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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47670 条记录，以下是241-250 订阅

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Mobile Robot Manipulator Localization in Indoor Environments for Object Picking Tasks 6

Mobile Robot Manipulator Localization in Indoor Environments...

引用

6th international conference on Control and robotics, ICCR 2024

作者： Xie, Yunfan Pongthanisorn, Goragod Kaneko, Shin-Ichiro Capi, Genci Graduate School of Science and Engineering Hosei University Dept. of Mechanical Eng. Tokyo Japan National Institute of Technology Toyama College Department of Electrical and Control Systems Engineering Toyama Japan

ISBN: (纸本)9798331518158

In dynamic and unpredictable work environments such as manufacturing, logistics, and automated warehouses, achieving high-precision self-localization estimation for efficient object picking are critical challenges for mobile robots. Ensuring accuracy and adaptability is essential for their successful operation. This paper presents a new method that integrates RGB cameras with Contrastive Learning (CL) for self-localization estimation and advanced object picking for mobile manipulators. The effectiveness of the proposed method for location estimation in object picking is verified in indoor environments. © 2024 IEEE.

关键词： Contrastive Learning

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DETReg Incorporating Semi-Supervised Learning for Object Detection in the Advanced Driver-Assistance Systems 1

DETReg Incorporating Semi-Supervised Learning for Object Det...

引用

1st international conference on robotics, engineering, science, and technology, RESTCON 2024

作者： Nakano, Keita Matsushima, Kousuke National Institute of Technology Kurume College Advanced Engineering School Fukuoka Japan National Institute of Technology Kurume College Dept. of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350348842

In Advanced Driver-Assistance Systems (ADAS) and automatic driving, it is important to accurately recognize objects around the vehicle. DETReg is one of the unsupervised pre-Training methods using Transformer, which is self-supervised by combining localization and categorization. DETReg performs self-supervised learning on unlabeled images. Then, it extracted a wide range of features from rich aspects of the data and gained the flexibility to adapt to many variations. Fine tuning then used the labeled dataset of the target task to fine tune the model to fit the specific dataset. This allowed DETReg to achieve higher accuracy in the object detection task. However, it is difficult to learn DETReg efficiently because of its slow learning time. In this paper, we propose a new pre-Training method for object detection, called Semi-DETReg, that utilizes a few supervised labels during self-supervised learning. We incorporate semi-supervised learning into DETReg by using a portion of the supervised training data in the pre-Training to improve efficiency. We demonstrate the effectiveness of our method by conducting experiments and comparing our method to a similarly trained DETReg. © 2024 IEEE.

关键词： Object detection

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DeepCerviNet: Enhanced Cervical Cancer Classification Using Supervised Learning Model 3

DeepCerviNet: Enhanced Cervical Cancer Classification Using ...

引用

3rd IEEE international conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahaman, Md Anikur Binte Rabbani Nuha, Maksura Tahsin, Md Hossain, Al Islam, Raihan Ul Rifat Ahmmad Rashid, Mohammad Reza, Ahmed Wasif Ripon, Shamim H. East West University Department of Computer Science and Engineering Dhaka Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331534400

Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research, the rate of death from cervical cancer remains strikingly high among women. Therefore, accurate early-stage cervical cancer identification is imperative for effective diagnosis. It facilitates cost-effective and timely treatment to increase the survival rate of patients, leading to an optimal diagnosis improvement in disease classification. Deep learning approaches demonstrate superior accuracy in medical data classification, motivating us to consider these techniques in this research. In our study, we propose DeepCerviNet, a custom CNN model that achieves 99% precision, which exceeds the accuracy of other deep learning models, MobileNetV2 (97%), ResNET34 (95%), and GoogleNet (94%). Furthermore, to assess our model's generalizability, we use another Multicancer dataset where the proposed model classifies various cancers with 99% accuracy. The DeepCerviNet model's strong performance and adaptability can assist in identifying more aggressive or recurrent cervical cancer types, aiding the development of targeted therapeutic strategies. © 2024 IEEE.

关键词： Self-supervised learning

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Mechanical Characteristics and Microstructural Analysis of Additively Fabricated Low-Carbon Steel Structure via Direct Energy Deposition Process 1

Mechanical Characteristics and Microstructural Analysis of A...

引用

1st international conference on robotics, engineering, science, and technology, RESTCON 2024

作者： Yadav, Ashish Srivastava, Manu Jain, Prashant K Rathee, Sandeep PDPM Indian Institute of Information Technology Design & Manufacturing Mechanical Engineering Department Jabalpur India National Institute of Technology Mechanical Engineering Department Srinagar India

ISBN: (纸本)9798350348842

The use of wire-Arc additive manufacturing, an arc-based direct energy deposition (DED) process, to create different components out of structural materials has gained much traction in recent years. This experimental work focused on the use of wire-Arc additive manufacturing to deposit the structure of low-carbon alloy steel (ER70S-6). The microstructure and mechanical characteristics of the deposited structure were analyzed. Microstructures consisting of polygonal ferrite in the center of the melt pools and interlayer regions of bainite, allotriomorphic ferrite, and acicular ferrite were found in the deposited structure. The microstructure showed polygonal and closed acicular phases near the grain boundary. The mechanical characteristics of the structure were examined at various orientations (i.e., horizontal, and vertical building directions). WAAM fabricated multi-layered structures have average tensile test values of ultimate tensile strength (UTS), yield strength (YS), and the percentage of elongation (EL%) are 520.25 MPa, 333.35 MPa, and 32.45%, respectively. The uniform ferritic microstructure of the structure was shown to be responsible for its improved mechanical characteristics. The results show great potential for DED as a practical and affordable process for producing precisely designed parts with remarkable strength and hardness. © 2024 IEEE.

关键词： 3D printing

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Vision Transformer and Residual Network-Based Autoencoder for RGBD Data Processing in Robotic Grasping of Noodle-Like Objects 1

Vision Transformer and Residual Network-Based Autoencoder fo...

引用

1st international conference on robotics, engineering, science, and technology, RESTCON 2024

作者： Koomklang, Nattapat Gamolped, Prem Hayashi, Eiji Kyushu Institute of Technology Department of Creative Informatics Fukuoka Japan

ISBN: (纸本)9798350348842

In this innovative study, a Vision Transformer and Residual Network-based Autoencoder is employed for the efficient encoding of RGBD data, aimed at enhancing robotic precision in grasping noodle-like objects. The project successfully compresses 50x50 pixel RGBD images to a 1024-element format, optimizing data processing for robotic applications. Utilizing a novel combination of vision transformers and residual networks, the autoencoder maintains critical data features during compression and decompression, essential for accurate robotic manipulation. The efficacy of this approach is evaluated using various metrics such as Relative Absolute Error (RAE), Relative Squared Error (RSE), Root Mean Square Error (RMSE), and accuracy thresholds. The results are promising, demonstrating high fidelity in the reconstructed data with accuracy thresholds at 1.25 reaching 0.993 for RGB images and 0.989 for depth images. These findings confirm the autoencoder's effectiveness in representing full data with excellent accuracy, underscoring its potential in robotic grasping applications, particularly for objects with complex shapes and textures. © 2024 IEEE.

关键词： Deep learning

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Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Drivers Through Haptic Feedback 4

Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Dr...

引用

4th international conference on robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Chowdhury, Shah Fahim Rabbi, Abid Mahamud Saif, Mushfiq Qayyum Khan, Nafisa Anjum Hasan, Khandaker Tabin Deparment of Computer Science and Engineering Dhaka Bangladesh Faculty of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9798331530938

This research explores the use of haptic technology in heavy vehicle driving safety, focusing on its potential to enhance driver assistance systems and improve driving experience. The study addresses driver-induced causes of traffic accidents, including drivers' inability to see around the vehicle due to its length and height. The proposed system provides 360-degree cognitive awareness through haptic feedback, which can improve road safety and mitigate challenges in diverse driving conditions. By using haptic feedback, the driver will be able to get a tangible sense of the presence of objects surrounding the vehicle. Through a comprehensive review of existing literature and experimental studies, the research demonstrates the potential of haptic feedback in revolutionizing the driving experience. The findings highlight the optimum mapping between sensors and vibration motors positions on the human body © 2025 IEEE.

关键词： Vehicle safety

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A Review of Cooperative Adaptive Cruise Control for Road Vehicles with Network Uncertainty 7

A Review of Cooperative Adaptive Cruise Control for Road Veh...

引用

7th international conference on Intelligent robotics and Control engineering, IRCE 2024

作者： Cao, Wanke Wu, Rutao Lv, Haijun Guo, Shuxun Beijing Institute of Technology National Engineering Research Center of Electric Vehicles Shenzhen Automotive Research Institute Beijing100081 China Byd Auto Industry Company Limited Byd Company Limited Shenzhen518000 China

ISBN: (纸本)9798350352399

Cooperative adaptive cruise control (CACC) allows connected vehicles in a platoon to maintain smaller headway, which has the potential to enhance driving safety and traffic efficiency. However, various network uncertainty in internet of vehicles, such as communication delay, network attacks, packet loss, can seriously affect the performance of CACC. In this paper, we focus on the research progress of CACC for road vehicles with network uncertainty. Basic issues including information flow topology, inter-vehicle spacing policy, and string stability of platoon are systematically discussed. This paper comprehensively reviews three important aspects of CACC for road vehicles with network uncertainty: communication delay, network attacks and packet loss. Finally, the future research direction of CACC is summarized and prospected. © 2024 IEEE.

关键词： Adaptive cruise control

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Malware Detection in Industrial IoT using Federated Cloud

Malware Detection in Industrial IoT using Federated Cloud

引用

2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Lamech Vedaraj, J. Maheswaran, C.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350306927

Using BaIoT, a network stress simulator for actual IoT devices that have been compromised by computer viruses, the suggested methodology was developed. The ability to identify malware that impacts both visible and hidden N- BaIoT IoT bias has been taught and validated using supervised and unsupervised allied models. Furthermore, their performance has been evaluated using two standards. In fact, it appears that the step used in the majority of related literacy algorithms, the birth model aggregation composed phase, is particularly vulnerable to multiple attacks made against a single adversary. The effectiveness of colourful model aggregation procedures as a deterrent is therefore evaluated using attack scripts. This feature significantly improves the defences against malicious actors, while developing allied tactics will require more work. © 2023 IEEE.

关键词： Internet of things

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Asynchronous Guaranteed Cost Control of 2-D Hidden Bernoulli Jump Roesser Systems 24

Asynchronous Guaranteed Cost Control of 2-D Hidden Bernoulli...

引用

2024 international conference on Industrial Automation and robotics, IAR 2024

作者： Liu, Jiazheng Yang, Yang Song, Yaru School of Electronic Information Engineering Changchun University of Science and Technology Jilin Changchun China

ISBN: (纸本)9798400711268

This paper focuses on the issue of asynchronous guaranteed cost control for two-dimensional Bernoulli jump Roesser systems. To represent the asynchronous dynamics caused by mode mismatch, a hidden Bernoulli model is proposed, and the asynchronous control law is developed based on this model. By utilizing the two-dimensional Lyapunov technique and the linear matrix inequality approach, sufficient criteria are established to guarantee the asymptotic stability of the closed-loop system while ensuring a bound on the predetermined guaranteed cost. Finally, an algorithm is proposed to summarize the design process for the optimal guaranteed cost controller, accompanied by a numerical example to illustrate its effectiveness. © 2024 Copyright held by the owner/author(s).

关键词： Stability criteria

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Review of robotics’ Role in Unsafe Site Conditions in the Construction Industry

Review of Robotics’ Role in Unsafe Site Conditions in the C...

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international conference on Civil engineering Innovative Development in engineering advances, ICC-IDEA 2023

作者： Kumar, Sourav Murugesan, Balasubramanian Sathyanarayanan, Aishwarya Poyyamozhi, Mukilan Department of Civil Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

ISBN: (纸本)9789819962327

Construction industry has a tremendous impact on both the national and global economies, but it also faces numerous obstacles, despite rapid economic expansion and modernization. Construction industry is plagued with inefficiencies in terms of accidents, fatalities, and productivity. Unsafe site conditions, which are a dangerous physical state that leads to fatalities or a death toll, are one of the leading causes of accidents. Some of the risky actions and situations identified on construction sites are also examined in this review study. To achieve long-term improvement, researchers must address the accident's root causes. Stating to construction technology, using a robot could solve this problem by operating at great heights and providing genuine benefits such as greater construction productivity, shorter construction lead times, fewer employees, and reduced construction costs. Construction, a rapidly expanding industry, will quickly adopt unmanned aerial vehicles (UAVs) as a technology that saves time and money while also improving worker safety and control. Robots will play a major role in the coming future, and they will take the construction industry to another level. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Construction industry

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