize different emotional states. Emotion recognition based on full EEG channels leads to the use of redundant data and increases the hardware complexity. This paper presents a channel selection method based on a new binary Many-Objective particle swarm optimization with Cooperative Agents (BMaOPSO-CA). More specifically, we perform unsupervised feature learning with recurrent convolutional layers based on autoencoder architecture directly from clean EEG signals. Extensive validation on three public effective benchmarks, i.e. DASPS, DEAP, and SEED which are different in channel number used stimuli, and participant ratings, was carried out with subject independent scheme. As result, the experimental study highlights the optimal electrode locations related to emotions, leading to analyze the relationship between specific brain regions and emotions.

关键词： Unsupervised EEG feature learning Convolutional LSTM Channel selection binary particle swarm optimization Local learning strategy

来源：评论

学校读者我要写书评

暂无评论

Accelerating Analytics Using Improved binary particle swarm optimization for Discrete Feature Selection

引用

COMPUTER JOURNAL 2022年第10期65卷 2547-2569页

作者： Moorthy, Rajalakshmi Shenbaga Pabitha, P. St Josephs Inst Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India Anna Univ Chennai Madras Inst Technol Campus Dept Comp Technol Chennai 600044 Tamil Nadu India

Feature selection, a combinatorial optimization problem, remains broadly applied in the area of Computational Learning with the aim to construct a model with reduced features so as to improve the performance of the model. Feature selection algorithm aims to identify admissible subgroup of features without sacrificing the accuracy of the model. This research works uses Improved binary particle swarm optimization (IBPSO) to optimally identify subset of features. The problem of stagnation, trapping in local optima and premature convergence of binary particle swarm optimization (BPSO) for solving discrete feature selection dispute has been tackled using IBPSO. IBPSO prevents the model from overfitting and also takes less computational time for constructing the model because of reduced feature subset. The sine function, cosine function, position of the random particle and linear decrement of inertial weight are integrated in IBPSO, which balances between exploration and exploitation to identify optimal subset of features. The linear decrement of inertial weight tends to do good level of exploration at the starting phase, whereas at the end it tends to exploit solution space to find the optimal subset of features that are more informative and thereby discarding redundant and irrelevant features. Experimentation is carried out on seven benchmarking datasets obtained from University of California, Irvine repository, which includes various real-world datasets for processing with machine learning algorithms. The proposed IBPSO is compared with conventional metaheuristic algorithms such as BPSO, Simulated Annealing, Ant Colony optimization, Genetic Algorithm and other hybrid metaheuristic feature selection algorithms. The result proves that IBPSO maximizes the accuracy of the classifier together with maximum dimensionality reduction ratio. Also, statistical tests such as T-test, Wilcoxon signed-pair test are also carried out to demonstrate IBPSO is better than other algorithms

关键词： feature selection binary particle swarm optimization Improved binary particle swarm optimization accuracy dimension reduction statistical test

来源：评论

学校读者我要写书评

暂无评论

A New Affine Arithmetic-Based Optimal Network Reconfiguration to Minimize Losses in a Distribution System Considering Uncertainty Using binary particle swarm optimization

引用

ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第6-7期48卷 628-639页

作者： Raj, Vinod Kumar, Boddeti Kalyan Indian Inst Technol Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

In the present work, binary particle swarm optimization (BPSO) based optimal re-configuration for balanced and unbalanced radial distribution networks using Affine Arithmetic (AA), with uncertainty in generation and load, is proposed to minimize the system losses. An expression for three phase real affine power loss is derived with partial deviations of real power loss in lines with respect to power injections in other buses and also with respect to power injections in other phases in case of unbalanced distribution systems. The major contribution of the present work is the application of AA based optimal network reconfiguration, to both balanced and unbalanced radial distribution networks with uncertainty. The proposed method is tested on IEEE 16, 33, 85 and 119 bus balanced distribution systems and an unbalanced 123 bus system with Distributed Generation (DG) connected at some buses. The optimal loss intervals obtained by the proposed method are compared with that obtained by Interval Arithmetic (IA) and Monte Carlo (MC) simulations based methods. The simulation results show that proposed AA based analysis gives an optimal reconfiguration, for both balanced and unbalanced radial distribution systems with uncertainty as compared to existing IA based method.

关键词： affine arithmetic binary particle swarm optimization distribution system network reconfiguration real power loss minimization uncertainty

来源：评论

学校读者我要写书评

暂无评论

Improved binary particle swarm optimization for the deterministic security-constrained transmission network expansion planning problem

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 150卷

作者： Garcia-Mercado, Josue Isai Gutierrez-Alcaraz, Guillermo Gonzalez-Cabrera, N. Tecnol Nacl Mexico IT Morelia Dept Elect Engn Morelia Michoacan Mexico Natl Autonomous Univ Mexico UNAM Dept Elect Energy Mexico City DF Mexico

- This paper proposes an improved version of binary particle swarm optimization (BPSO) to solve the securityconstrained transmission network expansion planning (TNEP) problem using the DC network model. Some modifications, such as superior population configurations, are made to improve the efficiency of the BPSO by adapting it to the needs of the TNEP to obtain better results. A parallel implementation of the improved BPSO reduces execution times. Transmission losses are included using a quadratic function and piecewise linear approximation approach. Considering transmission losses in the long-term planning horizon can avoid underinvestment in network capacity and result in a different network expansion plan. The proposed approach is validated with numerical simulations of the classic Garver system, and its performance is tested on the IEEE 24bus and 118-bus systems for a ten-year planning horizon. The simulation results are compared to those obtained from classic BPSO to verify the efficiency of the improved version of the BPSO.

关键词： Transmission network expansion planning binary particle swarm optimization Transmission losses, direct current optimal power flow Security constrained

来源：评论

学校读者我要写书评

暂无评论

SEM-Net: Deep features selections with binary particle swarm optimization Method for classification of scanning electron microscope images

引用

MATERIALS TODAY COMMUNICATIONS 2021年 27卷

作者： Kavuran, Gurkan Malatya Turgut Ozal Univ Fac Engn & Nat Sci Dept Elect & Elect Engn Malatya Turkey

Materials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques used in the characterization of nanomaterial with designed properties to meet the needs of applications in the literature. However, the increase in image size and complexity in its content restricts the use of traditional methods. Recent advances in machine learning have been used to benefit computers' potential to make sense of these images. The approach proposed in this paper aims for the feature reduction with the binary particle swarm optimization method to execute the classification process on SEM images by concatenating the deeper layers of pre-trained CNN models AlexNet and ResNet-50. The feature vectors were used as input to support vector machine classifier (SVMC) after dimension reduction to obtain the final model. Finally, the trained model's performance was tested using SEM images of Ag-doped SnO2 nanoparticles, which were prepared by the author using the low-temperature hydrothermal method. To the best of the author knowledge, these images were not available in the databases. The best accuracy value was observed with 3112 features for the SEM dataset with optimized vectors as 99.3 %. An example was illustrated where the feature selection with the BPSO technique could provide novel insight into nanoscience research and test the model with the SEM images of Ag-doped SnO2 particles that are obtained by the hydrothermal method.

关键词： Hydrothermal method Nanoscience Ag-doped SnO2 Feature selection binary particle swarm optimization Deep learning

来源：评论

学校读者我要写书评

暂无评论

A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs

引用

NEUROCOMPUTING 2024年 608卷

作者： Tmamna, Jihene Fourati, Rahma Ben Ayed, Emna Passos, Leandro A. Papa, Joao P. Ben Ayed, Mounir Hussain, Amir Univ Sfax Natl Engn Sch Sfax ENIS Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Univ Jendouba Fac Sci Jurid Econ & Gest Jendouba Jendouba 8189 Tunisia Polytech Sfax IPSAS Ind Res Lab 4 0 Ave 5 AugustRue Said Aboubaker Sfax 3002 Tunisia Sao Paulo State Univ Sch Sci Sao Paulo Brazil Univ Sfax Fac Sci Sfax Comp Sci & Commun Dept Sfax Tunisia Edinburgh Napier Univ Sch Comp Edinburgh Scotland

Deep Convolutional Neural Networks (CNNs), continue to demonstrate remarkable performance across various tasks. However, their computational demands and energy consumption present significant drawbacks, restricting their practical deployment and contributing to a substantial carbon footprint. This paper addresses this challenge by proposing a novel method named binary particle swarm optimization Layer Pruner (BPSOLPruner), aimed at achieving substantial computational reduction and mitigating environmental impact during CNN inference. BPSO-LPruner utilizes a constrained binary particle swarm optimization for CNN layer pruning, integrating a masked-bit strategy and a new population initialization strategy to enhance search performance. We illustrate the effectiveness of our method in reducing model computational costs and carbon footprint emissions while improving performance across multiple models (VGG16, VGG19, DenseNet-40, ResNet18, ResNet20, ResNet34, ResNet44, ResNet56, ResNet110, ResNet50, and MobileNetv2) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, COVID-19 X-ray dataset). Promising results underscore the performance of the proposed method. Additionally, we demonstrate that layer pruning yields benefits beyond enhanced computational performance. Our experimentation reveals that BPSO-LPruner enhances the model's reliability and robustness by effectively addressing variations in input data, inherent ambiguity in model parameters, and adversarial images.

关键词： Green deep learning Layer pruning binary particle swarm optimization Layer weighting initialization Bit mask strategy Adversarial attacks

来源：评论

学校读者我要写书评

暂无评论

Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on binary particle swarm optimization

引用

IEEE ACCESS 2019年 7卷 182289-182295页

作者： Xiong, Baoping Li, Yurong Huang, Meilan Shi, Wuxiang Du, Min Yang, Yuan Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350116 Peoples R China Fujian Univ Technol Dept Math & Phys Fuzhou 350116 Peoples R China Fuzhou Univ Fujian Key Lab Med Instrumentat & Pharmaceut Tech Fuzhou 350116 Peoples R China Northwestern Univ Dept Phys Therapy & Human Movement Sci Chicago IL 60208 USA Wuyi Univ Fujian Prov Key Lab Ecoind Green Technol Wuyishan 354300 Peoples R China

Joint moment is an important parameter for a quantitative assessment of human motor function. However, most existing joint moment prediction methods lacking feature selection of optimal inputs subset, which reduced the prediction accuracy and output comprehensibility, increased the complexity of the input sensor structure, making the portable prediction equipment impossible to achieve. To address this problem, this paper develops a novel method based on the binary particle swarm optimization (BPSO) with the variance accounted for (VAF) as fitness function to reduce the number of input variables while improves the accuracy in joint moment prediction. The proposed method is tested on the experimental data collected from ten healthy subjects who are running on a treadmill with four different speeds of 2, 3, 4 and 5m/s. The BPSO is used to select optimal inputs subset from ten electromyography (EMG) data and six joints angles, and then the selected optimal inputs subset be used to train and predict the joint moments via artificial neural network (ANN). Prediction accuracy is evaluated by the variance accounted for (VAF) test between the predicted joint moment and multi-body dynamics moment. Results show that the proposed method can reduce the number of input variables of five joint moment from 16 to less than 11. Furthermore, the proposed method can better predict joint moment (mean VAF. 94.40 +/- 0 .84%) in comparison with the state-of-the-art methods, i.e. Elastic Net (mean VAF. 93.38 +/- 0 .96%) and mutual information (mean VAF. 86.27 +/- 1.41%). In conclusion, the proposed method reduces the number of input variables and improves the prediction accuracy that may allow the future development of a portable, non-invasive system for joint moment prediction. As such, it may facilitate real-time assessment of human motor function.

关键词： Joint moment prediction artificial neural network binary particle swarm optimization feature selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：