检索结果-内蒙古大学图书馆

Research progress and development trend of bionic harvesting technology

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 222卷

作者： Luo, Yuanqiang Li, Junlin Yao, Beihuo Luo, Qing Zhu, Zhicheng Wu, Weibin South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China

In the context of natural selection, organisms have evolved various excellent characteristics that can guide improved design optimization in engineering. Scholars at home and abroad have carried out a large number of efficient and favorable bionic -type researches by imitating the information of biological signs. In particular, in the field of agricultural picking, the bionic concept of "learning from nature and feeding back to nature " will undoubtedly facilitate the automation of various picking tasks in agriculture, which can not only solve the social problems and operational efficiency problems caused by manual harvesting, but also effectively reduce the damage rate of target crops. Based on these, the present study conducted a systematic literature review of the latest advancements in bionic technology implementation for agricultural picking, utilizing multiple scientific databases and online development resources. In particular, the bionic end -effector structures and bionic -inspired algorithms (BIAs) are deeply studied, and both types of bionic applications are well categorized. The bionic endeffector structures are considered in this study to be divided into three main categories: (1) cutting type, (2) clawing type, and (3) clamping type. The BIAs mainly consider four common categories: (1) artificial neural network, (2) genetic algorithm, (3) artificial bee colony, (4) ant colony optimization. In addition, this review also explores the experimental treatments used to develop the bionic picking structure, which aims to guide how to learn the excellent characteristics of biological signs to optimize the mechanical design. Finally, the potential challenges and future development trends of bionic harvesting technology are prospected.

关键词： bionic harvesting End-effector bio-inspired algorithms Agricultural biomimicry

来源：评论

学校读者我要写书评

暂无评论

Summarizing social media content via bio-inspired influence maximization algorithms 2020

Summarizing social media content via bio-inspired influence ...

引用

The 9th International Conference on Smart Media and Applications

作者： Christian Esposito Vincenzo Moscato Giancarlo Sperli Chang Choi University of Salerno Italy University of Naples Federico II italy Gachon University S. Korea

ISBN: (纸本)9781450389259

In this paper, we describe a multimedia summarization technique for Online Social Networks (OSNs) using a bio-inspired influence maximization algorithm. As first step, we model each OSN using an hypergraph based approach that the authors have presented in some previous works. Then, we leverage an influence analysis methodology based on the bees' behaviors within an hive to determine the most important multimedia objects with respect to one or more topics of interest. Finally, a summarization technique is exploited to determine from the list of candidates a multimedia summary in according to a model that favors priority (w.r.t. some user keywords), continuity, variety and not repetitiveness features. Several preliminary experiments on Flickr dataset show the effectiveness of the proposed summarization approach and encourage future work.

关键词： bio-inspired algorithms

来源：评论

学校读者我要写书评

暂无评论

Towards Enhancing Computational Complexity in Explainable Decision Support System: Exploring bio-inspired Approaches

引用

Procedia Computer Science 2025年 258卷 430-442页

作者： Ijeoma Noella Ezeji Matthew Adigun Kayode Oki University of Zululand University of Zululand Kwadlagezwa South Africa Department of Information Technology Walter Sisulu Univesrsity East London South Africa

Decision support systems play an important role in aiding decision-making processes by providing valuable insights and recommendations driven by data analysis. However, as decision support systems increasingly employ complex Artificial Intelligence (AI) techniques, the need for explainability becomes essential to build trust and transparency with users. As a result, explainable AI (XAI) has emerged as a paradigm that seeks to make AI decision-making processes transparent and understandable to humans. While explainability enhances user comprehension, it introduces computational complexity challenges. In the evolving landscape of Explainable Decision Support Systems (XDSS), the quest for optimizing computational complexity without sacrificing explainability presents a challenge. This paper delves into the potential of bio-inspired algorithms as a novel approach to addressing this challenge. Through a comprehensive theoretical analysis, this paper explores various bio-inspired algorithms, such as Genetic algorithms, Particle Swarm Optimization, and Ant Colony Optimization, to assess their applicability and effectiveness in enhancing the computational efficiency of XDSS. By establishing a set of selection criteria to identify the most promising bio-inspired algorithm for optimizing computational complexity in XDSS. The paper sets the groundwork for future research by highlighting theoretical foundations, potential applications, and a clear direction for empirical validation. This exploration not only contributes to the theoretical advancement in the field of XDSS but also opens new avenues for practical implementations that can significantly improve explainable decision support systems’ performance and usability.

关键词： bio-inspired algorithms Computational Complexity Explainable Decision Support System (XDSS) Explainable AI Optimization techniques

来源：评论

学校读者我要写书评

暂无评论

Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO algorithms

引用

ENERGIES 2024年第4期17卷 822页

作者： Camas-Nafate, Monica Coronado-Mendoza, Alberto Vargas-Salgado, Carlos Aguila-Leon, Jesus Alfonso-Solar, David Univ Guadalajara Dept Water & Energy Studies Guadalajara 44430 Mexico Univ Inst Energet Engn Univ Politecn Valencia Valencia 46022 Spain Univ Politecn Valencia Dept Elect Engn Valencia 46022 Spain Univ Politecn Valencia Dept Appl Thermodynam Valencia 46022 Spain

In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 degrees C;3.6 C, 455 s) and 0.1705 (25 degrees C;3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

关键词： Particle Swarm Optimization (PSO) Grey Wolf Optimizer (GWO) lithium-ion battery modeling charge-discharge cycle predictions bio-inspired algorithms

来源：评论

学校读者我要写书评

暂无评论

An efficient approach for load balancing of VMs in cloud environment

引用

APPLIED NANOSCIENCE 2021年第2期13卷 1313-1326页

作者： Assudani, Purshottam J. Balakrishnan, P. Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Shri Ramdeobaba Coll Engn & Management Dept Informat Technol Nagpur Maharashtra India

Cloud computing provides a number of resources over the internet to the users based on their request. These resources need to be scheduled in an efficient manner so that not only the provider gets benefited out of it, but the user also can take its advantage to the full extent. Therefore, resource scheduling is a critical and demanding requirement in a cloud environment. In this paper, we are proposing a bio-inspired approach, in which we have modified the existing particle swarm optimization (PSO) Algorithm and have combined it with genetic algorithm (GA) which in turn has the features and advantages of both the approaches. The proposed inventive particle swarm optimization with genetic algorithm (IPSO-GA) not only schedules resources efficiently, but also effectively manage the resources. The proposed approach is compared with traditional approaches on CloudSim simulator, where the proposed algorithm outperforms the traditional algorithms in terms of makespan time, execution time and resource utilization. Our proposed approach IPSO-GA has given better results than the existing approaches.

关键词： Cloud computing Resource scheduling Load balancing Resource utilization Resource mapping bio-inspired algorithms

来源：评论

学校读者我要写书评

暂无评论

Implementation of a bio-inspired neural architecture for autonomous vehicle on a reconfigurable platform 31

Implementation of a bio-inspired neural architecture for aut...

引用

IEEE 31st International Symposium on Industrial Electronics (ISIE)

作者： Elouaret, Tarek Colomer, Sylvain Demelo, Frederic Cuperlier, Nicolas Romain, Olivier Kessal, Lounis Zuckerman, Stephane Univ CY Cergy Paris Lab ETIS ENSEA CNRS F-95000 Cergy France VEDECOM Inst 23 Bis Allee Marronniers F-78000 Versailles France

ISBN: (纸本)9781665482400

The efficiency of the self-localisation task is of tremendous importance for autonomous vehicles. Cameras are the most common kinds of sensors to perform this task, as they provide rich input at very low cost. The computational intensity of visual localisation varies depending on the complexity of the environment surrounding the vehicle, which influences which real-time task should run at any moment. Real-time image processing in this context requires fast processing, as well as the ability to store enough information to take decisions in an energy-efficient manner, especially in the case of electric vehicles. Hence, leveraging FPGAs is a natural answer to many issues raised by such an application, in order to facilitate application prototyping and estimate the proper energy savings. A FPGA-based solution is introduced to implement a bio-inspired visual localisation model. The proposed workflow is as follows: (1) an image processing IP which yields the pixels information per each visual landmark detected in each captured image;(2) an implementation of a bio-inspired neural architecture. Comparisons with a pure software solution show that our hardware-based IP implementation yields up to 1000x lower latency times, 220x higher throughput (frames/second) than the reference software implementation, and a low footprint, with 0.237W (i.e., 23x less power than a regular high-end microprocessor).

关键词： FPGA bio-inspired algorithms neural networks hardware acceleration

来源：评论

学校读者我要写书评

暂无评论

Trip Itinerary Planning: A bio-inspired Metaheuristic Approach

Trip Itinerary Planning: A Bio-inspired Metaheuristic Approa...

引用

IEEE International Conference on Smart Mobility (ICSM)

作者： Jia, Jingkai Chen, Yuli Liu, Yuxuan Khamis, Alaa Univ Toronto Elect & Comp Engn Toronto ON Canada Gen Motors Canada Canadian Tech Ctr Oshawa ON Canada

ISBN: (纸本)9781665499545

Trip itinerary planning plays an important role in the tourism industry and in our daily lives. In this paper, trip itinerary planning problem is modelled as Team Orienteering Problem with Time Window (TOPTW) with travel distance as a soft constraint. Three bio-inspired meta-heuristic algorithms, namely, genetic algorithm, adaptive genetic algorithm and artificial bee colony algorithm are considered to solve this problem. These solvers are evaluated in terms of algorithms' execution time, optimality, and coverage time using real data from City of Toronto. The experiment results show that adaptive genetic algorithm outperforms the other algorithms in terms of optimality and robustness.

关键词： Trip planning multi-criteria optimization meta-heuristics bio-inspired algorithms genetic algorithms and artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm

引用

NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2515-2547页

作者： Braik, Malik Sheta, Alaa Al-Hiary, Heba Al Balqa Appl Univ Salt Jordan Southern Connecticut State Univ Dept Comp Sci 501 Crescent St New Haven CT 06515 USA

Meta-heuristic search algorithms were successfully used to solve a variety of problems in engineering, science, business, and finance. Meta-heuristic algorithms share common features since they are population-based approaches that use a set of tuning parameters to evolve new solutions based on the natural behavior of creatures. In this paper, we present a novel nature-inspired search optimization algorithm called the capuchin search algorithm (CapSA) for solving constrained and global optimization problems. The key inspiration of CapSA is the dynamic behavior of capuchin monkeys. The basic optimization characteristics of this new algorithm are designed by modeling the social actions of capuchins during wandering and foraging over trees and riverbanks in forests while searching for food sources. Some of the common behaviors of capuchins during foraging that are implemented in this algorithm are leaping, swinging, and climbing. Jumping is an effective mechanism used by capuchins to jump from tree to tree. The other foraging mechanisms exercised by capuchins, known as swinging and climbing, allow the capuchins to move small distances over trees, tree branches, and the extremities of the tree branches. These locomotion mechanisms eventually lead to feasible solutions of global optimization problems. The proposed algorithm is benchmarked on 23 well-known benchmark functions, as well as solving several challenging and computationally costly engineering problems. A broad comparative study is conducted to demonstrate the efficacy of CapSA over several prominent meta-heuristic algorithms in terms of optimization precision and statistical test analysis. Overall results show that CapSA renders more precise solutions with a high convergence rate compared to competitive meta-heuristic methods.

关键词： Optimization Meta-heuristics bio-inspired algorithms Capuchin search algorithm Optimization techniques

来源：评论

学校读者我要写书评

暂无评论

A framework for the analysis and synthesis of Swarm Intelligence algorithms

引用

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2021年第4期33卷 659-681页

作者： Cruz, Davila Patricia Ferreira Maia, Renato Dourado de Castro, Leandro Nunes Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab Lcon Grad Program Elect Engn & Comp Consolacao St 930 BR-01302000 Sao Paulo SP Brazil Univ Estadual Montes Claros Comp Sci Dept Montes Claros MG Brazil

Over the last decades, the number of Swarm Intelligence algorithms proposed in the literature has increased considerably. However, most algorithms do not follow an adequate scientific rigour neither the principles of Swarm Intelligence, reproducing similar computational procedures of many other approaches, but by means of a different metaphor. In this scenario, the aim of this paper is to propose a framework for the analysis and synthesis of Swarm Intelligence algorithms that can contribute to a structured understanding (analysis) and development (synthesis) of these algorithms. The main objective of the proposed framework is to guide the construction of Swarm Intelligence metaphors in a consistent and well-founded way, as well as to contribute with the analysis and development of new algorithms that had better encompass the features and abilities of insects' societies.

关键词： Swarm Intelligence bio-inspired algorithms natural computing information processing decision-making social insects

来源：评论

学校读者我要写书评

暂无评论

Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France

Ever-increasing data in various fields like bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection Moth flame optimization algorithm Quantum computing Microarray data Cancer classification bio-inspired algorithms Molecular biology Optimization algorithms Evolutionary algorithms Swarm intelligence

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：