Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Utility and Application of a Versatile Analytical Method for MMF Calculation in AC Machines

A versatile analytical method(VAM) for calculating the harmonic components of the magnetomotive force(MMF) generated by diverse armature windings in AC machines has been proposed, and the versatility of this method has been established in early literature. However, its practical applications and significance in advancing the analysis of AC machines need further elaboration. This paper aims to complement VAM by augmenting its theory, offering additional insights into its conclusions, as well as demonstrating its utility in assessing armature windings and its application of calculating torque for permanent magnet synchronous machines(PMSM). This work contributes to advancing the analysis of AC machines and underscores the potential for improved design and performance optimization.

Electromagnetic Performance Analysis of Variable Flux Memory Machines with Series-magnetic-circuit and Different Rotor Topologies

In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Recent Advances in Video Coding for Machines Standard and Technologies

To improve the performance of video compression for machine vision analysis tasks,a video coding for machines(VCM)standard working group was established to promote standardization procedures.In this paper,recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases,requirements,evaluation frameworks and corresponding metrics of the VCM standard are given.Then the existing methods are presented,introducing the existing proposals by category and the research progress of the latest VCM conference.Finally,we give conclusions.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the client.Clients can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and value.One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’projects.The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients.The projects belong to OSMO vendors,having offices in developing countries while providing services to developed countries.In the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model.The proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden layers.The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies.The current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

Two-Agent Makespan Minimization Problem on Parallel Machines

A two-agent scheduling problem on parallel machines is considered in this paper. Our objective is to minimize the makespan for agent A, subject to an upper bound on the makespan for agent B. In this paper, we provide a new approximation algorithm called CLPT. On the one hand, we compare the performance between the CLPT algorithm and the optimal solution and find that the solution obtained by the CLPT algorithm is very close to the optimal solution. On the other hand, we design different experimental frameworks to compare the CLPT algorithm and the A-LS algorithm for a comprehensive performance evaluation. A large number of numerical simulation results show that the CLPT algorithm outperformed the A-LS algorithm.

The Advantages of Using Rotating Machines with Profiled Rotors

In order to achieve a lower consumed energy, the performance of a new type of rotating volumetric pump with two profiled rotors (variant I) which is compared with a centrifugal pump (variant II) is presented. The analysis regarding the same flow rate of transported liquid and the same pressure increases points out the conduct of the system at the variation of the key operating parameters. The actual driving power of the rotating volumetric pump is higher stating that is more advantageous in operation. The effective efficiency of the system is improved due to the original constructive solution.

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization,the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where informa-tion regarding all the buses connecting in a city will be gathered,processed and accurate bus arrival time prediction will be presented to the user.Various linear and time-varying parameters such as distance,waiting time at stops,red signal duration at a traffic signal,traffic density,turning density,rush hours,weather conditions,number of passengers on the bus,type of day,road type,average vehi-cle speed limit,current vehicle speed affecting traffic are used for the analysis.The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area.Multiple ELMs(MELM)for explicitly training dynamic,road and trajectory information are used in the proposed approach.A large-scale dataset(historical data)obtained from Kerala State Road Transport Corporation is used for training.Simulations are carried out by using MATLAB R2021a.The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week.It can manage huge volumes of data with less human intervention at greater learning speeds.It is found MELM yields prediction with accuracy in the range of 96.7%to 99.08%.The MAE value is between 0.28 to 1.74 minutes with the proposed approach.The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy.The research has proved that MELM is superior for arrival time pre-dictions in terms of accuracy and error,compared with other approaches.

Homogeneous Batch Memory Deduplication Using Clustering of Virtual Machines

Virtualization is the backbone of cloud computing,which is a developing and widely used paradigm.Byfinding and merging identical memory pages,memory deduplication improves memory efficiency in virtualized systems.Kernel Same Page Merging(KSM)is a Linux service for memory pages sharing in virtualized environments.Memory deduplication is vulnerable to a memory disclosure attack,which uses covert channel establishment to reveal the contents of other colocated virtual machines.To avoid a memory disclosure attack,sharing of identical pages within a single user’s virtual machine is permitted,but sharing of contents between different users is forbidden.In our proposed approach,virtual machines with similar operating systems of active domains in a node are recognised and organised into a homogenous batch,with memory deduplication performed inside that batch,to improve the memory pages sharing efficiency.When compared to memory deduplication applied to the entire host,implementation details demonstrate a significant increase in the number of pages shared when memory deduplication applied batch-wise and CPU(Central processing unit)consumption also increased.

A Study of Spatial Construction in Machines Like Me

Technology has advanced quickly in recent years,cutting-edge artificial intelligence research has been conducted,and artificial intelligence is now pervasive in all aspects of our daily lives.Since its publication,Machines Like Me by Ian McEwan has drawn a lot of interest from people from all walks of life.Interest in AI has never been higher.The book is regarded as a sincere examination of humanity’s dilemma in the future.This thesis will examine McEwan’s outlook on the future of robots and his treatment of them as fellow humans by focusing on the construction of space in the book.

Machines,Tools and Tool Transporter Concurrent Scheduling in Multi⁃machine FMS with Alternative Routing Using Symbiotic Organisms Search Algorithm

This study explored the concurrent scheduling of machines, tools, and tool transporter(TT) with alternative machines in a multi-machine flexible manufacturing system(FMS), taking into mind the tool transfer durations for minimization of the makespan(MSN). When tools are expensive, just a single copy of every tool kind is made available for use in the FMS system. Because the tools are housed in a central tool magazine(CTM), which then distributes and delivers them to many machines, because there is no longer a need to duplicate the tools in each machine, the associated costs are avoided. Choosing alternative machines for job operations(jb-ons), assigning tools to jb-ons, sequencing jb-ons on machines, and arranging allied trip activities, together with the TT’s loaded trip times and deadheading periods, are all challenges that must be overcome to achieve the goal of minimizing MSN. In addition to a mixed nonlinear integer programming(MNLIP) formulation for this simultaneous scheduling problem, this paper suggests a symbiotic organisms search algorithm(SOSA) for the problem’s solution. This algorithm relies on organisms’ symbiotic interaction strategies to keep living in an ecosystem. The findings demonstrate that SOSA is superior to the Jaya algorithm in providing solutions and that using alternative machines for operations helps bring down MSN.

Hybrid Multi-Object Optimization Method for Tapping Center Machines

This paper proposes a hybrid multi-object optimization method integrating a uniform design,an adaptive network-based fuzzy inference system(ANFIS),and a multi-objective particle swarm optimizer(MOPSO)to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control(CNC)machines.First,rigid tapping parameters and uniform(including 41-level and 19-level)layouts were adopted to collect representative data for modeling.Next,ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data.In tapping center machines,the synchronization errors and cycle times are important consid-erations,so these two objects were used to build the ANFIS models.Then,a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously.The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the nonsystematic method.Additionally,the optimal parameter combination was used to optimize existing CNC tools during the commissioning process.Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping.The posi-tion gain and prefeedback coefficient can reduce the synchronization errors significantly,and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors.The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms;it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield,there-by helping factories achieve carbon reduction.

日光温室薄膜全自动清洗机研制与试验

针对日光温室薄膜人工清洗劳动强度大、自动化清洗设备缺乏的现状,该研究设计了一种日光温室薄膜全自动清洗机。为减小整机质量并降低成本,采用双蜗轮蜗杆电机分别驱动清洗主机的毛刷与爬升轴;设计地面移位换行装置,用以承载清洗主机沿温室长度方向移动;为实现自动换行清洗,设计棚顶移位换行装置,调整棚顶吊绳安装高度,避免吊绳在换行作业时与棚面产生干涉而影响换行质量或损伤薄膜。基于多传感器融合与数据校验技术,保证清洗主机、棚顶与地面移位装置间的协作实时性、一致性及可靠性。为验证设计方案的合理性与可行性,以参照日光温室跨度、脊高、肩高参数10:1制作模型温室,按外形尺寸5:1加工清洗样机,并进行对齐、倾斜偏移及清洗效果验证试验。结果表明,清洗主机升降时的水平偏移量在±3°以内,左右偏移量在±7 mm以内(换行操作时各偏移量无累积);地面与棚顶移位换行装置的单次换行误差与多次换行累计误差均在1 mm左右;毛刷材料选型与关键参数设计合理,可对薄膜表面灰尘进行有效清洗,洗净率为95.4%,清洗效果明显。该清洗机在丰富国内日光温室清洗装备类型的同时,有效解决了团队前期研发清洗机质量大、易伤膜、自动化水平低等问题,为温室大棚薄膜相关清洗设备的设计研发提供了参考。

可解释人工智能在电力系统中的应用综述与展望

可解释人工智能(XAI)作为新型人工智能(AI)技术,具有呈现AI过程逻辑、揭示AI黑箱知识、提高AI结果可信程度的能力。XAI与电力系统的深度耦合将加速AI技术在电力系统的落地应用,在人机交互的过程中为电力系统的安全、稳定提供助力。文中梳理了电力系统XAI的历史脉络、发展需求及热点技术,总结了XAI在源荷预测、运行控制、故障诊断、电力市场等方面的电力应用,并围绕解释含义、迭代框架、数模融合等方面展望了电力系统XAI的应用前景,可为推动电力系统智能化转型与人机交互迭代提供理论参考与实践思路。

智慧学习环境中的人机协同设计

作为教育数字化转型的首要任务,智慧学习环境建设过分强调技术之于教育的能力,而忽略教育主体的价值与地位,涌现出场景割裂、数据孤岛等问题。人机协同旨在充分发挥人与机器的优势,弥补彼此的劣势,成为指导智慧学习环境创设与优化的最优解。研究将人机协同视为智慧学习环境设计的基线思维,构建了由数据模型层、技术支撑层和场景应用层三个层级,包含场景、数据、模型、资源、工具与服务等六个要素的智慧学习环境概念模型。基于普瑞斯的人机功能分配决策矩阵理论,提出了AI讲师、执行型AI+人类助手、伙伴型AI+人类同侪、助教型AI+人类教练、人类导师等五种人机协同模式。在此基础上,研究制定了智慧学习环境各层级的设计原则,分析了数据模型层的决策协同设计、技术支撑层的交互协同设计和场景应用层的流程协同设计,讨论了人机协同模式中人机互信和价值对齐的建构策略,以期指导智慧学习环境中的人机协同设计。

机器学习方法在盾构隧道工程中的应用研究现状与展望

随着盾构隧道工程信息化水平的提升,隧道掘进设备作业过程监测技术日益完善,记录的工程数据蕴含了掘进设备内部信息及其与外部地层的相互作用关系。机器学习因其数据分析能力强,无需先验的理论公式和专家知识,相较于传统的建模统计分析方法具有更大的应用空间。通过机器学习方法对收集的信息与数据进行深度挖掘并分析其内在联系,有助于提升盾构隧道工程建设的效率和安全保障水平。简述机器学习方法的基本原理,总结和分析机器学习方法在盾构工程中的应用研究状况,综述基于机器学习的盾构设备状态分析、盾构设备性能预测、围岩参数反演、地表变形预测和隧道病害诊断等5个方面的进展,并分析当前研究的不足。最后,分析盾构隧道工程向智能化方向发展需重点攻克的难题。

改进YOLOv7算法的钢材表面缺陷检测研究

当前,基于深度学习的智能检测技术逐步应用于钢材表面缺陷检测领域,针对钢材表面缺陷检测精度低的问题,提出一种高精度实时的缺陷检测算法CDN-YOLOv7。加入CARAFE轻量化上采样算子来改善网络特征融合能力,融合级联注意力机制和解耦头重新设计YOLOv7检测头网络,旨在解决原始头网络特征利用效率不高的问题,使其充分利用各尺度、通道、空间的多维度信息,提升复杂场景下模型表征能力。引入归一化Wasserstein距离重新设计Focal-EIoU损失函数,提出NF-EIoU替换CIoU损失,平衡各尺度缺陷样本对Loss的贡献,降低各尺度缺陷的漏检率。实验结果表明,CDN-YOLOv7的检测精度可达80.3%,较于原YOLOv7精度提升了6.0个百分点,模型推理速度可达60.8帧/s,满足实时性需求,CDN-YOLOv7在提升各尺度缺陷检测精度的同时显著降低了缺陷的漏检率。