Permanent Magnet Temperature Estimation for PMSMs Using Virtual Position-offset Injection

This paper proposes a virtual position-offset injection based permanent magnet temperature estimation approach for permanent magnet synchronous machines(PMSMs). The concept of virtual position-offset injection is mathematically transforming the machine model to a virtual frame with a position-offset. The virtual frame temperature estimation model is derived to calculate the permanent magnet temperature(PMT) directly from the measurements with computation efficiency. The estimation model involves a combined inductance term, which can simplify the establishment of saturation compensation model with less measurements. Moreover, resistance and inverter distorted terms are cancelled in the estimation model, which can improve the robustness to the winding temperature rise and inverter distortion. The proposed approach can achieve simplified computation in temperature estimation and reduced memory usage in saturation compensation. While existing model-based approaches could be affected by either the need of resistance and inverter information or complex saturation compensation. Experiments are conducted on the test machine to verify the proposed approach under various operating conditions.

Robust H_(∞)state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks

We investigate the dynamic event-triggered state estimation for uncertain complex networks with hybrid delays suffering from both deception attacks and denial-of-service attacks.Firstly,the effects of time-varying delays and finitedistributed delays are considered during data transmission between nodes.Secondly,a dynamic event-triggered scheme(ETS)is introduced to reduce the frequency of data transmission between sensors and estimators.Thirdly,by considering the discussed plant,dynamic ETS,state estimator,and hybrid attacks into a unified framework,this framework is transferred into a novel dynamical model.Furthermore,with the help of Lyapunov stability theory and linear matrix inequality techniques,sufficient condition to ensure that the system is exponentially stable and satisfies H∞performance constraints is obtained,and the design algorithm for estimator gains is given.Finally,two numerical examples verify the effectiveness of the proposed method.

Investigating the characteristic delay time in the leader–follower behavior in children single-file movement

The single-file movement experiment offered a convenient way to investigate the one-dimensional leader–follower behavior of pedestrians. This study investigated the time delays of children pedestrians in the leader–follower behavior by introducing a time-dependent delayed speed correlation. A total of 118 German students from the fifth grade(aged11–12 years old) and the 11th grade(aged 17–18 years old) participated the single-file experiment. The characteristic delay time for each pedestrian was identified by optimising the time-dependent delayed speed correlation. The influences of the curvature of the experimental scenario, density, age, and gender on the delay time were statistically examined. The results suggested that to a large extent, the revealed characteristic delay time was a density-dependent variable, and none of the curvatures, the age and gender of the individual, and the age and gender of the leader had a significant influence on it. The findings from this study are variable resources to understand the leader–follower behavior among children pedestrians and to build related simulation models.

Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable responses.However,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic responses.In this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training corpus.The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation.Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation.For reproducibility,we release the code and data publicly at:https://github.com/siat‐nlp/HSEMEC‐code‐data.

Adaptive distributed observer design for containment control of heterogeneous discrete-time swarm systems

This paper develops both adaptive distributed dynamic state feedback control law and adaptive distributed measurement output feedback control law for heterogeneous discrete-time swarm systems with multiple leaders.The convex hull formed by the leaders and the system matrix of leaders is estimated via an adaptive distributed containment observer.Such estimations will feed the followers so that every follower can update the system matrix of the corresponding adaptive distributed containment observer and the system state of their neighbors.The followers cooperate with each other to achieve leader-follower consensus and thus solve the containment control problem over the network.Numerical results demonstrate the effectiveness and computational feasibility of the proposed control laws.

SpikeGoogle:Spiking Neural Networks with GoogLeNet-like inception module

Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work presents a new network called SpikeGoogle,which is implemented with GoogLeNet-like inception module.In this inception module,different convolution kernels and max-pooling layer are included to capture deep features across diverse scales.Experiment results on small NMNIST dataset verify the results of the authors’proposed SpikeGoogle,which outperforms the previous Spiking Convolutional Neural Network method by a large margin.

Mechanical Analysis and Performance Optimization for the Lunar Rover’s Vane-Telescopic Walking Wheel

It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance.First,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the moon.Next,the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption.This was achieved via analysis of the wheel force during movement.Finally,the effectiveness of the proposed method was demonstrated by several simulation experiments.The newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.

Workspace,stiffness analysis and design optimization of coupled active-passive multilink cable-driven space robots for on-orbit services

The use of space robots(SRs)for on-orbit services(OOSs)has been a hot research topic in recent years.However,the space unstructured environment(i.e.:confined spaces,multiple obstacles,and strong radiation interference)has greatly restricted the application of SRs.The coupled active-passive multilink cable-driven space robot(CAP-MCDSR)has the characteristics of slim body,flexible movement,and electromechanical separation,which is very suitable for extreme space environments.However,the dynamic and stiffness modeling of CAP-MCDSRs is challenging,due to the complex coupling among the active cables,passive cables,joints,and the end-effector.To deal with these problems,this paper proposes a workspace,stiffness analysis and design optimization method for such type of MCDSRs.Firstly,the multi-coupling kinematics relationships among the joint,cables and the end-effector are established.Based on hybrid series-parallel characteristics,the improved coupled active–passive(CAP)dynamic equation is derived.Then,the maximum workspace,the maximum stiffness,and the minimum cable tension are resolved,among them,the overall stiffness is the superposition of the stiffness produced by the active and the passive cable.Furthermore,the workspace,the stiffness,and the cable tension are analyzed by using the nonlinear optimization method(NOPM).Finally,an 8-DOF CAP-MCDSR experiment system is built to verify the proposed modeling and trajectory tracking methods.The proposed modeling and analysis results are very useful for practical space applications,such as designing a new CAP-MCDSR,or utilizing an existing CAP-MCDSR system.

Temporal-spatial dynamic characteristics of vehicle emissions on intercity roads in Guangdong Province based on vehicle identity detection data

Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities.Considering the variability of emissions caused by vehicles,roads,and traffic,the 24-hour change characteristics of air pollutants(CO,HC,NO_(X),PM_(2.5))on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July 2018.The results showed that the spatial diversity of emissions caused by the unbalanced economywas obvious.The vehicle emissions in the Pearl River Delta region(PRD)with a higher economic level were approximately 1–2 times those in the non-Pearl RiverDelta region(non-PRD).Provincial roads with high loads became potential sources of high emissions.Therefore,emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater reduction.Gasoline passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions,contributing more than 50%in the daytime(7:00–23:00)and higher than 26%at night(0:00–6:00).Diesel trucks made up 10%of traffic,but were the dominant player at night,contributed 50%–90%to NO_(X) and PM_(2.5) emissions,with amarked 24-hour change rule of more than 80%at night(23:00–5:00)and less than 60%during daytime.Therefore,targeted control measures by time-section should be set up on collaborative control.These findings provide time-varying decision support for variable vehicle emission control on a large scale.

Toward a personalized autonomous transportation system:Vision,challenges,and solutions

The fragmented design of intelligent transportation systems creates isolated intelligent systems.Resource competition and information gaps are fierce and widespread,worsening traffic issues and degrading overall service levels.Therefore,empowered by advanced technologies,an evolution toward an autonomous transportation system(ATS)is observed.This evolution aims to develop a collaborative and sustainable ecosystem,prompting interoperability within the cloud-edge-device continuum.

Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

Dear Editor,Electric vehicle(EV)sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation.1 However,the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity,resulting in longer reserve and waiting times.2 Moreover,severe mobility restrictions caused by infectious diseases,such as coronavirus disease 2019(COVID-19),have greatly affected people’s travel behavior3,4 and hindered their willingness to use EVs,given that charging in public spaces consumes time and increases the risk of contracting the virus.5 This implies that in the postpandemic era,in which individuals coexist with the virus,the interplay between the two important trends,namely vehicle electrification and mobility restrictions,can extensively affect people’s daily commuting by using EVs.

Variation of spatio-temporal distribution of on-road vehicle emissions based on real-time RFID data

High-resolution vehicular emissions inventories are important for managing vehicular pollution and improving urban air quality. This study developed a vehicular emission inventory with high spatio-temporal resolution in the main urban area of Chongqing, based on realtime traffic data from 820 RFID detectors covering 454 roads, and the differences in spatiotemporal emission characteristics between inner and outer districts were analysed. The result showed that the daily vehicular emission intensities of CO, hydrocarbons, PM2.5, PM10,and NO_(x) were 30.24, 3.83, 0.18, 0.20, and 8.65 kg/km per day, respectively, in the study area during 2018. The pollutants emission intensities in inner district were higher than those in outer district. Light passenger cars(LPCs) were the main contributors of all-day CO emissions in the inner and outer districts, from which the contributors of NO_(x) emissions were different. Diesel and natural gas buses were major contributors of daytime NO_(x) emissions in inner districts, accounting for 40.40%, but buses and heavy duty trucks(HDTs) were major contributors in outer districts. At nighttime, due to the lifting of truck restrictions and suspension of buses, HDTs become the main NO_(x) contributor in both inner and outer districts,and its three NO_(x) emission peak hours were found, which are different to the peak hours of total NO_(x) emission by all vehicles. Unlike most other cities, bridges and connecting channels are always emission hotspots due to long-time traffic congestion. This knowledge will help fully understand vehicular emissions characteristics and is useful for policymakers to design precise prevention and control measures.

Numerical study on aero-optical imaging deviations of vehicles at different altitudes

When the vehicle is flying in the atmosphere at high speed, the optical head and the atmosphere will have severe friction, thus forming a complex flow field, which makes the target image shift in the optical imaging system. The influence of altitude on aero-optical imaging deviation is studied in this paper. The geometric modeling and mesh generation of a typical blunt nosed high-speed vehicle were carried out, and the three-dimensional(3 D) flow field density was obtained by a large amount of computational fluid dynamic calculation. In order to complete the optical calculation, the backward ray tracing method and the backward ray tracing stop criterion were used. The results show that as the height increases, the imaging deviation decreases gradually, and the imaging deviation slope increases and tends to be flat and close to zero.

Evaluation of external costs in road transport under the openness of a gated community

Residential quarters in Chinese cities are usually walled off from their surrounding roads for security purposes.Recently,the Chinese government has decided to thoroughly open gated residential communities in order to improve traffic capacity and coordinate major roads in the road network,which will inevitably pose challenges,such as environmental pollution,for community members.Unfortunately,before this decision,there were no comprehensive investigations into whether this measure works for road traffic or how much the adverse impact exerts upon residents.Here,we propose a comprehensive method combining microscopic traffic simulation with a vehicle exhaust emission and dispersion model and a noise emission and attenuation model,in addition to a consideration of social cost,to evaluate the possible influence of opening an enclosed residential community to surrounding roads.The validity of the hybrid model was assessed by an assumptive case of two rectangular gated communities under varying traffic flow and five community opening modes.Preliminary results indicate that the opened community outperforms the gated in the most of 49 percent reduction in comprehensive cost.A more detailed analysis reveals that the appropriate extent of openness should rely on the actual situation,and potentially serves as a foundation for the healthy development of communities and cities.Based on the case study results,this paper outlines some strategical suggestions for improving enclosed residential areas by striking a better balance between traffic capacity and environmental risks.

TCMBank-the largest TCM database provides deep learningbased Chinese-Western medicine exclusion prediction

Dear Editor,In the modernization of traditional Chinese medicine(TCM),two key aspects are determining the active ingredients in herbs and elucidating the mechanism of action between the active ingredients and targets.The construction of a comprehensive and highly-reliability TCM database is highly desirable.Since its establishment in 2011,our TCM Database@Taiwan1 has been used extensively and heavily cited,and it also has been included in the ZINC database.2 Using natural language processing,we set up a knowledge graph and molecular signaling pathways to establish a TCM database,TCMBank(https://TCMBank.cn/),which extends from TCM Database@Taiwan and includes 9192 herbs,61,966 ingredients,15,179 targets,and 32,529 diseases.The updated TCMBank expanded the number of herbal ingredients from 32,364 to 61,966(unduplicated),and two new data fields,targets,and diseases,have been added.The number of herbs with connection information is 9010,and the average number of connection edges of herbs is 16.05.The number of ingredients with connection information is 54,676,and the average number of connection edges of herbs is 5.26.

Aquaporin 9 is involved in CRC metastasis through DVL2-dependent Wnt/β-catenin signaling activation

Background:Aquaporin 9(AQP9)is permeable to water or other small molecules,and plays an important role in various cancers.We previously found that AQP9 was related to the efficacy of chemotherapy in patients with colorectal cancer(CRC).This study aimed to identify the role and regulatory mechanism of AQP9 in CRC metastasis.Methods:The clinical significance of AQP9 was analysed by using bioinformatics and tissue microarray.Transcriptome sequencing,Dual-Luciferase Reporter Assay,Biacore,and co-immunoprecipitation were employed to demonstrate the regulatory mechanism of AQP9 in CRC.The relationship between AQP9 and CRC metastasis was verified in vitro and in vivo by using real-time cell analysis assay,high content screening,and liver metastasis models of nude mice.Results:We found that AQP9 was highly expressed in metastatic CRC.AQP9 overexpression reduced cell roundness and enhanced cell motility in CRC.We further showed that AQP9 interacted with Dishevelled 2(DVL2)via the C-terminal SVIM motif,resulting in DVL2 stabilization and the Wnt/b-catenin pathway activation.Additionally,we identified the E3 ligase neural precursor cell expressed developmentally downregulated 4-like(NEDD4L)as a modulator regulating the ubiquitination and degradation of AQP9.Conclusions:Collectively,our study revealed the important role of AQP9 in regulating DVL2 stabilization and Wnt/β-catenin signaling to promote CRC metastasis.Targeting the NEDD4L–AQP9–DVL2 axis might have therapeutic usefulness in metastatic CRC treatment.

Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

Lane-change path planning and control method for self-driving articulated trucks

Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles.A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads.With different steering and braking maneuvers,minimum safe distances were modeled and calculated.Considering safety and ergonomics,the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change.Furthermore,a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability.Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.Findings–The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks.The proposed trajectory model could provide safety lane-change path planning,and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.Originality/value–This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles.There are two main contributions:thefirst is a more quantifiable trajectory model for self-driving articulated vehicles,which provides the opportunity to adapt vehicle and scene changes.The second involves designing a feedback linearization controller,combined with a multi-objective decision-making mode,to improve the comprehensive performance of intelligent vehicles.This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

Router and gateway node placement in wireless mesh networks for emergency rescue scenarios

The focus of this paper is on base functionalities required for UAV-based rapid deployment of an ad hoc communication infrastructure in the initial phases of rescue operations.The main idea is to use heterogeneous teams of UAVs to deploy communication kits that include routers,and are used in the generation of ad hoc Wireless Mesh Networks(WMN).Several fundamental problems are considered and algorithms are proposed to solve these problems.The Router Node Placement problem(RNP)and a generalization of it that takes into account additional constraints arising in actual field usage is considered first.The RNP problem tries to determine how to optimally place routers in a WMN.A new algorithm,the RRT-WMN algorithm,is proposed to solve this problem.It is based in part on a novel use of the Rapidly Exploring Random Trees(RRT)algorithm used in motion planning.A comparative empirical evaluation between the RRT-WMN algorithm and existing techniques such as the Covariance Matrix Adaptation Evolution Strategy(CMA-ES)and Particle Swarm Optimization(PSO),shows that the RRT-WMN algorithm has far better performance both in amount of time taken and regional coverage as the generalized RNP problem scales to realistic scenarios.The Gateway Node Placement Problem(GNP)tries to determine how to locate a minimal number of gateway nodes in a WMN backbone network while satisfying a number of Quality of Service(QoS)constraints.Two alternatives are proposed for solving the combined RNP-GNP problem.The first approach combines the RRT-WMN algorithm with a preexisting graph clustering algorithm.The second approach,WMNbyAreaDecomposition,proposes a novel divide-and-conquer algorithm that recursively partitions a target deployment area into a set of disjoint regions,thus creating a number of simpler RNP problems that are then solved concurrently.Both algorithms are evaluated on real-world GIS models of different size and complexity.WMNbyAreaDecomposition is shown to outperform existing algorithms using 73%to 92%fewer router nodes while at the same time satisfying all QoS requirements.