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Continuous advantage learning for minimum-time trajectory planning of autonomous vehicles

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Science China(Information Sciences) 2024年第7期67卷 285-294页

作者： Zhuo LI Weiran WU Jialin WANG Gang WANG Jian SUN School of Automation Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation center China Academy of Launch Vehicle Technology Intelligent Game and Decision Laboratory

This paper investigates the minimum-time trajectory planning problem of an autonomous vehicle. To deal with unknown and uncertain dynamics of the vehicle, the trajectory planning problem is modeled as a Markov decision process with a continuous action space. To solve it, we propose a continuous advantage learning(CAL) algorithm based on the advantage-value equation, and adopt a stochastic policy in the form of multivariate Gaussian distribution to encourage exploration. A shared actor-critic architecture is designed to simultaneously approximate the stochastic policy and the value function, which greatly reduces the computation burden compared to general actor-critic methods. Moreover, the shared actor-critic is updated with a loss function built as mean square consistency error of the advantage-value equation, and the update step is performed several times at each time step to improve data efficiency. Simulations validate the effectiveness of the proposed CAL algorithm and its better performance than the soft actor-critic algorithm.

关键词： trajectory planning continuous advantage learning stochastic policy shared actor-critic

Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning

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Cancer Innovation 2024年第3期3卷 65-80页

作者： Enzhao Zhu Linmei Zhang Jiayi Wang Chunyu Hu Qi Jing Weizhong Shi Ziqin Xu Pu Ai Zhihao Dai Dan Shan Zisheng Ai School of Medicine Tongji UniversityShanghaiChina Shanghai Engineering Research center of Tooth Restoration and Regeneration Research Institute of StomatologyStomatological Hospital and Dental School of Tongji UniversityShanghaiChina Tenth People's Hospital of Tongji University School of MedicineTongji UniversityShanghaiChina Department of Anesthesiology and Perioperative Medicine Shanghai Fourth People's HospitalSchool of MedicineTongji UniversityShanghaiChina Shanghai Hospital development center ShanghaiChina Columbia University New YorkNYUSA School of Medicine Royal College of Surgeons in IrelandUniversity of Medicine and Health SciencesDublinIreland Department of Biobehavioral Sciences Columbia UniversityNew YorkNYUSA Department of Medical Statistics School of MedicineTongji UniversityShanghaiChina Clinical Research center for Mental Disorders Chinese-German Institute of Mental HealthShanghai Pudong New Area Mental Health CenterSchool of MedicineTongji UniversityShanghaiChina

Background:The role of surgery in metastatic breast cancer(MBC)is currently *** novel statistical and deep learning(DL)methods promise to infer the suitability of surgery at the individual ***:The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery ***:We introduced the deep survival regression with mixture effects(DSME),a semi-parametric DL model integrating three causal inference *** models were trained to make individualized treatment *** who received treatments in line with the DL models'recommendations were compared with those who underwent treatments divergent from the *** probability weighting(IPW)was used to minimize *** effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal ***:In total,5269 female patients with MBC were *** was an independent protective factor,outperforming other models in recommending surgery(IPW-adjusted hazard ratio[HR]=0.39,95%confidence interval[CI]:0.19–0.78)and type of surgery(IPW-adjusted HR=0.66,95%CI:0.48–0.93).DSME was superior to other models and traditional guidelines,suggesting a higher proportion of patients benefiting from surgery,especially breast-conserving *** debiased effect of patient characteristics,including age,tumor size,metastatic sites,lymph node status,and breast cancer subtypes,on surgery decision was also ***:Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery *** method can facilitate the development of efficient,reliable treatment recommendation systems and provide quantifiable evidence for decision-making.

关键词： breast surgery causal inference deep learning metastatic breast cancer

The Impact of Urban and Rural Education Gap on student Achievement Differences

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Journal of Contemporary Educational Research 2024年第9期8卷 145-150页

作者： Weiping Fu Abdul Talib bin Mohamed Hashim Faculty of Human development Universiti Pendidikan Sultan Idris35900 Tanjong MalimPerakMalaysia

With our country’s social development,the gap between urban and rural education has become increasingly obvious,especially the difference in student *** study aims to explore the impact of the urban-rural education gap on student *** study adopts a quantitative analysis method to collect and analyze the academic performance,learning resources,and social resources of urban and rural *** results show that the unbalanced distribution of educational resources in urban and rural areas is the key factor leading to the gap in students’*** example,urban education resources are abundant while rural education resources are relatively *** the same time,the difference in social environment and family economic status also have a significant impact on student *** differences between urban and rural areas,particularly in terms of family economic conditions and their ability to invest in education,further widen the gap between urban and rural *** results of this study can provide a reference for reducing the gap between urban and rural education and improving the academic performance of rural students.A fair education policy is essential to narrow the gap between urban and rural education resources distribution,so as to improve the equality of education in society.

关键词： Urban and rural education gap student achievement differences Allocation of educational resources Social and environmental differences Family economic situation

LDAS&ET-AD:Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

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Computers, Materials & Continua 2024年第5期79卷 2331-2359页

作者： Shuyi Li Hongchao Hu Xiaohan Yang Guozhen Cheng Wenyan Liu Wei Guo National Digital Switching System Engineering&Technological R&D center The PLA Information Engineering UniversityZhengzhou450000China

Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial ***,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for ***,the reliability of guidance from static teachers diminishes as target models become more *** paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD ***,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target *** calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers *** evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 *** experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline *** comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustne

关键词： Adversarial training adversarial distillation learnable distillation attack strategies teacher evolution strategy

Machine learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting

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Acta Metallurgica Sinica(English Letters) 2024年第3期37卷 453-466页

作者： Zhihong Zhu Wenhang Ning Xuanyang Niu Yuhong Zhao Beijing Advanced Innovation center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-Performance Al/Mg Alloy MaterialsNorth University of ChinaTaiyuan 030051China Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China

SiC is the most common reinforcement in magnesium matrix composites,and the tensile strength of SiC-reinforced magnesium matrix composites is closely related to the distribution of *** a uniform distribution of SiC requires fine control over the parameters of SiC and the processing and preparation ***,due to the numerous adjustable parameters,using traditional experimental methods requires a considerable amount of experimentation to obtain a uniformly distributed composite ***,this study adopts a machine learning approach to explore the tensile strength of SiC-reinforced magnesium matrix composites in the mechanical stirring casting *** analyzing the influence of SiC parameters and processing parameters on composite material performance,we have established an effective predictive ***,six different machine learning regression models have been developed to predict the tensile strength of SiC-reinforced magnesium matrix *** validation and comparison,our models demonstrate good accuracy and reliability in predicting the tensile strength of the composite *** research findings indicate that hot extrusion treatment,SiC content,and stirring time have a significant impact on the tensile strength.

关键词： Machine learning Stirring casting Magnesium matrix composite Tensile strength

Research progress on temperature field evolution of hot reservoirs under low-temperature tailwater reinjection

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Journal of Groundwater Science and Engineering 2024年第2期12卷 205-222页

作者： Xin Wang Guo-qiang Zhou Yan-guang Liu Ying-nan Zhang Mei-hua Wei Kai Bian School of Earth science and Engineering Hebei University of EngineeringHandan 056038HebeiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuang 050061China Technology Innovation center of Geothermal&Hot Dry Rock Exploration and development Ministry of Natural ResourcesShijiazhuang 050061China Shanxi Key Laboratory for Exploration and Exploitation of Geothermal Resources Taiyuan 030024China Shanxi Geological Engineering Exploration Institute Taiyuan 030024China

This paper focuses on the study of the evolutionary mechanism governing the temperature field of geothermal reservoir under low-temperature tailwater reinjection conditions,which is crucial for the sustainable geothermal energy *** advancing exploitation of geothermal resources deepens,precise understanding of this mechanism becomes paramount for devising effective reinjection strategies,optimizing reservoir utilization,and bolstering the economic viability of geothermal energy *** article presents a comprehensive review of temperature field evolution across diverse heterogeneous thermal reservoirs under low-temperature tailwater reinjection conditions,and analyzes key factors influ-encing this *** evaluates existing research methods,highlighting their strengths and *** study identifies gaps in the application of rock seepage and heat transfer theories on a large scale,alongside the need for enhanced accuracy in field test results,particularly regarding computational effi-ciency of fractured thermal reservoir models under multi-well reinjection *** address these shortcomings,the study proposes conducting large-scale rock seepage and heat transfer experiments,coupled with multi-tracer techniques for field testing,aimed at optimizing fractured thermal reservoir models'computational efficiency under multi-well reinjection ***,it suggests integrat-ing deep learning methods into research *** initiatives are of significance in deepening the understanding of the evolution process of the temperature field in deep thermal reservoirs and enhancing the sustainability of deep geothermal resource development.

关键词： Geothermal reinjection Seepage heat transfer Tracer test Numerical simulation Thermal breakthrough

Simulation and forecasting of fishery weather based on statistical machine learning

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Information Processing in Agriculture 2024年第1期11卷 127-142页

作者： Xueqian Fu Chunyu Zhang Fuhao Chang Lingling Han Xiaolong Zhao Zhengjie Wang Qiaoyu Ma National Innovation center for Digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and Technology Research center for Internet of Things in Agriculture BeijingChina College of Information and Electrical Engineering China Agricultural UniversityBeijingChina

As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery *** fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery *** objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery ***,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather *** also illustrate the main technical means and principles of SML ***,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

关键词： Fishery weather Statistical machine learning Weather generation Weather forecasting Extreme weather warning

Privacy-Preserving Federated Deep learning Diagnostic Method for Multi-Stage Diseases

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace Research center Harbin150001China Information Network Engineering and Research center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

Crop pest image recognition based on the improved ViT method Author links open overlay panel

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Information Processing in Agriculture 2024年第2期11卷 249-259页

作者： Xueqian Fu Qiaoyu Ma Feifei Yang Chunyu Zhang Xiaolong Zhao Fuhao Chang Lingling Han College of Information and Electrical Engineering China Agricultural UniversityBeijingChina National Innovation center for Digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and Technology Research center for Internet of Things in Agriculture BeijingChina

The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable ***,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low *** recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict *** study introduced the improved Vision Transformer(ViT)method for crop pest image *** them,the region with the most obvious features can be effectively selected by block *** self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion *** the experiment,data with 7 classes of examples are used for *** can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.

关键词： Crop diseases recognition Fruit tree leaf disease Vision Transformer Deep learning Computer Vision