基于深度学习的AMT智能地质解译技术

在砂岩型铀矿勘探中,开展音频大地电磁法(Audio Magnetotelluric Method,AMT)测深能够有效地获得深部砂体发育范围、基底隆凹形态等与铀成矿相关的地质信息。其测量成像结果包含的地质意义往往需要通过主观地质推断后才能为后续地质勘探工作提供支撑,解译结果严重依赖解译人员的地质认知和专业经验。与此同时,随着三维AMT测量技术的发展,人工地质解译的效率较为低下。文章以砂岩型铀矿成矿环境为基础,采用随机电阻率模型构建AMT图像样本库,并建立U-net网络开展砂体、基底等岩性识别训练,从而实现智能推断反演电阻率体中的砂体和基底。通过与人工解译结果对比,智能解译结果匹配程度较高,解译时间大大缩短,表明该方法在砂岩型铀矿勘探中具有较好的应用前景。

基于GMT脚本的AMT数据曲线图批量绘制方法与应用研究

AMT法观测天然交变电磁场,通过数据处理和反演可获得地下介质的电性分布特征,近年来得到了广泛应用。AMT数据曲线图包括测点信息、视电阻率和阻抗相位曲线及数据,可直观反映测点数据质量和曲线类型,是重要的原始资料。加拿大Phoenix Geophysics公司的MTU-5A和V8大地电磁仪是国内最常见的AMT仪器,原始数据经预处理,用MT-Editor软件编辑后可以批量导出PLT格式文件,使用awk命令可从PLT文件中提取测点信息和视电阻率、阻抗相位数据,利用GMT绘图软件将测点信息和数据绘制成图,结合循环语句可达到批量成图的目的。AMT数据曲线图可应用于原始数据质量评价和AMT测深曲线类型判别,在工作区地电规律研究中具有重要意义。

基于多目标粒子群与模糊控制的AMT自适应换挡规律研究

搭载自动机械变速器(AMT)的车辆,其换挡规律是提升动力性与经济性的关键.本文以3挡AMT纯电动城市客车为研究对象,基于多目标粒子群算法(MOPSO)对不同加速踏板开度下的换挡车速进行优化,建立了兼顾动力性与经济性的双参数MOPSO换挡规律,并构建了以车辆载荷与加速度变化为输入,车速调整量为输出的模糊控制器对MOPSO规律进行自适应调整,得到了自适应换挡规律Fuzzy-MOPSO.最后,对Fuzzy-MOPSO规律开展了动力性与经济性验证,并与其他换挡规律进行比较.结果表明,Fuzzy-MOPSO规律的加速性能比经济性规律提升了15.3%,其动力性能比MOPSO规律更优越.经济性方面,在4段实际道路工况下,Fuzzy-MOPSO规律的经济性比动力性规律分别提升了6.08%、7.28%、6.88%、5.63%,比MOPSO规律更具节能潜力.此外,Fuzzy-MOPSO规律在实际道路工况下的换挡频率与MOPSO规律相当,节能的同时能够有效抑制频繁换挡,提升传动系统的寿命.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

AMT手动挡锁挡控制策略研究

为实现纯电动商用车机械式自动变速箱(AMT)适应复杂行驶工况,可通过整车控制器(VCU)控制变速箱进行手动挡锁挡控制。通过使用比例积分(PI)调节的控制方式对电机扭矩进行限制,从而将驱动电机的转速控制在限值之内。然后基于控制器局域网(CAN)标定协议(CCP)进行在线标定,截取相应的报文数据进行分析,验证其功能。试验结果显示,文章的控制方法能够实现车辆的手动挡锁挡功能,电机转速波动范围在±20r/min之内,且车身无抖动现象,具有较好的驾驶平顺性。

AMT控制器电磁发射干扰特性研究

基于GJB 151B—2013中对军用设备和分系统的电磁发射要求,以某型号AMT控制器为研究对象,进行AMT控制器电磁发射干扰特性分析,开展CE102电源线传导发射试验和RE102电场辐射发射试验。试验结果表明:试验用AMT控制器的电源线传导发射和电场辐射发射均满足GJB 151B—2013的电磁发射要求,电源正线和电源负线的电磁传导发射情况基本一致,均存在以50 Hz为基波的多次谐波干扰,在50 Hz处安全裕度最小,约为10 dBμV;经屏蔽处理后的AMT控制器在垂直极化和水平极化两种天线状态下的电磁辐射情况基本一致。为进一步提高AMT控制器电磁发射干扰的安全裕度,既要在电源线端口处采取以50 Hz为基波的多次谐波干扰抑制措施,同时还要对AMT控制器上的孔缝、接口以及线缆进一步采取屏蔽防护措施。

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

矿用防爆无轨胶轮车用AMT技术探讨

介绍了矿用防爆无轨胶轮车用变速箱的使用现状及需求,结合工程机械常用的自动变速箱系统的特点,对其性能进行比较。根据比较结果,着重分析了机械式自动变速箱(AMT)的优缺点和国内外发展现状。根据防爆无轨胶轮车的发展趋势和煤炭市场的使用需求,综合论述了AMT在矿用防爆无轨胶轮车上推广使用的可行性及重要意义;并分析了AMT在矿用防爆无轨胶轮车上的应用前景及市场需求。

Artificial Intelligence-Based Automated Actuarial Pricing and Underwriting Model for the General Insurance Sector

The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.

物探AMT法在矿山工程地质勘查中的应用

伴随着国内社会经济的不断发展,诸多基础建设的工程项目数量越来越多。在这些工程项目中,矿山工程地质勘查工作最为重要,因为它直接关系着我国矿产资源的开采量,决定了资源经济效益的发挥水平。矿山工程地质勘查工作中,常常会遭遇到较多不良地质体,比如断层、岩溶、暗河等,在矿山工程地质勘查时,如果没有提前预警并且应用合理方法展开处理,必定会加大安全事故发生概率。要想避免这些问题产生,应当注重应用物探AMT技术,如此能够有效避免不良地质体。本文接下来将会对此展开分析,希望为工程人员提供参考。

Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

两挡AMT纯电动汽车的换挡协调控制及仿真研究

与传统内燃机汽车相比,纯电动汽车在环保方面具有显著优势,大力发展电动汽车已经成为汽车产业发展主流趋势.以纯电动汽车自动变速器(Automated Mechanical Transmission,AMT)为研究对象,对其参数换挡协调控制进行了研究.按照控制参数的变化实现换挡,根据电动机特性和汽车行驶动力学方程,应用MATLAB/Simulink搭建车辆模型,进一步制定换挡控制策略,并对其进行仿真模拟验证.仿真结果表明,建立的仿真模型和设计的换挡规律是合理可行的.

AGV Scheduling Optimization of Automated Port Based on Disruption Management

The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unloading efficiency of container terminals. These terminals can also increase the port’s transportation volume while ensuring the quality of cargo loading and unloading, which has become an inevitable trend in the future development of ports. However, the continuous growth of the port’s transportation volume has increased the horizontal transportation pressure on the automated terminal, and the problems of route conflicts and road locks faced by automated guided vehicles (AGV) have become increasingly prominent. Accordingly, this work takes Xiamen Yuanhai automated container terminal as an example. This work focuses on analyzing the interference problem of path conflict in its horizontal transportation AGV scheduling. Results show that path conflict, the most prominent interference factor, will cause AGV scheduling to be unable to execute the original plan. Consequently, the disruption management was used to establish a disturbance recovery model, and the Dijkstra algorithm for combining with time windows is adopted to plan a conflict-free path. Based on the comparison with the rescheduling method, the research obtains that the deviation of the transportation path and the deviation degree of the transportation path under the disruption management method are much lower than those of the rescheduling method. The transportation path deviation degree of the disruption management method is only 5.56%. Meanwhile, the deviation degree of the transportation path under the rescheduling method is 44.44%.

Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias

In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation efficiency.This glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias.Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model selection.To validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our methodology.In the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for training.The second pipeline is dedicated to feature extraction and classification,utilizing deep learning models.Notably,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class arrhythmias.An ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model pipeline.In our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

Glaucoma drainage device implantation and cyclophotocoagulation in the management of refractory glaucoma after Descemet-stripping automated endothelial keratoplasty

AIM:To compare the surgical outcomes of glaucoma drainage device implantation(GDI)and trans-scleral neodymium:YAG cyclophotocoagulation(CPC)in the management of refractory glaucoma after Descemetstripping automated endothelial keratoplasty(DSAEK).METHODS:This retrospective study on observational case series enrolled 29 patients who underwent DSAEK and posterior anti-glaucoma surgery(15 with GDI and 14 with CPC).The main outcome measures were intraocular pressure(IOP),glaucoma surgery success rate(defined as IOP of 6–21 mm Hg without additional anti-glaucoma operation),number of glaucoma medications,endothelial graft status,and best-corrected visual acuity(BCVA).RESULTS:The mean follow-up time was 34.1 and 21.0mo for DSAEK or glaucoma surgeries,both for the GDI and CPC groups.Both groups showed significant IOP reduction after glaucoma surgery.The GDI group presented a significantly higher success rate in IOP control than the CPC group(60%vs 21.4%,P=0.03).Both procedures significantly decreased the number of glaucoma medications(P=0.03).Forty percent and 57%of cases in the GDI and the CPC group,respectively,experienced endothelial graft failure during follow-up(P=0.36).Significantly worse BCVA after surgery was observed in the CPC group but not in the GDI group.CONCLUSION:Both GDI and CPC significantly decrease IOP in eyes with glaucoma after DSAEK.GDI is preferable to CPC in refractory glaucoma cases after DSAEK,as it manifests a significantly higher success rate for IOP control,similar endothelial graft failure rate,and relatively preserves BCVA than CPC.

An Effective Optimization Method for Integrated Scheduling of Multiple Automated Guided Vehicle Problems

Automated Guided Vehicle(AGV)scheduling problem is an emerging research topic in the recent literature.This paper studies an integrated scheduling problem comprising task assignment and path planning for AGVs.To reduce the transportation cost of AGVs,this work also proposes an optimization method consisting of the total running distance,total delay time,and machine loss cost of AGVs.A mathematical model is formulated for the problem at hand,along with an improved Discrete Invasive Weed Optimization algorithm(DIWO).In the proposed DIWO algorithm,an insertion-based local search operator is developed to improve the local search ability of the algorithm.A staggered time departure heuristic is also proposed to reduce the number of AGV collisions in path planning.Comprehensive experiments are conducted,and 100 instances from actual factories have proven the effectiveness of the optimization method.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.