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Brain-inspired artificial intelligence research: A review

Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Online Computation Offloading and Trajectory Scheduling for UAV-Enabled Wireless Powered Mobile Edge Computing

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China Communications 2022年第4期19卷 257-273页

作者： Han Hu Xiang Zhou Qun Wang Rose Qingyang Hu The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China The Department of Electrical and Computer Engineering Utah State UniversityLoganUTUSA.

The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is *** to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

关键词： energy efficiency mobile edge computing UAV-enabled wireless power transfer trajectorys cheduling

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Analysis on D2D Heterogeneous Networks with State-Dependent Priority Traffic

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computers, Materials & Continua 2023年第2期74卷 2981-2998页

作者： Guangjun Liang Jianfang Xin Linging Xia Xueli Ni Yi Cao Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina National and Local Joint Engineering Laboratory of Radio Frequency Integration and Microassembly Technology Nanjing University of Posts and TelecommunicationsNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center NanjingChina Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

In this work,we consider the performance analysis of state dependent priority traffic and scheduling in device to device(D2D)heterogeneous *** are two priority transmission types of data in wireless communication,such as video or telephone,which always meet the requirements of high priority(HP)data transmission *** there is a large amount of low priority(LP)data,there will be a large amount of LP data that cannot be *** situation will cause excessive delay of LP data and packet dropping *** order to solve this problem,the data transmission process of high priority queue and low priority queue is *** the priority jump strategy to the priority queuing model,the queuing process with two priority data is modeled as a two-dimensionalMarkov chain.A state dependent priority jump queuing strategy is proposed,which can improve the discarding performance of low priority *** quasi birth and death process method(QBD)and fixed point iterationmethod are used to solve the causality,and the steady-state probability distribution is further ***,performance parameters such as average queue length,average throughput,average delay and packet dropping probability for both high and low priority data can be *** simulation results verify the correctness of the theoretical ***,the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.

关键词： Stochastic geometry queuing theory D2D heterogeneous networks quasi-birth and death process

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FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training

FlashMix: Fast Map-Free LiDAR Localization via Feature Mixin...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Raktim Gautam Goswami Naman Patel Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, and demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://***/raktimgg/FlashMix.

关键词： Location awareness Training Measurement Point cloud compression Laser radar Accuracy Pose estimation Benchmark testing Mixers Convergence

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On a novel tracking differentiator design based on iterative learning in a moving window

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control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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research of Improved TD3 Robotic Arm Path Planning using Evolutionary Algorithm 5

Research of Improved TD3 Robotic Arm Path Planning using Evo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Feng Xiong, Jianqiang Yuan, Shuai Wen, Ke Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center School of Electrical and Control Engineering Shenyang110168 China Shenyang Jianzhu University School of Electrical and Control Engineering Shenyang110168 China Shenyang Jianzhu University School of Computer Science and Engineering Shenyang110168 China

ISBN: (纸本)9798350307320

Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm has the inadequacies of low efficiency and slow speed. Although the deep reinforcement learning has accomplished many achievements in the path planning of control manipulator, there are still some problems such as long training time and low planning accuracy. To solve the abovementioned issues, we propose an improved Twin Delayed Deep Deterministic policy gradient (TD3) algorithm (Improved Cross-Entropy Method-TD3: ICEM-TD3) for the path planning of the robotic arm. First, this paper combines evolutionary strategies with TD3 to generate action networks. Then the exploration of TD3 algorithm in the action space is replaced by the exploration in the parameter space. In addition, this paper designs a new reward function to weaken the redundancy of planning and accelerate the convergence speed of training. Finally, the Gazebo simulator is adopted to verify the proposed algorithm, and the results illustrate that the proposed algorithm can greatly improve the accuracy of the path planning of the manipulator using deep reinforcement learning. © 2023 IEEE.

关键词： Motion planning

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Multi-Timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2023年第4期1卷 322-335页

作者： Wu, Sirui Lin, Jin Li, Jiarong Liu, Feng Song, Yonghua Xu, Yanhui Cheng, Xiang Yu, Zhipeng Tsinghua University State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Beijing100087 China Tsinghua University Sichuan Energy Internet Research Institute Chengdu610213 China University of Macau Department of Electrical and Computer Engineering Macau999078 China Tsinghua University Department of Electrical Engineering Beijing100087 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China

Renewablepower to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified timeframe. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of the RePtA VPP joining multiple markets over the conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor flexibility. © 2023 IEEE.

关键词： Ammonia

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High-Dimensional controller Tuning through Latent Representations

High-Dimensional Controller Tuning through Latent Representa...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alireza Sarmadi Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we propose a method to automatically and efficiently tune high-dimensional vectors of controller parameters. The proposed method first learns a mapping from the high-dimensional controller parameter space to a lower dimensional space using a machine learning-based algorithm. This mapping is then utilized in an actor-critic framework using Bayesian optimization (BO). The proposed approach is applicable to complex systems (such as quadruped robots). In addition, the proposed approach also enables efficient generalization to different control tasks while also reducing the number of evaluations required while tuning the controller parameters. We evaluate our method on a legged locomotion application. We show the efficacy of the algorithm in tuning the high-dimensional controller parameters and also reducing the number of evaluations required for the tuning. Moreover, it is shown that the method is successful in generalizing to new tasks and is also transferable to other robot dynamics.

关键词： Legged locomotion Machine learning algorithms Heuristic algorithms Dynamics Aerospace electronics Vectors Quadrupedal robots

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High-Performance Image Compression and Reconstruction Using Lightweight Deep Neural Networks

High-Performance Image Compression and Reconstruction Using ...

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2024 Academic Conference of China Instrument and control Society, ACCIS 2024

作者： Gao, Shuhua Qin, Zekun Jiang, Xingyu Wang, Zhengfang Xu, Wenjun Wang, Jing School of Control Science and Engineering Shandong University Jinan China National University of Singapore Electrical and Computer Engineering Singapore Peng Cheng Laboratory Research Institute of MA&EI Shenzhen China

ISBN: (纸本)9798350350326

In many remote areas, tunnels, and underground spaces, network bandwidth is extremely limited. However, with the development of high-resolution cameras, the size of image data has significantly increased, making it challenging to frequently transmit large-scale images for detection and monitoring purposes. To address this issue, this paper proposes an image compression and reconstruction method based on lightweight neural networks, utilizing a dual-module deep learning architecture comprising a Compressed Convolutional Neural Network (CCNN) and a Reconstructed Convolutional Neural Network (RCNN). The lightweight design facilitates easy deployment on edge devices. The CCNN module compresses the original image for fast network transmission, and upon reception, the RCNN module reconstructs the image. This approach significantly reduces the transmission payload and enhances speed under limited bandwidth conditions. Furthermore, the developed method outperforms the classic JPEG algorithm across four representative metrics as evaluated on a widely-used public dataset. To further optimize the CCNN for edge deployment, a model distillation technique is employed, reducing the model size by approximately 70% with negligible performance impact. In summary, the proposed deep neural network model offers high performance and a low memory footprint for image compression and reconstruction, making it particularly suitable for resourcelimited edge devices and bandwidth-constrained applications. © 2024 IEEE.

关键词： Convolutional neural networks

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High-Value Target Escort and Defense Scenario with Differential Subgame Cost 63

High-Value Target Escort and Defense Scenario with Different...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Judy, Rachael L. Fuchs, Zachariah E. Casbeer, David W. University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States Control Science Center of Excellence Air Force Research Laboratory 2210 8th St. WPAFBOH45433 United States

ISBN: (纸本)9798350316339

An Escort and Defense Scenario is presented in which a high-value Target maneuvers through a high-risk region while being escorted by a mobile, defensive agent. Along this trajectory, an Attacker may be launched from one of several possible launch locations. If a launch occurs, the three agents play out a Target-Attacker-Defender (TAD) differential game in which the Defender attempts to intercept the Attacker at maximal distance from the Target while the Attacker strives to maneuver as close as possible to the Target. Prior to launch, the Target and Defender strive to preposition themselves in advantageous positions to effectively respond to a potential threat while simultaneously moving towards a safety region. An augmented collocation-based direct optimal control method is developed to solve the escort problem by solving and utilizing the value of the TAD differential subgame at each collocation point while simultaneously optimizing the primary optimal control problem. © 2024 IEEE.

关键词： Optimal control systems

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