Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization

The decision-making method of tunnel boring machine(TBM)operating parameters has a significant guiding significance for TBM safe and efficient construction,and it has been one of the TBM tunneling research hotspots.For this purpose,this paper introduces an intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization.First,linear cutting tests and numerical simulations are used to investigate the physical rules between different cutting parameters(penetration,cutter spacing,etc.)and rock compressive strength.Second,a dual-driven mapping of rock parameters and TBM operating parameters based on data mining and physical rules of rock breaking is established with high accuracy by combining rock-breaking rules and deep neural networks(DNNs).The decision-making method is established by dual-driven mapping,using the effective rock-breaking capacity and the rated value of mechanical parameters as constraints and the total excavation cost as the optimization objective.The best operational parameters can be obtained by searching for the revolutions per minute and penetration that correspond to the extremum of the constrained objective function.The practicability and effectiveness of the developed decision-making model is verified in the SecondWater Source Channel of Hangzhou,China,resulting in the average penetration rate increasing by 11.3%and the total cost decreasing by 10%.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients

BACKGROUND:To promote the shared decision-making(SDM)between patients and doctors in pediatric outpatient departments,this study was designed to validate artificial intelligence(AI)-initiated medical tests for children with fever.METHODS:We designed an AI model,named Xiaoyi,to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic.We calculated the sensitivity,specificity,and F1 score to evaluate the efficacy of Xiaoyi’s recommendations.The patients were divided into the rejection and acceptance groups.Then we analyzed the rejected examination items in order to obtain the corresponding reasons.RESULTS:We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics.The recommended examinations given by Xiaoyi for 10,636(89.6%)patients were qualified.The average F1 score reached 0.94.A total of 58.4%of the patients accepted Xiaoyi’s suggestions(acceptance group),and 41.6%refused(rejection group).Imaging examinations were rejected by most patients(46.7%).The tests being time-consuming were rejected by 2,133 patients(43.2%),including rejecting pathogen studies in 1,347 patients(68.5%)and image studies in 732 patients(31.8%).The difficulty of sampling was the main reason for rejecting routine tests(41.9%).CONCLUSION:Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients,and is worth promoting in facilitating SDM.

An intelligent singular value diagnostic method for concrete dam deformation monitoring

Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status.As an intelligent singular value diagnostic method for concrete dam deformation monitoring, shallow neural network models result in local optima and overfitting, and require manual feature extraction.To obtain an intelligent singular value diagnosis model that can be used for dam safety monitoring, a convolutional neural network (CNN) model that has advantages of deep learning (DL), such as automatic feature extraction, good model fitting, and strong generalizability, was trained in this study.An engineering example shows that the predicted result of the intelligent singular value diagnostic method based on CNN is highly compatible with the confusion matrix, with a precision of 92.41%, receiver operating characteristic (ROC) coordinates of (0.03, 0.97), an area-under-curve (AUC) value of 0.99, and an F1-score of 0.91.Moreover, the performance of the CNN model is better than those of models based on decision tree (DT) and k-nearest neighbor (KNN) methods.Therefore, the intelligent singular value diagnostic method based on CNN is simple to operate, highly intelligent, and highly reliable, and it has a high potential for application in engineering.

Decision-Making Models Based on Meta-Reinforcement Learning for Intelligent Vehicles at Urban Intersections

Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections.To deal with this,a deep deterministic policy gradient(DDPG)decision-making algorithm(T-DDPG)based on a time-series Markov decision process(T-MDP)was developed,where the state was extended to collect observations from several consecutive frames.Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm.Furthermore,model-agnostic meta-learning(MAML)was incorporated into the T-DDPG algorithm to improve the training method,leading to a decision algorithm(T-MAML-DDPG)based on a secondary gradient.Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models.The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios,which could improve traffic safety and efficiency.The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.

Research on an Intelligent Maintenance Decision-making Support System

A new synthetic model of maintenance decision-making, which is made by anartificial neural network (ANN) , expert system (ES) and emulation technology, is put forward. Bymeans of this model all kinds of maintenance resources with low cost can be effectively harmonized;accordingly, the reliability, maintenance efficiency and quality of equipment can be improved, soservice life of equipments is enhanced.

An Intelligent Algorithm for Solving Weapon-Target Assignment Problem:DDPG-DNPE Algorithm

Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

Building a Business and Strategic Intelligence Policy as a Strategy for Promoting Congolese Business Progress and Healthy Economic Development in Eastern DRC

The aim of this study was to verify the existence of business and strategic intelligence policies at the level of Congolese companies and at the state level, likely to foster progress and healthy development in the east of the DRC. The study was based on a mixed perspective consisting of objective analysis of quantitative data and interpretative analysis of qualitative data. The results showed that business and strategic intelligence policies have not been established at either company or state level, as this is an area of activity that is not known to the players in companies and public departments, and there are no units or offices in their organizational structures responsible for managing strategic information for competitiveness on the international market. In addition, there is a real need to establish strategic information management units within companies, upstream, and to set up a national strategic information management department or agency to help local companies compete in the marketplace, downstream. This reflects the importance and timeliness of building business and strategic intelligence policies to ensure economic progress and development in the eastern DRC. Business and strategic intelligence provides companies with an appropriate tool for researching, collecting, processing and disseminating information useful for decision-making among stakeholders, in order to cope with a crisis or competitive situation. The study suggests a number of key recommendations based on its findings. To the government, it is recommended to establish the national policy of business and strategic intelligence by setting up a national agency of strategic intelligence in favor of local companies;and to companies to establish business intelligence units in their organizational structures in favor of stakeholders to foster advantageous decision-making in the competitive market and achieve progress. Finally, the study suggests that studies be carried out to fully understand the opportunities and impact of business and strategic intelligence in African countries, particularly in the DRC.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

The pioneer of intelligent construction—An overview of the development of intelligent compaction

As the pioneer in the intelligent construction technologies(ICT)of transportation infrastructure,intelligent compaction(IC)has been applied in the infrastructure construction of various countries.It is currently the technology that best reflects the intelligence of engineering construction.This article overviews the latest developments and trends in IC.Firstly,the basic meaning of ICT is defined based on the essential characteristics of intelligent construction of transportation infrastructure,“perception,analysis,decision-making,execution”(PADE).The concept of intelligent compaction technology classification is also introduced.The PADE requirements that intelligent compaction should meet are proposed.Secondly,according to the sequence of“perception,analysis,decision-making,execution,”the workflow and key technologies of intelligent compaction are analyzed,and the mechanism of using the response of the roller to solve the modulus is given and verified.On this basis,The IC feasibility test methods,including compaction degree,compaction stability,and compaction uniformity,are briefly described.The implementation scheme of feedback control is given.Then,the use of artificial neural networks(ANN),hybrid expert systems,and reinforcement learning in intelligent compaction are briefly introduced.Finally,several extended applications of intelligent compaction are expounded,including the development ideas of intelligent road rollers and the role of intelligent compaction in virtual construction,the layer-specific mechanical parameters of fillers,etc.

Utilizing Artificial Intelligence (AI) for the Identification and Management of Marine Protected Areas (MPAs): A Review

The article discusses the application of artificial intelligence (AI) and automation in marine conservation, specifically in relation to the protection of marine ecosystems and the definition of marine protected areas (MPAs). It highlights the threats that marine ecosystems face due to human activities and emphasizes the importance of effective management and conservation efforts. By improving data gathering, processing, monitoring, and analysis, artificial intelligence, and automation, they can revolutionize marine research. In conclusion, this study emphasizes the importance of AI and automation in marine conservation responsibly and ethically. In order to integrate these technologies into decision-making processes, stakeholders and marine conservation professionals must collaborate. Through the use of artificial intelligence and automation, marine conservation efforts can be transformed by establishing new methods of collecting and analyzing data, making informed decisions, and managing marine ecosystems.

公路路基变形破坏及其测试技术研究进展与展望

路基变形是公路病害的主要原因之一,其在公路建设、管理、运营、养护过程中带来的隐患日益凸显。目前以遥感测绘、地球物理探测、多传感器为代表性的相关技术,在路基变形测试中发挥了重要作用。为研究道路工程领域动态和行业发展趋势,梳理近年来道路工程领域理论、方法、技术及装备研究现状,阐述了公路路基变形的主要形式及影响因素,分析了路基变形测试技术类型及其优缺点,进一步讨论了对全周期智慧监测体系的认识及其发展趋势。构建路基变形监测体系是感知路基状态、路基稳定性评估和灾害预警的主要途径。随着人工智能和先进测试技术的快速进步,路基变形测试技术呈现出动态化、透明化、智慧化等新特点。加强公路测试基础理论、技术与装备的研发,形成基于立体空间的多手段路基健康监测体系是未来道路安全运维的重要发展方向。

基于点云数据的大型复杂钢结构智能化施工方法

大型复杂钢结构施工过程中,常面临施工尺寸质量难以把控、构件提升变形监测困难和合拢段现场配切效率低等问题。三维激光扫描技术可全覆盖地、快速精准地获取复杂构部件在施工过程中的点云数据,这为解决上述问题提供了新方法。为此,该文以重庆两江新区寨子路钢拱桥为工程背景,开展基于点云数据的大型复杂钢拱桥智能化施工方法的全流程研究。基于标靶球检测算法、快速四点一致集算法、迭代最近邻算法等实现标靶球点云数据的自动检测及多站点云数据之间的自动配准;通过BIM点云化技术、kNN算法等完成目标点云数据的半自动化提取,实现拱肋尺寸的智能化检测;基于八叉树算法、区域增长算法等实现拱肋提升变形的智能检测;为缩短拱肋的合拢工期,基于BIM模型焊缝信息提取技术、主成分分析算法、Canny边界检测算法、霍夫变换算法等提出数字化预拼装算法,得到合拢段的配切量。工程应用表明,该文所提出的智能施工方法效率高、自动化程度好,研究成果可为大型复杂钢结构的施工质量和安装效率的提升提供理论和算法支撑。

薄壁结构加工误差预测与控制研究进展

薄壁结构因质量轻、比强度高被广泛应用于航空航天领域,但因其结构复杂、刚性弱、材料去除率高等特点,铣削加工的零件成品容易出现形位误差,严重影响其使用性能,因此对薄壁结构加工误差的控制具有重要现实意义。通过对薄壁结构加工特点进行分析,总结薄壁结构加工误差来源;根据加工误差来源介绍了薄壁结构不同类型的加工误差,即装夹定位、切削负载、切削振动、机床误差、残余应力以及多工序加工引起的加工误差;然后从这6个方面总结归纳薄壁结构加工误差的预测模型和控制方法;最后阐述了薄壁结构铣削加工误差控制方法的当前挑战和未来发展趋势。

岩石变形局部化智能识别的DSCM-CNN方法

岩石变形局部化的识别对于岩石破坏机理、岩土工程灾害预测预警有着重要的意义。本文将数字散斑相关方法(digital speckle correlation methods,DSCM)与卷积神经网络(convolutional neural networks,CNN)相结合,提出了一种用于岩石变形局部化智能识别的DSCM-CNN模型。通过DSCM获取岩石试件在单轴压缩实验过程中的最大剪应变场云图,根据变形局部化带位置进行标注,完成数据集的构建;利用训练数据集对DSCM-CNN智能识别模型进行训练。通过红砂岩单轴压缩实验对该方法进行验证,结果表明:DSCM-CNN模型可以实现岩石变形局部化带位置的自动识别,子集准确率、精确度、召回率等指标分别达到94.19%,97.21%和96.41%,证明了岩石变形局部化智能识别的DSCM-CNN模型的可行性,为岩石变形局部化智能监测提供了一种新的思路。

集成学习在土木工程中的预测研究进展

建筑工程项目随着施工进度推进,结构变形程度增大,超过变形临界值时将出现安全事故,且在工程活动和外界气候环境共同作用下,易诱发滑坡等地质灾害。为及时有效发现并解决建设过程中的变形问题,以及可能诱发的地质灾害,将Bagging算法、Boosting算法、Stacking集成学习方法应用于土木工程预测中,以便更加合理地应用智能预测技术。并指出目前存在无法预测前期结构变形、集成学习种类繁多、滞后效应时效性明显的问题,对该技术应用前景进行展望。

人工智能正颌手术(牙合)板的设计与精确性评价

目的:建立基于人工智能(artificial intelligence,AI)的正颌外科手术(牙合)板设计方法,并比较其与普通数字化(牙合)板的精度差异。方法:通过相关软件程序建立正颌外科手术(牙合)板的设计算法,得到可用的(牙合)板自动设计程序,比较同一病例进行手术(牙合)板设计所需时间。将40例骨性Ⅱ类错(牙合)颌畸形患者(20例单颌,20例双颌)咬合模型纳入比较,分别由AI软件和人工数字化软件设计并打印手术(牙合)板。将同一组模型分别戴入AI(牙合)板与普通(牙合)板后扫描获取数字化数据,比较整体偏差与部分牙尖位置偏差。结果:牙列模型整体偏差分析结果显示,整体偏差均小于0.1 mm,表示AI(牙合)板与普通(牙合)板在引导模型就位上未见明显区别;牙尖位置偏差比较显示,AI(牙合)板与普通(牙合)板偏差距离均值约为0.10~0.14 mm;使用AI软件在单颌手术[(10.7±2.4) s]和双颌手术[(21.5±3.9) s](牙合)板设计中均可极大减少设计所需时间。结论:AI(牙合)板精度与普通(牙合)板相比,未见明显差异,同时其能够提升正颌手术(牙合)板的设计效率。

人工智能在脊柱畸形领域研究热点的可视化分析

背景:随着人工智能技术在治疗脊柱畸形领域的不断完善与进步,已有大量的研究投入到该领域当中,但主要研究现状、热点和发展趋势尚不明确。目的:采用文献计量学的方法可视化分析人工智能在脊柱畸形领域的相关文献,明确该领域的研究热点和不足,为今后研究工作研究提供参考。方法:在Web of Science核心集数据库检索建库至2023年收录的人工智能在脊柱畸形领域相关文献,通过Citespace 5.6.R5和VOSviewer 1.6.19软件对文献数据进行可视化分析。结果与结论:(1)共纳入文献165篇,此领域年发文量呈波动上升趋势,发文量最多的作者是Lafage V,发文量最多的国家是中国。(2)关键词分析结果显示,青少年早发性脊柱侧弯、深度学习、分类、精度和机器人是该研究领域的主要高频关键词。(3)文献共被引和高被引文献深度分析结果显示,人工智能在脊柱畸形领域有3大热点,包括利用U型架构(深度学习卷积神经网络的一种成熟模式)来自动测量影像学参数(Cobb角、棘旁肌准确分割等)、多视图相关网络架构(即脊柱曲度评估框架)及机器人引导脊柱手术。(4)在人工智能治疗脊柱畸形领域,基因组学等机制研究十分薄弱,未来可利用无监督分层聚类等机器学习技术,运用全基因组关联分析等基因组学研究方法,来进行脊柱畸形领域的易感基因等基础机制研究。