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Categorizing methods for integrating machine learning with executable specifications

Science China(information Sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF department of Computer Science and Applied Mathematics Weizmann Institute of Science department of Software and information systems engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite readily.That promise is already delivering extremely impressive results in a variety of areas.However,while DL-enabled systems achieve excellent performance,they are far from perfect.It has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto encountered.Furthermore,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human engineers.At the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system behavior.In this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered here.We then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with BP.We conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted Computational Offloading

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1885-1905页

作者： Baofeng Ji Ying Wang Weixing Wang Shahid Mumtaz Charalampos Tsimenidis School of information engineering Henan University of Science and TechnologyLuoyang471000China Research and Innovation Center of Intelligent system Longmen LaboratoryLuoyang471000China State Key Laboratory of Complex Electromagnetic Environment Effects on electronics and information system(CEMEE) Luoyang471000China Sustainable Digital Communications and Energy systems(DIGCOM)Group Nottingham Trent UniversityNottinghamUK

The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency communication.This study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system model.Specifically,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference power.Furthermore,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ratio.Subsequently,the analytical expression for the outage probability of the system in Rayleigh channels is derived and analyzed.The study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through simulation.The proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link transmission.The simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.

关键词： Unmanned aerial vehicle(UAV) intelligent reflective surface(IRS) energy harvesting computational offloading outage probability

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Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

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China Communications 2024年第2期21卷 183-196页

作者： Yueyue Su Nan Qi Zanqi Huang Rugui Yao Luliang Jia College of Electronic information engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China School of electronics and information Northwestern Polytechnical UniversityXi’an 710072China School of Space information Space Engineering UniversityBeijing 101416China

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

关键词： channel selection cooperative antijamming and interference mitigation local altruistic game Stackelberg game unmanned aerial vehicle(UAV)

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Extension of Distribution Transformer Life in the Presence of Smart Inverter-based Distributed Solar Photovoltaic systems

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CSEE Journal of Power and Energy systems 2024年第1期10卷 88-95页

作者： Kanhaiya Kumar Saran Satsangi Ganesh Balu Kumbhar department of Electrical and electronics engineering Motihari College of EngineeringMotihariBiharIndia department of Electrical engineering National Institute of Technology(NIT)PatnaPatnaBiharIndia department of Electrical engineering Indian Institute of Technology(IIT)RoorkeeRoorkeeUttarakhandIndia

A transformer is an essential but expensive power delivery equipment for a distribution utility.In many distribution utilities worldwide,a sizable percentage of transformers are near the end of their designed life.At the same time,distribution utilities are adopting smart inverter-based distributed solar photovoltaic(SPV)systems to maximize renewable generation.The central objective of this paper is to propose a methodology to quantify the effect of smart inverter-based distributed SPV systems on the aging of distribution transformers.The proposed method is first tested on a modified IEEE-123 node distribution feeder.After that,the procedure is applied to a practical distribution system,i.e.,the Indian Institute of Technology(IIT)Roorkee campus,India.The transformer aging models,alongside advanced control functionalities of grid-tied smart inverter-based SPV systems,are implemented in MATLAB.The open-source simulation tool(OpenDSS)is used to model distribution networks.To analyze effectiveness of various inverter functionalities,time-series simulations are performed using exponential load models,considering daily load curves from multiple seasons,load types,current harmonics,etc.Findings show replacing a traditional inverter with a smart inverter-based SPV system can enable local reactive power generation and may extend the life of a distribution transformer.Simulation results demonstrate,simply by incorporating smart inverter-based SPV systems,transformer aging is reduced by 15%to 22%in comparison to SPV systems operating with traditional inverters.

关键词： Distribution transformer loss of life smart inverter functions solar photovoltaic systems thermal models Volt-VAr control

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Loyal wingman task execution for future aerial combat:A hierarchical prior-based reinforcement learning approach

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Chinese Journal of Aeronautics 2024年第5期37卷 462-481页

作者： Jiandong ZHANG Dinghan WANG Qiming YANG Zhuoyong SHI Longmeng JI Guoqing SHI Yong WU School of electronics and information Northwestern Polytechnical UniversityXi’an 710129China

In modern Beyond-Visual-Range(BVR)aerial combat,unmanned loyal wingmen are pivotal,yet their autonomous capabilities are limited.Our study introduces an advanced control algorithm based on hierarchical reinforcement learning to enhance these capabilities for critical missions like target search,positioning,and relay guidance.Structured on a dual-layer model,the algorithm’s lower layer manages basic aircraft maneuvers for optimal flight,while the upper layer processes battlefield dynamics,issuing precise navigational commands.This approach enables accurate navigation and effective reconnaissance for lead aircraft.Notably,our Hierarchical Prior-augmented Proximal Policy Optimization(HPE-PPO)algorithm employs a prior-based training,prior-free execution method,accelerating target positioning training and ensuring robust target reacquisition.This paper also improves missile relay guidance and promotes the effective guidance.By integrating this system with a human-piloted lead aircraft,this paper proposes a potent solution for cooperative aerial warfare.Rigorous experiments demonstrate enhanced survivability and efficiency of loyal wingmen,marking a significant contribution to Unmanned Aerial Vehicles(UAV)formation control research.This advancement is poised to drive substantial interest and progress in the related technological fields.

关键词： Beyond-visual-range Loyal wingmen Hierarchical prior-augmented proximal policy optimization Unmanned aerial vehicles Warfare

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Audio-visual keyword transformer for unconstrained sentence-level keyword spotting

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 142-152页

作者： Yidi Li Jiale Ren Yawei Wang Guoquan Wang Xia Li Hong Liu Key Laboratory of Machine Perception Peking UniversityShenzhen Graduate SchoolShenzhenChina College of electronics and information engineering Sichuan UniversityChengduChina department of Computer Science ETH ZurichZurichSwitzerland

As one of the most effective methods to improve the accuracy and robustness of speech tasks,the audio-visual fusion approach has recently been introduced into the field of Keyword Spotting(KWS).However,existing audio-visual keyword spotting models are limited to detecting isolated words,while keyword spotting for unconstrained speech is still a challenging problem.To this end,an Audio-Visual Keyword Transformer(AVKT)network is proposed to spot keywords in unconstrained video clips.The authors present a transformer classifier with learnable CLS tokens to extract distinctive keyword features from the variable-length audio and visual inputs.The outputs of audio and visual branches are combined in a decision fusion module.As humans can easily notice whether a keyword appears in a sentence or not,our AVKT network can detect whether a video clip with a spoken sentence contains a pre-specified keyword.Moreover,the position of the keyword is localised in the attention map without additional position labels.Exper-imental results on the LRS2-KWS dataset and our newly collected PKU-KWS dataset show that the accuracy of AVKT exceeded 99%in clean scenes and 85%in extremely noisy conditions.The code is available at https://***/jialeren/AVKT.

关键词： artificial intelligence multimodal approaches natural language processing neural network speech processing

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A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

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Computer Modeling in engineering & Sciences 2024年第5期139卷 1357-1379页

作者： Zhiwei Lin Hui Wang Tianding Chen Yingtao Jiang Jianmei Jiang Yingpin Chen Key Laboratory of Light Field Manipulation and system Integration Applications in Fujian Province School of Physics and Information EngineeringMinnan Normal UniversityZhangzhou363000China department of Electrical and Computer engineering University of NevadaLas Vegas89154USA College of Material engineering Fujian Agriculture and Forestry UniversityFuzhou350000China

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

关键词： Reverse path planning Dyna-Q bidirectional search posture angle joint motion

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Visible Light Indoor Positioning system Based on Pisarenko Harmonic Decomposition and Neural Network

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Chinese Journal of electronics 2024年第1期33卷 195-203页

作者： Li ZHAO Yi REN Qi WANG Lange DENG Feng ZHANG School of Electronic information engineering Xi’an Technological University

Visible-light indoor positioning is a new generation of positioning technology that can be integrated into smart lighting and optical communications. The current received signal strength(RSS)-based visible-light positioning systems struggle to overcome the interferences of background and indoor-reflected noise. Meanwhile, when ensuring the lighting, it is impossible to use the superposition of each light source to accurately distinguish light source information; furthermore, it is difficult to achieve accurate positioning in complex indoor environments. This study proposes an indoor positioning method based on a combination of power spectral density detection and a neural network. The system integrates the mechanism for visible-light radiation detection with RSS theory, to build a back propagation neural network model fitting for multiple reflection channels. Different frequency signals are loaded to different light sources at the beacon end, and the characteristic frequency and power vectors are obtained at the location end using the Pisarenko harmonic decomposition method. Then, a complete fingerprint database is established to train the neural network model and conduct location tests. Finally, the location effectiveness of the proposed algorithm is verified via actual positioning experiments. The simulation results show that, when four groups of sinusoidal waves with different frequencies are superimposed with white noise, the maximum frequency error is 0.104 Hz and the maximum power error is 0.0362 W. For the measured positioning stage, a 0.8 m × 0.8 m × 0.8 m solid wood stereoscopic positioning model is constructed, and the average error is 4.28 cm. This study provides an effective method for separating multi-source signal energies, overcoming background noise, and improving indoor visible-light positioning accuracies.

关键词： Visible-light communication Indoor positioning Frequency estimation Pisarenko harmonic decomposition Back propagation neural network

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N:ZnO/MoS_(2)-heterostructured flexible synaptic devices enabling optoelectronic co-modulation for robust artificial visual systems

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Nano Research 2024年第3期17卷 1902-1912页

作者： Lei Xu Wenxiao Wang Yang Li Yonghui Lin Wenjing Yue Kai Qian Qinglei Guo Jeonghyun Kim Guozhen Shen School of information Science and engineering University of JinanJinan 250022China School of Microelectronics Shandong UniversityJinan 250101China department of Electronic Convergence engineering Kwangwoon UniversitySeoul 01897Republic of Korea School of Integrated Circuits and electronics Beijing Institute of TechnologyBeijing 100081China

With the merits of non-contact,highly efficient,and parallel computing,optoelectronic synaptic devices combining sensing and memory in a single unit are promising for constructing neuromorphic computing and artificial visual chip.Based on this,a N:ZnO/MoS_(2)-heterostructured flexible optoelectronic synaptic device is developed in this work,and its capability in mimicking the synaptic behaviors is systemically investigated under the electrical and light signals.Versatile synaptic functions,including synaptic plasticity,long-term/short-term memory,and learning-forgetting-relearning property,have been achieved in this synaptic device.Further,an artificial visual memory system integrating sense and memory is emulated with the device array,and the visual memory behavior can be regulated by varying the light parameters.Moreover,the optoelectronic co-modulation behavior is verified by applying mixed electric and light signals to the array.In detail,a transient recovery property is discovered when the electric signals are applied in synergy during the decay of the light response,of which property facilitates the development of robust artificial visual systems.Furthermore,by superimposing electrical signals during the light response process,a differentiated response of the array is achieved,which can be used as a proof of concept for the color perception of the artificial visual system.

关键词： flexible synaptic device synaptic plasticity optoelectronic synapse robust visual memory optoelectronic co-modulation artificial visual system

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of system Control and information Processing Ministry of Education of ChinaShanghai 200240China Shanghai engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown department of Electrical and Computer engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR problem.In particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value function.This method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing methods.Three numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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