Miracle-dz/UniFAR-FTUs huggingface dataset:
CUDA-Based Parallel Computation Model for State Estimation

With the development of the active distribution network (ADN), distributed state estimation (DSE) has become an inevitable trend for state estimation (SE). The efficient partitioned strategy is an essential prerequisite for DSE. However, the existing methods only consider the basic requirements of equilibrium connectivity, and the similarity of buses in one sub-region is ignored. A new partitioned strategy is proposed in this paper, this method learns from the idea of hierarchical clustering, the buses of the distribution network are aggregated to form sub-regions, and CUDA platform is used to realize the parallel computation of SE. The improved IEEE33-node system and a real distribution network are analyzed as a case study. The results show that, compared with traditional CSE, the estimation accuracy of DSE is higher than that of CSE, which indicates that the estimation accuracy of the proposed method is higher than that of CSE. Besides, compared with serial computation, this method can effectively reduce the running time of DSE in each sub-region and improve the overall calculation efficiency.
Accurate ship segmentation via ship contour prediction

Accurate ship segmentation in optical remote sensing images is challenging. In this paper, ship contour prediction network is introduced to improve the performance of ship segmentation network. Ship contour prediction network predicts the ship contour, which can promote the learning of ship segmentation network, producing more accurate segmentation results. The ship contour prediction network can be naturally embedded into any ship segmentation network with encoder-decoder architecture. Moreover, ship contour prediction network is only used in the training process, and thus the network not only can improve segmentation accuracy, but also does not increase computational cost in the testing process.
Robust static output feedback based ILC design with finite frequency specifications for batch processes with time-varying uncertainties

For industrial batch processes with time-varying uncertainties, a robust output feedback based iterative learning control (ILC) design is proposed by introducing finite frequency specifications, based on only the output measurement. The integral of output tracking error in the time domain is incorporated into the ILC updating law to improve the tracking performance from batch to batch. Based on the generalized Kalman-Yakubovich-Popov lemma, robust stability of the resulting closed-loop ILC system along both time and batch directions is analyzed in the repetitive system framework. The corresponding ILC law can be determined from the established tractable linear matrix inequality conditions. Moreover, it is shown that the obtained stability condition can also guarantee the bounded output tracking error and system input in the presence of time- and/or batch-varying uncertainties. An illustrative example from the literature is adopted to demonstrate the effectiveness of the proposed design.
A tracking system based on 2-DOF motion platform for flight simulator

With the development of flight control technologies, there is an increasing demand of flight simulators. Although the 6-DOF and 3-DOF flight simulators, can simulate different kinds of motion states, it has complex structure and is difficult to control and implement. Meanwhile, to improve the smooth movements, the paper designed a tracking system based on a 2-DOF motion platform, which consisted of a motion control system and 3D flight simulation software (Cessna 172 Simulator kits). The motion platform has a 2-DOF hybrid mechanism, where the motion control system realizes the tracking algorithm. The 3D flight simulation software calculates the flight data in real time. The quintic and the cubic polynomial trajectory tracking algorithms are proposed to solve the jitter problem of data transmission time. Finally the flight simulation platform is implemented to test the tracking system, and the results showed that it can track precise posture of aircraft with smooth velocity and acceleration.
Circuit Design of Moving Object Recognition System

The traditional moving object recognition and tracking systems are usually based on software environment of PC, so the performance of processing speed, real-time and the size have limitations. In view of this, the circuit design based on Field Programmable Gate Array(FPGA) is proposed, which improves the performance of processing speed and real-time and miniaturization. Aiming at the recognition, tracking and feature point extraction of moving object, 300,000 pixel camera is used for image acquisition, and hardware circuit is used for image processing, including real-time image caching, gray-scale processing, improved bit plane median filtering, segmentation of self-adaptive threshold binarizing, fusing of frame difference and background difference methods for moving object detecting, tracking and feature point extracting. Finally, the recognition results are displayed by HDMI interface displayer. The design takes full advantage of the high-speed and parallel processing ability of FPGA, and combines the high-speed on-chip RAM with the large capacity off-chip SDRAM to realize the processing and storage of video data. After written by Verilog HDL and verified by Modelsim, the physical circuit is implemented in FPGA. This system has the characteristics of strong anti-interference, compact, flexibility, high speed, low power consumption, versatility and scalability, which is suitable for both industrial field and home use.
Path Planning of Stand-off Jamming Electronic Warfare Aircraft

An improved particle swarm optimization (PSO) approach was proposed to plan the optimum interference path for electronic warfare aircraft (EWA). In the initial part, a detailed flight scheme is given to ensure continuous jamming size of EWA to the enemy is constantly changing in the course of flight. On this basis, a simulation model of path planning, which combined a variety of airspace battlefield constraints and main influencing factors was constructed by using an improved PSO. A clustering method has been applied to enhance the exploration ability of global solutions by adding multiplier factors, and a small numerical value is applied in formulas as a suffix to increase the variation range for the duration. The simulation results show that the applying of improved flight path can effectively suppress the effective detection range of enemy radar and significantly increase the flight safety distance of penetration aircraft.
Multi-UAVs Cooperative Ground Moving Target Tracking and Positioning Method

To solve the problems that the target cannot be continuously positioned and the positioning accuracy of the moving target is low, a multiple unmanned aerial vehicles(Multi-UAVs) cooperative ground moving target tracking and positioning method based on solving nonlinear equations, cooperative tracking control and Kalman filtering is proposed. Firstly, the target position is calculated by solving the nonlinear eauations, which solves the problem that the target height value cannot be accurately solved in the traditional method. Then under static conditions, the UAVs formation shape when the positioning accuracy is optimal is obtained through simulation analysis. Secondly, to dispose of the problem that the UAVs cannot continuously locate the target with high precision, a multi-UAVs cooperative ground moving target tracking and positioning method is provided to ensure that the UAVs formation in the target positioning process always maintains the best formation shape to achieve continuous optimal target positioning. Thirdly, to solve the problems that the traditional phase angle-based phase control method is not easy to implement in engineering and the phase control is not robust, a phase control method based on UAVs spacing and sliding mode control is proposed. Finally, to achieve optimal high-precision target continuously positioning, Kalman filtering is introduced into the UAVs position information and target positioned results, which further improves the accuracy of target positioning. In the simulation environment, the performance of the multi-UAVs cooperative tracking control algorithm, the effectiveness of the positioning result filtering algorithm, and the continuous high accuracy of the tracking positioning results are verified.
H∞ Controller Design for Networked Control Systems with Quantization

In this paper, the quantized state feedback H∞ stabilization problem of networked control system (NCS) with timevarying delay is addressed. The signals are quantized by logarithmic quantizer. A novel Lyapunov-Krasovskii function is proposed, in which the sector property of the quantization nonlinearity is fully utilized. In order to analyze the asymptotical stability of NCS, the decomposition method of variable time delay is introduced. The developed criteria are characterized by linear matrix inequalities (LMIs), which can be used to optimize the allowable upper bounds of time delays. The relationship between network and controller parameters is established. Numerical examples are given to demonstrate the effectiveness of our results.
Remaining Useful Life Prediction Based on Stacked Sparse Autoencoder and Echo State Network

Nowadays, the prediction of industry components’ remaining useful life (RUL) has already become a hot topic. In this paper, a RUL prediction method based on stacked sparse autoencoder (SAE) and echo state network (ESN) is proposed. Autoencoder (AE) is an unsupervised learning method that can be used for feature extraction to obtain a health index (HI). To enhance the performance of the HI, a sparse constraint is added and a stacked structure is used to increase depth. The RUL of the industry components is then predicted using a method that maps directly to the RUL value based on the health factor curve. With the capability of encapsulating dynamic time behavior and saving historical information of input data, ESN is selected as the prediction network. The proposed method is verified using the C-MPASS dataset. The experimental results show that the method has better performance than the stacked AE based method.
Backstepping Sliding Mode Control for the Speed and Tension System of Reversible Cold Strip Rolling Mill Based on Extended State Observers

In order to weaken the influences of system uncertainties on the coordinated tracking control performance of the speed and tension system of the reversible cold strip rolling mill, a backstepping sliding model control strategy is proposed based on the extended state observers (ESOs). First, the ESOs are constructed to dynamically observe the system's unmatched uncertainties, which effectively improve the system's tracking control precision. Next, based on the backstepping control and the tracking differentiators (TDs), the integral sliding mode controllers (ISMCs) for the speed and tension system of the reversible cold strip rolling mill are designed, which solve the "differential explosion" problem of conventional backstepping control, and improve the robust stability of the system, Finally, simulation research is carried out on the speed and tension system of a 1422 mm reversible cold strip rolling mill by using actual data, and the results show the validity of the proposed control strategy.
Minimax Joint Detection and Estimation with the Bayesian Cost

Many practical problems involve joint detection and estimation, where performance of estimation and detection largely affects each other. This paper considers the hypothesis testing problem with unknown parameters in a jointly optimal way, whose goal is to choose the true hypothesis and to estimate the unknown parameters simultaneously. By minimizing the maximum Bayesian estimation cost function subject to the constraints on Bayesian or Non-Bayesian detection performance, we obtain the corresponding joint optimal detectors and estimators. Under the proposed framework, it yields a lower estimation cost compared with the method that deals with the detection and estimation problem separately, and also provides tradeoff between detection and estimation performance. Finally, numerical examples about spectrum sensing in cognitive radio systems show the flexibility and the superiority of the proposed approach compared with the classical Neyman-Pearson test and the generalized likelihood ratio test.
Towards Building Reliable and Cost-Efficient Distributed Storage Systems

Reliability and cost are two important targets for distributed storage systems. For many years, numerous schemes have been proposed to improve the reliability or cost of distributed storage systems, and they can be divided into three categories: (1) data redundancy schemes; (2) data placement schemes; and (3) data repair schemes. However, it is still unclear regarding how to build a reliable and cost-efficient distributed storage system, because (i) insufficient considerations on the combinations of different schemes; and (ii) insufficient considerations on failures and recoveries of different subsystems (racks, nodes, disks, and sectors). To measure the reliability and cost caused by different schemes, we design and implement CR-SIM, a Comprehensive Reliability SIMulator for distributed storage systems. It considers various affecting factors, such as the system topology, the data redundancy scheme, the data placement scheme, the data repair scheme, and the failure/recovery models of different subsystems. By using CR-SIM, we conduct various simulation-based experiments, and the experimental results reveal several important findings, which are helpful to build reliable and cost-efficient distributed storage systems. For public use, we have open-sourced our source code at https://github.com/yichuan0707/CR-SIM.
The Open Domain-Specific Architecture

Chiplet technology can significantly reduce the cost and time needed to develop custom high performance silicon products. In order to realize a Chiplet-based product, a die-to-die (D2D) network to interconnect the chiplets is required. Almost all of today’s chiplet-based products use proprietary D2D interfaces. Industry has primarily paid attention to PHY connectivity between chiplets in a package. A design also requires logical information flow between chiplets. Current designs use proprietary logic protocols between components. This approach makes it challenging to integrate chiplets from multiple vendors. The Open Domain-Specific Architecture (ODSA) is a project within the Open Compute Project (OCP) community that aims to establish open physical and logical D2D interfaces for chiplets. Ultimately, ODSA aims to create a marketplace of interoperable chiplets from multiple vendors based on open interfaces. This will allow product designers to develop best in class chiplets and System-In -a-Package (SIP) from multiple vendors. This paper reviews technical developments within the ODSA.
Identification of seizure from single channel EEG using Support Vector Machine & Hilbert Vibration Decomposition

A well-known neurological brain dysfunction named epilepsy which is caused by recrudescent seizures. Because of higher temporal resolution, brain activities measured by electroencephalography (EEG) are usually utilized for confinement of seizures and distinguishing proof of epileptic dysfunctions. Detection of EEG seizures by using traditional Fourier-based methods and manual interpretation is tedious and challenging because of non-linear and non-stationary dynamics of EEG. In our research, at first, we have done robust statistical analysis to detect and classify the seizure and nonseizure. But, the result was not accurate enough to detect and classify seizure effectively. For identification of ordinary and epileptic EEG measurement, we approached a novel algorithm based on Hilbert vibration decomposition (HVD). HVD accomplishes Hilbert transform demonstration of instantaneous frequency and bring outs mono components that have particular time-differing amplitudes and instantaneous frequencies from non-stationary signals. Least squares support vector machine (LS-SVM) is used for identifying epileptic seizures in this research. In addition, it is attracting for real-time physiological signal processing applications because of its lower mathematical complexity. The classification accuracy of 97.66% was attained on a test, which was conducted on a benchmark EEG data set. In addition, area of 0.9914 under the receiver operating characteristics (ROC) curve utilizing the delta, theta & alpha rhythms.
An Adaptive Distributed Compressed Video Sensing Algorithm Based on Normalized Bhattacharyya Coefficient for Coal Mine Monitoring Video

Compared with traditional video surveillance systems, wireless video sensor systems are more suitable for emergency application scenarios, such as underground coal mine disaster rescue, due to their low power consumption and rapid deployment. Considering the limited computing power and transmission bandwidth of video sensor nodes, we propose an adaptive compression and hybrid multiple hypothesis based residual reconstruction algorithm based on normalized Bhattacharyya coefficient (NBCAC-MHRR) to solve the high efficiency video coding (HEVC) problem in underground coal mines. First, a low-complexity adaptive sampling rate allocation method is performed on the encoding side. Second, by integrating the background subtraction idea, we combine the high-quality reconstruction of the foreground with the multi-hypothesis residual reconstruction of the background to improve the overall reconstruction effect of the video sequence. Simulation results show that the proposed algorithm can achieve higher reconstruction quality and efficiency than previous methods, especially in underground coal mine application scenarios.
Robot Manipulation in Open Environments: New Perspectives

The problem of performing everyday manipulation tasks robustly in open environments is currently beyond the capabilities of artificially intelligent robots; humans are required. The difficulty arises from the high variability in open environments; it is not feasible to program for, or train for, every variation. This correspondence paper presents the case for a new approach to the problem, based on three mutually dependent ideas: 1) highly transferable manipulation skills; 2) choice of representation: a scene can be modeled in several different ways; and 3) top-down processes by which the robot’s task can influence the bottom-up processes interpreting a scene. The approach we advocate is supported by evidence from what we know about humans, and also the approach is implicitly taken by human designers in designing representations for robots. We present brief results of an implementation of these ideas in robot vision, and give some guidelines for how the key ideas can be implemented more generally in practical robot systems.
Line Segment Semi-direct Tracking Based on Stereo Odometry

The feature based visual SLAM self locates by detecting feature points in the image. However, it is difficult to extract effective feature points in the lack of texture environment, which is easy to cause tracking loss. In order to improve the robustness of slam in a thousand texture environment, some researchers put forward the slam method which combines feature points and feature lines. However, the extraction and matching of feature lines in these methods will consume a lot of computing resources. The paper proposes a direct tracking algorithm for line segment features, LSD algorithm is first used to extract the initial feature line segment in the key frame; in the ordinary frame, the direct tracking of line segment is realized by sampling, tracking and fitting steps. The algorithm is applied to the visual odometer, and experiments are carried out on the data sets of EuRoC and KITTI. The results show that the algorithm can improve the running speed of the visual odometer to 1.5 times compared with other algorithms, while maintaining similar trajectory accuracy.
Soft Robotic Gripper With Compliant Cell Stacks for Industrial Part Handling

Robot object grasping and handling requires accurate grasp pose estimation and gripper/end-effector design, tailored to individual objects. When object shape is unknown, cannot be estimated, or is highly complex, parallel grippers can provide insufficient grip. Compliant grippers can circumvent these issues through the use of soft or flexible materials that adapt to the shape of the object. This letter proposes a 3D printable soft gripper design for handling complex shapes. The compliant properties of the gripper enable contour conformation, yet offer tunable mechanical properties (i.e., directional stiffness). Objects that have complex shape, such as non-constant curvature, convex and/or concave shape can be grasped blind (i.e., without grasp pose estimation). The motivation behind the gripper design is handling of industrial parts, such as jet and Diesel engine components. (Dis)assembly, cleaning and inspection of such engines is a complex, manual task that can benefit from (semi-)automated robotic handling. The complex shape of each component, however, limits where and how it can be grasped. The proposed soft gripper design is tunable by compliant cell stacks that deform to the shape of the handled object. Individual compliant cells and cell stacks are characterized and a detailed experimental analysis of more than 600 grasps with seven different industrial parts evaluates the approach.
Consensus of discrete-time multi-agent systems with unbounded time-varying delays

This paper studies the modeling and convergence of discrete-time distributed consensus algorithms with unbounded time-varying delays. Firstly, we reveal that when the consensus algorithm with unbounded time-varying delays is modeled as a time-delay system, the maximum delay in the system equation increases to infinity as time goes on. While this property prevents us from using most tools for convergence analysis of conventional discrete time-delay systems, we study the properties of the system state by building a finite dimensional delay system model in the interval [0, k] for any finite positive integer k. Next, we prove that, under the assumption that the growth rate of the maximum delay is slower than the growth rate of a ${\mathcal{K}_\infty }$ class function, and the system topology contains a spanning tree, the consensus algorithm with unbounded time-varying delays can achieve a consensus. And the convergence rate of the algorithm is also characterized. Finally, the simulation experiment demonstrates the correctness of the theoretic result of the paper.
State Estimation for Induction Motor Speed-sensorless Control Based on Strong Tracking Filter

In order to solve the poor performance of state estimation and instability at low speeds, a strong tracking filter (STF) method for the joint estimation of speed, rotor flux and load torque is proposed. The torque equation and load torque are introduced into the state equation, and the filter gain matrix is adjusted online by introducing the time-varying fading factor into the covariance matrix of the predicted state. Compared with the state observer algorithm, simulation results show that the STF algorithm can effectively realize the joint state estimation of speed, rotor flux and load torque of induction motor. The proposed method can improve the estimation performance and stability at low speeds, and realize smooth transition of speed and stator current when switching between motoring mode and regenerating mode with superior dynamic performance, high estimation accuracy and strong robustness against load disturbance.