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Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： Nebojsa Bacanin Khaled Alhazmi Miodrag Zivkovic K.Venkatachalam Timea Bezdan Jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at ***,to a very large search space,it is very difficult to find the proper values of connection weights and *** traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local *** commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient *** an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network *** thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural *** the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization *** results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local *** proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on *** mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

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Sitpose: A Siamese Convolutional Transformer for Relative Camera Pose Estimation

Sitpose: A Siamese Convolutional Transformer for Relative Ca...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Kai Leng Cong Yang Wei Sui Jie Liu Zhijun Li School of Computer Science and Technology Harbin Institute of Technology Shenzhen School of Future Science and Engineering Soochow University Horizon Robotics School of Computer Science and Technology Harbin Institute of Technology

Relative Camera Pose Estimation (RCPE) aims to calculate the translation and rotation between two frames with overlapped regions, which is crucial to computer vision and robotics. This paper presents a novel siamese convolutional transformer model, SiTPose, to regress relative camera pose directly. SiTPose is distinguished in three aspects: (1) With a cross-attention feature extractor and a compact transformer encoder, extreme rotation errors (> 150°) are significantly reduced: from 9.7‰ with the state-of-the-art 8-Points to 1‱ on the 7Scenes dataset. (2) SiTPose is also robust to narrow-baseline cases (slight rotation angle and large translation between neighboring frames), while existing RCPE methods mainly focus on wide-baseline cases. (3) SiTPose can be flexibly extended to geometry-based vSLAM (namely SiTSLAM) in a multi-threaded way to prevent tracking lost and scale ambiguity problems. Results on multiple datasets show that SiT-SLAM yields a marked improvement in robustness and localization accuracy in complex scenarios, e.g., RMSE error is reduced from 26.36m with the classic ORBSLAM3 method to 6.94m on the KITTI-09.

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Computational Intelligence Router of Machine and Artificial Intelligence Learning for the Expansion of Agriculture's Manufacturing Sector 3

Computational Intelligence Router of Machine and Artificial ...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Alanya-Beltran, Joel Akram, Shaik Vaseem Shah, Heli Sanjay, P.C. Ponnusamy, R. Symbiosis Institute of Business Management Pune Maharashtra Pune India Universidad Tecnológica Del Perú Peru Uttaranchal University Uttaranchal Institute of Technology India Parul University Mechatronics & Robotics Pit India India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

The economic sector includes agriculture as a major player. Around the world, the primary issue and hot topic is agriculture automation. As a result of the population boom, there is a huge increase in the need for both jobs and food. Farmers employed traditional techniques, which included insufficient to meet these needs. This led to the introduction of new automated techniques. These innovative techniques supplied the world's food needs while giving billions of people access to jobs. An agricultural revolution has been sparked by artificial intelligence. With the use of this technology, crop yields are now more resistant to many conditions like population increase, climate change, job troubles, and food security concerns. The primary goal of this study is to evaluate the numerous ways artificial intelligence is being used in agriculture, including the use of sensors and other tools built into drones and robots for weeding, spraying, and irrigation. The excessive use of pesticides, herbicides, water, and other resources is reduced by these technologies, and the soil fertility is maintained. They also aid in the effective use of labor, which raises productivity and quality. In order to provide a concise summary of the current state of agricultural automation, including the use of drones and robots for weeding, this study analyses the research of several researchers. Along with two automated weeding strategies, the various ways of soil water sensing are described. This study discusses the use of drones as well as the numerous spraying and crop monitoring techniques that may be utilized with them. © 2023 IEEE.

关键词： Drones

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Acoustic Neural 3D Reconstruction Under Pose Drift

arXiv

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arXiv 2025年

作者： Lin, Tianxiang Qadri, Mohamad Zhang, Kevin Pediredla, Adithya Metzler, Christopher A. Kaess, Michael the Robotics Institute Carnegie Mellon University PittsburghPA United States the Department of Computer Science the University of Maryland College Park United States The Computer Science Department Dartmouth College United States

We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses. The accuracy of current state-of-the-art 3D acoustic modeling algorithms is highly dependent on accurate pose estimation;small errors in sensor pose can lead to severe reconstruction artifacts. In this paper, we propose an algorithm that jointly optimizes the neural scene representation and sonar poses. Our algorithm does so by parameterizing the 6DoF poses as learnable parameters and backpropagating gradients through the neural renderer and implicit representation. We validated our algorithm on both real and simulated datasets. It produces high-fidelity 3D reconstructions even under significant pose drift. © 2025, CC BY.

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Alzheimer's Disease Prediction using SVM Kernal's with PCA Architecture 21

Alzheimer's Disease Prediction using SVM Kernal's with PCA A...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Patel, Ruchi Verma, Ashok Kumar Kshirsagar, Ravindra V. Sahu, Neelesh Kumar Gyan Ganga Institute of Technology and Sciences Department of Computer Science and Engineering MP Jabalpur India Gyan Ganga Institute of Technology and Sciences Department of Electronics and Communication MP Jabalpur India Gyan Ganga Institute of Technology and Sciences Department of Artificial Intelligence and Robotics MP Jabalpur India

ISBN: (纸本)9798350358230

Memory and other mental processes are both severely disrupted by Alzheimer's disease (AD), a neurodegenerative condition. It interferes with almost every cognitive process that the brain is capable of. This ultimately results in a smaller and less volumetric brain as time passes. There is currently no treatment that can reverse the effects of Alzheimer's disease. The only way for early detection and therapy to be possible is for the symptoms to be recognized promptly. Throughout the years, there has been a significant amount of research conducted on the application of machine learning to the diagnosis and classification of Alzheimer's disease. The performance of linear, polynomial, and RBF (Radial Basis Function) classification kernels in the context of AD classification using the Support Vector Machine (SVM) with the Principal Component Architecture (PCA) is investigated in this study. As a result, a framework is provided for the categorization of AD, which is comprised of the following steps: data input, model building, hyperparameter tuning, prediction on test data, performance evaluation, and selection of the most effective SVM kernel model and PCA for dimension reduction. ADNI and the Alzheimer's MRI Preprocessed dataset that is available on Kaggle are used to conduct an evaluation of the proposed framework. The performance of the model is evaluated using accuracy measure for both the datasets. Across both datasets, the SVM linear kernel with PCA achieved the maximum accuracy, which was 99.99 percent. © 2023 IEEE.

关键词： Support vector machines

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Gravity Compensation of the dVRK-Si Patient Side Manipulator based on Dynamic Model Identification

arXiv

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arXiv 2025年

作者： Zhou, Haoying Yang, Hao Deguet, Anton Fichera, Loris Wu, Jie Ying Kazanzides, Peter Department of Robotics Engineering Worcester Polytechnic Institute MA United States Laboratory for Computational Sensing and Robotics JHU MD United States Department of Computer Science Vanderbilt University TN United States Department of Computer Science Johns Hopkins University MD United States

The da Vinci Research Kit (dVRK, also known as dVRK Classic) is anopen-source teleoperated surgical robotic system whose hardware is obtainedfrom the first generation da Vinci Surgical System (Intuitive, Sunnyvale, CA,USA). The dVRK has greatly facilitated research in robot-assisted surgery overthe past decade and helped researchers address multiple major challenges inthis domain. Recently, the dVRK-Si system, a new version of the dVRK which usesmechanical components from the da Vinci Si Surgical System, became available tothe community. The major difference between the first generation da Vinci andthe da Vinci Si is in the structural upgrade of the Patient Side Manipulator(PSM). Because of this upgrade, the gravity of the dVRK-Si PSM can no longer beignored as in the dVRK Classic. The high gravity offset may lead to relativelylow control accuracy and longer response time. In addition, althoughsubstantial progress has been made in addressing the dynamic modelidentification problem for the dVRK Classic, further research is required onmodel-based control for the dVRK-Si, due to differences in mechanicalcomponents and the demand for enhanced control performance. To address theseproblems, in this work, we present (1) a novel full kinematic model of thedVRK-Si PSM, and (2) a gravity compensation approach based on the dynamic modelidentification. © 2025, CC BY.

关键词： Surgical equipment

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Enhancement of Two-Photon Fluorescence Microscopy Through Auxiliary Microspheres for Microscopic Observation 14

Enhancement of Two-Photon Fluorescence Microscopy Through Au...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Zhang, Feng Jin, Zijian Jiang, Chaodi Yuan, Shuai Yang, Yongliang Wang, Xiaoduo School of Electrical & Control Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798331506056

At present, it is still a great challenge to improve the observation accuracy of two-photon fluorescence microscope. A new method that using auxiliary microspheres as movable components on the sample is proposed to solve this challenge in this study, it can improve high-resolution imaging. In experiments using two-photon fluorescence microscopy, the half-width height (FWHM) of red fluorescent nanoparticles with a diameter of 20 nm was observed to be 296 nm using a primitive microscope system. In this paper by combining barium titanate microspheres with two-photon fluorescence microscopy, the accuracy of the point spread function of the nanoparticles can be improved by a factor of about 1.65, which results in a significant reduction of their half-width height measurements to 179 nm. The research is of great significance to improve the resolution of two-photon fluorescence microscope. This progress is very valuable for studying biological tissues and cell structures, it provides a new impetus for the sustainable development in the microscopic imaging field. © 2024 IEEE.

关键词： Microspheres

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Heat transfer simulation of optogenetics-LED based on microchannel heat dissipation

Heat transfer simulation of optogenetics-LED based on microc...

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2024 International Conference on Optoelectronic Information and computer Engineering, OICE 2024

作者： Liu, Lusheng Le, Song Zhang, Yuan Zhang, Jing Wang, Aiping Bin, Jianxiong Niu, Lan Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9781510682504

Optogenetics technology has greatly promoted the development of neuroscience. Flexible gene manipulation tools and sensitive light activation methods have provided convenience for neural function research. However, the thermal effect generated in the process of light stimulation affects the activation of neurons by optogenetic technology, which makes the results of light-activated light-sensitive proteins to generate neural currents inaccurate. Here, we established an in vivo activation model of LED-based optogenetic technology through simulation and analyzed the thermal effect produced by it. We propose to use microchannels to dissipate heat from implanted devices, reducing the thermal effect of light stimulation for neural activation. Through simulation, we propose the use of the vortex-like and cobweb-like microchannels for heat dissipation, which can control the temperature rise within 0.28K and 0.31K, respectively. © 2024 SPIE.

关键词： Heat losses

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Implicit Graph Search for Planning on Graphs of Convex Sets

arXiv

引用

arXiv 2024年

作者： Natarajan, Ramkumar Liu, Chaoqi Choset, Howie Likhachev, Maxim The Robotics Institute Carnegie Mellon University United States Department of Computer Science The University of Illinois Urbana-Champaign United States

Smooth, collision-free motion planning is a fundamental challenge in robotics with a wide range of applications such as automated manufacturing, search & rescue, underwater exploration, etc. Graphs of Convex Sets (GCS) is a recent method for synthesizing smooth trajectories by decomposing the planning space into convex sets, forming a graph to encode the adjacency relationships within the decomposition, and then simultaneously searching this graph and optimizing parts of the trajectory to obtain the final trajectory. To do this, one must solve a Mixed Integer Convex Program (MICP) and to mitigate computational time, GCS proposes a convex relaxation that is empirically very tight. Despite this tight relaxation, motion planning with GCS for real-world robotics problems translates to solving the simultaneous batch optimization problem that may contain millions of constraints and therefore can be slow. This is further exacerbated by the fact that the size of the GCS problem is invariant to the planning query. Motivated by the observation that the trajectory solution lies only on a fraction of the set of convex sets, we present two implicit graph search methods for planning on the graph of convex sets called INSATxGCS (IxG) and IxG*. INterleaved Search And Trajectory optimization (INSAT) is a previously developed algorithm that alternates between searching on a graph and optimizing partial paths to find a smooth trajectory. By using an implicit graph search method INSAT on the graph of convex sets, we achieve faster planning while ensuring stronger guarantees on completeness and optimality. Moveover, introducing a search-based technique to plan on the graph of convex sets enables us to easily leverage well-established techniques such as search parallelization, lazy planning, anytime planning, and replanning as future work. Numerical comparisons against GCS demonstrate the superiority of IxG across several applications, including planning for an 18-degree-of-freedom multi

关键词： Motion planning

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A Digital Twin for Telesurgery Under Intermittent Communication

A Digital Twin for Telesurgery Under Intermittent Communicat...

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International Symposium on Medical robotics (ISMR)

作者： Junxiang Wang Juan Antonio Barragan Hisashi Ishida Jingkai Guo Yu-Chun Ku Peter Kazanzides Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

Telesurgery is an effective way to deliver service from expert surgeons to areas without immediate access to specialized resources. However, many of these areas, such as rural districts or battlefields, might be subject to different problems in communication, especially latency and intermittent periods of communication outage. This challenge motivates the use of a digital twin for the surgical system, where a simulation would mirror the robot hardware and surgical environment in the real world. The surgeon would then be able to interact with the digital twin during communication outage, followed by a recovery strategy on the real robot upon reestablishing communication. This paper builds the digital twin for the da Vinci surgical robot, with a buffering and replay strategy that reduces the mean task completion time by 23 % when compared to the baseline, for a peg transfer task subject to intermittent communication outage. The relevant code can be found here: https://***/LCSR-CIIS/dvrk_digital_twin_teleoperation.

关键词： Medical robotics Codes Surgery Hardware Digital twins Mirrors

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