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检索条件"主题词=software algorithms"

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Detection of Bacteria on Food using Deep Learning

Detection of Bacteria on Food using Deep Learning

引用

Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Sai Deepthi Challamalla Kalpana Seelam Veda charan Sanapathi Lekhana Donepudi Electronics and instrumentation Engineering V.R.Siddhartha Engineering College Vijayawada India Dept. of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Due to their large global prevalence, foodborne illnesses have a significant negative impact on both the social economy and public health. Foodborne pathogens, the primary cause of foodborne illnesses, are crucial to the management and avoidance of foodborne illnesses. However, there is a limited percentage of true clinical pathogen detection in real life, and foodborne illnesses caused by many bacteria lack specificity in their clinical symptoms. Our objectives were to categorise foodborne illness pathogens using gas sensors and machine learning techniques, analyse foodborne disease case data, and choose relevant characteristics based on analysis findings in order to predict foodborne diseases.

关键词： Support vector machines Pathogens Machine learning algorithms software algorithms Signal processing algorithms Transforms Bacteria Prediction algorithms Universal Serial Bus Public healthcare

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The Metal Defect Detection Model Based on Non-Local Fusion Attention and Residual Networks

The Metal Defect Detection Model Based on Non-Local Fusion A...

引用

Cloud Computing, Big Data Applications and software Engineering (CBASE), International Conference on

作者： Jian Zheng Jun Zhang College of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi China Yichun Lithium New Energy Industry Research Institute Jiangxi University of Science and Technology Yichun Jiangxi China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

In response to the challenges in extracting features, low detection accuracy, and large parameter sizes in current metal surface defect detection, a metal defect detection model based on non-local fusion attention and residual networks was designed. First, Ghost convolution was used to replace traditional convolution modules to reduce the network parameters. Second, to address the issue of defect information loss during feature extraction in deep networks, a fusion of residual networks and defect downsampling was proposed, resulting in the Residual Downsampling Network (RDNet). Finally, to enhance the feature extraction capability of the model, the Non-local Fusion Attention Network (NFANet) was introduced, which effectively utilizes global information to improve defect detection accuracy. The model achieved average precision (AP) of 99.8% and 87.9% on the publicly available metal defect datasets KSDD and KSDD2, respectively. The rate of missed and false detections significantly decreased, and the extracted defect features became more refined, achieving high detection performance while keeping the model's parameter size relatively small.

关键词： Accuracy Convolution software algorithms Metals Feature extraction Data models Data mining Defect detection Residual neural networks software engineering

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A Hybrid Method of SVG Parameter Calibration Based on Particle Swarm Optimization and Soft Actor-Critic

A Hybrid Method of SVG Parameter Calibration Based on Partic...

引用

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Ao Zhao Xuanchen Xiang Ligang Zhao Hongyue Zhen Ruisheng Diao Kaibiao Yan Guangjun Xu ZJU-UIUC Institute Zhejiang University Haining China Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Static var generators (SVGS) are essential for compensating reactive power and maintaining bus voltage levels before and after large disturbances. Accurate modeling of SVG controller parameters is critical to ensure the reliability of dynamic simulation in power system operation. This paper introduces a novel SVG parameter identification method, which integrates the benefits of particle swarm optimization (PSO) and reinforcement learning (RL) to improve the efficiency and accuracy of parameter calibration. We use simulation data generated by PSD BPA software combined with actual power grid data as the environment to optimize the parameter settings of SVG. This study combined PSO with the Soft Actor-Critic (SAC) algorithm to optimize and calibrate these parameters. Compared with the simulation results, our method shows significant advantages in calibration accuracy and processing speed and effectively improves the simulation quality and practicability of the SVG model.

关键词： Reactive power Accuracy Computational modeling software algorithms Power system dynamics Reinforcement learning Voltage software Calibration Particle swarm optimization

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A gradient and double threshold-based algorithm for improved wideband spectrum sensing

A gradient and double threshold-based algorithm for improved...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. Monisha P. Vijayalakshmi S Gokulakkannan M Ajayraman Department of ECE Vels Institute Of Science Technology And Advanced Studies Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

There is a scarcity of wireless spectrum because of the industry’s growth spurred by the quick development of wireless communication. Future communication systems that use dynamic spectrum access and cognitive radio may be able to resolve this problem. In order to maximize spectrum use and transmission dependability, cognitive radios (CRs) adapt their settings in response to environmental inputs. This allows them to detect and take advantage of spectrum gaps. A key element of CR is spectrum sensing, which is the periodical monitoring of the spectrum of radio bands to guarantee interference-free operation. However, there are issues with spectrum sensing algorithms in noisy and heavily shadowed locations, such as the concealed primary user dilemma. Several factors need to be considered by cooperative spectrum detection algorithms, such as decision fusion methods, cooperation overheads, and sensing speed and time. The purpose of this work is to improve sensing performance and suggest. The objective of this research is to improve the performance of sensing and suggest sophisticated algorithms for basic user signal identification and wide-band sensing effectiveness. The work aims at optimizing spectrum usage, lowering computational complexity, improving detection performance, and improving noise uncertainty response by evaluating radio frequency white spaces across bands. To ensure their efficacy, the suggested algorithms are tested on hardware and software platforms.

关键词： Wireless sensor networks Uncertainty Accuracy Heuristic algorithms software algorithms Noise White spaces

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Requirements Elicitation for Machine Learning Applications: A Research Preview

Requirements Elicitation for Machine Learning Applications: ...

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AI, Science, Engineering, and Technology (AIxSET), Conference on

作者： Timothy Elvira Tyler Thomas Procko Omar Ochoa Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States

ISBN: (数字)9798350390995

ISBN: (纸本)9798350391008

The development of software systems is preceded by an important first phase, requirements elicitation, wherein developers establish the intended functionality of a system to be developed in a series of interviews with a customer. These requirements can often be raw, requiring refinement in an iterative process. Traditional software requirements are of the form 'The system shall … ‘, where each requirement is written to be clear, concise, consistent, complete, testable and traceable through development. Because Machine Learning (ML) is stochastic, or uncertain, in the face of unseen data, ML behavior cannot be precisely defined. As such, it is posited that software requirements specific to ML must delineate a window of acceptable behavior, e.g., a plus/minus value for evaluated model metrics. The provision of such is essential for the quality of future ML software systems, as the current trend of large-scale generative ML has brought about a paradigm of fine-tuning, e.g., of Large Language Models, in which pre-trained large models are taken “off the shelf” and made fit for very specific purposes. These specific purposes vary with different use cases, but they are effectively undeclared requirements. The present paper discusses requirements elicitation in the context of ML by considering the four-software engineering requirement elicitation techniques: conversational, observational, analytic and synthetic, providing future researchers with insight for eliciting quality ML requirements.

关键词： Measurement Technological innovation Large language models software algorithms Stochastic processes Prototypes Machine learning software systems Market research Iterative methods

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Research on Automatic Security Sensing Technology Based on Multi-Version Characterization

Research on Automatic Security Sensing Technology Based on M...

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Power System and New Energy Technology Innovation Forum (NPSIF), Boao New Power System International Forum -

作者： Jiangang Lu Shiming Li Ruifeng Zhao Wenxin Guo Chen Wang Power Dispatching Control Center Guangdong Power Grid Co. Ltd. China

ISBN: (数字)9798331504557

ISBN: (纸本)9798331504564

This paper investigates a security auto-perception technique based on multi-version characterization, aiming to improve the security and reliability of software systems. By analyzing the characteristics of different versions of software, a new automatic sensing method is proposed and its effectiveness is verified. The method combines machine learning and data mining techniques, and is able to automatically identify the security threats of software systems and provide corresponding warnings and solutions without human intervention. The experimental results show that the technique significantly outperforms the traditional method in terms of detection efficiency and accuracy, providing strong support for the security protection of software systems.

关键词： Technological innovation Accuracy software algorithms Machine learning software systems Sensors Power systems software reliability Security software development management

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Strengthening Teaching and Learning Based Multi-population Symbiotic Organism Search Algorithm

Strengthening Teaching and Learning Based Multi-population S...

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software Engineering, Social Network Analysis and Intelligent Computing (SSAIC), Asia-Pacific Conference on

作者： Qiwen Zhang Bingting Liu Lanzhou University of Technology Lanzhou Gansu China

ISBN: (数字)9798350393682

ISBN: (纸本)9798350393699

A Strengthening Teaching and Learning based Multi-population Symbiotic Organism Search algorithm (STLMSOS) is proposed to address the shortcomings of the symbiotic biological algorithm in solving complex high-dimensional problems, such as low accuracy, poor diversity and the tendency to fall into local optimality. The algorithm divides the ecosystem into three populations based on the evolutionary characteristics of organisms, and draws on the powerful search capability of the “teaching” phase and the excellent search capability of the “learning” phase of the teaching optimization algorithm, so that the mutualism population and commensalism population can evolve in stages, enhancing the solution accuracy and diversity of the algorithm. At the same time, by taking into account the complex connection between biological migration and species evolution, the information interaction between multiple populations is improved so that stagnant populations can interact in time to jump out of the local optimum. The proposed STLMSOS was fully evaluated on the CEC2017 problem set, and the simulation results show that STMSOS outperforms other state-of-the-art algorithms and is an effective algorithm for solving complex high-dimensional problems.

关键词： Symbiosis Accuracy Sociology Education software algorithms Search problems Organisms

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Application of Genetic algorithms for Finding Optimal Modes in Polymer Synthesis Processes

Application of Genetic Algorithms for Finding Optimal Modes ...

引用

International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Eldar Miftakhov Sofia Mustafina Anastasia Kashnikova Alexander Kubryak Research Department Ufa University of Science and Technology Ufa Russia Department of Mathematical Modeling Ufa University of Science and Technology Ufa Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

This paper presents a methodology and algorithm for solving the problem of finding optimal modes for organizing continuous polymer synthesis processes. Due to the high dimensionality of the parameter space, the large number of local solutions, and the nonlinear nature of the mathematical description, a genetic algorithm is proposed for solving optimization problems. The algorithm's software implementation enables optimization tasks to be solved for various efficiency criteria. To validate this approach, a computational experiment was conducted aimed at finding the optimal mode for the copolymerization process of butadiene with styrene. A kinetic modeling approach was employed for the mathematical description of this process. The software implementation of the proposed genetic algorithm and a series of computational trials allowed for the establishment of a two-point regulation mode of molecular mass, which contributed to maintaining the product's intrinsic viscosity value while increasing conversion to 82%. The study results demonstrated that altering the mode of regulator additive supply helps achieve the specified efficiency criterion and is a key factor in controlling continuous polymer synthesis processes.

关键词： Regulators software algorithms Process control software Mathematical models Computational efficiency Polymers Feeds Optimization Genetic algorithms

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Identification of Urban Traffic Accident Hotspots Using Getis-Ord Gi and DBSCAN algorithmsimes

Identification of Urban Traffic Accident Hotspots Using Geti...

引用

Cloud Computing, Big Data Applications and software Engineering (CBASE), International Conference on

作者： Lejia Zhang Weiyang Wan Bolong Lv Qiuling Li Ke Sun Qian Wan Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

This study aims to identify urban traffic accident hotspots by integrating spatial statistics and clustering techniques. Initially, the Getis-Ord Gi* method is employed to preliminarily identify areas with high accident frequencies. Subsequently, the DBSCAN algorithm is applied for further analysis of these hotspot areas. To optimize the two key parameters of DBSCAN (eps and min_samples), this study utilizes GridSearchCV for parameter search and adopts the adjusted silhouette score as the evaluation criterion. The results indicate that the proposed method effectively overcomes the limitations of traditional approaches, enhancing the accuracy of traffic accident hotspot identification.

关键词： Accuracy software algorithms Clustering algorithms Safety management Real-time systems Internet of Things Data mining Accidents Python Geographic information systems

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Power System Stability Improvement using Modified African Vulture Optimization Tuned STATCOM Controller

Power System Stability Improvement using Modified African Vu...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： R. Shivakumar S. Sowranchana Department of Electrical and Electronics Engineering Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In power system, stability plays a major role to obtain the stable condition, but is affected by the presence of low frequency oscillations in the system. The complex design of generators, loads and transmission lines will also cause the disturbance to the system. In this paper, damping is done by using FACTS controller, which can retain the stability of the system by damping the low frequency oscillations. The proposed STATCOM controller is tuned using Modified African vulture optimization algorithm to compute the optimal parameters for damping. Simulation is done by MATLAB software to determine the speed and power angle deviation responses of various operating criterions of the system. The performances of the proposed modified African Vulture Optimization Algorithm tuned STATCOM is compared with conventional African Vulture and Genetic Algorithm tuned controllers. The proposed approach gives better performance when compared to conventional algorithms.

关键词： Damping Stability criteria software algorithms Power system stability Automatic voltage control software Optimization Oscillators Frequency control Genetic algorithms

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