Intelligent Vehicle Auxiliary Handling System Based on the Internet of Things Technology

This paper proposes an intelligent vehicle auxiliary handling system based on Internet of Things(IoT)technology,featuring an innovative holding mechanism design that adjusts to the length and width of various vehicles.The system combines precise positioning using satellite tracking technology,intelligent recognition via OpenCV,and the interconnectivity of IoT.This intelligent vehicle auxiliary handling system can independently identify vehicle positions and plan optimal handling paths,eliminating the traditional reliance on manual operation.It offers efficient and accurate handling,setting a new trend in the handling industry.Additionally,the system integrates seamlessly with parking lot management systems,providing real-time updates on vehicle locations and statuses.This allows managers to monitor the parking lot operations clearly and efficiently.This intelligent coordination greatly enhances overall work efficiency and streamlines parking management.Overall,the innovative design of the intelligent vehicle auxiliary handling system represents a significant breakthrough in both function and performance,gaining user favor with its smooth operation.Looking ahead,continued technological advancements and the expansion of application fields will bring even more vitality and intelligence to societal development.

基于TCN-GRU的Handle标识解析系统负载均衡算法

数联网技术是解决数据管理困难及数据孤岛等问题的关键。近年来,随着数联网技术的飞速发展,Handle系统作为数联网标识解析领域中最具有代表性的系统得以快速落地。然而,Handle标识注册量和解析请求量的快速增长使得Handle系统缓存/递归解析服务节点的解析请求压力加大,且难以保证标识解析请求的服务质量。针对上述问题,提出一种基于TCN-GRU的缓存/递归解析服务节点的负载均衡算法,旨在优化缓存/递归解析服务节点的标识解析效率。首先,将用户的解析请求合理地分配到缓存/递归解析服务节点的服务器集群当中,以提升服务器集群整体的工作效率和吞吐量;其次,基于Handle系统中节点的层级架构,考虑到循环神经网络中普遍存在的梯度弥散问题,引入TCN时序神经网络,有助于提取服务器集群负载的时序性信息。相较于传统的负载均衡算法,所提算法根据服务器负载状况动态调整负载转发策略,服务器平均响应时间缩短50%左右,吞吐率提高40%左右,请求失败次数大幅减少。

Predicting the returns of the US real estate investment trust market:evidence from the group method of data handling neural network

Purpose:The Group Method of Data Handling(GMDH)neural network has demon-strated good performance in data mining,prediction,and optimization.Scholars have used it to forecast stock and real estate investment trust(REIT)returns in some coun-tries and region,but not in the United States(US)REIT market.The primary goal of this study is to predict the US REIT market using GMDH and then compare its accuracy with that derived from the traditional prediction method.Design/methodology/approach:To forecast the return on the US REIT index,this study used the GMDH neural network and the generalized autoregressive conditional heteroscedasticity(GARCH)model.In this test,the training samples,testing samples,and kernel functions of the GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach.Corresponding experiments were performed using the GARCH model,and the accuracies of these two approaches were compared.Findings:Compared with GARCH,GMDH’s accuracy is much higher,indicating that the machine learning approach can provide a highly accurate prediction of REIT prices.The size of the training samples and the kernel functions in the GMDH model affect the accuracy of the prediction results.In particular,the kernel function has a signifi-cant impact on prediction accuracy.The linear and linear covariance kernel functions are simple to train and yield accurate predictions,whereas the quadratic function is difficult to train.Even with small training samples,GMDH can outperform GARCH in prediction accuracy.Research limitations/implications:Although GMDH shows good performance in predicting the US REIT return,it is still a black-box model,and the algorithm is difficult for financial analysts to develop and customize.The data used in this study come from the US REIT market,which is the world’s largest and most liquid market.Social implications:This research shows that the GMDH model outperforms the GARCH model in forecasting REIT returns.Hence,investors can use the machine thus better investment decisions.Future investors and researchers may use GMDH to forecast the performance of REITs in other markets.Originality/value:This is the first study to apply the GMDH neural network to the US REIT market and determine the impact of the two factors on its performance.For example,this research first discusses the impact of kernel functions on the US REIT market using the GMDH neural network.It also includes short-term daily prediction returns that were not previously considered,making it a valuable reference for financial industry analysts.

Construction and application of knowledge graph for grid dispatch fault handling based on pre-trained model

With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.

Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

Online Fault Detection Configuration on Equipment Side of a Variable-Air-Volume Air Handling Unit

With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.

工业互联网Handle标识解析技术在桥梁行业中的创新应用研究

当前,新一轮科技革命和产业变革蓬勃兴起,工业互联网作为第四次工业革命的重要基石,融合人工智能、区块链和大数据、云边协同、数字孪生、边缘计算、5G等新一代信息技术,通过网络、平台、安全3大功能体系构建,实现全要素、全产业链、全价值链的全面连接。Handle标识解析技术作为工业互联网的重要组成部分,也在不断探索在不同场景中的应用。目前,标识解析技术对桥梁建设行业的应用还处于发展探索期,正在不断探索在桥梁全生命周期、供应链管理、上下游对接等方面的应用实践。本文主要描述标识解析技术在打通从桥梁建设到桥梁监测和管养的数据链路方面的应用探索和研究。

基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型研究

为提高大坝变形预测精度,针对大坝变形数据的复杂性和非线性等特征,基于自适应噪声完备集成经验模态分解(CEEMDAN)、数据处理群集法(GMDH)和差分自回归移动平均模型算法(ARIMA)进行大坝变形预测研究。采用CEEMDAN将大坝变形原始数据分解为高频随机分量、中频周期分量和低频趋势分量,再分别采用GMDH模型、ARIMA模型对高中频分量、低频分量进行预测,建立基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型。以江西上犹江水电站为例,将该模型预测结果与反向传播(BP)、径向基函数(RBF)、GMDH和CEEMDAN-GMDH模型的预测结果进行对比分析。结果表明:CEEMDAN-GMDH-ARIMA模型的均方根误差(E_(RMS))、平均绝对误差(E_(MA))、相关系数(r)分别为0.048 mm、0.035 mm、0.994,均优于BP、RBF、GMDH、CEEMDAN-GMDH模型,模型预测效果最好,能够很好地体现监测点水平位移变化趋势。

Fault isolation of air handling unit based on neural network

Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.

Thermal Effects and RF Power Handling of DC～5GHz MEMS Switch

A DC to 5GHz series MEMS switch is designed and fabricated for wireless communication applications,and thermal effect and power handling of the series switch are discussed.The switch is made on glass substrate,and gold platinum contact is used to get a stable and little insert loss.From DC to 5GHz,0 6dB insertion loss,30dB isolation,and 30μs delay are demonstrated.Thermal effect of the switch is tested in 85℃ and -55℃ atmosphere separately.From DC to 4GHz,the insert loss of the switch increases 0 2dB in 85℃ and 0 4dB in -55℃,while the isolation holds the same value as that in room temperature.To measure the power handling capability of the switch,we applied a continuous RF power increasing from 10dBm to 35 1dBm with the step of 1 0dBm across the switch at 4GHz.The switch keeps working and shows a decrease of the insert loss for 0 1～0 6dB.The maximum continuous power handling (35 1dBm,about 3 24W) is higer than the reported value of shunt switch (about 420mW),which implies series switches have much better power handling capability.

Approach to evaluating exception handling of programs

To solve the problems that the exception handling code is hard to test and maintain and that it affects the robustness and reliability of software, a method for evaluating the exception handling of programs is presented. The exception propagation graph （EPG） that describes the large programs with exception handling constructs is proposed by simplifying the control flow graph and it is applied to a case to verify its validity. According to the EPG, the exception handling code that never executes is identified; the points that are the most critical to controlling exception propagation are found; and the irrational exception handling code is corrected. The constructing algorithm for the EPG is given; thus, this provides a basis for automatically constructing the EPG and automatically correcting the irrational exception handling code.

Study on AIDS Epidemic from Unsafe Blood Handling among Rural Doctors

Objectives: This paper examines the basic knowledge ofAIDS and HIV transmission through unsafe blood collec-tion and supply among rural Chinese doctors. It also ex-plores the accessibility of AIDS intervention methods inrural area. Methods: We did Case studies, held focus group discus-sions and provided questionnaires to all rural doctors inone township where the epidemic of HIV was known to bespread through blood collection and supply. Data were col-lected and analyzed with software EPI 6.0.Results: The effective response rate to the questionnairewas 100%. The results showed that more than 95% of in-formants gave correct answers to the questions about thesexual and blood-bourne transmission of HIV as well as itscontagiousness. Half of the participants were ignorant aboutmother to child transmission of HIV and did not know thatHIV couldn’t be transmitted by saliva, sweat, mosquito bites,sharing of bathtubs or toilets. More than 80% of infor-mants were opposed to blood selling and reportedly under-stood the objective of the blood organizers in their villages,knew the peak time of blood selling by the villagers, and ,were aware of the risks of diseases being spread throughblood. . 27.3% used disposable syringes ‘once in a while’,and 15.2% discarded or sold used disposable syringes.Conclusions: The authors assert that there are severe lurk-ing perils of iatrogenic cross infection in rural areas. Ruraldoctors urgently need formal training on prevention andtreatment of HIV infection. We believe that rural doctorsshould become the key force in AIDS prevention and con-trol in rural area.

心肌梗塞细胞L型钙通道、钙运作(Ca^(2+)handling)及钠/钙交换功能研究

目的：研究心肌梗塞细胞钙通道、钙运作及钠／ 钙交换功能变化。方法：运用膜片钳全细胞记录法测细胞膜钙电流，荧光技术测细胞内钙浓度。结果：心肌梗塞细胞钙离子密度明显下降（ 正常细胞：９－８ ±１－４ ｐＡ／ｐＦ，心肌梗塞细胞：３－７ ±０－６ ｐＡ／ｐＦ，Ｐ＜ ０ ．０１） ，肌浆网钙诱发钙释放功能下降，钙回落缓慢而钠／ 钙交换功能无变化。结论：钙通道及钙运作功能异常可能与心肌梗塞后心力衰竭。

Correction Method of Light Hydrocarbons Losing and Heavy Hydrocarbon Handling for Residual Hydrocarbon (S_1) from Shale

In China, hot researches on shale oil were raised by the important breakthrough of shale oil in America. Obviously, the first important issue is the actual shale oil resource potential of China, and the selection of the key appraisement parameter is vital to the shale oil resource amount. Among the appraisement parameters, the oil content parameter（S1） is the key one, but the evaluation result is generally lower because of light hydrocarbon losing and heavy hydrocarbon handling. And the more important thing is that the light hydrocarbon with small molecular weight is more recoverable, and therefore its amount is important to the total shale oil yields. Based on pyrolysis experiments and the kinetic model of hydrocarbon generation, correction factors and a model of light hydrocarbon losing and heavy hydrocarbon handling were established. The results show that the correction factor of heavy hydrocarbon handling is 3.2, and that of light hydrocarbon losing is controlled by kerogen type, maturity and hydrocarbon generation environment（closed or open）.

Interchange between grooming and infant handling in female Tibetan macaques(Macaca thibetana)

In some non-human primates, infants function as a social tool that can bridge relationships among group members. Infants are a desired commodity for group members, and mothers control access to them. The biological market theory suggests that grooming is widespread and represents a commodity that can be exchanged for infant handling. As a limited resource, however, the extent to which infants are interchanged between mothers(females with an infant) and non-mothers(potential handlers,females without an infant) remains unclear. In this study, we collected behavioral data to investigate the relationship between grooming and infant handling in free-ranging Tibetan macaques(Macaca thibetana)at Mt. Huangshan, China. Our results showed that females with infants received more grooming than females without infants. After her infant was handled,mother females received more grooming than they did during daily grooming interactions. However, with the increasing number of infants within the social group, both the grooming that mothers received and the grooming that non-mothers invested for handling infants decreased. We also found that non-mothers invested more time in grooming to gain access to younger infants than older infants. Our results provide evidence that infants are social commodities for both mother and non-mother females. Mothers use infants for obtain grooming and non-mothers use grooming to gain access to infants. The current study implies a bidirectional and complex interchange pattern between grooming and infant handling to compensate for the dyadic grooming disparity in non-human primates.

Direct Yaw Moment Control for Distributed Drive Electric Vehicle Handling Performance Improvement

For a distributed drive electric vehicle（DDEV） driven by four in-wheel motors, advanced vehicle dynamic control methods can be realized easily because motors can be controlled independently, quickly and precisely. And direct yaw-moment control（DYC） has been widely studied and applied to vehicle stability control. Good vehicle handling performance： quick yaw rate transient response, small overshoot, high steady yaw rate gain, etc, is required by drivers under normal conditions, which is less concerned, however. Based on the hierarchical control methodology, a novel control system using direct yaw moment control for improving handling performance of a distributed drive electric vehicle especially under normal driving conditions has been proposed. The upper-loop control system consists of two parts： a state feedback controller, which aims to realize the ideal transient response of yaw rate, with a vehicle sideslip angle observer; and a steering wheel angle feedforward controller designed to achieve a desired yaw rate steady gain. Under the restriction of the effect of poles and zeros in the closed-loop transfer function on the system response and the capacity of in-wheel motors, the integrated time and absolute error（ITAE） function is utilized as the cost function in the optimal control to calculate the ideal eigen frequency and damper coefficient of the system and obtain optimal feedback matrix and feedforward matrix. Simulations and experiments with a DDEV under multiple maneuvers are carried out and show the effectiveness of the proposed method： yaw rate rising time is reduced, steady yaw rate gain is increased, vehicle steering characteristic is close to neutral steer and drivers burdens are also reduced. The control system improves vehicle handling performance under normal conditions in both transient and steady response. State feedback control instead of model following control is introduced in the control system so that the sense of control intervention to drivers is relieved.

Coaxial Twin-shaft Magnetic Fluid Seals Applied in Vacuum Wafer-Handling Robot

Compared with traditional mechanical seals,magnetic fluid seals have unique characters of high airtightness,minimal friction torque requirements,pollution-free and long life-span,widely used in vacuum robots.With the rapid development of Integrate Circuit（IC）,there is a stringent requirement for sealing wafer-handling robots when working in a vacuum environment.The parameters of magnetic fluid seals structure is very important in the vacuum robot design.This paper gives a magnetic fluid seal device for the robot.Firstly,the seal differential pressure formulas of magnetic fluid seal are deduced according to the theory of ferrohydrodynamics,which indicate that the magnetic field gradient in the sealing gap determines the seal capacity of magnetic fluid seal.Secondly,the magnetic analysis model of twin-shaft magnetic fluid seals structure is established.By analyzing the magnetic field distribution of dual magnetic fluid seal,the optimal value ranges of important parameters,including parameters of the permanent magnetic ring,the magnetic pole tooth,the outer shaft,the outer shaft sleeve and the axial relative position of two permanent magnetic rings,which affect the seal differential pressure,are obtained.A wafer-handling robot equipped with coaxial twin-shaft magnetic fluid rotary seals and bellows seal is devised and an optimized twin-shaft magnetic fluid seals experimental platform is built.Test result shows that when the speed of the two rotational shafts ranges from 0-500 r/min,the maximum burst pressure is about 0.24 MPa.Magnetic fluid rotary seals can provide satisfactory performance in the application of wafer-handling robot.The proposed coaxial twin-shaft magnetic fluid rotary seal provides the instruction to design high-speed vacuum robot.

Nonlinear adaptive optimal control for vehicle handling improvement through steer-by-wire system

A control algorithm for improving vehicle handling was proposed by applying right angle to the steering wheel,based on the nonlinear adaptive optimal control(NAOC).A nonlinear 4-DOF model was initially developed,then it was simplified to a 2-DOF model with reasonable assumptions to design observer and optimal controllers.Then a simplified model was developed for steering system.The numerical simulations were carried out using vehicle parameters for standard maneuvers in dry and wet road conditions.Moreover,the hardware in the loop method was implemented to prove the controller ability in realistic conditions.Simulation results obviously show the effectiveness of NAOC on vehicle handling and reveal that the proposed controller can significantly improve vehicle handling during severe maneuvers.

Multi-objective Optimal Trajectory and Placement of a Robot for Cotton Yarns Handling

This study deals with a robot manipulator for yarn bobbin handling in the cotton yarns lattice distortion modification system.The aim is to achieve an operation of yarn bobbin handling with minimal execution time,energy consumption and jerk in motion together.The placement of the robot,in relation to the yarn bobbin stations,is also optimized in conjunction of trajectory optimization.Three possible techniques for building the handling traj'ectory were considered:the quaternion spherical linear interpolation in Cartesian space,the quintic polynomial spline and quintic B-spline in joint space.The genetic algorithm(GA) was used to optimize the trajectories of the robot,with a penalty function to handle nonlinear constraints associated in the robot motion.Two simulations of the optimal trajectory in joint space and the placement of robot were carried out and the results obtained were presented and discussed.It is concluded that the quintic polynomial spline constructs a better trajectory in joint space and the proper placement of robot makes better performance.