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检索条件"主题词=software algorithms"

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Simulation of the Active Filter Tuning Systems Based on Delay Times, Periods and Phases Comparison

Simulation of the Active Filter Tuning Systems Based on Dela...

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IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)

作者： Alexander Gubin Evgenii Balashov Peter the Great St.Petersburg Polytechnic University Saint Petersburg Russia

ISBN: (数字)9798350388879

ISBN: (纸本)9798350388886

In this paper is described an auto tuning methods of active RC filters. The tuning range and the tuning accuracy are analyzed. To verify the method, analog and digital part of the tuning system are cosimulated together. The simulation results shows that the time constant tuning accuracy is ±3.8% for tuning circuit based on delay times comparison, 5.5% for circuit based on period comparison and 3.9% for tuning circuit based on phase comparison by 4-bit code sequence and time-constant variation ±28%.

关键词： Accuracy Simulation software algorithms Signal processing algorithms Active filters Delays System software Logic Tuning Photonics

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Utilizing Decision Tree,SVM And KNN algorithms For Optimal Speed Detection And Control With Superior Accuracy

Utilizing Decision Tree,SVM And KNN Algorithms For Optimal S...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： N. Ramya K Amrutha H Krishalini K Ramya Department of CSE Sri Sairam Engineering College Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Over-speeding is a major contributor to traffic accidents and fatalities worldwide. AI and ML algorithms like decision tree, SVM and KNN can be used within vehicles to automatically detect and control speed limits, thus improving road safety. This paper explores the potential of AI/ML-based systems for vehicle speed limit detection and control, analyzing their functionalities, challenges and future directions. The paper finds that AI/ML-based systems can effectively detect and control speed limits, leading to a significant reduction in traffic accidents and fatalities. Improving algorithm accuracy and ensuring data reliability are fundamental to unlock the full potential of this technology. The paper also discusses some future directions for AI/ML-based speed limit detection and control, such as the use of more sophisticated algorithms and the development of more comprehensive data sets.

关键词： Support vector machines software algorithms Road safety software Telecommunication computing Safety Decision trees

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A Diffusion Model Augmented Indoor Robot Local Navigation Planner Based On Decision Transformer

A Diffusion Model Augmented Indoor Robot Local Navigation Pl...

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software Engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Yangfan Wu Junzhi Li Jianmin Ji School of Computer Science and Technology University of Science and Technology of China Anhui China

ISBN: (数字)9798350374346

ISBN: (纸本)9798350374353

As the demand for indoor service robots grows, reliable local navigation techniques are crucial for their applications. Due to the speed limitations of interacting with the physical world, training robot navigation policies from pre-gathered of-fline datasets is becoming increasingly popular. However, training local navigation policies on offline datasets is not easy and faces two main challenges: (1) Myopia: Common assumptions based on Markov Decision Processes and limited local observations impose higher demands on navigation policies, making decision-making difficult for planners in complex scenarios. (2) Distribution Shift: Limited offline datasets only cover a portion of the state-action space, resulting in learned policies differing from behavioral policies and performing poorly in real world scenarios, while common non-generative data augmentation methods are not suitable for navigation scenarios. Therefore, we introduce a global information guided- diffusion model augmented local planner based on decision transformer. Our approach combines offline reinforcement learning and sequence modeling to train a local navigation algorithm, while a diffusion model guided by global information generates trajectories similar to the distribution of offline datasets but with different global paths, further improving navigation policies with synthetic data. Results in simulator demonstrate that this approach effectively improves the performance of various imitation learning algorithms on offline datasets and has good modeling capabilities for trajectories in multiple scenarios. Real-world tests also show that the algorithm can plan smooth, safe paths in complex scenarios, demonstrating excellent obstacle avoidance capabilities.

关键词： Training Navigation Service robots software algorithms Transformers Diffusion models Trajectory

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Realization of Conceptual Model of IT-Infrastructure of Technology of Differential Diagnostics of Clinical and Hematological Syndromes for Health Passport

Realization of Conceptual Model of IT-Infrastructure of Tech...

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AITU: Digital Generation, IEEE

作者： Indira Uvaliyeva David Borozenets School of Information Technology & Intelligent Systems D.Serikbayev East Kazakhstan Technical University Ust-Kamenogorsk Kazakhstan

ISBN: (数字)9798350364378

ISBN: (纸本)9798350364385

Clinical and hematologic syndromes are a group of diseases that have both clinical manifestations and changes in the composition of blood and tissue fluids. Differential diagnosis of these syndromes requires a comprehensive approach, including the analysis of clinical symptoms, results of laboratory tests, instrumental methods and other data. This article describes the development of a software complex for diagnosing clinical and hematological syndromes for the electronic passport, which allows to automate the process of clinical decision support based on differential diagnosis algorithms and models of intellectual analysis of medical data. In the process of implementing the conceptual model of IT infrastructure of the technology of differential diagnosis of clinical and hematological syndromes for the health passport, new computational and analytical modules such as DataAsQuisition, DataPre-processing, Morphological_Classification and Ensembling were created.

关键词： Analytical models Pathology Computational modeling software algorithms Machine learning Differential diagnosis software

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Behavior Generation Approach for Quadruped Robots Based on 3D Action Design and Proximal Policy Optimization

Behavior Generation Approach for Quadruped Robots Based on 3...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Chuang Luo Qin Zhang Shuxin Li Hui Chai Wei Xu Kang Wang School of Electrical Engineering University of Jinan Jinan China School of Control Science and Engineering Shandong University Jinan China China North Artificial Intelligence and Innovation Research Institute Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

To improve quadrupedal robots' movement diversity and capacity to learn a wide range of behavioral skills, a quadrupedal robot behavior generation method based on 3D animation software and imitation learning is proposed. First, 3D animation software is used to provide expert experience of feasible control strategies for specific task skills; then, a motion skill optimization method based on imitation learning algorithm is designed to achieve the optimization of the designed actions. Experiments conducted on simulation and physical prototypes demonstrate that the Panda5 quadruped robot can perform extremely dynamic and agile behaviors like hopping in place and trot in place, verifying the efficacy of the method provided in this work.

关键词： Legged locomotion Visualization Three-dimensional displays Imitation learning software algorithms Prototypes Animation software Trajectory Quadrupedal robots

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From Concepts to Applications: A Comprehensive Overview of ROS

From Concepts to Applications: A Comprehensive Overview of R...

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International Conference on Computational Cybernetics (ICCC)

作者： Esmeralda Kadena Marsidi Gupi Beatrix Fregan Aniko Horvath-Kiss Xhezmije Palushi Zoltan Rajnai Banki Donát Faculty of Mechanical and Safety Engineering Obuda University Budapest Hungary Faculty of Economics Governance and Law Barleti University Tirana Albania National University of Public Service Budapest Hungary Faculty of Information Technology Tirana Business University Tirana Albania

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

This paper explores the Robot Operating System (ROS), a foundational open-source middleware designed to facilitate robotics development by integrating hardware and software. It provides essential tools and algorithms enabling efficient programming of complex robotic systems. This work delves into the core components of ROS, including its computational graph model, parameter servers, and messaging systems, while highlighting tools that enhance system capabilities. Additionally, the paper reviews navigation algorithms, from traditional ones to advanced path-planning techniques, emphasizing their applications in autonomous mobile robotics. Comparisons of robotics software platforms illustrate the diversity and adaptability required to meet the demands of modern robotics. In the end, this paper emphasizes its critical role in advancing academic research and industrial applications.

关键词： Service robots Navigation Reviews Operating systems software algorithms Hardware Servers Middleware Robots Robot programming

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A Cross Strategy-Based Agent-Assisted Multi-Population Particle Swarm Optimization Algorithm

A Cross Strategy-Based Agent-Assisted Multi-Population Parti...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Yuhan Wang Han Yu Jiahui Ma Nanping Li Lin Wang School of Cyber Science and Engineering Qufu Normal University Qufu China School of Computer Science Qufu Normal University Rizhao China School of Humanities and Law Tianjin University of Science and Technology China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

With the rapid increase in the complexity of modern engineering systems and scientific computing problems, Large-Scale Expensive Optimization Problems (LSEOPs) have become a core challenge in the field of optimization. Traditional Evolutionary algorithms (EAs) encounter the "curse of dimensionality" in high-dimensional scenarios, and most of the existing Surrogate-Assisted Evolutionary algorithms (SAEAs) are designed for low-dimensional problems and struggle to adapt to the characteristics of high-dimensional data. In this paper, a Cross Strategy-Based Agent-Assisted Multi-Population Particle Swarm Optimization algorithm (CS-AAMPSO) is proposed to improve the efficiency and robustness of methods for working out LSEOPs, and the experimental results show that the performance is improved to some extent.

关键词： Scientific computing High dimensional data software algorithms Network security Robustness Complexity theory Particle swarm optimization Optimization software engineering

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Multi-objective Flexible flow shop scheduling with limited buffers based on model-driven differential evolution algorithm

Multi-objective Flexible flow shop scheduling with limited b...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Lixin Liang Zhifeng Hao School of Mathematics and Computer Science Shantou University Shantou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The multi-objective flexible flow shop scheduling problem with limited buffers (MO-FFSS-LB) is widely encountered in modern manufacturing systems. However, the existence of limited buffers greatly increases the complexity of job scheduling and needs to consider job completion time, replacement costs of different recipes, and dynamic buffer balancing simultaneously. To address the challenge of MO-FFSS-LB, this paper proposes a model-driven differential evolution (MDE) algorithm. Firstly, it designs a multi-layer coding mechanism to represent the solution of the problem according to the characteristics of the scheduling problem and then introduces an initialization mechanism to improve the quality of the initial solution and the solving efficiency. Moreover, the proposed MDE utilizes a machine learning-based model trained on historical scheduling data to approximate fitness, enabling rapid population evolution. To further improve the convergence speed and solution quality, a neighborhood search-based local optimization strategy and dynamically adjusted crossover rates and mutation factors strategy are introduced, enhancing the algorithm’s adaptability to complex scheduling problems. Experimental results demonstrate that the MDE algorithm can generate high-quality solutions for the MO-FFSS-LB and outperforms existing traditional scheduling algorithms in several performance metrics. This research not only provides an effective optimization tool for solving complex scheduling problems with limited buffer constraints but also offers new insights and methods for theoretical research and practical applications in related fields.

关键词： Job shop scheduling Machine learning algorithms Scheduling algorithms Heuristic algorithms software algorithms Dynamic scheduling Approximation algorithms Encoding Data models Optimization

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Suppressing Speckle Noise in Ultrasound Images Through Multi-scale Structural Information Distillation

Suppressing Speckle Noise in Ultrasound Images Through Multi...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Xiangfei Feng Jianhua Hu Zhanjiang Yuan Feiyong He Juxiu Zhu Wei Nie Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

When ultrasound waves propagate through the human body, they produce reflections and scatterings, resulting in speckle noise in ultrasound images. Additionally, these speckles, being multiplicative noise, present a significant challenge for despeckling processing. Existing methods can suppress speckle noise, but they often struggle to adequately protect details while smoothing the images. In this paper, we propose a novel algorithm for ultrasound images by using multi-scale structural information distillation. Firstly, a denoising network is utilized to efficiently reduce speckle noise in ultrasound images. Next, a pre-trained network is leveraged to extract multi-scale features from both the noise ultrasound image and denoised ultrasound image. Multi-scale feature extraction is crucial as it enables the network to grasp diverse structural details across various resolutions, facilitating a deeper comprehension of the image content. Lastly, based on these extracted multi-scale features, a structural distribution similarity metric loss function is constructed. This loss function serves as a critical guide during the optimization process of the speckle noise suppression network. By minimizing this loss function, the network is trained to balance noise suppression with the preservation of fine structural details, aiming to overcoming the challenges posed by multiplicative speckle noise in ultrasound images. The proposed method is evaluated on a open access ultrasound image dataset, and the experimental results demonstrate the effectiveness of the proposed method.

关键词： Ultrasonic imaging Smoothing methods Noise Noise reduction software algorithms Scattering Speckle Feature extraction Reflection software engineering

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The Thermal Cutting Process of Flat Material on Figured Parts: Digital Twin Based on the Voxel Representation

The Thermal Cutting Process of Flat Material on Figured Part...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Mikhail Verkhoturov Galina Verkhoturova Nail Khannanov Department of Computer Science Ufa University of Science and Technology Ufa Russian Federation Department of Computational Mathematics and Cybernetics Ufa University of Science and Technology Ufa Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper presents a novel approach to constructing a digital twin (DT) for the thermal cutting process of flat materials, such as those used in laser or gas cutting. The primary goal is to optimize the cutting tool path while accounting for thermal effects on the material, which can lead to overheating and deformation. The proposed method utilizes a discrete-logical (voxel-based) representation to model temperature changes in the material during cutting. A computational experiment demonstrates the effectiveness of the developed algorithm, which minimizes both idle travel and local overheating zones. The results show a 14% improvement in path optimization compared to traditional methods, highlighting the potential of integrating real-time temperature data into the cutting process. This approach enhances the quality of cut blanks and reduces production costs, making it a valuable tool for industrial applications.

关键词： Temperature distribution software algorithms Production Traveling salesman problems Routing Minimization Real-time systems software Digital twins Optimization

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