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2023 Genetic and Evolutionary Computation Conference, GECCO 2023

作者： Gong, Cheng Nan, Yang Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798400701191

A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on the performance of EMO algorithms. However, in most existing EMO algorithms, one of the commonly-used initialization methods is to randomly generate a set of solutions as an initial population. One possible approach to improve random initialization is to include one or more Pareto optimal (near Pareto optimal) solution(s) in the initial population, which are expected to provide useful information and knowledge on the optimized problem. In this paper, to investigate the effectiveness of this initialization idea, we examine and quantify the effects of including one or more Pareto optimal solution(s) in the initial population on the performance of EMO algorithms. Experimental results demonstrate that it is worthwhile to first obtain and then include some Pareto optimal solutions in the initial population. Through a number of experiments and algorithm behavior analysis, this study provides supports and insights into EMO algorithm design and motivates further research on population initialization for EMO algorithms. © 2023 ACM.

关键词： Evolutionary algorithms

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MITA: Bridging the Gap between Model and Data for Test-time Adaptation

arXiv

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arXiv 2024年

作者： Yuan, Yige Xu, Bingbing Xiao, Teng Hou, Liang Sun, Fei Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Pennsylvania State University United States Kuaishou Technology China

Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at batch level, often exhibit suboptimal performance in complex real-world scenarios, particularly when confronting outliers or mixed distributions. This phenomenon stems from a pronounced over-reliance on statistical patterns over the distinct characteristics of individual instances, resulting in a divergence between the distribution captured by the model and data characteristics. To address this challenge, we propose Meet-In-The-Middle based Test-Time Adaptation (MITA), which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions, thereby meeting in the middle. MITA pioneers a significant departure from traditional approaches that focus solely on aligning the model to the data, facilitating a more effective bridging of the gap between model’s distribution and data characteristics. Comprehensive experiments with MITA across three distinct scenarios (Outlier, Mixture, and Pure) demonstrate its superior performance over SOTA methods, highlighting its potential to significantly enhance generalizability in practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Data assimilation

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Attitude control method for tilt-trirotor UAV based on adaptive fuzzy PID 3

Attitude control method for tilt-trirotor UAV based on adapt...

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2024 3rd International Conference on Aerospace and Control Engineering, ICoACE 2024

作者： Jin, Yukang Gao, Zhenhao Cai, Zhiqin Peng, Haijun School of Mechanics and Aerospace Engineering Dalian University of Technology Liaoning Dalian510000 China School of Mechanics and Aerospace Engineering State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Liaoning Dalian510000 China

This study introduces an adaptive fuzzy PID control-based approach for managing the attitude of a tilt-trirotor unmanned aerial vehicle (UAV). This approach employs fuzzy logic to dynamically adjust the parameters of the PID controller in real time, enabling the control system to effectively adapt to changing flight conditions and external perturbations. Simulation results indicate that, in contrast to conventional PID control techniques, the adaptive fuzzy PID-based control method exhibits significant improvements in response speed, steady-state error, and disturbance rejection capability. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Disturbance rejection

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Multimodal process monitoring based on transition-constrained Gaussian mixture model

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Chinese Journal of Chemical Engineering 2020年第12期28卷 3070-3078页

作者： Shutian Chen Qingchao Jiang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is *** multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical *** study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process *** technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online *** process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode *** studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

关键词： Multimodal process monitoring Gaussian mixture model State transition matrix Process control Process systems systems engineering

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A Modified Construction Method of Blended Rolled Edge and Its Application

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Progress In Electromagnetics Research C 2022年 120卷 77-92页

作者： Jiang, Yongquan Yin, Hongcheng Mo, Chongjiang Kong, Dewang Science and Technology on Electromagnetic Scattering Laboratory Beijing100854 China School of Information and Communication Engineering Communication University of China Beijing100024 China

—It is of great practical value to study the blended rolled edge of reflector used in Compact Antenna Test Range (CATR). Taking a rectangular aperture reflector as the benchmark, a reflector with ideal blended rolled edge is obtained by means of parameter iterative optimization after accurately establishing the position relationship between the local and global coordinates where the blended rolled edge is located, precisely deriving the geometric equation of the main reflector zone and blended rolled edge zone in the local coordinate and optimizing continuity condition of curvature radius. On the basis, a blended rolled edge reflector with minimum operating frequency of 0.8 GHz and quiet zone size of 2 m is designed. The simulation results show that the performance of the reflector with blended rolled edge obtained by the proposed method is better than that obtained by the traditional construction method, and the designed reflector has excellent performance. The work in this paper provides a theoretical support for the optimal design and engineering application of the blended rolled edge reflector. © 2022, Electromagnetics Academy. All rights reserved.

关键词： Iterative methods

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Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data

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Artificial Intelligence in Agriculture 2022年第1期6卷 129-137页

作者： Ilham Ihoume Rachid Tadili Nora Arbaoui Mohamed Benchrifa Ahmed Idrissi Mohamed Daoudi Solar Energy and Environment Laboratory Mohammed V University in RabatFaculty of SciencesB.P 1014RabatMorocco

In the uncertainties within which the worldwide food security lies nowadays,the agricultural industry is raising a crucial need for being equipped with the state-of-the-art technologies for a more efficient,climate-resilient and sustainable *** traditional production methods have to be revisited,and opportunities should be given for the innovative solutions henceforth brought by big data analytics,cloud computing and internet of things(IoT).In this context,we develop an optimized tinyML-oriented model for an active machine learningbased greenhouse microclimate management to be integrated in an on-field *** design an experimental strawberry greenhouse from which we collect multivariate climate data through installed *** obtained values'combinations are labeled according to a five-action multi-label control strategy,then used to prepare a machine learning-ready *** dataset is used to train and five-fold cross-validate 90 Multi-Layer Perceptrons(MLPs)with varied hyperparameters to select the most performant–yet optimized–model instance for the addressed *** multi-label control approach enables designing highly scalable models with reduced computational complexity,comprising only n control neurons instead of(1+∑n k=1Cn k)neurons(usually generated from a classic single-label approach from n input variables).Our final selected model incorporates 2 hidden layers with 7 and 8 neurons respectively and 151 parameters;it scored a mean accuracy of 97%during the cross-validation phase,then 96%on our supplementary test *** model enables an intelligent and autonomous greenhouse management with the less required *** can be efficiently deployed in microcontrollers within real world operating conditions.

关键词： Agricultural greenhouse Microclimate control Machine learning optimization TinyML

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Stochastic Computing Based on Volatile Ovonic Threshold Switching Devices

Stochastic Computing Based on Volatile Ovonic Threshold Swit...

引用

International Conference on ASIC

作者： Z. Chai W. Zhang J. F. Zhang School of Engineering Liverpool John Moores University UK State Key Laboratory for Mechanical Behavior of Materials and School of Materials Science and Engineering Xi’an Jiaotong University Xi’an China

Ovonic Threshold Switching (OTS) selector is essential in the 1S (selector) -1R (resistive switching device) crossbar memory array to suppress the sneak current paths. OTS exhibits inherent stochastic characteristics in its switching process and can be used for implementing true random number generators (TRNGs). Stochastic computing (SC) can be further designed and realized by exploiting the probabilistic switching behavior in the OTS. The stochastic bit streams generated by OTS are demonstrated with good computation accuracy in both multiplication operation and edge detection circuit for image processing. Moreover, the distribution of random bit in the stochastic streams generated by OTS has been statistically studied and linked to the defect de/localization behavior in the chalcogenide material. Weibull distribution of the delay time supports the origin of such probabilistic switching, facilitates further optimization of the operation condition, and lays the foundation for device modelling and circuit design. Considering its other advantages such as simple structure, fast speed, and volatile nature, OTS is a promising material for implementing SC in a wide range of novel applications, such as image processors, neural networks, control systems and reliability analysis.

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A Gradient-Based Adaptive Quantum-behaved Particle Swarm optimization

A Gradient-Based Adaptive Quantum-behaved Particle Swarm Opt...

引用

Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Hao Mei Jingjing Zhang Qingchun Wang Yuchun Wu Guoping Guo AHU-IAI AI Joint Laboratory Anhui University Hefei Anhui China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei Anhui China School of Electrical Engineering and Automation Anhui University Hefei Anhui China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Based on the quantum-behaved particle swarm optimization and gradient-based methods, an improved particle swarm optimization algorithm is proposed. In this modified particle swarm algorithm, particles alternate between utilizing quantum behavior and gradient information to optimize parameters. The algorithm also incorporates local random search to enhance the search ability. Tests on some benchmark functions across various dimensions demonstrates its strong global search capabilities and precision. The experimental results indicate promising prospects for the application of this algorithm.

关键词： Performance evaluation Gradient methods Costs Heuristic algorithms Scalability Sociology Robustness

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Query-Enhanced Motion Transformer with Dilated Static Query and Bridged Dynamic Query

SSRN

引用

SSRN 2024年

作者： Kang, Miao Shi, Liushuai Ye, Ke Zhou, Sanping Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence China National Engineering Research Center for Visual Information and Applications China Institute ofArtificial Intelligence and Robotics Xi'an Jiaotong University China

Motion forecasting is integral to autonomous driving, as it aims to anticipate future trajectory of traffic agents within the complicated environment by comprehending their various motion intentions. Recent effective query-based methods have focused on designing static queries and learning dynamic queries through cascaded Transformer decoders to represent motion multimodality. However, static queries are usually generated by clustering the goal of motion samples, whose coverage of motion patterns suffers from the limited valid range of the clustered samples. Additionally, dynamic queries, affected by the sequential connections between decoder layers, lack communication between distant decoders, leading to suboptimal optimization. In this paper, we propose a simple yet effective Query-Enhanced Motion TRansformer (QE-MTR) for motion forecasting. This approach includes both Dilated Static Query (DSQ) and Bridged Dynamic Query (BDQ) to enhance the representation of motion multimodality. Specifically, the DSQ expands the valid range of motion samples from their future sequence to complete sequence, providing more diverse motion patterns for query generation and effectively dilating the coverage of static queries. The BDQ introduces cross connection across decoders instead of direct connections, promoting harmonious dynamic optimization and improving the effectiveness of final dynamic queries. QE-MTR achieves state-of-the-art performance on the Waymo Open Motion Dataset, earning 2nd place finish in the 2023 Waymo Motion Prediction Challenge. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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