Atom substitution of the solid-state electrolyte Li_(10)GeP_(2)S_(12)for stabilized all-solid-state lithium metal batteries

Solid-state electrolyte Li_(10)GeP_(2)S_(12)(LGPS)has a high lithium ion conductivity of 12 mS cm^(-1)at room temperature,but its inferior chemical stability against lithium metal anode impedes its practical application.Among all solutions,Ge atom substitution of the solid-state electrolyte LGPS stands out as the most promising solution to this interface problem.A systematic screening framework for Ge atom substitution including ionic conductivity,thermodynamic stability,electronic and mechanical properties is utilized to solve it.For fast screening,an enhanced model Dop Net FC using chemical formulas for the dataset is adopted to predict ionic conductivity.Finally,Li_(10)SrP_(2)S_(12)(LSrPS)is screened out,which has high lithium ion conductivity(12.58 mS cm^(-1)).In addition,an enhanced migration of lithium ion across the LSr PS/Li interface is found.Meanwhile,compared to the LGPS/Li interface,LSrPS/Li interface exhibits a larger Schottky barrier(0.134 eV),smaller electron transfer region(3.103?),and enhanced ability to block additional electrons,all of which contribute to the stabilized interface.The applied theoretical atom substitution screening framework with the aid of machine learning can be extended to rapid determination of modified specific material schemes.

A dynamic database of solid-state electrolyte(DDSE)picturing all-solid-state batteries

All-solid-state batteries(ASSBs)are a class of safer and higher-energy-density materials compared to conventional devices,from which solid-state electrolytes(SSEs)are their essential components.To date,investigations to search for high ion-conducting solid-state electrolytes have attracted broad concern.However,obtaining SSEs with high ionic conductivity is challenging due to the complex structural information and the less-explored structure-performance relationship.To provide a solution to these challenges,developing a database containing typical SSEs from available experimental reports would be a new avenue to understand the structureperformance relationships and find out new design guidelines for reasonable SSEs.Herein,a dynamic experimental database containing>600 materials was developed in a wide range of temperatures(132.40–1261.60 K),including mono-and divalent cations(e.g.,Li^(+),Na^(+),K^(+),Ag^(+),Ca^(2+),Mg^(2+),and Zn^(2+))and various types of anions(e.g.,halide,hydride,sulfide,and oxide).Data-mining was conducted to explore the relationships among different variates(e.g.,transport ion,composition,activation energy,and conductivity).Overall,we expect that this database can provide essential guidelines for the design and development of high-performance SSEs in ASSB applications.This database is dynamically updated,which can be accessed via our open-source online system.

Hitachi——Fully Automatic Washing Machine

The new model Hitachi fully automatic washing machine is made by the Shanghai Hitachi Shangling Machinery Co. Ltd, using Japanese Hitachi technology and equipment. The product adopts a smooth, clean and abrasion-corrosion-proof tub made of stainless titanium alloy steel. The wash tub has a large capacity and does not damage the clothing. The rotation speed is 900 per minute. The drying ability is 10％ higher than original

Fully Integrated Machine Control for Ultra Precision Machining

To meet the demands for highly advanced components with ultra precise contour accuracy and optical surface quality arising in the fields of photonics and optics, automotive, medical applications and biotechnology, consumer electronics and renewable energy, more advanced production machines and processes have to be developed. As the complexity of machine tools rises steadily, the automation of manufacture increases rapidly, processes become more integrated and cycle times have to be reduced significantly, challenges of engineering efficient machine tools with respect to these demands expand every day. Especially the manufacture of freeform geometries with non-continuous and asymmetric surfaces requires advanced diamond machining strategies involving highly dynamic axes movements with a high bandwidth and position accuracy. Ultra precision lathes additionally equipped with Slow Tool and Fast Tool systems can be regarded as state-of-the-art machines achieving the objectives of high quality optical components. The mechanical design of such ultra precision machine tools as well as the mechanical integration of additional highly dynamic axes are very well understood today. In contrast to that, neither advanced control strategies for ultra precision machining nor the control integration of additional Fast Tool systems have been sufficiently developed yet. Considering a complex machine setup as a mechatronic system, it becomes obvious that enhancements to further increase the achievable form accuracy and surface quality and at the same time decrease cycle times and error sensitivity can only be accomplished by innovative, integrated control systems. At the Fraunhofer Institute for Production Technology IPT a novel, fully integrated control approach has been developed to overcome the drawbacks of state-of-the-art machine controls for ultra precision processes. Current control systems are often realized as decentralized solutions consisting of various computational hardware components for setpoint generation, machine control, HMI （human machine interface）, Slow Tool control and Fast Tool control. While implementing such a distributed control strategy, many disadvantages arise in terms of complex communication interfaces, discontinuous safety structures, synchronization of cycle times and the machining accuracy as a whole. The novel control approach has been developed as a fully integrated machine control including standard CNC （computer numerical control） and PLC （programmable logic controller） functionality, advanced setpoint generation methods, an extended HMI as well as an FPGA （field programmable gate array）-based controller for a voice coil driven Slow Tool and a piezo driven Fast Tool axis. As the new control system has been implemented as a fully integrated platform using digital communication via EtherCAT, a continuous safety strategy could be realized, the error sensitivity and EMC susceptibility could be significantly decreased and the overall process accuracy from setpoint generation over path interpolation to axes movements could be enhanced. The novel control at the same time offers additional possibilities of automation, process integration, online data acquisition and evaluation as well as error compensation methods.

Compilation and Analysis Method for Computer Aided Pattern Design System Based on Fully-Fashioned Computerized Flat Knitting Machine

The knitting industry CAD/CAM system＇s development needs a solution of computer representation of the designed objects. One of them is a transformation of knitting rows and stitches form knitting pattern and its automatic analysis is to control data from the knitting design data. A method of the pattern CAD system including the analyzing and the compiling of the pattern data was developed in this paper. By rasterizing the pattern map, the drafted pattern could be compiled into knitting row data and stitches. The storage format of knitting rows data and stitches data could be defined by analyzing the motion of computerized fiat knitting machine. By reassembling the control data, it could be analyzed into the execute data of the controller in computerized fiat knitting machine. This method mainly focused on the weak areas of multi-needle-bed technology and fully-fashioned knitting technology in inland and could be applied to most of control systems in the master-slave mode. The knitting row data and pattern row data in a ＂ ＊. ptn＂ format file were integrated. Giving a corresponding analytic calculation process of control data and realizing independent development of the upper machine and the lower machine, the final knitting control data not only have low computation and transmission cost but also have fast transfer speeds.

An approximation algorithm for parallel machine scheduling with simple linear deterioration

In this paper, a parallel machine scheduling problem was considered , where the processing time of a job is a simple linear function of its starting time. The objective is to minimize makespan. A fully polynomial time approximation scheme for the problem of scheduling n deteriorating jobs on two identical machines was worked out. Furthermore, the result was generalized to the case of a fixed number of machines.

Recent Development of Reluctance Machines with Different Winding Configurations,Excitation Methods,and Machine Structures

This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double layer conventional(DLC-),double layer mutually-coupled(DLMC),single layer conventional(SLC-),and single layer mutually-coupled(SLMC-),as well as fully-pitched(FP)winding configurations have been considered for both rectangular wave and sinewave excitations.Different conduction angles such as unipolar􀫚120°elec.,unipolar/bipolar􀫚180°elec.,bipolar􀫛240°elec.and bipolar􀫜360°elec.have been adopted and the most appropriate conduction angles have been obtained for the SRMs with different winding configurations.In addition,with appropriate conduction angles,the 12-slot/14-pole SRMs with modular stator structure is found to produce similar average torque,but lower torque ripple and iron loss when compared to non-modular 12-slot/8-pole SRMs.With sinewave excitation,the doubly salient synchronous reluctance machines with the DLMC winding can produce the highest average torque at high currents and achieve the highest peak efficiency as well.In order to compare with the conventional synchronous reluctance machines(SynRMs)having flux barriers inside the rotor,the appropriate rotor topologies to obtain the maximum average torque have been investigated for different winding configurations and slot/pole number combinations.Furthermore,some prototypes have been built with different winding configurations,stator structures,and slot/pole combinations to validate the predictions.

A secure outsourced Turing- equivalent computation scheme against semi-honest workers using fully homomorphic encryption

A scheme that can realize homomorphic Turing- equivalent privacy-preserving computations is proposed, where the encoding of the Turing machine is independent of its inputs and running time. Several extended private information retrieval protocols based on fully homomorphic encryption are designed, so that the reading and writing of the tape of the Turing machine, as well as the evaluation of the transition function of the Turing machine, can be performed by the permitted Boolean circuits of fully homomorphic encryption schemes. This scheme overwhelms the Turing-machine-to- circuit conversion approach, which also implements the Turing-equivalent computation. The encoding of a Turing- machine-to-circuit conversion approach is dependent on both the input data and the worst-case runtime. The proposed scheme efficiently provides the confidentiality of both program and data of the delegator in the delegator-worker model of outsourced computation against semi-honest workers.

Machine learning promotes the development of all-solid-state batteries

Lithium-ion batteries(LIBs)are a promising energy storage system for green energy applications.However,the use of liquid electrolytes in LIBs results in safety and lifespan issues.To address these challenges,researchers have been focusing on the development of all-solid-state batteries that use solid electrolytes.Unfortunately,traditional methods are time-consuming and expensive for exploring solid-state batteries,limiting their ability to keep up with growing social demand.In recent years,the development of big data has opened up new avenues for materials discovery,allowing for large-scale materials screening through computer simulations and machine learning models that can disclose the structure-activity relationship of materials.This review provides an overview of the basic procedures and common algorithms used in machine learning for designing solid-state batteries,with particular emphasis on recent research progress in applying machine learning to cathode materials and solid electrolytes,as well as predicting the condition of solid-state batteries.Additionally,this review offers a brief outlook on the challenges and opportunities facing machine learning methods in the realm of solid-state batteries.

Unsupervised machine learning to classify crystal structures according totheir structural distortion: A case study on Li-argyrodite solid-state electrolytes

High-throughput approaches in computational materials discovery often yield a combinatorial explosionthat makes the exhaustive rendering of complete structural and chemical spaces impractical. A commonbottleneck when screening new compounds with archetypal crystal structures is the lack of fast and reliabledecision-making schemes to quantitatively classify the computed candidates as inliers or outliers (too distortedstructures). Machine learning-aided workflows can solve this problem and make geometrical optimizationprocedures more efficient. However, for this to occur, there is still a lack of appropriate combinations ofsuitable geometrical descriptors and accurate unsupervised models which are capable of accurately differentiating between systems with subtle structural changes. Here, considering as a case study the compositionalscreening of cubic Li-argyrodites solid electrolytes, we tackle this problem head on. We find that Steinhardtorder parameters are very accurate descriptors of the cubic argyrodite structure to train a range of commonunsupervised outlier detection models. And, most importantly, the approach enables us to automatically classifycrystal structures with uncertainty control. The resulting models can then be used to screen computed structureswith respect to an user-defined error threshold and discard too distorted structures during geometricaloptimization procedures. Implemented as a decision node in computer-aided materials discovery workflows,this approach can be employed to perform autonomous high-throughput screening methods and make the useof computational and data storage resources more efficient.

基于多层全连接神经网络的6C地震波极化向量识别研究

利用机器学习原理,提出一种基于多层全连接(multi-layer fully connected, MFC)神经网络的六分量(six-component, 6C)地震波极化向量识别方法。首先利用6C地震波各波型极化向量数学模型和一系列仿真参数得到5种波型和噪声波型各5 000个极化向量数据集,然后随机选取其中5 000个作为测试集,其余划分为训练集,进行MFC神经网络与支持向量机(support vector machine, SVM)的综合辨识性能对比实验。结果表明,MFC神经网络模型识别5种极化向量类型(SH波和Love波视为一类)和6种极化向量类型的效果均显著优于SVM模型,平均识别率分别达到99.786%和87.940%。

基于YOLOv8的气象设备识别监控算法

在人烟稀少的地区,气象设备的监测与检查面临设备安置位置偏僻、缺乏实时巡检等问题。为解决这一难题,基于在图像识别领域表现卓越的YOLOv8算法,提出了一种新的气象设备识别监控模型,通过将原有的高效的空间金字塔池化(spatial pyramid pooling-fast,SPPF)层替换为空间金字塔池化-全连接空间金字塔卷积(spatial pyramid pooling-fully connected spatial pyramid convolution,SPPFCSPC)层,成功降低了计算量,提升了气象设备检测的速度。为了进一步提升模型在复杂环境下的性能,提出了YOLOv8-SA模型,通过在主干网络(backbone)中加入多头自注意力机制,更精准地捕获图像中不同区域之间的关联性,有力地提高了模型的准确性。为了验证模型的有效性,创建了一个专门的气象设备数据集,并进行了对比实验。实验结果表明,本文提出的YOLOv8-SA模型在检测速度和准确性方面均取得了显著的提升,在自制的数据集中检测精度为98.6%,与传统的YOLOv8模型相比,检测精度提升了0.6%。该模型可有效解决人烟稀少地区气象设备的监测问题,为提升监测系统的实用性和效率提供新思路。

基于STAMP与模型检验的全自动无人驾驶复杂运营场景安全验证方法

与传统列控系统相比,全自动无人驾驶运营场景更加复杂多变,潜在的危险及致因具有更强的隐蔽性和复杂性,给运营安全带来了新的挑战。针对以上问题,提出一种STAMP(Systems-Theoretic Accident Model and Process)与模型检验相结合的复杂运营场景安全验证方法。首先,基于STAMP理论构建运营场景分层控制结构模型,辨识潜在的不安全控制行为、分析危险致因和安全约束;其次,定义分层控制结构模型与安全状态机模型间的基本转换规则,基于分层控制结构模型、安全约束和转换规则,构建运营场景安全状态机模型;最后,针对提取的安全约束,利用数据流图建立安全属性验证模型,结合模型检验技术,对运营场景安全状态机模型进行形式化验证。以全自动无人驾驶运营场景中列车自动进站停车为例,对方法进行验证分析。结果表明,当STAMP理论提取的安全约束通过了场景安全状态机模型的验证时,表示在该场景中对应的不安全控制行为没有发生且不导致相应危险。该方法结合系统安全分析与形式化建模验证的优势,降低了运营场景建模的难度,构建的运营场景形式化模型满足系统安全约束,可以作为全自动无人驾驶系统安全设计和安全改进的重要基础。

环保大数据在区块链中的隐私计算

近年来,随着我国网络安全、医疗及环保相关政策的先后出台,环境数据的价值与日俱增。但我国对于环境数据的科学管理与安全共享仍处于起步阶段,具有隐私保护需求的环境数据数量急剧增加,但在数据共享上却面临数据孤岛化、泄露风险高等诸多难题。针对环境数据共享场景,为从根本上满足用户数据云存储和云计算的隐私保护需求,文章将区块链和隐私计算相结合,采用国密算法构建了一个基于全同态加密和可搜索加密技术的数据密态管理系统。依托区块链部署和云服务存储、隐私计算支持,系统可实现全同态加密机器学习和可搜索加密两种功能。文章基于全同态加密技术实现了神经网络预测模型,并完成了密态数据的云计算;同时,文章采用对称可搜索加密方案,支持数据在全程密态下的远程托管,能够实现密文检索并保护查询关键词的隐私。在保护隐私安全前提下,该方案有效打通了各方数据流通通道,保证数据可用不可见。

蜂窝梁一体化架桥机力学性能研究

蜂窝梁一体化架桥机作为全预制装配式桥梁的重要施工机械,使用期间结构的静动力性能是影响一体化安装施工的重要因素.为揭示其静动力性能,分别采用了应变传感器,基于数字图像相关(DIC)技术的光电图像测量仪对关键截面的应变、挠度进行监测,并对结构的强度、刚度进行分析评估.采用简单移动平均法提取结构的动位移,并进行动力特性分析.此外,建立了结构主梁的有限元模型,并将其分析结果与监测结果进行对比.研究结果表明:有限元分析结果与实测结果一致,验证了有限元分析的准确性,两者均证明了其强度及刚度满足规范要求.基于动位移数据采用峰值拾取法能够有效识别架桥机的自振频率,有限元计算的一阶自振频率为4.08 Hz,相对于实测分析结果的最大误差仅为0.73%.

基于Creo的吹瓶机封口凸轮研究

为了提高全自动旋转式吹瓶机的稳定性,对其封口凸轮进行优化设计。对封头组件的运动过程进行分析,根据组合摆线运动规律,使用Creo软件进行曲线编程,获得封口凸轮曲线。对封口凸轮曲线进行压力角分析、最大加速度验证,并对封口凸轮实物进行试验验证。结果显示,采用组合摆线运动规律设计封口凸轮的吹瓶机单腔平均产能为2750瓶/h,整机平均噪声为78.90 dB(A);和普通方法设计封口凸轮的吹瓶机对比,分别提升22.2%和降低6.18 dB(A)。研究为机械行业的凸轮设计提供借鉴。

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

全自动粉料包装机包装精度的影响因素

介绍了全自动粉料包装机的称重原理,分析了充气压力和环境温度对全自动粉料包装机包装精度的影响。

综采工作面智能化开采技术的发展及应用

综采工作面智能化开采技术代表着煤炭产业的现代化和未来发展。智能化开采技术的发展受综采工作面特性、地质情况和安全要求的制约。通过广泛应用传感技术、不断改进自动化控制系统以及应用功能强大的数据处理与分析技术,智能化开采技术在提高生产效率和安全性方面取得了重要进展。此外,人工智能和机器学习等技术在综采工作面管理和风险预测中的应用,也为煤炭产业带来了巨大的潜力。未来,智能化开采技术前景广阔,包括更高级的自动化系统、精确的地质数据分析和智能决策支持系统。这一发展将提高煤炭产业的生产效率、安全性和可持续性,为行业的可持续发展开辟了新的路径。

我国煤矿智能化综采开采技术装备应用现状与发展思考

煤矿智能化技术是煤炭工业实现高质量发展的核心技术支撑,而煤矿智能开采技术与煤机装备是煤矿智能化建设的关键。在详细分析我国煤矿智能化建设初级阶段现状和煤矿智能化技术装备发展制约因素的基础上,剖析了采煤机自适应规划截割控制、刮板输送设备智能化综合控制、液压支架电液控制、透明工作面地质保障、工作面直线度监控、工作面人员精确定-位及主动防护、工作面AI识别监测等煤矿智能化开采技术的优势和不足,分析结果表明:煤矿智能化建设需求为高端煤机装备产业发展和产业升级带来了前所未有的机遇,现代信息技术与高端煤机装备的深度融合将革新我国煤炭的智能化生产方式,但各种技术瓶颈依旧制约着其高质量发展,距离煤矿实现“少人化、无人化”开采仍存在较大差距。煤矿智能化建设作为一项“人、机、环、管”协同的系统工程,笔者提出了由打破传统的“单兵作战”理念向秉承“有机智能协同”建设理念来破解当前技术瓶颈的建议,例如:加强高端煤机装备向新-数-智转变攻关、完善地-边-端三级垂直安全管控技术、规划感知引导装置由叠加扩充向有机融合转变、建立设备全生命周期数字化管理系统、引导单机智能向群组协同智能转变等建议,以此推动我国煤矿智能化开采煤机装备技术的高质量发展。