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The telemetry drill string is a high-speed data transmission system of downhole measurement while drilling. The drill string is formed by connected wired drill pipes in series to transmit downhole information to the ground. The electromagnetic signal realizes non-contact cascade transmission through the electromagnetic coupler formed by the drill pipe joint coil. The voltage transfer function of the electromagnetic coupler seriously affects the signal transmission characteristics of the telemetry drill string. The electromagnetic coupler can be regarded as a high-frequency transformer with a special structure. Its structural parameters have a great influence on the equivalent circuit parameters of the coupler. The coupling coefficient of the coupler is directly related to the transmission capacity of electromagnetic signals. In this paper, the above influence factors are systematically studied through the electromagnetic simulation analysis of the coupler. Research shows that the coil groove width of the magnetic core in the coupler affects on the coil inductance and the coupling coefficient; the coil groove depth of the magnetic core has a certain effect on the coil inductance, but it has a great influence on the coupling coefficient. The gap between the contact surfaces of the magnetic core has a big effect on the coil inductance and coupling efficient. In the structural design of the coupler, shallow groove depth should be used and the gap between contact surfaces of the magnetic cores should be reduced as much as possible to obtain a larger coupling coefficient, which is beneficial for the coupling transmission of electromagnetic signals. The research in this article has a certain guidance on the structure design of electromagnetic coupler.

With more attention being paid to large scale virtual environments, the demand for more users to collaborate in the same physical space is growing rapidly. Due to the complex collision problem caused by the limitation of physical space, the multi-user redirected walking methods are devoted to improving the ability of multi-user navigation in large-scale virtual environments by reducing the disturbance of resets. Because the existing multi-user redirected walking methods do not consider the density of users in the physical space, there are more boundary conflicts in the real walking for the multi-user virtual environment. In order to decrease the boundary conflicts, this paper presents a novel method of dynamic density-based redirected walking towards multi-user virtual environments. This method dynamically adjusts the user distribution to a state with high center density and low boundary density through the density force, which is generated by the density difference between standard density and actual density. In our method, the users in high-density areas are guided by a repulsive force away from the central area while the users in low-density areas are guided by the gravitational forces towards the central area. Our method can select a double-density optimal gravitational point as the turning target, so all users can move to the area of minimum density to make better use of the whole physical space. Our method also adopts the artificial potential field (APF) forces to prevent user collisions caused by usergathering. The users are guided to move in the direction of the resultant force vector of density force, gravitational force and APF force. In addition, this paper introduces a matching resetting method to further adjust the density distribution while dealing with user conflicts. The results of experiments show that our method successfully reduces the potential conflicts about 30% on average compared with the existing reactive multi-user redirection algorithms. Especially as the number of users increases, our method can avoid more boundary conflicts by using the adjustment of density.

This paper firstly focuses on yield estimation problem on post-layout-simulation of high dimensional SRAM arrays. Post-layout-simulation is much more credible than pre-simulation. However, it introduces strong relationship among SRAM columns. The Multi-Fidelity Gaussian Process model between the small and the large SRAM arrays near Optimal Shift Vector (OSV) is built. An iterative strategy is proposed and Multi-Modal method is applied to obtain more prior knowledge of the small SRAM arrays and further accelerate convergence. Experimental results show that the proposed method can gain 5-7x speedup with less relative errors than the state-of-the-art method for 384D cases.

We present direct measurements using reflection-mode terahertz (THz) time domain spectroscopy (TDS) of amplifying quantum-cascade (QC) metasurfaces used in THz QC-VECSELs. Devices resonant at 2.2 and 2.5 THz are studied under different bias currents, and exhibit gain-induced partial compensation of losses.

The development of intelligent agricultural machinery and equipment technology is highly valued in China. Unmanned driving technology is an important part of it. For the unmanned driving technology of agricultural vehicles, the flexibility of agricultural vehicles and the accuracy of path tracking must be ensured to reduce the damage to farmland. This paper proposes a new type of unmanned high clearance sprayer, which use the four-wheel independent driving (4WID) with front-rear dual steering axle as its drive chassis. This flexible steering structure can effectively reduce the turning radius and make the rutting marks of the front and rear wheels consistent, reducing the damage to seedlings. Based on the sprayer kinematics model, the target path and vehicle parameters are combined to build a preview model prediction path tracking controller. Finally, co-simulation in the ADAMS and MATLAB show that new structure high clearance sprayer can achieve high precision path tracking and the trajectories of the front and rear wheels are almost consistent.

As an important tool for exploring the ocean, autonomous underwater vehicle (AUV) plays an irreplaceable role in various marine activities. Due to the complexity and uncertainty of the marine environment, AUV is required to develop in a more intelligent direction. How to ensure that AUV avoids obstacles and reaches the target point smoothly is a key research issue of the AUV. The dynamic window approach (DWA) is adopted to AUV in this paper to achieve AUV's autonomous obstacle avoidance for static obstacles. The DWA is used to search for the optimal velocity command in its admissible velocity space by maximizing the objective function, however, the weights of its objective function are constant, which makes AUV lack flexibility in complex environments, and even unable to avoid obstacles. To address the above problem, reinforcement learning is introduced to optimize DWA. Q-learning, a reinforcement learning algorithm, is used to learn the weights of the DWA's objective function, which enables appropriate weights can be selected in different environments and improves the applicability of DWA in the complex environment. Compared with the original DWA, the DWA combined with Q-learning is effective and suitable for complex obstacle environments.

The short-circuit current(SCC) is far larger than the rated current of existing circuit breakers, which has been a serious problem threatening the safety of the power grid. The economical high-voltage AC current limiter based on high coupled split reactor(HCSR) and rapid switch can be used to suppress the SCC. In this paper, the composition structure and working principle of the limiter are introduced at first. The main source of electromagnetic interference(EMI) in the system is explained then. In order to improve the reliability of the economical limiter and also to reduce the interference from its own, both electromagnetic compatibility(EMC) immunity tests and EMC emission tests should be done. As there is no testing standard basis for this new type of limiter, this paper gives the EMC test requirements, test methods and test criterion. According to different EMC tests and tested port types, specific test parameters and test procedures are given as well. At the end of the paper, the practical anti-interference measures are given to ensures the reliability of the design. Beyond that, however, this paper also offers reference for establishing testing standards of the economical high-voltage AC current limiter.

The problem of “Jingri (Chinese mad about Japanese Militarism psychologically)” among a few students reflects the lack of awareness of the history around the Anti-Japanese War, the absence of the history education about the War and the prominent historical identity crisis of the War. Through literature research, group discussion and expert interviews, a questionnaire on the historical cognition status of the War among vocational college students is preliminarily compiled. Through EFA, three factors are extracted, including 20 items, with a cumulative variance contribution rate of 67.069%. Under $\text{CFA},\ 2 0.9,\ \text{NFI} > 0.9, \text{TLI} > 0.9, \text{IFI} > 0.9$ , the fitting indexes are up to the standard, and the load of each factor is greater than 0.7. The Cronbach Alpha coefficient about three dimensions is 0.928, 0.906 and 0.908, and the Combined Reliability is 0.929, 0.907 and 0.909 respectively. The correlation coefficients of each dimension, dimension and the scale meet the requirements. The AVE is more than 0.5, which is greater than the correlation coefficients. Therefore, the scale has high reliability and validity. It has guiding significance for vocational colleges to carry out a survey of the status quo of students' historical cognition about the War and take targeted historical education of it.

NeuroEvolution is one of the most competitive evolutionary learning frameworks for designing novel neural networks for use in specific tasks, such as logic circuit design and digital gaming. However, the application of benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT) remains a challenge, in terms of their computational cost and search time inefficiency. This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches -- NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -- have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines. Additionally, we conduct a robustness test to evaluate the resilience of the proposed NEAT algorithms. The results show that the two proposed strategies deliver improved performance, characterized by (1) a higher accumulated reward within a finite number of time steps; (2) using fewer episodes to solve problems in targeted environments, and (3) maintaining adaptive robustness under noisy perturbations, which outperform the baselines in all tested cases. Our analysis also demonstrates that RET expends potential future research directions in dynamic environments. Code is available from https://github.com/HaolingZHANG/ReverseEncodingTree., Accepted to IEEE Congress on Evolutionary Computation (IEEE CEC) 2020. Lecture Presentation

The object of this study is to develop a fractional-order fuzzy proportional-integral-derivative (FOFPID) control system for controlling the mover position of a voice coil motors (VCMs)-based x-y motion stage. First, the operating principle and dynamics of the VCMs-based x-y motion stage are analyzed. Then, a design of the fractional-order proportional-integral-derivative (FOPID) control system is introduced on the basis of the fractional calculus. With the additional degree of freedom to the control parameters, the FOPID controller can upgrade the contouring performance levels of the conventional proportional-integral-derivative (PID) controller in transient and steady-state responses with respect to the dynamic of the VCMs-based x-y motion stage. To further improve the control response of the FOPID controller, the FOFPID control system is thus designed by integrating a fuzzy logic controller. Finally, to evaluate and compare the contouring performances of the PID, FOPID, FOFPID control systems, two test conditions are provided which are the nominal condition and payload condition. Experimental results demonstrated that the developed FOFPID controller outperforms conventional PID and FOPID controllers with regard to the VCM-based x-y motion stage under both conditions. The good stability and smoothness against to the parameter variations of the FOFPID can also be observed clearly.

this research work proposes model predictive control (MPC) to control the motion of a newly design steering-less Electric Vehicle with independent four in-wheels electric vehicle (SL-EV). The EV consists of four electric motors in each wheel. A novel steering system is introduced and can be controlled by varying the speed of each of the motor. The EV vehicle is designed for agricultural purpose and can reduce the damage of crops. The control models are obtained using kinematic and dynamic models of the forces of the in-wheel electric motors. The MPC is tested with different parameters on the proposed EV. The experimental results show that the proposed design has the features of modest assembly, high communication ability, low energy consumption and slight rotating power. The additional advantage is that, it can be turned simply by varying the speed of the right and left steering-wheels. These characteristics make the 4WD EV scheme extra governable. Besides, the harm to crops is reduced by using the EV as it has the similar path of both left and right wheels.

It is really a frustrating occurrence of how fruit diseases can cause a reduction in production and the economy in the agricultural field all over the world. A growing body of research proves that fruits are critical to promoting good health. In fact, fruits should be the foundation of a healthy diet. This paper presents a better and modern proposed solution for the detection of fruit diseases using their physical attributes. The proposed methods are composed of K-Means clustering technique, ANN and SVM. K-Means is used for the image Segmentation. It has a function of mapping images to their corresponding disease classes based on the phenotypic characteristics such as the texture, color, structure of holes on the fruit and physical make-up. ANN (Artificial Neural Network) is pragmatic in achieving enhanced results in relations to the accuracy of detection and classification have some advantages over the other algorithms. They utilize quite little pre-processing in regards to other image classification methods. It implies the filters were studied by the network that in traditional methods were hand-engineered. This autonomy from earlier information and human exertion in feature design is a major advantage. A Support Vector Machine SVM also is used with the ANN to increase the high rate of classification. The proposed solution can significantly engineer precise detection and automatic classification of fruit diseases.

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for ocean exploration, military and industrial applications. Motion control of AUV is the key to completing these missions. Sliding mode control (SMC) method has a good performance in the motion control system of AUV. Nevertheless, when the system is not fully or previously known, classical techniques are not entirely suitable for the SMC tuning. Moreover, the variation of drag and lift coefficients of the AUV are very sensitive to the AUV speed, and it is difficult to achieve the accurate control of AUV motion by fixed SMC parameters at different speeds. In this paper the tuning process at different speeds is automated through the use of a model-free reinforcement learning algorithm-deep deterministic policy gradient (DDPG) based SMC. The robustness and effectiveness of the proposed control method are tested and validated through extensive simulation results. The results show that the SMC-DDPG achieve motion control at different AUV speeds with fine stability, fast convergence speed, high precision and little chattering.

This paper proposed a Fuzzy PID (FPID) control system based on Extended State Observer (ESO). In this paper, fuzzy logic control effectively solves the problem of parameter adjustment of AUV. ESO is used to obtain the state of the system, to realize the estimation of the interference inside and outside the system and to compensate the interference of the control system. Genetic Algorithm (GA) is used to optimize the initial parameters of FPID controller, which solves the problem of determining the searching space of parameters scientifically and overcomes the blindness of subjective setting. Simulation results show the superiority of FPID control method based on ESO.

In order to improve the performance of motion control for autonomous underwater vehicle (AUV), a Fractional Order PID (FOPID) strategy is proposed. It is an extension of the classic PID controller, which enhances the system control performance due to increased two tuning parameters. The Seeker Optimization Algorithm (SOA) is employed to tune coefficients of FOPID controller. Simulation and experiment results verify that the FOPID strategy allow better control of steady-state error and overshoot. The SOA is efficient to find the optimal coefficients of FOPID controller.

AUV is a typical nonlinear system which uncertainty of system model, and it is also affected by many external disturbances during the process of sailing, especially ocean currents and waves. Therefore, it is difficult to ensure path tracking control of AUV during navigation. A tracking control method based on the Extreme Learning Machine (ELM) and line of sight (LOS) was proposed to achieve robust path tracking. This method can add lateral velocity during sample training to reduce the impact of ocean currents and use the good real-time nature of ELM to predict path points, then desired heading is calculated based on the principle of LOS and the classic PID is used as a controller for heading, depth and speed of AUV. Experimental results demonstrate that our tracking control system based on ELM and LOS enable AUV to move steadily along the planning path in a complex real-world environment.

Navigation algorithm is the key of AUV positioning. Particle Filtering (PF) is a Bayesian estimation based on Monte Carlo method, which is suitable for the analysis of nonlinear and non-Gaussian systems. It is widely used in the research of target tracking, navigation positioning. However, the biggest drawback of particle filters is the degradation of particles. One of the ways to mitigate the effects of this phenomenon is to resample the particles. This paper describes the basic principle of particle filter and introduces polynomial and deterministic resampling algorithms in detail. By comparing the simulation results of the Extended Kalman Filtering (EKF) algorithm, the Particle Filtering (PF) algorithm and the Deterministic PF(DPF) algorithm in AUV navigation and positioning, the feasibility and accuracy of the DPF algorithm are confirmed.

In the complex sea conditions, autonomous underwater vehicle (AUV) need to perform some high-precision tasks, which requires the motion control of AUV have the best command tracking performance, anti-interference ability and robustness. We adopt an effective feed-forward two degree-of-freedom (2-DOF) PID control to ensure the motion control system of AUV have better anti-interference ability and command tracking performance, and use Extreme Learning Machine (ELM) to learn and train corresponding parameters set in order to obtain appropriate parameters quickly and reduces the cumbersome process of artificially adjusting the parameters. The horizontal experiments have been carried out in December 2017 and April 2018 at TuanDao bay, QingDao using own AUV platform, Sailfish.

Convolutional neural networks (CNNs) show commendable performance in computer vision, approaching high accuracy in a broad number of application domains. However, the training process of feature kernels in CNNs is easily affected by illumination intensity and feature interaction, which leads to over-fitting. In this paper, we propose a covariance neural network (CovNN), which replaces the original convolutional operation with our covariance algorithm, to make the learned kernels more robust to different illumination conditions and irrelevant features. This covariance layer uses the 3D covariance between all the input feature maps and the corresponding group of kernels by sliding window method, and regularizes them without additional parameters. Moreover, the covariance layer can be seamlessly transplanted to a variety of neural network architectures extended from CNNs (e.g., ResNet, Faster R-CNN). We evaluate the proposed CovNN on several popular datasets for image recognition (MNIST, Fashion-MNIST, CIFAR 10 and AR) and classification of organs (Abdominal Ultrasound Dataset). Experimental results demonstrate that CovNN achieves significant improvements over the state-of-the-art on most of them.

In wafer fab and assembly processes, non-stick on pad (NSOP) problem impacts seriously yields of the products as the electrical communication between the integrated circuit (IC) chips and other components taking place via the bondpad of the chip. NSOP (Non-Stick On pad) is a failure mode of the IC chip that occurs as a result of poor adhesion between the Aluminium (A1) bondpad and either the bond wire or solder contact. Such failures can be observed even for nanoscale chips such as the 65nm, 45nm, 40nm, and 28nm nodes and beyond. In order to eliminate NSOP problem to enhance the yield of products, process engineers from wafer fab and assembly house together with failure analysis engineers have to know what a good quality bondpad is and how to evaluate and qualify. On these common questions and concerns, in this paper, we will study and introduce Al bondpad qualification methodologies (OSAT and OSSD) and eliminate NSOP problem.

In this paper, a LiDAR-based interactive target detection system was developed to characterized tree canopy structures under various laser sensor travel speed and detection distances. The system composed of a sliding motion control system and a Lidar-based target detection unit. The target detection unit used a 270° range laser scanning sensor to measure target object surface distances based on the time-of-flight principle. The laser sensor travel speed and travel distance was controlled via the control system by specifying a position and speed as a set point. A real-time data acquisition and data post-processing programs were developed based on C++ and MATLAB programming languages respectively. The entire system was tested in the laboratory for a wide range of parameters and operating conditions. The test result showed that the system could detect and characterize tree canopy structure at very low travel speed (0.3 m/s) and high travel speed (5.0 m/s), respectively with acceptable accuracy.

Several methods have been revealed in the reference for detection and localization of power quality (PQ) transient disturbances utilizing S-transform, Fourier transforms, Gabor-Wigner, Gabor transform, Hilbert transform and families of the wavelet transform. However, this paper presents an alternative approach to detect and locate the power quality (PQ) transient disturbances based on singular spectrum analysis (SSA). SSA is a non-parametric technique, which does not require any supposition to generate the observed signal, and affords an effective way to recognize weak transient PQ signal. It has the capability to decompose the PQ transient disturbance signals into a sum of a small number of detectable oscillatory components, removing noise and reconstructing the original signal. Based on the proposed method, transient signals are decomposed into approximate and detail signal. The experiment is performed using PQ long-and-short duration transient disturbances such as high and low-frequency oscillations, impulsive transient and voltage sag. The results of the experiment are compared with multi-resolution db8 wavelet transform. As shown in the simulation experiment results, the proposed SSA technique can be used effectively to detect and locate the transient disturbance. The proposed technique is efficient and alternative technique comparatively wavelet transform and SSA works well even in noisy environment.

The localization of passive source in water is always the hot topic of acoustics. We used to estimate azimuth by using horizontal line array, and range-depth by using vertical line array. However, it is a three-dimension waveguide that the underwater signals propagate in. The horizontal and vertical line arrays lack the ability to localize the target simultaneously in this three-dimensional environment. This paper presents a threedimensional (3-D) spatial sampling scheme by using the Helix array. Particularly, the mode matched field localization in three-dimension Fourier transform domain processing is presented and verified both in simulations and experiments. At the same time, we compare the localization performance of Helix array and other arrays, the robustness of mode matched processing and matched field processing in simulations.

With the demand of power information construction and the application of D5000 platform, the standardization and management of all kinds of data becomes a necessary condition for data sharing and application integration between systems. In order to realize the safe, convenient and efficient information resource sharing of power enterprises and improve the information management level, the fusion method is proposed, which uses the semantic Web technology and Hadoop technology. Through the semantic Web technology, the data quantization analysis and logic interconnection is realized in the monitoring point tables, then the associated attributes of information point tables could be established. Through Hadoop technology, the information point tables after the semantic analysis are processed by the redundant backup, file classification, dynamic monitoring. The information point table data can be traceable, combined, improved. Through the simulation analysis, the effect is verified separately in the case of heterogeneous information integration and integration rule. Finally, the optimization purpose of the monitoring information point tables fusion are achieved in the aspects of the practicability and timeliness.

A feedback-based multilayer measurement matrix (FBMM) within compressive sensing (CS) framework is introduced. In CS, the restricted isometry property (RIP) condition of measurement matrix determines the reconstruction precision. The smaller coherence between measurement matrix and sparse dictionary, the better signal reconstruction performs. However, the construction of a small coherence measurement matrix with large size (e.g., Gaussian matrix) consumes numerous of storage and calculation. Therefore, we introduce a FBMM algorithm where small measurement matrix is applied by feedback method for multilayer to reduce the consumption of resource. Finally, we prove that the FBMM algorithm satisfies the RIP conditionally and provides several selectable measurement matrix with different measuring dimensions. In addition, we established several experiment to test the performance of the proposed algorithm and compare with the traditional algorithm. we conclude that the FBMM algorithm can reduce the consumption of resource for hardware implementation with acceptable reconstruction precision.

Heading control is an important part of Autonomous Underwater Vehicle (AUV) control. But it's control performance is restricted to the uncertainty environments, and lack of understanding of dynamic characteristics of AUV. As a model-free method, the Q-learning achieves its control motivation by interacting with the environment and maximizing a reward, so suits the complicated applications in heading control of AUV. However, Q Learning algorithms are not competent for continuous space problems. So, Extreme Learning Machine(ELM) is proposed to guarantee the generalization performance and work with continuous states and actions. In this paper, the method of using ELM based Q Learning is proposed for heading control. The results have shown that the proposed method for heading control has good performance.

In order to complete nonlinear path following smoothly and accurately, this paper proposes a method utilizing the Serret-Frenet Line-of-Sight (LOS) guidance with adaptive compensation in the horizontal plane. All regular paths are feasible. Our method takes three steps to accomplish the path following. First, the guidance law calculates the desired yaw angle. Then an adaptive compensation is added on the desired yaw which considering uncertainty and input saturation. Last, the PID controller is extended to cope with the yaw tracking and velocity control. Simulations and outfield experiments are conducted to verify the feasibility and superiority of the novel approach.

Integrated Modular Avionics is widely used in modern civil aircraft. As a public resource platform, IMA doesn't have independent aircraft functions. So the function failures are not determined, and this will result in IMA system's safety assessment difficult to be developed. This paper gives an integral bottom-top approach based on the IMA components to perform the safety analysis: through selecting the IMA components, we analyze the comprehensive their failure effects and demonstrate the compliance to related airworthiness regulations.

Development Assurance Standards (DAS), generally developed from Process Management technology are defined according to requirements that evaluate products in specific domains. Similar with Process Management, problems are encored when using those standards in practice. Current researches on Software Process Improvement (SPI) focused on technology, methods and experience of process from a higher level of abstraction, but ignore the impact of different application areas. A lack of effective analysis means to ensure the selection of the process and to ensure the development of the standard limited the use of DAS. This paper analyses DASs used in airborne software, electrical hardware and complex system of Civil Aircraft that generally follow a similar backgrounds, environments in development process. Process Standards Maturity Model is defined and used on different DASs to evaluate projects selection of those standards. Additionally, shortages of current Development Assurance are discussed.