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Optimization of fracture reduction robot controller based on improved sparrow algorithm

Biomimetic intelligence & Robotics 2023年第4期3卷 16-28页

作者： Baichuan An Jianwen Chen Hao Sun Minghuan Yin Zicheng Song Chao Zhuang Cheng Chang Minghe Liu School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Limb Deformity Correction and Function Reconstruction Department Southern University of Science and Technology HospitalShenzhen 518000China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of *** the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is *** achieve this,a sparrow search algorithm(SSA)based on the Levy flight operator was proposed in this study for self-tuning the robot controller *** inverse kinematic analysis of the FRR was also *** robot dynamics model was established using Simulink,and the inverse dynamics controller for the fracture reduction mechanism was designed using the computed torque control *** simulation and physical experiments were also *** actual motion trajectory of the actuator drive rod and its error with a desired trajectory was obtained through *** optimized Levy-sparrow search algorithm(Levy-SSA)crack reduction robot controller demonstrated an overall reduction of two orders of magnitude in the reduction error,with an average error reduction of 98.74%compared with the traditional unoptimized *** Levy-SSA increased the convergence of the crack reduction robot control system to the optimal solution,improved the accuracy of the motion trajectory,and exhibited important implications for robot controller optimization.

关键词： Fracture reduction robot The sparrow search algorithm Levy flight Reduction accuracy

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Machine learning-based classification study of rice blast disease 5

Machine learning-based classification study of rice blast di...

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5th International Conference on Computer Vision and data Mining, ICCVDM 2024

作者： Yang, Xu Liu, Fangyuan Song, Shaozhong An, Xiaofeng Wu, Yonghao School of Electronic Information Engineering Changchun University of Science and Technology Changchun China School of Data Science and Artificial Intelligence JiLin Engineering Normal University Changchun China Education Quality Monitoring Center JiLin Engineering Normal University Changchun China

ISBN: (纸本)9781510682863

Begin Rice, as the largest food crop grown in China, has shown a high incidence of rice blast disease, which seriously threatens the sustainability of rice production. A UAV multispectral remote sensing platform is used to realize the identification and monitoring of the incidence of rice blast disease and early warning at the field level, which assists variable fertilizer and drug application by plant protection UAVs. This research is based on a rice blast experiment in a large field in Wanchang Town, Yongji County, Jilin City, based on a malefactor, using a UAV multispectral remote sensing platform to obtain images of two fertility stages, namely, the nodulation stage(NS) and the spike stage(SS) of rice, and using six different vegetation index parameters as inputs to establish a DBO-XGBoost classification model, with an accuracy rate of more than 90% for both fertility stages. © 2024 SPIE.

关键词： Vegetation

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Text Extraction with Optimal Bi-LSTM

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Computers, Materials & Continua 2023年第9期76卷 3549-3567页

作者： Bahera H.Nayef Siti Norul Huda Sheikh Abdullah Rossilawati Sulaiman Ashwaq Mukred Saeed Computer Techniques Engineering Department Ibn Khaldun University CollegeBaghdad10011Iraq Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University MalaysiaSepang43900Malaysia

Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the *** Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant *** study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain *** pass them to deep learning layers to extract feature maps and reform the predicted *** Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training *** Connectionist Temporal Classification(CTC)function calcifies the training and validation loss *** addition to decoding the extracted feature to reform characters again and linking them according to their time *** proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)*** result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and *** the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.

关键词： Deep neural network text features dual max-pooling concatenating convolution neural networks bidirectional long short memory text connector characteristics

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An Enhanced Deep Learning Technique for Crack Identification in Composite Materials

An Enhanced Deep Learning Technique for Crack Identificatio...

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International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Ramanathan, Saveeth Sankareswaran, Uma Maheswari Mohanraj, Prabhavathy Department of Computer Science Engineering Coimbatore Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Coimbatore Institute of Technology Coimbatore India Department of Artificial Intelligence and Data Science Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9789819909803

Composite material consists of more than one substance, which inherit the superior properties of the constituent’s elements for light weight and tensile strength. The identification of cracks in composite material at an earlier stage reduces the cost and effort. Deep learning is a complex model in machine learning to extract significant features from a large dataset. The suitable construction of convolutional neural network topology for the application will give better results. Our proposed model uses Bayesian optimization for hyper-parameter tuning which updates the hyper-parameter value for each iteration to improvise the accuracy. In this work real time, composite material images were taken as dataset captured by Silicon Electron Microscope and Evolution VF camera. Applied proposed model on Silicon Electron Microscope dataset, obtained accuracy over 88% and on Evolution VF dataset obtained accuracy over 91%. The integration of optimization techniques with CNN reduces the computational time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electron microscopes

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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Deep Learning Classification Techniques on Detecting Diabetic Retinopathy dataset 7

Deep Learning Classification Techniques on Detecting Diabeti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Revathi, B. Usharani, C. Kezial Elizabeth, S.K. Nagaraj, P. Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence And Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science And Engineering Madurai India Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Theni Kammavar Sangam College of Technology Department of Computer Science And Engineering Theni India

ISBN: (纸本)9798350359299

Deep learning algorithms can summarize images to understand how to carry out necessary tasks. The purpose of this study is to compare several deep learning methods. Both experience-based and explanation-based learning are possible in deep learning. The most widely utilized algorithms, such as Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Generative Adversarial Networks (GAN), Radial Basis Function Networks (RBFN), and Deep Belief Networks (DBN), and the Diabetic Retinopathy dataset is utilized in this study to evaluate the effectiveness of the algorithms. A comparative study of the classifiers reveals that CNN performs more accurately than the other approaches. © 2024 IEEE.

关键词： Classification (of information)

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient 3D Hilbert Curve Encoding and Decoding Algorithms

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Chinese Journal of Electronics 2022年第2期31卷 277-284页

作者： JIA Lianyin LIANG Binbin LI Mengjuan LIU Yong CHEN Yinong DING Jiaman Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Library Yunnan Normal University School of Information Science and Engineering Guangxi University for Nationalities School of Computing Informatics and Decision Systems Arizona State University

Hilbert curve describes a one-to-one mapping between multidimensional space and 1 D *** traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm(JFK-3HE) and a new decoding algorithm(JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0 s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.

关键词： order-wise manner multidimensional space encoding state-of-the-arts algorithms encoding algorithm parallel algorithms JFK-3HE statistical distributions decoding solid modelling design efficient 3D JFK-3HD traditional 3D Hilbert encoding equivalent computing costs different input data decoding algorithm Hilbert curve state views iterative methods

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Using Machine Learning to Forecast the Risk of Suicide Attempts over Time 1

Using Machine Learning to Forecast the Risk of Suicide Attem...

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1st International Conference on Computational intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Shetty M, Swara Praveen, Varsha Chinmayi, H.S. Bhavitha, K.S. Kamal, Shoaib Rao, Trupthi Global Academy of Technology Department of Computer Science Engineering Bengaluru India Global Academy of Technology Department of Electonics and Communication Engineering Bengaluru India Global Academy of Technology Department of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350339727

Suicide is recognized as a grave public health concern affecting individuals of all ages, from children and adolescents to young adults and beyond. It represents a significant cause of mortality within this population, influenced by various factors. Conventional methods for predicting suicide attempts have restricted the accuracy and scope of detecting these perilous behaviors. To enhance the accuracy and scope of detecting suicide attempts, we utilized machine learning on electronic health records from a vast medical database. Our study involved 469 adults exhibiting suicidal tendencies, among whom 85 patients made suicide attempts (cases). Additionally, 384 participants who did not attempt suicide but had depression showed promising signs of suicidal behavior, as confirmed by expert record review. This approach surpasses the limitations of traditional methods in predicting suicide attempts. Through the development of machine learning algorithms, we achieved accurate predictions of future suicide attempts (AUC = 0.80, precision= 0.30, recall = 0.54, Fl-score = 0.32, support=13). Our findings are a significant stride towards precise and scalable detection of suicide risk. Additionally, they offer valuable insights into how the risk of suicide attempts fluctuates over time. © 2023 IEEE.

关键词： Machine learning

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