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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Zuo, Yang Duan, Xuting Xia, Haiying Zhou, Jianshan School of Transportation Science and Engineering Beihang Univ. Beijing China State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China Zhongguancun Laboratory Beijing China Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

With the development of computer technology, intelligent vehicles have become a popular orientation at present. This paper provides a solution for the traditional GNSS/IMU integrated navigation to mitigate the influence of non-line-of-sight (NLOS) environments and achieve high-precision navigation. The LOS/NLOS switching is treated as a transition in a Markov chain, and the DIMM filtering is applied to obtain GNSS-filtered data free from the influence of NLOS. GNSS/IMU integrated navigation is loose coupling in that the method combines IMU information with processed GNSS measurement using the ESKF (Error-state Kalman Filter) to achieve accurate target tracking and positioning by utilizing the state difference as the state variable. Simulation results demonstrate that the proposed method significantly improves tracking and positioning accuracy by effectively mitigating NLOS effects compared to traditional methods. This research provides an effective solution for real-world autonomous vehicle navigation systems and optimizes the city driving experience. © ASCE.

关键词： Markov chains

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Nickel-iron in the second coordination shell boost single-atomicsite iridium catalysts for high-performance urea electrooxidation

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Nano Research 2024年第5期17卷 3919-3926页

作者： Xiaoyu Chen Jiawei Wan Jing Chai Liang Zhang Fang Zhang Qinghua Zhang Lin Gu Lirong Zheng Ranbo Yu Department of Physical Chemistry School of Metallurgical and Ecological EngineeringUniversity of Science and Technology BeijingBeijing 100083China State Key Laboratory of Biochemical Engineering Key Laboratory of Biopharmaceutical Preparation and DeliveryChinese Academy of SciencesInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China Center for Combustion Energy School of Vehicle and MobilityState Key Laboratory of Intelligent Green Vehicle and MobilityTsinghua UniversityBeijing 100084China Analysis and Testing Center Beijing Institute of TechnologyBeijing 100081China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China Beijing National Center for Electron Microscopy and Laboratory of Advanced Materials Department of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Beijing Synchrotron Radiation Facility Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China

Single-atom catalysts(SACs)with high catalytic activity as well as great stability are demonstrating great promotion in electrocatalytic energy conversion,which is also a big challenge to ***,we proposed a facile synthetic strategy to construct nickel-iron bimetallic hydroxide nanoribbon stabilized single-atom iridium catalysts(Ir-NiFe-OH),where the nickel-iron hydroxide nanoribbon not only can serve as good electronic conductor,but also can well stabilize and fully expose single-atom *** as catalyst for urea oxidation reaction(UOR),it exhibited excellent UOR performance that it only needed a low operated potential of 1.38 V to achieve the current density of 100 mA·cm^(-2).In-situ Fourier transform infrared spectroscopy,X-ray absorption spectrum,and density functional theory calculations proved that Ir species are active centers and the existence of both Ni and Fe in the local structure of Ir atom can optimize the d-band center of Ir species,promoting the adsorption of intermediates and desorption of products for *** hydrogen evolution reaction(HER)/UOR electrocatalytic cell demanded voltages of 1.46 and 1.50 V to achieve 50 and 100 mA·cm^(-2),respectively,which demonstrated a higher activity and better stability than those of conventional *** work opens a new avenue to develop catalysts for UORs with boosted activity and stability.

关键词： single-atom iridium coordinate structure nanoribbon urea electrooxidation

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Optimization of Grayscale Image Security through the use of Hybrid Cryptographic Techniques based on Sine and Logistics Maps 4

Optimization of Grayscale Image Security through the use of ...

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4th International Conference on Innovative Research in Applied science, Engineering and technology, IRASET 2024

作者： Koulouh, Fatima Safae, Amine Es-Sabry, Mohammed Mostafa, Merras Elakkad, Nabile Sidi Mohamed Ben Abdellah University Department Of Computer Science Morocco Abdelmalek Essaadi University Laboratory Of Information Security Intelligent Systems And Applications Tetouan Morocco High School Of Technology Moulay Ismail University Image Laboratory Morocco Sidi Mohamed Ben Abdellah University Laboratory Of Engineering Systems And Applications Morocco

ISBN: (纸本)9798350309508

This paper presents an innovative method for enhancing security improve the security of grayscale images by integrating hybrid cryptographic techniques, specifically taking advantage of the sine map and a dual application of the logistic map. The synergy of these chaotic systems reinforces the encryption process, the sinusoidal card generating pseudo-random sequences essential for the generation of cryptographic keys, and the double logistic card adding complexity and unpredictability. Hybridization exploits their complementary characteristics, contributing to the overall robustness of the encryptions system. The theoretical foundations of the sinusoidal map and the double logistic map are examined, highlighting their suitability for cryptographic applications. A comparative examination with established techniques showcases the effectiveness of the proposed method, both in terms of security and computational efficiency, experimental results showing resilience against various attacks, providing information for practical integration into image security frameworks. © 2024 IEEE.

关键词： Chaotic systems

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Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning

Synthetically Generating Human-like Data for Sequential Deci...

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Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications 2023

作者： Brandt, Bryan Dasgupta, Prithviraj Distributed Intelligent Systems Section Information Technology Division Naval Research Laboratory WashingtonDC United States Computer Science Department University of Wisconsin-Whitewater WI United States

ISBN: (纸本)9781510661721

We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same tasks. © 2023 SPIE.

关键词： Decision making

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Camouflaged Object Segmentation with Transformer 6th

Camouflaged Object Segmentation with Transformer

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Wang, Haiwen Wang, Xinzhou Sun, Fuchun Song, Yixu State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9789811692468

The Vision Transformer (ViT) [6] directly applies a Transformer architecture to image classification and achieves an impressive result compared with convolutional networks. This paper presents a new ViT-base camouflaged object segmentation method, called COS Transformer, which aims to identify and segment objects concealed in a complex environment. The high intrinsic similarities between object and surrounding makes the task challenging than salient object detection. Most recent camouflaged object segmentation methods(e.g., EGNet [29], PraNet [10] and SINet [9]) adopt convolutional network with an encoder-decoder architecture and focused on increasing the receptive field, which is limited by the depth of the network. In camouflaged object segmentation (COS) task, the camouflage is mainly relied on contrast of the whole surrounding instead of the local information. We introduce transformer with global context awareness in this paper, for self-attention allowing COS Transformer to aggregate features globally even in the lowest layers. Specifically, the architecture is composed of a transformer-based encoder and a multi-layers feature aggregation refinement module. After training on the COD10K [9] dataset, COS Transformer attains excellent results compared to state-of-the-art convolutional networks, e.g. 11.7% improvement of E [8] on the COD10K contrasted to SINet. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 24

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhou, Hang Yu, Wentao Wan, Sheng Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Jinan University Engineering Research Center of Trustworthy AI Ministry of Education China

ISBN: (纸本)9798331506681

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed 'personalized Federated learning with Traffic Pattern Sharing' (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Highway administration

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Microstructures and micromechanical behaviors of high -entropy alloys investigated by synchrotron X-ray and neutron diffraction techniques: A review

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International Journal of Minerals,Metallurgy and Materials 2024年第6期31卷 1333-1349页

作者： Yubo Huang Ning Xu Huaile Lu Yang Ren Shilei Li Yandong Wang Beijing Advanced Innovation Center of Materials Genome Engineering State Key Laboratory for Advanced Metals and MaterialsUniversity of Science and Technology BeijingBeijing 100083China AnSteel Beijing Research Institute Co.Ltd. Beijing 102200China Spallation Neutron Source Science Center Dongguan 523803China Department of Physics City University of Hong KongHong Kong 999077China Institute for Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China

High-entropy alloys(HEAs)possess outstanding features such as corrosion resistance,irradiation resistance,and good mechan-ical properties.A few HEAs have found applications in the fields of aerospace and *** studies on the deformation mech-anisms of HEAs can guide microstructure control and toughness design,which is vital for understanding and studying state-of-the-art structural *** X-ray and neutron diffraction are necessary techniques for materials science research,especially for in situ coupling of physical/chemical fields and for resolving macro/microcrystallographic information on ***,several re-searchers have applied synchrotron X-ray and neutron diffraction methods to study the deformation mechanisms,phase transformations,stress behaviors,and in situ processes of HEAs,such as variable-temperature,high-pressure,and hydrogenation *** this review,the principles and development of synchrotron X-ray and neutron diffraction are presented,and their applications in the deformation mechanisms of HEAs are *** factors that influence the deformation mechanisms of HEAs are also *** review fo-cuses on the microstructures and micromechanical behaviors during tension/compression or creep/fatigue deformation and the application of synchrotron X-ray and neutron diffraction methods to the characterization of dislocations,stacking faults,twins,phases,and intergrain/interphase stress *** on future developments of synchrotron X-ray and neutron diffraction and on research directions on the deformation mechanisms of novel metals are discussed.

关键词： high-entropy alloys microstructures micromechanical behaviors synchrotron X-ray diffraction neutron diffraction

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Classifying Authentic and Fake Anger Based on High-Performance LSTM Networks Model, Fuzzy Logic Model and Fully Connected Neural Network 4

Classifying Authentic and Fake Anger Based on High-Performan...

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4th International Conference on Advances in Electrical Engineering and computer Applications, AEECA 2023

作者： Chen, Kangyu Peng, Zhiyuan The University of Hong Kong Department of Computer Science Hong Kong Nanjing University State Key Laboratory For Novel Software Technology Nanjing China

ISBN: (纸本)9798350308082

Understanding human's emotion is a vital task in the computer science and machine learning. People could easily distinguish the genuine and posed anger while it could be difficult for computers to judge the authenticity of anger. In this paper, apart from the fully connected Neural Networks built in the previous research, methods of LSTM (Long short-term memory) Networks and Fuzzy logic classification are also built for telling the posed angers from genuine ones using limited data. Then the paper compared the performance of these three popular classification methods. Carefully designed modifications have been made on these models to increase the performance. © 2023 IEEE.

关键词： Long short-term memory

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Development of a Vibration-driven Capsule Robot with Protruding Magnetic Bristles with Enhanced Adaptability in Wet and Dry Environments

Development of a Vibration-driven Capsule Robot with Protrud...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Zhang, Yujie Wei, Tanyong He, Shujing Hong, Yuying Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798350325706

Colonoscopy is a widely used method for diagnosing bowel cancers, requiring coordinated efforts from medical professionals due to its invasive nature. In contrast, miniature capsule robots, measuring just a few centimeters, offer a less invasive diagnostic option by integrating multiple functions. However, current capsule robots face challenges with complex designs, limited mobility, size, and cost, which hinder their adoption and reduce gastrointestinal disorder screening rates. To address these issues, we introduce the vibration-driven capsule robot (VCR) with magnetic protruding bristle structures. Four simple components (shell structure, electronic components, permanent magnet, and magnetic soft bristles) are designed to improve the robot's mobility in dry and wet environments. The magnetic bristle structures are inspired by flexible organisms and enhance the robot's adaptability. The robot is propelled by centrifugal force from a vibration motor, allowing efficient movement along the intestinal tract while protecting the patient's lining. The fabrication process of the magnetic soft bristles has been refined to optimize material ratios and enhance the robot's performance. The vibration motor's output is analyzed using load cells to ensure efficiency. For precise path planning, an external permanent magnet (EPM) is applied to guide the capsule's direction. Experiments confirm the VCR's versatility, performing fluid linear and curved motions in different environments. These findings underscore the feasibility of the VCR as a potent tool for non-invasive, timely screening of gastrointestinal disorders. © 2023 IEEE.

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