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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Chraigui Mouna Nizar Rokbani Haykal Chaabane Sofiène Mansouri High Institute of Applied Sciences & Technology University of Sousse Sousse Tunisia Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Research Group on Intelligent machines REGIM-lab High Institute of Applied Sciences & Technology of Sousse Tunisia High Institute Applied Sciences & Technology of University of Sousse University of Tunis EI Manar Higher Institute of Medical Technologies of Tunis Laboratory of Biophysics and Medical Technologies Tunis Tunisia University of Tunis El Manar Higher Institute of Medical Technologies of Tunis Laboratory of Biophysics and Medical Technologies Tunis Tunisia

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

In robotics, inverse kinematics (IK) consist in finding the actuation solution needed for a robotic system to achieve a given work position. Traditional analytical methods may suffer from singularities and may be time consuming especially for manipulators with high degrees of freedom (DoF). This paper proses a new neural metaheuristic approach, consisting in embedding a neural network within a swarm, where the neural network will be generating a first solution which considered as the starting configuration of the swarm. The swarm will then prospect better solutions if needed while starting from the neural network initial position. The approach is called the neural-meta-swarm, N-M-swarm. This paper is a typical implementation of the approach where an artificial neural networks in embedded in the initialization process of the Dragonfly Algorithm. The ANN is trained on forward kinematics data to map end-effector positions to joint angles, while the DA optimizes the ANN's up to the needed precision. Experimental results demonstrate that this method retuned acceptable results for a 4 DoF manipulator. N-DA showed also acceptable performance for an approximated circular path planning.

关键词： Green energy Metaheuristics Kinematics Artificial neural networks approximation algorithms Path planning End effectors

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Application of Convolutional Neural Network in Livestock Target Detection of IoT Images

Application of Convolutional Neural Network in Livestock Tar...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Maohong Tian Feiyang Zhao Nengmei Chen Dequan Zhang Jie Li Hualin Li School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Shu Yi Xin Credit Management Co. LTD Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges and Universities Engineering Research Center Chongqing China Chongqing Da Tong Experimental School Chongqing China Shu Yi Xin Credit Management Co. LTD Chongqing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

The national rural revitalization strategy has advanced, and new requirements for modern internet-of-things (IoT)-based farm supervision tools have been proposed. This study proposes a “you-only-look-once” (YOLO) v5 convolutional neural network to optimize the candidate frame's center point algorithm. Five YOLOv5 models are used to identify and classify captive pigs. According to the experimental results, the K-median algorithm has a better effect than using the K-means algorithm, and its average intersection over union is approximately 1-2% higher. In our scenario, the commercial application value of the YOLOv5s model is higher, and the corresponding performance results of F1-score, mAP0.5, mAP0.5:0.95, FPS, and R 2 are 92.21%, 95.18%, 64.85%, 75 FPS, and 85.54%, respectively.

关键词： YOLO Seminars Image recognition Target recognition approximation algorithms Agriculture Classification algorithms

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Correlation Matching Method for Speech Signals at Different Sampling Rates

Correlation Matching Method for Speech Signals at Different ...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Wenxiu Zheng Fukang Yang Xianao Zhu School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Aiming to the problem of determining the signal source of speech signals with different sampling rates, a method is proposed to match speech signals with different sampling rates. This method performs bilinear interpolation on the speech signal with the smaller sampling rate to transform its sampling rate, making the sampling rates of the two digital speech signals approximately equal. Then, it calculates the cross-correlation function of the two digital speech signals. Based on the observation of significant correlation peaks, it calculates the peak-to-mean ratio of the cross-correlation function and the sliding correlation coefficient to determine if the speech signals are from the same source. Simulation results show that this method can effectively determine whether two speech signals with different sampling rates belong to the same speech signal source.

关键词： Correlation coefficient Interpolation Correlation Simulation Transforms Speech recognition Knowledge discovery approximation algorithms Fuzzy systems

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Procedural Generative Design for Spatial Layout Planning Using Hexagonal Cellular Automata

Procedural Generative Design for Spatial Layout Planning Usi...

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International Conference on Cyberworlds

作者： Chelsea Shan Xian Ng Chun-Hsien Chen Peer Mohideen Sathikh School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore School of Art Design and Media Nanyang Technological University Singapore Singapore

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Spatial layout planning in architecture is a complex, highly constrained problem with multiple objectives and wide range of possible solutions. Due to its iterative and time-consuming nature, it is desirable to automate and optimize the spatial layout design process. Here we present a procedural model with hexagonal cellular automata to automatically generate various spatial layouts from basic user inputs of design space, area, and adjacency. The user is also allowed to specify the positions of one or more spaces within the design space. The spatial layout generator can output various 2D models representing the allocated spaces that conform to all the user inputs. It is envisioned that the generated layouts can serve as design guides and alternatives to ease the tedious spatial layout planning process.

关键词： Design automation Computational modeling Layout Automata approximation algorithms Generators Planning Iterative methods

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Communication and Control Co-Design in Industrial Internet of Things via Multi-hop Relay

Communication and Control Co-Design in Industrial Internet o...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Minxing Fu Xiong Li Shen Wang Qimei Cui Bing Luo Gang Deng Beijing University of Posts and Telecommunications Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Multi-hop relay is regarded as a promising and effective method to provide highly reliable communication service between the control center and the terminal under a fragile and unstable industrial wireless channel environment. However, wireless communication and control in the Industrial Internet of Things (IIoT) are tightly coupled, making multi-hop relay path construction more challenging. Therefore, we propose an efficient multi-hop relay node selection algorithm to decrease the control cost of the closed-loop system to ensure the stability of the system. Specifically, we establish the relationship between node selection and control cost using the age of information as an intermediary. Then, an improved elite ant colony (IEACO) algorithm is proposed to find a relay path to minimize control cost so as to ensure the system quickly converges to a stable state. Finally, we carried out extensive experiments to verify the effectiveness of the proposed algorithm, and the results reveal that the IEACO algorithm can achieve the approximately optimal control performance. Specifically, in the example of the temperature control system, the IEACO algorithm can also make the system reach the stable state faster and improve the convergence performance by about 5% to 30%.

关键词： Wireless communication Costs Optimal control Spread spectrum communication approximation algorithms Temperature control Relays Thermal stability Industrial Internet of Things Convergence

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H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odometry

H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odomet...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Aydemir, Eren Fetic, Naida Unel, Mustafa Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Ford Otosan Prod Dev Istanbul Turkey

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In this paper, we propose a hybrid visual-LiDAR odometry (H-VLO) framework that fuses predicted visual depth map and completed LiDAR map. Compared to the previous visual-LiDAR odometry methods, our approach leverages 2D feature matching and 3D association by utilizing deep depth map, deep flow map and deep LiDAR depth completion networks. Rather than extraction of the depth values from LiDAR measurements for each visual feature, our method first densifies a LiDAR scan with a deep depth completion network and then fuses it with visual deep depth map estimation in a Bayesian framework. This method reduces pose estimation drift by improving feature-to-feature and point-to-feature matching, as well as scale recovery. The evaluations on the public KITTI odometry benchmark show that our technique achieves better or at least comparable estimates than the state-of-the-art visual-LiDAR and monocular visual odometry approaches.

关键词： Visualization Laser radar Three-dimensional displays Fuses approximation algorithms Feature extraction Cameras

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An Improved Grey Wolf Optimization Algorithm for Planting Structure Optimization in North China Mountainous Area

An Improved Grey Wolf Optimization Algorithm for Planting St...

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Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR), International Conference on

作者： Yutong Gao Taohong Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331534035

ISBN: (纸本)9798331534042

This study addresses the agricultural planting strategy optimization problem in North China's mountainous area by constructing three progressively in-depth mathematical models. These models consider factors such as planting restrictions, sales, and cost assumptions. An Improved Grey Wolf Optimization algorithm (IGWO) is proposed to solve the established mathematical models. Compared with the standard GWO algorithm, the particle swarm optimization (PSO) algorithm, and the differential evolution (DE) algorithm, the IGWO algorithm increases the economic benefits of agricultural planting structure by approximately 15%. Additionally, the ecological protection index is improved by 10%, and the social development index is increased by 8 %. This research provides a novel approach and perspective for optimizing agricultural planting structures.

关键词： Heuristic algorithms approximation algorithms Mathematical models Robustness Indexes Protection Particle swarm optimization Optimization Standards Robots

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Joint Transceiver Design and Mode Selection for NOMA-ISAC in NAFD Cell-Free mMIMO Systems

Joint Transceiver Design and Mode Selection for NOMA-ISAC in...

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International Conference on Communication Technology (ICCT)

作者： Jilong Wu Wei Jia Yiting Chen Pengcheng Zhu National Mobile Communication Research Laboratory Southeast University Nanjing P. R. China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Integrated sensing and communication (ISAC) has garnered considerable attention for its capacity to simultaneously communicate and sense the surrounding environment. To enhance ISAC systems, non-orthogonal multiple access (NOMA) can be leveraged to mitigate interference between communication and sensing functions. This paper explores the transceiver design and mode selection for NOMA-ISAC in network-assisted fullduplex (NAFD) cell-free massive multiple-input multiple-output (mMIMO) systems. We firstly derive expressions for both the data rates of communication users and the sensing signal-to-interference-plus-noise ratios (SINRs) of radar targets. Subsequently, we formulate a joint optimization problem aimed at maximizing the sum sensing SINR while respecting communication constraints. To address this, we propose an algorithm based on sequential convex approximation (SCA) coupled with simulated annealing (SA) to obtain the optimal solution. Simulation results demonstrate the efficacy of our proposed algorithm in significantly enhancing system performance.

关键词： NOMA System performance Simulation Interference Radar Integrated sensing and communication approximation algorithms Transceivers Optimization Signal to noise ratio

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Geometric Localization of Homology Cycles

arXiv

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arXiv 2024年

作者： Dhar, Amritendu Natarajan, Vijay Rathod, Abhishek Department of Computer Science and Automation Indian Institute of Science Bangalore India Zuse Institute Berlin Germany Department of Computer Science Ben Gurion University of the Negev

Computing an optimal cycle in a given homology class, also referred to as the homology localization problem, is known to be an NP-hard problem in general. Furthermore, there is currently no known optimality criterion that localizes classes geometrically and admits a stability property under the setting of persistent homology. We present a geometric optimization of the cycles that is computable in polynomial time and is stable in an approximate sense. Tailoring our search criterion to different settings, we obtain various optimization problems like optimal homologous cycle, minimum homology basis, and minimum persistent homology basis. In practice, the (trivial) exact algorithm is computationally expensive despite having a worst case polynomial runtime. Therefore, we design approximation algorithms for the above problems and study their performance experimentally. These algorithms have reasonable runtimes for moderate sized datasets and the cycles computed by these algorithms are consistently of high quality as demonstrated via experiments on multiple datasets. © 2024, CC BY.

关键词： approximation algorithms

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Discovering Options That Minimize Average Planning Time 39

Discovering Options That Minimize Average Planning Time

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ivanov, Alexander Bagaria, Akhil Konidaris, George Brown University ProvidenceRI United States Amazon New YorkNY United States

ISBN: (纸本)157735897X

We present an option discovery algorithm that accelerates planning by minimizing the shortest distance between any two states in the MDP. The proposed algorithm produces options that approximately minimize planning time in the multi-goal setting: it is shown to be a worst-case (4α, 2)-approximation of the optimal option set, where α is the approximation ratio of the k-medians with penalties subroutine. We then present a variation, Fast Average Options, with improved run-time and describe a general means of producing similar algorithms based on the selection of a k-medians subroutine. We empirically evaluate our method on four discrete and two continuous control planning domains where it outperforms other leading option discovery algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

关键词： approximation algorithms

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