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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatla, Shruti Gautam, Devendra Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

In this report, we explore the key challenges and threats facing network security in wired and wireless environments, as well as the technologies and best practices for addressing these challenges. We provide an overview of the key concepts and terminology related to network security, trust, and privacy, and discuss the role of encryption in maintaining trust and privacy in these environments. We also examine common attack vectors and countermeasures and offer some recommendations for best practices in the realm of network security. © 2023 IEEE.

关键词： Network security

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Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

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Science China(Technological Sciences) 2023年第10期66卷 2862-2870页

作者： GAO QinHe HUANG Tong ZHAO Ke SHAO HaiDong JIN Bo LIU ZhiHao WANG Dong National Key Discipline Laboratory of Armament Launch Theory&Technology Rocket Force University of EngineeringXi’an 710025China Key Laboratory of Road Construction Technology&Equipment Ministry of Education Chang’an UniversityXi’an 710054China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Institute of Systems and Robotics Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbra 3030-290Portugal

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains,often neglecting the influence of class information,leading to inaccurate alignment *** by this observation,this paper proposes an adaptive inter-intra-domain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target ***,an adaptive factor is introduced to dynamically assess their relative *** upon the proposed adaptive inter-intradomain discrepancy approach,we develop an inter-intradomain alignment network with a class-aware sampling strategy(IDAN-CSS)to distill the feature *** classaware sampling strategy,integrated within IDAN-CSS,facilitates more efficient *** multiple transfer diagnosis cases,we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.

关键词： unsupervised domain adaptation inter-class domain discrepancy intra-class domain discrepancy class-aware sampling strategy

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi) 1

Applying CNN Models with Stereo Camera for Object Detection ...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Harimanto, Farrell Putra Hasanah, Nurul Pradana, Muhammad Khristamto Aditya Puji, Muhammad Nurul Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia National Research and Innovation Agency Kst Samaun Samadikun Research Center for Smart Mechatronics Bandung40135 Indonesia

ISBN: (纸本)9798350368918

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%. © 2024 IEEE.

关键词： Automotive industry

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Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach

arXiv

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arXiv 2025年

作者： Alsakabi, Mohammed Erickson, Aidan Dolan, John M. Tonguz, Ozan K. Department of Electrical and Computer Engineering College of Engineering The Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

We present a cost-effective new approach for generating denser depth maps for Autonomous Driving (AD) and Autonomous Vehicles (AVs) by integrating the images obtained from deep neural network (DNN) 4D radar detectors with conventional camera RGB images. Our approach introduces a novel pixel positional encoding algorithm inspired by Bartlett’s spatial spectrum estimation technique. This algorithm transforms both radar depth maps and RGB images into a unified pixel image subspace called the Spatial Spectrum, facilitating effective learning based on their similarities and differences. Our method effectively leverages high-resolution camera images to train radar depth map generative models, addressing the limitations of conventional radar detectors in complex vehicular environments, thus sharpening the radar output. We develop spectrum estimation algorithms tailored for radar depth maps and RGB images, a comprehensive training framework for data-driven generative models, and a camera-radar deployment scheme for AV operation. Our results demonstrate that our approach also outperforms the state-of-the-art (SOTA) by 27.95% in terms of Unidirectional Chamfer Distance (UCD). © 2025, CC BY-NC-ND.

关键词： Radar imaging

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A Compliant Robot-Assisted Mold Polishing Application Using the Franka Emika Robot 49

A Compliant Robot-Assisted Mold Polishing Application Using ...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Xenya, Michael Christopher Cortesao, Rui Institute of Systems and Robotics University of Coimbra Electrical and Computer Engineering Department Coimbra3030-290 Portugal FOE GCTU PMB100 Accra Ghana

ISBN: (纸本)9798350331820

Metal mold casting for many application areas remains imperfect since this involves the control of numerous factors that affect workpiece quality. Surface roughness which remains one of the many issues related to metal mold cast is often mitigated by means of grinding and polishing. As many attempts are being made to address this challenge through robot-assisted techniques, we focus on rapid mold polishing using the Franka Emika (FE) robot and its programming resources, including a built-in impedance control architecture. To accomplish the objective, mold surface segmentation is necessary. For each segment, the polishing approach is based on surface normals, relative motion, and torque sensor data. Results were obtained with open platform communication-unified architecture (opc-ua) client and server that record torque and force data. Results show that tuning internal impedance control parameters for polishing tasks is feasible. © 2023 IEEE.

关键词： Polishing

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Target Detection and Tracking of Survey Robots Using Multimodal Internet of Things Sensor data Fusion Using YOLOv5 2

Target Detection and Tracking of Survey Robots Using Multimo...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Ye, Chengjing Wu, Miaoyong Yang, Yefen Zhang, Ke Bu, Xinhua Huang, Miaomiao Guangdong Polytechnic of Science and Technology Faculty of Robotics Guangdong Zhuhai China Guangdong Polytechnic of Science and Technology Computer Engineering Technical College Guangdong Zhuhai China Faculty of Architectural Engineering Guangdong Polytechnic of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798350382952

In recent years, due to the rapid development of computer vision technology, real-time detection and tracking of objects using computer vision has become a hot topic. Object detection mainly consists of two parts: object segmentation and object recognition, that is, object acquisition. Accurate detection of targets is the key to achieving high-precision and high-efficiency tracking. Based on the above discussion, this article explored the target detection and tracking of survey robots using the multimodal Internet of Things (IoT) sensor data fusion of YOLOv5, and compared and analyzed the performance of direct frame difference method, traditional ORB method, and the algorithm in this paper through experiments. The results indicated that the accuracy and recall of the robot image object detection algorithm proposed in this paper were both above 90%, and higher than other algorithms, indicating that the algorithm has extremely high accuracy in object detection. © 2024 IEEE.

关键词： Internet of things

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Smart Robot: Artificial Intelligent Cleaning Robot with Wifi Boundary Sensing

Smart Robot: Artificial Intelligent Cleaning Robot with Wifi...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Tannous, Gresmarteneanos Ame, Ibrahim Kian, Nafiseh Yousefi Al-Turjman, Fadi Mlimwa, Cliford Peter Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Research Centre for AI and IoT Nicosia Cyprus Research Centre for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Cyprus Department of Computer Engineering Cyprus International University Nicosia Cyprus

ISBN: (纸本)9783031782756

The environment surrounds our daily lives, whether we want to or not. When we have a clean environment, we can enjoy very healthy lifestyles. However, when we have dirty environments, it can cause a myriad of diseases, health risks, and worse, global pandemics like plagues. This became very apparent during the years of the COVID-19 pandemic when we had to sanitize our hands on an hourly basis after touching suspicious items that might have had human contact. This study entails a way to keep our environment clean by picking up trash and items that are left on the floor by other people. A smart robot that uses artificial intelligence to be able to not only move around and understand its surroundings, but also know if there is an object that is identified as trash and needs to be removed from that area. The smart robot is fitted with a high-definition camera for object detection and can see them from 3–4 m. From the image, it will scan the angle direction of the object, change its course, and follow it. After reaching the said location, it will begin its procedure of picking up and storing it in its trash compartment. The robot can be tuned to only clean a certain area and not cross its boundary by comparing fingerprints of registered Wi-Fi RSSIs. This method is most accurate for indoor boundary setting. For outdoors, a GPS method is available to track and bind the robot within a map circumference. Finally, all these metrics and location services can be viewed on a custom-made mobile application that complements the robot's features. What ties all this together is a cloud-based server that feeds the app data from the robot and stores it for future review. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wi-Fi

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A study of multifactor quantitative analysis of sports game trends 24

A study of multifactor quantitative analysis of sports game ...

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2024 International Conference on Smart City and Information System, ICSCIS 2024

作者： Wang, Yuefan Gu, Zhongquan Liu, Yuhao Wu, Lihao School of Electrical Engineering Guangzhou City University of Technology Guangzhou Guangdong510800 China School of Mechanical Robotics Engineering Guangzhou City University of Technology Guangzhou Guangdong510800 China School of Computer Engineering Guangzhou City University of Technology Guangzhou Guangdong510800 China

ISBN: (纸本)9798400710155

This study analyzes the effect of momentum on the outcome of tennis matches, using the 2023 Wimbledon men's singles final as an example. The match data was preprocessed, including invalid sample treatment, outlier treatment, data normalization, feature screening and the innovative combination of wavelet analysis and Bayesian Optimization are used in data preprocessing. An ARIMA model was constructed to analyze the effect of match duration on the outcome. The study found that the longer the duration of the match, the more difficult it is to predict. A Pearson correlation analysis model was used to determine the correlation between the distance moved by players and wins and losses. The random forest model analysis showed that serve order, match point, and set point were the key factors influencing the match outcome. Additionally, Djokovic's momentum changes were evaluated through hierarchical analysis, and the model's accuracy was verified using the 2022 Qatar World Cup Final as an example. The study proposes a coaching strategy based on risky decision-making to help coaches effectively coach athletes during matches. © 2024 ACM.

关键词： Risk assessment

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Suppression of Vibration in PMSM Caused by Time-Varying Load for Reciprocating Compressor

Suppression of Vibration in PMSM Caused by Time-Varying Load...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Park, Soo-Hwan Kim, Jae-Hyun Park, Chi-Sung Won, Yun-Jae Lim, Myung-Seop Dongguk University Department of Mechanical Robotics and Energy Engineering Seoul Korea Republic of School of Mechanical Engineering Yeungnam University Daegu Korea Republic of Hanyang University Department of Automotive Engineering Seoul Korea Republic of Automotive-Computer Convergence Hanyang University Department of Automotive Engineering Seoul Korea Republic of

ISBN: (纸本)9798350370607

This paper deals with the vibration characteristics of permanent magnet synchronous motors (PMSMs) when there is time-varying load generated in the reciprocating compressor. As the reciprocating mechanism is necessary for compressing the refrigerant, the time-varying load torque is inevitable in compressor. However, this load torque affects to the system dynamics such as the speed fluctuation. These speed ripple cause unexpected vibration characteristics because they induce sideband components in the time harmonics of the electromagnetic force of PMSM. Therefore, we analyzed the vibration characteristics of PMSM according to time harmonics variation and conduct a study to suppress sideband vibration by adopting harmonic current injection method. © 2024 IEEE.

关键词： Permanent magnets

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Multispectral Registration Algorithm Based on Fusion of Infrared and Color Images 16

Multispectral Registration Algorithm Based on Fusion of Infr...

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Wan, Teng Du, Shaoyi Zhang, Qiang Qi, Ying Li, Ce Northwest Normal University The College of Computer Science and Engineering Lanzhou China Xi'an Jiaotong University The Institute of Artificial Intelligence and Robotics The College of Artificial Intelligence Xi'an China Lanzhou University of Technology The College of Electrical and Information Engineering Lanzhou China

ISBN: (纸本)9798331506896

Multispectral data fusion is a fundamental research in the fields of artificial intelligence and computer vision, which provides technical support and theoretical basis for remote sensing, autonomous driving, and medical imaging. In this paper, we present a robust registration algorithm to align RGB-D scans. In colored point clouds, limited by the principle of point cloud collection and generation, noise, and outliers are common and may cause registration failure. Therefore, the core of our method is to apply color information and kernel function to remove the influence of noise and outliers on the alignment accuracy. In the beginning, for the first time, we innovatively use the Cauchy kernel as the energy function of the point set registration to assign different weights to inliers, noise, and outliers to maximize their differentiation. Combined with the maximization optimization strategy, noise and outliers are removed to achieve robust registration. Secondly, since RGB-D data contain both shape and color information, the trimmed bidirectional color distance is applied to reduce the probability of noise and outliers being selected as the matching point pairs when establishing the correspondences. Moreover, the overall formulation is solved by the iterative optimization method. Experiments demonstrate that our algorithm is more robust and accurate than previous point set registration algorithms, especially when aligning point sets containing noise and outliers. © 2024 IEEE.

关键词： Image registration

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