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检索条件"任意字段=3rd International Conference on Robotics and Control Engineering, RobCE 2023"

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Skin Cancer Detection and control Techniques Using Hybrid Deep Learning Techniques

Skin Cancer Detection and Control Techniques Using Hybrid De...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Nagaraj P Suresh Kumar S V. Saijagadeeshkumar G. Pavan Kumar K. Yerriswamyreddy K. Jaya Krishna Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India

More than 70% of skin cancer fatalities are caused by the deadliest type of the disease, melanoma. Malignant melanocytes give rise to melanomas. Melanoma would have a better ranking based on years lost to cancer because relatively young people are affected by this cancer. The average age at which melanoma is discovered in patients is relatively young, and the overall number of melanoma sufferers is rising. As a result, it is hypothesized that Caucasian populations are experiencing an increase in the overall burden of melanoma. There is a large potential benefit for the prevention of melanoma because the overall burden of the disease is rising, prognosis strongly depends on the time of diagnosis, as well as the majority crucially, effective treatments for advanced stages are missing. However, the majority of the known risk factors for melanoma, including pale skin tone, freckles, light eyes, advanced age, history of sunburns, clinical atypical nevi, past melanoma, and family history of the disease, are not treatable. The only possible outcomes are sun exposure and sunburns. Melanoma preventive strategies include sun protection techniques. Comprehensive sun protection programs have been in place for more than ten years in several high-risk nations, and public awareness of the benefits of sun protection is strong. The general public's sun exposure habits have not changed even though these public health efforts have raised awareness of skin cancer and the harmful effects of excessive sun exposure. Several researchers have expressed an interest in melanoma early detection, and a tremendous amount of work has been done on melanoma diagnosis. Several approaches to early detection were explored in this research work, along with the benefits and drawbacks of each approach.

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Adaptive control of Multi-Rotor UAV Using Enhanced Learning Technology

Adaptive Control of Multi-Rotor UAV Using Enhanced Learning ...

引用

2018 3rd international conference on Communications, Information Management and Network Security(CIMNS2018)

作者： Chin-Hsiung Lee College of Robotics Fuzhou Polytechnic

Due to the development and maturation of embedded processor,micro-sensor technology and control theory,multi-rotor UAV is *** continuously increasing efficiency and multifunctionality make multi-rotor UAV widely used in military,civil aerial shooting,search and rescue,monitoring and other *** the special mechanical structure and dynamic characteristics,it also plays an important role in the scientific ***,UAV is susceptible to the disturbance of environmental factors during the *** study uses reinforcement learning to enhance the stability of flight control of multi-rotor *** reinforcement learning method,also known as reinforcement learning,is one of the learning methods in the field of machine learning and artificial *** is the most commonly used algorithm in the agent system,which is suitable for the unknown *** particular,the agent system will get feedback from changes in the environment,so it can learn from experience and optimize the overall *** research mainly uses the Q-learning algorithm to realize the enhanced learning mechanism,which learns the best response and control corresponding to different UAV attitude,adopts the PID control parameter of reinforcement learning adjustment,and uses it to maintain the stable posture of UAV in the unknown environment.

关键词： multi-rotor UAV enhanced learning PID control machine learning self-adaptive control

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engineering Social Learning Mechanisms for Minimalistic Multi-agent Robots

Engineering Social Learning Mechanisms for Minimalistic Mult...

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international conference on control and Robots (ICCR)

作者： Owais Hamid Kerstin Dautenhahn Chrystopher L. Nehaniv Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175638

Social learning, which can include complex or simple social mechanisms, allow us to understand cooperation and communication in animals, giving them better chances to survive for longer and thrive as a society. In order to translate this understanding into socially rich behavior among multi-agent robots, this study utilizes social learning mechanisms that are simple yet quite effective. Small and simple swarm robots are utilized to understand how such social mechanisms might play a role in establishing rules for emergent group behavior and how social rules might be engineered to gain useful effects in a group of robots. The study investigates exploratory behavior without interaction (asocial) and with interaction (social) among a group of robots. The results from this exploratory study suggest that deterministic asocial exploration is best performed by Spiral exploration mechanisms. However, these asocial exploration strategies are eclipsed by certain types of social reward sharing strategies as long as sharing occurs for at least half the lifetime of the robots. Sharing locations of reward caches for all time is of course the most optimal, but comes at the cost of communicating longer and hence using more energy both on the sender and receiver's end. An analysis of a compromise strategy between completely asocial exploration and social reward location sharing is performed using strategies termed critical and conditional learning. It is found that the number of reward caches located through critical and conditional learning are intermediary to the two extremes, namely completely asocial and completely social foraging.

关键词： Learning systems Spirals Animals Switching systems Swarm robotics Switches Robots

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Research on Model-Based Cooperative Delivery Problem of Trucks and UAVs

Research on Model-Based Cooperative Delivery Problem of Truc...

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Digital Society and Intelligent Systems (DSInS), international conference on

作者： Jinchen Zhao Jun Lei Mian Ye Weiran Zhang Huichang Yang Engineering Research Center of Ministry of Education for Intelligent Air-Ground Fusion Vehicles and Control Xihua University Chengdu China Xihua University Chengdu China

ISBN: (数字)9798350331387

ISBN: (纸本)9798350331394

In order to address the increasing complexity of the urban logistics and distribution environment, a novel cooperative delivery model of trucks and UAVs was proposed, aiming to improve the efficiency of logistics and distribution, reduce the economic loss caused by untimely delivery, and meanwhile solve the problems of short distribution distance and insufficient range capacity in the UAV logistics industry. K-means clustering algorithm was utilized to determine the truck distribution points, and genetic algorithm was applied to solve the problem of the shortest driving distance under the cooperative distribution model. Through numerical experiments, it was verified that the proposed method featured favorable distribution performance with faster delivery speed and less time consumption. The research provided future development of urban logistics and distribution with regard to novel ideas and methods.

关键词： Industries Costs Clustering algorithms Transforms Autonomous aerial vehicles Numerical models Intelligent systems

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Research on Bad Data Identification Method for Power Information Communication Process

Research on Bad Data Identification Method for Power Informa...

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Computer Science, Electronic Information engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Fuxiang Zhang Zheng Li Meng Li Chong Li Haibo Sun State Grid Liaoning Information and Communication Company Shenyang China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

In traditional electrical power communication processes, the denoising ability of the existing methods for identifying faulty data is inadequate, which results in a diminished identification efficiency. To address this, the present study devises a novel method for identifying faulty data in electrical power information communication processes, grounded in wavelet analysis. Leveraging the singularity characteristic of the wavelet transformation, this method conducts localized singularity detection on the power information communication process. Following the detection, it discriminates between normal and faulty data utilizing neural network algorithms. Subsequently, to eliminate noise-containing signals within the faulty data, both soft and hard threshold denoising techniques are employed. After calculating the noise intensity, the wavelet transformation coefficients within a certain scale are arranged in ascending order based on their squares, and the likelihood estimation vector is computed. The method then proceeds to remove the noise segments within the signal, guided by the minimum and maximum values in the vector. Empirical results illustrate that this approach significantly enhances the denoising capability and bolsters identification efficiency, showcasing its potential as a robust solution in improving the quality and reliability of electrical power communication processes.

关键词： Wavelet transforms Fault diagnosis Noise reduction Noise Neural networks Process control Estimation

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Study of Skinner Automaton Implemented on a Two-Wheeled Robot

Study of Skinner Automaton Implemented on a Two-Wheeled Robo...

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2013 3rd international conference on Electric and Electronics(EEIC 2013)

作者： Xuan Wu XiaoGang Ruan XiaoPing Zhang Ouattara Sie Institute of Artificial Intelligence and Robots School of Electronic Information and Control Engineering Beijing University of Technology

Learning is the main aim of robotics. In this paper we present a new stochastic learning automaton called a Skinner automaton as a psychological model for formalizing the theory of operant conditioning. We identify animal operant learning with a thermodynamic process, and derive a so-called Skinner algorithm from Monte Carlo method and Metropolis algorithm and simulated annealing. The Skinner automaton is implemented on a two-wheeled robot with a flexible lumbar in a simulation experiment and it learns to keep balance successfully.

关键词： operant conditioning learning automaton two-wheeled robot

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Fast-speed Precise C-start of a Robotic Fish:Design and Implementation

Fast-speed Precise C-start of a Robotic Fish:Design and Impl...

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The 3rd international conference of Bionic engineering(ICBE’10)

作者： Zongshuai Su1, Junzhi Yu1, Min Tan1 and Jianwei Zhang2 1 Lab of Complex Systems and Intelligence Science, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China 2 Department of Informatics, University of Hamburg, D-22527 Hamburg, Germany

This paper presents a novel closed-loop method for a multilink robotic fish to mimic the C-start maneuver, in which the turning speed and precision are emphasized. The turning speed is maximized by carefully designed preparatory stage, and the turning precision is achieved by the feedback of the turning angle from a gyroscope and a new design of the propulsive stage. Different types of C-starts are studied based on the different sizes of caudal fins, in order to achieve the highest turning angular velocity. All the proposed types of C-starts are experimented and compared using a 4-joint robotic fish. The experimental results show the fastest angular velocity up to 200°/s and the distinctions between all these different types.

关键词： Bio-inspired robotics, robotic fish, turning control, C-start, Closed-loop maneuvering control.

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Pavement Crack Segmentation Network Based on Local Diagonal Gray Scale Transformation Algorithm

Pavement Crack Segmentation Network Based on Local Diagonal ...

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Computer Science, Electronic Information engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Tian Xie Song Wang Tao Li Chen Chang School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Pavement crack segmentation aims to identify and localize crack objects, in order to solve the problems such as the influence of complex texture noise in the crack background on segmentation and the misidentification of fine cracks, a crack segmentation network based on local diagonal grayscale transformation algorithm (LDGT-IterNet) is proposed. Firstly, the Local Diagonal Grayscale Transformation (LDGT) algorithm is used to preprocess the crack image, which converts the grayscale image into a 4-channel image with constant contrast according to the grayscale order between each pixel and its diagonal neighboring pixels along the four directions, preserving the structural intrinsic features of the crack curves and at the same time making the contrast change robust; Then, Convolutional Block Attention Module (CBAM) is introduced between the basic and refined modules of IterNet segmentation network to improve the performance of the segmentation network by mining the feature map detail information. In order to verify the robustness of the proposed method, experimental comparison and validation is carried out on Crack500, CFD and Crack200 datasets. The F 1 -score of the method is 85.47%, 87.95% and 86.82% on the three datasets, respectively. It shows that the method can effectively segment pavement cracks and provide more accurate information for road maintenance.

关键词： Image segmentation Roads Noise Process control Gray-scale Predictive models Robustness

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Torque Analysis of Spherical Permanent Magnetic Motor with Magnetic Equivalent Circuit and Maxwell Stress Tensor

Torque Analysis of Spherical Permanent Magnetic Motor with M...

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2011 3rd international Asia conference on Informatics in control, Automation and robotics(CAR 2011)

作者： Bin Li Chao Liu Hongfeng Li Guidan Li School of Electrical Engineering and Automation Tianjin University

The paper discusses the torque characteristics of spherical permanent magnetic(PM)motor adopting three-dimensional(3D)magnetic equivalent circuit(MEC).Taking into account the unsaturation of the magnetic field,the rotor and the stator are meshed in spherical coordinates and cylindrical coordinates respectively,and then the expressions of the reluctances and magnetomotive force(MMF)have been *** to the relative position between the coordinate systems,the magnetic field in the air gap has been obtained by transforming the coordinate *** on this,the Maxwell stress tensor(MST)method is applied to analyze the electromagnetic force and torque,the corresponding torque matrix is given,and the relationship between the motor torque and winding location is *** with the finiteelement method(FEM),this method is proved to have a high accuracy and helpful to the optimization design of the PM spherical motor.

关键词： PM spherical motor,Electromagnetic torque,Maxwell Stress Tensor,3D Magnetic Equivalent Circuit

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ABC-WRF-Based Gait Recognition for Exoskeleton control Systems: A Multi-Source Data Fusion Approach

ABC-WRF-Based Gait Recognition for Exoskeleton Control Syste...

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Networking Systems of AI (INSAI), international conference on

作者： Xi Yang Songbo Pan Yanbin Xu Chenguo Deng Guangdong power grid limited liability company Jiangmen power supply bureau Guangdong China School of Mechanical and Electrical Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

As the demand for exoskeleton robots continues to grow across various domains, the technology surrounding exoskeleton robots has attracted increased attention. The recognition of gait phases plays a crucial role in exoskeleton control systems. However, practical engineering applications have presented significant challenges for plantar pressurebased gait recognition technology. To address this, we have proposed a gait recognition approach that utilizes human joint angle data to mitigate the influence of environmental factors and movement patterns on plantar pressure signals. This study has designed a multi-source data fusion system for human motion sensing to capture motion attitude data across different modes of movement. Furthermore, in order to recognize gait phases, we have explored the optimization of the weighted random forest algorithm using the Artificial Bee Colony algorithm (ABC-WRF). Subsequently, the proposed method was evaluated through tests conducted on various terrains, including flat ground, stairs, slopes, and special terrain. The results demonstrated an average gait recognition accuracy of over 90%, with a maximum accuracy of 96%. This research contributes to the advancement of exoskeleton engineering by introducing the ABC-WRF-based gait recognition technology and fostering the development of related technologies.

关键词： Accuracy Roads Exoskeletons Data integration Stairs control systems Artificial bee colony algorithm Sensor systems Sensors Gait recognition

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