A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to collisions.Based on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is proposed.The algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame observation.By grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID systems.Simulation outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

Effects of drive imbalance on the particle emission from a Bose-Einstein condensate in a one-dimensional lattice

Time-periodic driving has been an effective tool in the field of nonequilibrium quantum dynamics,which enables precise control of the particle interactions.We investigate the collective emission of particles from a Bose-Einstein condensate in a one-dimensional lattice with periodic drives that are separate in modulation amplitudes and relative phases.In addition to the enhancement of particle emission,we find that amplitude imbalances lead to energy shift and band broadening,while typical relative phases may give rise to similar gaps.These results offer insights into the specific manipulations of nonequilibrium quantum systems with tone-varying drives.

Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Controlled thermally-driven mass transport in carbon nanotubes using carbon hoops

Controlling mass transportation using intrinsic mechanisms is a challenging topic in nanotechnology.Herein,we employ molecular dynamics simulations to investigate the mass transport inside carbon nanotubes(CNT)with temperature gradients,specifically the effects of adding a static carbon hoop to the outside of a CNT on the transport of a nanomotor inside the CNT.We reveal that the underlying mechanism is the uneven potential energy created by the hoops,i.e.,the hoop outside the CNT forms potential energy barriers or wells that affect mass transport inside the CNT.This fundamental control of directional mass transportation may lead to promising routes for nanoscale actuation and energy conversion.

A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Review of Three-phase Soft Switching Inverters and Challenges for Motor Drives

For electric vehicles (EVs),it is necessary to improve endurance mileage by improving the efficiency.There exists a trend towards increasing the system voltage and switching frequency,contributing to improve charging speed and power density.However,this trend poses significant challenges for high-voltage and high-frequency motor controllers,which are plagued by increased switching losses and pronounced switching oscillations as consequences of hard switching.The deployment of soft switching technology presents a viable solution to mitigate these issues.This paper reviews the applications of soft switching technologies for three-phase inverters and classifies them based on distinct characteristics.For each type of inverter,the advantages and disadvantages are evaluated.Then,the paper introduces the research progress and control methods of soft switching inverters (SSIs).Moreover,it presents a comparative analysis among the conventional hard switching inverters (HSIs),an active clamping resonant DC link inverter (ACRDCLI) and an auxiliary resonant commuted pole inverter (ARCPI).Finally,the problems and prospects of soft switching technology applied to motor controllers for EVs are put forward.

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Force Compensation Control for Electro-Hydraulic Servo System with Pump-Valve Compound Drive via QFT-DTOC

Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit(HDU),which features a high power-weight ratio.However,most HDUs are throttling-valve-controlled cylinder systems,which exhibit high energy losses.By contrast,pump control systems offer a high efficiency.Nevertheless,their response ability is unsatisfactory.To fully utilize the advantages of pump and valve control systems,in this study,a new type of pump-valve compound drive system(PCDS)is designed,which can not only effectively reduce the energy loss,but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of robots.Herein,considering the force control requirements of energy conservation,high precision,and fast response of the robot joint HDU,a nonlinear mathematical model of the PCDS force control system is first introduced.In addition,pressure-flow nonlinearity,friction nonlinearity,load complexity and variability,and other factors affecting the system are considered,and a novel force control method based on quantitative feedback theory(QFT)and a disturbance torque observer(DTO)is designed,which is denoted as QFT-DTOC herein.This method improves the control accuracy and robustness of the force control system,reduces the effect of the disturbance torque on the control performance of the servo motor,and improves the overall force control performance of the system.Finally,experimental verification is performed using the PCDS performance test platform.The experimental results and quantitative data show that the QFT-DTOC proposed herein can significantly improve the force control performance of the PCDS.The relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

Heberlein jet technology drives innovations in demanding technical applications

Heberlein Technology AG,Wattwil,Switzerland,March 7,2024–Unique technology for creating advanced technical textiles with exquisite characteristics was featured at the Techtextil in Germany.Heberlein,the leading supplier of air interlacing and air texturing jets,plans to convince visitors to the event with critical components–such as the latest PolyJet-TG-3–that can influence both yarn properties and processes.

Nullmax纽劢 MaxDrive行泊一体方案

是目前市面上极少有的真融合行泊一体4.0形态方案,具有高准度、高稳定、强性能、易落地的产品优势,传感器深度复用、芯片资源共享、安全高效经济,可满足客户各类需求。立足于Nullmax平台化BEV-AI技术架构,兼顾技术性能和部署成本的双重需求,支持高中低不同算力平台,适配任意传感器,可根据需求进行定制化开发,快速完成全场景智能驾驶功能的量产应用,且向上拓展性极强。

毫末智行 自动驾驶大模型 DriveGPT雪湖·海若

行业首个自动驾驶生成式大模型DriveGPT(雪湖·海若)为大模型技术落地自动驾驶提供了新范式。DriveGPT是由毫末自研的自动驾驶认知大模型升级而来,最初目的主要是解决自动驾驶的认知决策优化问题,其基本过程是在认知模块引入真实人驾数据,通过人类反馈强化学习技术,对自动驾驶认知决策模型进行持续优化。当前毫末正在通过引入感知大模型,将DriveGPT升级为通用大模型架构,目标是实现端到端自动驾驶。

A Physical Layer Algorithm for Estimation of Number of Tags in UHF RFID Anti-Collision Design

A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems.The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust access probability in random access protocols.Conventional researches estimate the number of tags in MAC layer based on statistics of empty slots,collided slots and successful slots.Usually,a collision detection algorithm is employed to determine types of time slots.Only three types are distinguished because of lack of ability to detect the number of tags in single time slot.In this paper,a physical layer algorithm is proposed to detect the number of tags in a collided slot.Mean shift algorithm is utilized,and some properties of backscatter signals are investigated.Simulation results verify the effectiveness of the proposed solution in terms of low estimation error with a high SNR range,outperforming the existing MAC layer approaches.

A Novel Time-aware Frame Adjustment Strategy for RFID Anti-collision

Recently,object identification with radio frequency identification(RFID)technology is becoming increasingly popular.Identification time is a key performance metric to evaluate the RFID system.The present paper analyzes the deficiencies of the state-of-the-arts algorithms and proposes a novel sub-frame-based algorithm with adaptive frame breaking policy to lower the tag identification time for EPC global C1 Gen2 UHF RFID standard.Through the observation of slot statistics in a sub-frame,the reader estimates the tag quantity and efficiently calculates an optimal frame size to fit the unread tags.Only when the expected average identification time in the calculated frame size is less than that in the previous frame size,the reader starts the new frame.Moreover,the estimation of the proposed algorithm is implemented by the look-up tables,which allows dramatically reduction in the computational complexity.Simulation results show noticeable throughput and time efficiency improvements of the proposed solution over the existing approaches.

An Improved Binary Search Anti-Collision Protocol for RFID Tag Identification

Radio frequency identification(RFID)has been widespread used in massive items tagged domains.However,tag collision increases both time and energy consumption of RFID network.Tag collision can seriously affect the success of tag identification.An efficient anti-collision protocol is very crucially in RFID system.In this paper,an improved binary search anti-collision protocol namely BRTP is proposed to cope with the tag collision concern,which introduces a Bi-response mechanism.In Bi-response mechanism,two groups of tags allowed to reply to the reader in the same slot.According to Bi-response mechanism,the BRTP strengthens the tag identification of RFID network by reducing the total number of queries and exchanged messages between the reader and tags.Both theoretical analysis and numerical results verify the effectiveness of the proposed BRTP in various performance metrics including the number of total slots,system efficiency,communication complexity and total identification time.The BRTP is suitable to be applied in passive RFID systems.

Performance Analysis of RFID Framed Slotted Aloha Anti-Collision Protocol

In this paper, we develop a novel mathematical model to estimate the probability distribution function of the number of tags discovered after a certain number of interrogation rounds. In addition, the pdfs of the number of rounds needed to discover all the tags are also calculated. The estimation of such pdfs will be helpful in estimating the number of interrogation rounds and the optimal parameter configuration of the RFID system which in turn will be helpful in estimating the time needed to discover all tags. Our results show that the proposed model accurately predicts the tags detection probability. We then use the proposed model to optimally configure the reader parameters (i.e. the frame size and the number of interrogation rounds).

Distance-Measuring Technology Using LD for Auto Anti-Collision

Auto anti-collision technology is one of the main research subjects of automobiles’ safety technology. Aiming at the key technology of Auto anti-collision, measuring the distance from obstacles, based on the theory of phase laser distance ranging, Laser Diode (LD) distance-measuring system for auto anti-collision has been developed to solve the problem of on-line measuring distance technology in middle to long distance utilizing the good characteristics of LD when modulating its optical intensity and adopting typical kinds of filter techniques in this paper. By theoretical analysis, adopting typical kinds of filter techniques can reduce the interference of strong light, so distance-measuring range can be 0.5–100 m in daytime or 1–200 m at night. And more, from theoretical analysis and experiment result, it can guarantee the high measuring resolution which can be less than 24.5 mm, utilizing the method of two Laser Diode optical intensity modulating wavelength and complimenting precise calibration and revision. The idea of LD distance-measuring technology is novel and feasible and this technology can be applied in Auto anti-collision. Key words laser diode - phase laser distance ranging - filter techniques - auto anti-collision CLC number TH 161 Foundation item: Supported by the National Natural Science Foundation of China (59675080, 59805006) and Wuhan Chenguang Foundation (20025001001)Biography: Zhang Xin-bao (1965-), male, Associate professor, research direction: precise mechanism and instrument.

A Frame Breaking Based Hybrid Algorithm for UHF RFIDAnti-Collision

Multi-tag collision imposes a vital detrimental effect on reading performanceof an RFID system. In order to ameliorate such collision problem and to improve thereading performance, this paper proposes an efficient tag identification algorithm termedas the Enhanced Adaptive Tree Slotted Aloha (EATSA). The key novelty of EATSA is toidentify the tags using grouping strategy. Specifically, the whole tag set is divided intogroups by a frame of size F. In cases multiple tags fall into a group, the tags of the groupare recognized by the improved binary splitting (IBS) method whereas the rest tags arewaiting in the pipeline. In addition, an early observation mechanism is introduced toupdate the frame size to an optimum value fitting the number of tags. Theoretical analysisand simulation results show that the system throughput of our proposed algorithm canreach as much as 0.46, outperforming the prior Aloha-based protocols.

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

A Dynamic Multi-ary Query Tree Protocol for Passive RFID Anti-collision

In this paper,a dynamic multi-ary query tree(DMQT)anti-collision protocol for Radio Frequency Identification(RFID)systems is proposed for large scale passive RFID tag identification.The proposed DMQT protocol is based on an iterative process between the reader and tags which identifies the position of collision bits through map commands and dynamically encodes them to optimize slots allocation through query commands.In this way,the DMQT completely eliminates empty slots and greatly reduces collision slots,which in turn reduces the identification time and energy costs.In addition and differently to other known protocols,the DMQT does not need to estimate the number of tags,reducing the protocol implementation complexity and eliminating the uncertainty caused by the estimation algorithm.A numerical analysis shows that DMQT has better performance than other algorithms for a number of tags larger than 300.Meanwhile,when the number of tags is 2000 and the tag identity(ID)length is 128 bits,the total identification time is 2.58 s and the average energy cost for a tag identification is 1.2 mJ,which are 16.9%and 10.4%less than those of state-of-the-art algorithms,respectively.In addition,a DMQT extension based on ACK command has also been presented to deal with capture effect and avoid missing identification.