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Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

Tsinghua Science and Technology 2024年第1期29卷 244-256页

作者： Wenjie Geng Zhiqiang Cao Peiyu Guan Fengshui Jing Min Tan Junzhi Yu State Key Laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China department of Advanced Manufacturing and robotics College of EngineeringPeking UniversityBeijing 100871China

Grasp detection plays a critical role for robot *** pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and ***,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are *** is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains ***,the channel information in high-level feature is also not well ***,the performance of grasp detection is *** solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle *** low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature *** a hierarchical fusion guarantees the quality of grasp ***,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective *** such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the ***,an information enhancement module is added before the encoder to reinforce input *** proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

关键词： grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual

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Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

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Computers, Materials & Continua 2024年第8期80卷 1889-1905页

作者： Abdulraqeb Alhammadi Zaid Ahmed Shamsan Arijit De Center for artificial intelligence and robotics(CaiRO) Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia department of Electrical engineering College of EngineeringImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia department of Information engineering Electronics and TelecommunicationsSapienza UniversityRome00185Italy

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation *** complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational *** localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online *** networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user *** networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to *** article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user *** proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s *** operates in both online and offline *** the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s *** results unequivocally showcase the superior performance of the proposed model when compared to other existing models and *** proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing ***,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

关键词： Localization positioning Bayesian fingerprinting received signal strength(RSS)

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A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1679-1694页

作者： Sanxiu Jiao Lecai Cai Xinjie Wang Kui Cheng Xiang Gao College of Automation and Information engineering Sichuan University of Science and EngineeringYibin644000China Sanjiang Research Institute of artificial intelligence and robotics Yibin UniversityYibin644000China College of Mechanical engineering Sichuan University of Science and EngineeringYibin644000China

As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data *** keeps data locally and trains local models through local data to protect the privacy of local *** federated learning method effectively solves the problem of artificial Smart data islands and privacy protection ***,existing research shows that attackersmay still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server *** solve this problem,differential privacy(DP)techniques are widely used for privacy protection in federated ***,adding Gaussian noise perturbations to the data degrades the model learning *** address these issues,this paper proposes a differential privacy federated learning scheme based on adaptive Gaussian noise(DPFL-AGN).To protect the data privacy and security of the federated learning training process,adaptive Gaussian noise is specifically added in the training process to hide the real parameters uploaded by the *** addition,this paper proposes an adaptive noise reduction *** the convergence of the model,the Gaussian noise in the later stage of the federated learning training process is reduced *** paper conducts a series of simulation experiments on realMNIST and CIFAR-10 datasets,and the results show that the DPFL-AGN algorithmperforms better compared to the other algorithms.

关键词： Differential privacy federated learning deep learning data privacy

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Assistive robots for Beijing Winter Paralympic torch relay: Accessible technologies to restore human functionality

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The Innovation 2024年第1期5卷 19-20页

作者： Bi Zhang Mei Shuai Bicheng Han Shuo Li Lianqing Liu Xingang Zhao State Key Laboratory of robotics Shenyang Institute of AutomationCASShenyang 110016China institutes for robotics and Intelligent Manufacturing CASShenyang 110169China department of Biomedical engineering Beihang UniversityBeijing 100191China Beijing ai-robotics Technology Co. Ltd.Beijing 100176China Zhejiang BrainCo Technology Co. Ltd.Hangzhou 311121China

The success of the Beijing Winter Olympics and Paralympics epitomized the vision of a community with a shared future for *** robots and artificial intelligence technologies were used in various games and events,such as assisting in parades,logistics,and security *** integration of unmanned robotic systems into torch relays was one of the highlights of the Winter Olympics.

关键词： Winter Access artificial

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Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin air Traffic Control Communications

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Computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software engineering University of Science and Technology LiaoningAnshan114051China Institute of Applied artificial intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and aiSHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

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The Journey/DAO/TAO of Embodied intelligence: From Large Models to Foundation intelligence and Parallel intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(ai)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading ai beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： intelligence DAO TAO

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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artificial intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia department of robotics engineering Karunya Institute of Technology and SciencesCoimbatoreIndia department of Mechatronics engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India department of Computer Science and engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable ai.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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Theory and verification of moiréfringes for x-ray three-phase grating interferometer

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Chinese Physics B 2024年第5期33卷 532-540页

作者：单雨征葛永帅杨君郭大育蔡学宝刘晓珂侯晓文郭金川 Key Laboratory of Optoelectronic Devices and Systems of the Ministry of Education Key Laboratory of Optoelectronic Devices and Systems of the Department of EducationGuangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Research Center for Medical artificial intelligence Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Institute of Advanced Science Facilities Shenzhen 518107China Institute of Electronic Paper Displays South China Academy of Advanced OptoelectronicsSouth China Normal UniversityGuangzhou 510006China College of Biophotonics South China Normal UniversityGuangzhou 510631China MinFound Medical Systems Co. Ltd.Hangzhou 310018China

Dual-phase and three-phase grating x-ray interference is a promising new technique for grating-based x-ray differential phase contrast ***-phase grating interferometers have been relatively completely studied and *** this paper,the corresponding imaging fringe formula of the three-phase grating interferometer is *** the same time,the similarities and differences between the three-phase grating interferometer and the dual-phase grating interferometer are investigated and verified,and that the three-phase grating interferometer can produce large-period moiréfringes without using the analyzing grating is demonstrated ***,a simple method of designing three-phase grating and multi-grating imaging systems from geometric optics based on the thin-lens theory of gratings is *** theoretical formulas and experimental results provide optimization tools for designing three-phase grating interferometer systems.

关键词： x-ray phase contrast phase grating moiréfringe

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Energy-efficient computation offloading assisted by RIS-based UAV

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The Journal of China Universities of Posts and Telecommunications 2024年第1期31卷 37-48页

作者： Li Linpei Zhao Chuan Su Yu Huo Jiahao Huang Yao Li Haojin School of intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of artificial intelligence University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China School of Computer and Communication engineering University of Science and Technology BeijingBeijing 100083China Research department of 5G and Drone China Mobile(Chengdu)Institute of Research and DevelopmentChengdu 610200China College of Communication engineering Chengdu University of Information TechnologyChengdu 610225China Research and Development Center Sony(China)LimitedBeijing 100027China

The new applications surge with the rapid evolution of the mobile *** explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local *** is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading *** edge computing(MEC)assisted by reflecting intelligent surfaces(RIS)-based unmanned aerial vehicle(UAV)is a promising method to assist the users in executing the computation tasks in proximity at low *** this paper,we propose an energy-efficient MEC system assisted by RIS-based UAV,where the UAV with RIS mounted relays the computation tasks to the MEC *** energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory,the transmission power of all users,and the phase shifts of the reflecting elements placed on the *** that the optimization problem is non-convex,we propose a deep deterministic policy gradient(DDPG)-based *** combining the DDPG algorithm with the energy efficiency maximization problem,the optimization problem can be ***,the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.

关键词： mobile edge computing unmanned aerial vehicle reflecting intelligent surface

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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Computer Modeling in engineering & Sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and artificial intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software engineering department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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