Holographic alloy positioning design system and holographic network phase diagrams of Au-Cu system

Taking Au?Cu system as an example, three discoveries and two methods were presented. First, a new way for boosting sustainable progress of systematic metal materials science (SMMS) and alloy gene engineering (AGE) is to establish holographic alloy positioning design (HAPD) system, of which the base consists of measurement and calculation center, SMMS center, AGE center, HAPD information center and HAPD cybernation center; Second, the resonance activating-sychro alternating mechanism of atom movement may be divided into the located and oriented diffuse modes; Third, the equilibrium and subequilibrium holographic network phase diagrams are blueprints and operable platform for researchers to discover, design, manufacture and deploy advanced alloys, which are obtained respectively by the equilibrium lever numerical method and cross point numerical method of isothermal Gibbs energy curves. As clicking each network point, the holographic information of three structure levels for the designed alloy may be readily obtained: the phase constitution and fraction, phase arranging structure and properties of organization; the composition, alloy gene arranging structure and properties of each phase and the electronic structures and properties of alloy genes. It will create a new era for network designing advanced alloys.

Portable Dynamic Positioning Control System on A Barge in Short-Crested Waves Using the Neural Network Algorithm

This paper develops a nonlinear mathematical model to simulate the dynamic motion behavior of the barge equipped with the portable outboard Dynamic Positioning （DP） system in short-crested waves. The self-tuning Proportional- Derivative （PD） controller based on the neural network algorithm is applied to control the thrusters for optimal adjustment of the barge position in waves. In addition to the wave, the current, the wind and the nonlinear drift force are also considered in the calculations. The time domain simulations for the six-degree-of-freedom motions of the barge with the DP system are solved by the 4th order Runge-Kutta method which can compromise the efficiency and the accuracy of the simulations. The technique of the portable alternative DP system developed here can serve as a practical tool to assist those ships without being equipped with the DP facility while the dynamic positioning missions are needed.

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

Residual Network with Enhanced Positional Attention and Global Prior for Clothing Parsing

Clothing parsing, also known as clothing image segmentation, is the problem of assigning a clothing category label to each pixel in clothing images. To address the lack of positional and global prior in existing clothing parsing algorithms, this paper proposes an enhanced positional attention module(EPAM) to collect positional information in the vertical direction of each pixel, and an efficient global prior module(GPM) to aggregate contextual information from different sub-regions. The EPAM and GPM based residual network(EG-ResNet) could effectively exploit the intrinsic features of clothing images while capturing information between different scales and sub-regions. Experimental results show that the proposed EG-ResNet achieves promising performance in clothing parsing of the colorful fashion parsing dataset(CFPD)(51.12% of mean Intersection over Union(mIoU) and 92.79% of pixel-wise accuracy(PA)) compared with other state-of-the-art methods.

Research on train integrated positioning based on grey neural network

Train positioning is the key to ensure the transportation and efficient operation of the railway.Due to the low accuracy and the poor real-time of the train positioning,a train positioning system based on global navigation satellite system/inertial measurement unit/odometer(GNSS/IMU/ODO)combination framework and a train integrated positioning method based on grey neural network are put forward.A data updating method based on the established grey prediction model of train positioning is put forward,which uses the accumulation and summary of the grey theory for the rough prediction of the data.The purpose of the method is to reduce the noise of the original data.Moreover,the radial basis function(RBF)neural network is introduced to correct residual sequence of the grey prediction model.Compared with the single model calibration,this method can make full use of the advantages of each model,thus getting a high positioning accuracy in the case of small samples and poor information.Experiments show that the method has good real-time performance and high accuracy,and has certain application value.

New Distributed Positioning Algorithm Based on Centroid of Circular Belt for Wireless Sensor Networks

This paper presents a new distributed positioning algorithm for unknown nodes in a wireless sensor network. The algorithm is based exclusively on connectivity. First, assuming that the positions of the anchor nodes are already known, a circular belt containing an unknown node is obtained using information about the anchor nodes that are in radio range of the unknown node, based on the geometric relationships and communication constraints among the unknown node and the anchor nodes. Then, the centroid of the circular belt is taken to be the estimated position of the unknown node. Since the algorithm is very simple and since the only communication needed is between the anchor nodes and the unknown node, the communication and computational loads are very small. Furthermore, the algorithm is robust because neither the failure of old unknown nodes nor the addition of new unknown nodes influences the positioning of unknown nodes to be located. A theoretical analysis and simulation results show that the algorithm does not produce any cumulative error and is insensitive to range error, and that a change in the number of sensor nodes does not affect the communication or computational load. These features make this algorithm suitable for all sizes of low-power wireless sensor networks.

GPS Rapid Static and Kinematic Positioning Based on GPS Active Network

This paper presents a data processing strategy for GPS kinematic positioning by using a GPS active network to model the GPS errors in double difference observable.Firstly,the double difference residuals are estimated between the reference stations in the active network.Then the errors at a user station are predicted as the network corrections to user measurements,based on the location of the user.Finally conventional kinematic positioning algorithms can be applied to determine the position of the user station.As an example,continuous 24_hour GPS data in March 2001 has been processed by this method.It clearly demonstrates that,after applying these corrections to a user within the network,both the success rate for ambiguity resolution and the positioning accuracy have been significantly improved.

An artificial neural network for detecting weld position in arc welding process

A kind of self organizing artificial neural network used for weld detection is presented in this paper, and its concepts and issues are discussed. The network can transform the weld visual information into typical patterns and match with the weld data collected on line, and so realize the accurate detection of the weld position in arc welding process.

Vehicle Positioning Method Based on RFID in Vehicular Ad-Hoc Networks

With the rapid development of vehicular ad hoc network( VANET) technology,VANET applications such as safe driving and emergency rescue demand high position accuracy,but traditional GPS is difficult to meet new accuracy requirements. To overcome this limitation,a new vehicle positioning method based on radio frequency identification( RFID) is proposed. First RFID base stations are divided into three categories using fuzzy technology,and then Chan algorithm is used to calculate three vehicles' positions,which are weighed to acquire vehicles' accurate position. This method can effectively overcome the problem that vehicle positioning accuracy is not high resulting from the factors such as ambient noise and base distribution when Chan algorithm is used. Experimental results show that the performance of the proposed method is superior to Chan algorithm and 2-step algorithm based on averaging method,which can satisfy the requirements of vehicle positioning in VANETs.

Positioning a Node of Wireless Sensor Networks in 3 Dimensional Space

To know the location of nodes is very important and valuable for wireless sensor networks (WSN), we present an improved positioning model (3D-PMWSN) to locate the nodes in WSN. In this model, grid in space is presented. When one tag is detected by a certain reader whose position is known, the tag’s position can be known through certain algorithm. The error estimation is given. Emulation shows that the positioning speed is relatively fast and positioning precision is relatively high.

Mobile Positioning System Based on the Wireless Sensor Network in Buildings

Established on the Intel Multi-Core Embedded platform, using 802.11 Wireless Network protocols as the communication medium, combining with Radio Frequency-Communication and Ultrasonic Ranging, imple-ment a mobile terminal system in an intellectualized building. It can provide its holder such functions: 1) Accurate Positioning 2) Intelligent Navigation 3) Video Monitoring 4) Wireless Communication. The inno-vative point for this paper is to apply the multi-core computing on the embedded system to promote its com-puting speed and give a real-time performance and apply this system into the indoor environment for the purpose of emergent event or rescuing.

Application of the Spectrum Peak Positioning Technology Based on BP Neural Network in Demodulation of Cavity Length of EFPI Fiber Optical Sensor

An Extrinsic Fabry-Perot Interferometric (EFPI) fiber optical sensor system is an online testing system for the gas density. The system achieves the measurement of gas density information mainly by demodulating the cavity length of EF- PI fiber optical sensor. There are many ways to achieve the demodulation of the cavity length. For shortcomings of the big intensity demodulation error and complex structure of phase demodulation, this paper proposes that BP neural net-work is used to locate the special peak points in normalized interference spectrum and combining the advantages of the unimodal and bimodal measurement achieves the demodulation of the cavity length. Through online simulation and actual measurement, the results show that the peak positioning technology based on BP neural network can not only achieve high-precision demodulation of the cavity length, but also achieve an absolute measurement of cavity length in large dynamic range.

Hybrid H_2/H_∞ force/position control based on neural networks

A neural network control scheme with mixed H2/H∞performance was proposed for robot force/position control under parameter uncertainties and external disturbances. The mixed H2/H∞tracking performance ensures both robust stability under a prescribed attenuation level for external disturbance and H2optimal tracking. The neural network was introduced to adaptively estimate nonlinear uncertainties, improving the system’s performance under parameter uncertainties as well as obtaining the H2/H∞tracking performance. The simulation shows that the control method performs better even when the system is under large modeling uncertainties and external disturbances.

Application of Artificial Neural Network in Robotic Hybrid Position/Force Control

A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of PIDcontroller.The experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.

Changes of China's Position in the World Food Trade Network

"Ensuring stability in foreign trade"and"ensuring food security"are essential parts of China's"six priorities"and"stability in six areas".Based on the data of bilateral food trade between countries during 1996-2018,using descriptive statistical analysis and social network analysis methods,this paper analyzed the changes of China's trade position in the world food trade network.It reached the following results.(i)China's position in the trade network has undergone a phased process of"remarkable improvement-continuous fluctuation-significant decline-steady growth".(ii)Export participation is better than import participation,the gap between trade export strength and import strength is widening,and the effective scale and constraint index gradually improve.(iii)The change of China's position in the food trade network is affected by geographical,economic,institutional and cultural factors.In view of these findings,it is recommended to improve node centrality,enhance network participation,improve network interconnection,and prevent international market risks,so as to better deal with the complex international environment.

Research and Development of Indoor Positioning

Indoor positioning systems have been sufficiently researched to provide location information of persons and devices.This paper is focused on the current research and further development of indoor positioning.The standard evolution and industry development are summarized.There are various positioning systems according to the scenarios,cost and accuracy.However,there is a basic positioning system framework including information extraction,measurement and calculation.In particular,the detailed positioning technologies mainly including cellular positioning and Local Area Network(LAN) positioning are listed and compared to provide a reference for practical applications.Finally,we summarize the challenges of indoor positioning and give a3-phase evolution route.

Flow Control for a Two-Stage Proportional Valve with Hydraulic Position Feedback

The current research mainly focuses on the flow control for the two-stage proportional valve with hydraulic position feedback which is named as Valvistor valve.Essentially,the Valvistor valve is a proportional throttle valve and the flow fluctuates with the change of load pressure.The flow fluctuation severely restricts the application of the Valvistor valve.In this paper,a novel flow control method the Valvistor valve is provided to suppress the flow fluctuation and develop a high performance proportional flow valve.The mathematical model of this valve is established and linearized.Fuzzy proportional-integral-derivative(PID)controller is adopted in the closed-loop flow control system.The feedback is obtained by the flow inference with back-propagation neural network(BPNN)based on the spool displacement in the pilot stage and the pressure differential across the main orifice.The results show that inference with BPNN can obtain the flow data fast and accurately.With the flow control method,the flow can keep at the set point when the pressure differential across the main orifice changes.The flow control method is effective and the Valvistor valve changes from proportional throttle valve to proportional flow valve.For the developed proportional flow valve,the settling time of the flow is very short when the load pressure changes abruptly.The performances of hysteresis,linearity and bandwidth are in a high range.The linear mathematical model can be verified and the assumptions in the system modeling is reasonable.

Design of 3D Space Following Positioning System Based on ZigBee

The research on positioning system and spatial alignment is a big topic. In this paper, we proposed a design (that) studies two issues. One is the study of range positioning algorithm based on ZigBee communication system. The other one is spatial alignment platform which is controlled with two servos. Hardware and software control system was realized, which also consists of two parts, ZigBee network positioning system and automatic orientation platform.