Miracle-dz/UniFAR-FTUs huggingface dataset:
Output Feedback Reliable Control for a Class of Uncertain Switched Fuzzy Systems with Control Delays

Based on the T-S fuzzy model, output feedback reliable control problem is studied for a class of uncertain switched fuzzy models with controlled time delay, which is developed by combing the switched fuzzy system, output feedback control and reliable control together . When the actuators seriously failedüthe unexpired part can not stabilize the original system, the output feedback reliable controller and the suitable switching law switching in the non-failure part of the actuator are designed to make sure that the closed-loop systems are asymptotically stable for all allowed uncertainties. A sufficient condition for the form of inequality that keep the closed-loop systems uniformly asymptotically stable is given with the method of multi-Lyapunov. In the end, the state curve and control curve of the proposed systems are given by simulink simulation to illustrate the feasibility and effectiveness of the output feedback reliable control method.
Visually Guided Extrinsic Manipulation for Assembly Tasks

Robotic assembly requires a system that can handle a wide range of the object poses for the complexity of real-world tasks. One problem during the handling is the uncertain pose of grasped objects. Objects might be grasped differently from simulation and change significantly due to unexpected contact and collisions. In this paper, we propose a visually guided extrinsic manipulation system to tackle these problems. Extrinsic manipulation enhances simple grippers dexterity to precisely reorient objects into target poses required for the assembly tasks through the interaction with a designed extrinsic manipulation platform. Closed loop manipulation is realized through the visual feedback which is provided to the system through AR markers fixed on the object. Experiments show that the proposed system can successfully handle arbitrary initial and final poses of the grasped object, and can correct and restore the uncertain object poses.
Delayed state synchronization of homogeneous discrete-time multi-agent systems in the presence of unknown communication delays

This paper studies delayed state synchronization of homogeneous discrete-time multi-agent systems (MAS) in the presence of unknown nonuniform communication delays. We develop a delay-free transformation to remove the effect comes from communication delays. A static and a dynamic protocols are designed for full- and partial-state coupling based on discrete-time algebraic Riccati equation, which only needs rough information of communication graph.
Dual-Function Radar Communication Systems: A solution to the spectrum congestion problem

To get the most use out of scarce spectrum, technologies have emerged that permit single systems to accommodate both radar and communications functions. Dual-function radar communication (DFRC) systems, where the two systems use the same platform and share the same hardware and spectral resources, form a specific class of radio-frequency (RF) technology. These systems support applications where communication data, whether as target and waveform parameter information or as information independent of the radar operation, are efficiently transmitted using the same radar aperture and frequency bandwidth. This is achieved by embedding communication signals into radar pulses. In this article, we review the principles of DFRC systems and describe the progress made to date in devising different forms of signal embedding. Various approaches to DFRC system design, including downlink and uplink signaling schemes, are discussed along with their respective benefits and limitations. We present tangible applications of DFRC systems and delineate their design requirements and challenges. Future trends and open research problems are also highlighted.
Shucked corn detection based on GMM and LBP features

Real-time detection of the degree of corn peeling is important to determine the operational status of the corn harvester. This paper presents a method to detect shucked corn. First, moving objects are detected from the background by using Gaussian Mixture Model (GMM) and morphological operations. Then, texture features, called Local Binary Pattern(LBP) features, are computed from multi-scale foreground images. Finally these texture features are sent to a trained support vector machine, which makes the decision whether a corn is shucked or not. Owing to the post-processing on the foreground image segmented after background modeling, our method can filter out redundant noise points. Due to the prominent difference of the LBP features of different objects, our method can make classification more robustly. Therefore, our method is accurate and efficient in the task of shucked corn detection, which is confirmed by experimental results.
Hard Ship Detection via Generative Adversarial Networks

In optical remote sensing images, many ships have very similar shapes and textures with backgrounds. In this case, it is very hard to accurately detect these ships. In this paper, we introduce generative adversarial networks (GANs) to perform hard ship detection. GANs consist of one generative network and one discriminator network. We take state-of-the-art object (ship) detection network Faster R-CNN as the generative network, which outputs the detection results as fake samples. The ground-truth ships in the input image are set as the real samples. The discriminator network is responsible for distinguishing between fake samples and real samples. The two networks are simultaneously trained. Through continuous adversarial training, the fake samples generated by the generative network can be very similar to the real samples, and the discriminator network would not correctly distinguish between fake samples and real samples. As a result, the ship detection network (generative network) correctly recognizes hard-detection ships, producing satisfactory detection results. What’s more, the discriminator network is only used in training process, and thus the proposed method not only improves detection accuracy, but also does not increase computational cost.
A Wireless Extended Coverage Scheme for Electric Power Communication Network

With rapid construction and development of the distribution network, the hybrid network, composed of optical fiber and wireless private network, has become the major communication method for medium and low-voltage power distribution electric power business. In order to reduce the blind spot and extend coverage of current electric power communication networks, this article designs wireless communication network scheme and data acquisition equipment for power communication networks, based on low power wide area network technology. The scheme can realize the many kinds of equipment accessing to electric power communication networks, and satisfy the access control demands of electric power system. The communication capability experiments show that the wireless scheme has the advantages of long transmission distance, strong signal penetration and high scalability.
V-REP-based virtual platform on multi-pattern neural control of robotic manipulator

The paper is concerned with a virtual experimental platform on multi-pattern intelligent control of robotic manipulator, which achieves the joint simulation of V-REP and Matlab. A novel pattern-based control scheme is developed based on deterministic learning mechanism. In the proposed scheme, the unknown dynamics of the manipulator with single reference trajectory is approximated by RBF neural network (NN) and stored in static NN with constant weights. Using such a method, for different reference trajectories, we construct a group of static NN learning controllers which embed constant NN, and then propose a novel switching control strategy. To make manipulator perform multiple control tasks autonomously, different recurrent reference trajectories are regarded as different patterns. RBF NNs are used to identify different patterns, and the corresponding identified results are stored in constant NN weight values. The stored values are employed to develop dynamical pattern estimators. The combination of pattern estimators and switching control strategy makes that the manipulator can rapidly identify different tasks and reuse the corresponding NN learning controller to achieve the desired control performance. To verify the effectiveness of the proposed scheme, V-REP and Matlab are jointed to build the virtual experimental platform. The paper also introduces how to set up the properties of manipulator joints inserted in the 2-link manipulator model in V-REP, the scripts types of embedded scripts, and the method of using remote API to realize the joint simulation. Finally, the operation environment and the result of joint simulation are exhibited to verify the validity of the proposed scheme and the applicability of joint simulation.
A Novel Smart Fault-Diagnosis Method with Procedures of Feature Productions and Extractions

In this paper, a set of novel smart timely-monitoring fault-diagnosis method is designed, which utilize the characteristics and mapping of the chaos system, provides a high efficiency and accurate identification strategy for the normal and abnormal states of rolling-element bearing. In the past, numerous techniques and approaches were first applied to the data recorded in the running object, and then the features were manually extracted and selected in raw data, and in order to obtain the features accurately, which had to rely on the domain-knowledge and experience of each line, while statistical methods were also necessary for data analysis. In this paper, a smart machine with feature-production is designed, which requires to be mathematically chaotic mapped, in order to reveal the key-features for experimental data. In addition, In addition, by applying the appropriate feature productions and extractions methods built in this paper, such as: Euclidean Feature Value (EFV), K-means Feature Value (KFV), etc., can be timely-monitored of normal and abnormal range feature-values (FV), and applied to the rolling-element bearing system for smart fault diagnosis. The experimental results show that the proposed method has a practical level of performance, and can achieve a high accurate classification rate between normal and abnormal states.
MOPSO-Based CNN for Keyword Selection on Google Ads

Google Ads is an advertising agency that provides ads to advertisers. Advertisers match the user’s search terms and push ads by selecting keywords related to their ad content. Keywords can determine the type of users an advertiser pushes, the effectiveness of the ad promotion, and the sales of the ad product. Automatically selecting keywords that are satisfactory to advertisers from a large number of keywords provided by Google Ads is the main task of this paper. But there is not too much time for the model to judge whether keywords are selected, choosing correct keywords in the shortest time is another task of this paper. Therefore, a structure of the model that can get some useful keywords for advertisers is designed and an improved multi-objective particle swarm optimization algorithm is proposed to achieve this multi-objective task. These are also the main contributions of this paper. To accomplish this multi-objective task, many technical issues need to be overcome, such as the mixed language problem, the imbalance problem, the problem of extracting features from corpora and so on. This paper proposes a corpus selection method to solve the mixed problem of Chinese and English in keywords, word embedding method to solve the representation of keywords, re-sampling to solve data imbalance problem, improved convolutional neural network (CNN) to solve classification problem, and a multi-objective particle swarm optimization algorithm (MOPSO) to achieve neural structure search of CNN so that the effect of the classification is improved and the training time is reduced. The keyword selection problem is solved with the combination of evolutionary computing, deep learning, machine learning, and text processing techniques. Experimental results show that the proposed algorithm greatly improved the accuracy of keyword selection and shortened the time of selecting keywords. Therefore, this algorithm has a good application value.
A Survey of Three-Dimensional Flight Path Planning for Unmanned Aerial Vehicle

The unmanned aerial vehicles (UAVs) have been widely used in various fields in recent years, where the three-dimensional (3D) flight path planning is very important to realize the autonomous flight and complete the desired missions for the UAVs. Although the 3D flight path planning have become an active topic in the filed of the UAVs, there are still several critical problems to be further investigated to the authors’ knowledge. Motivated by the aforementioned considerations, the purpose of this survey is to present several representative approaches already proposed in the literature and provide some problems to be further investigated for the 3D flight path planning of the UAVs.
Learning driving behavior for autonomous vehicles using deep learning based methods*

There are amounts of uncertainties in the driving environments. Existing autonomous driving technologies focus on the improvements of the sensing abilities to guaranty the safety of the vehicles. Furthermore, decision making methods could be designed. Human drivers could always make right decisions in the case of unexpected situations in driving activities. In this paper, a driving behavior learning method based on deep learning is proposed. In this method, the driving behavior could be learned in different driving environments. Finally, the proposed method is evaluated in the aspects of effectiveness and efficiency in the simulation experiments. The experimental results demonstrate that the proposed methods outperform other machine learning methods.
Simultaneous Localization and Mapping based on Lidar

To solve these problems of low accuracy of the proposed distribution, large number of particles required and particle degradation of traditional Rao-Blackwillised particle filter(RBPF), an improved RBPF simultaneous localization and mapping (SLAM) method based on lidar is proposed. The SLAM method uses the Point to Line Iterative closest point (PL-ICP) to register the laser scan data of two adjacent frames, and replaces the inter-frame matching result with the odometer reading to optimize the proposal distribution. Introducing a particle weight balancing strategy during sampling reduces the loss of particle diversity. The performance of the algorithm is verified by the robot operating system(ROS) and the differential robot platform. The experimental results show that the improved RBPF-SLAM method reduces the number of particles needed for mapping, and alleviates the problem of particle dissipation while improving the accuracy of interior mapping and location.
Shared Control For Omnidirectional Mobile Robots

A shared control strategy is proposed in this paper for an omnidirectional mobile robot(OMR). The operator is able to control the pose of the OMR through both voice and gesture commands, which are recognized by Keda Xunfei offline command word recognition and Myo armband. When executing the operator’s intention, OMR can avoid automatically the obstacles through vector field histogram(VFH+) method, and back to the path defined by the previous instruction after obstacles avoidance. Experiments show the effectiveness of the telecontrol and obstacles avoidance.
Empirical Analysis of Martingale Pricing for Convertible Bonds Based on Vasicek Model

This study is based on a reduced model of convertible bonds pricing in which the determined factors of convertible bond prices are stock prices and stochastic interest rates. Firstly, the assumption is that the stochastic process of interest rates obeys Vasicek model, three short-term interest rate series of Shanghai Interbank Offered Rate are selected to fit Vasicek model. Secondly, analysis of time series is used to test the stationarity of the three series and linear regression is used to get the stochastic processes. Thirdly, a convertible bond is given to verify the practicability of the Martingale pricing under Vasicek model. In order to get a more accurate consequence, three stochastic processes are applied to calculate the convertible bond price and make comparisons. Finally, due to the advantages of the Martingale pricing under stationary interest rate series, a convenient way to determine the benchmark interest rate in pricing for convertible bonds is put forward.
Robust Loop-Closure Detection with a Learned Illumination Invariant Representation for Robot vSLAM

Robust loop-closure detection plays a key role for the long-term robot visual Simultaneous Localization and Mapping(SLAM) in indoor or outdoor environment, due to illumination changes can greatly affect the accuracy of online image matching, and keypoints may fail to match between images taken at the same location but different seasons. In this paper, we propose a robust loop-closure detection method for robot visual SLAM, which adopts invariant representation as image descriptors composed of learned features and adapts to changes in illumination and seasons. We evaluate our method on real datasets and demonstrate its excellent ability to handle illumination changes.
Adaptive Inverse Kinematics of a 9-DOF Surgical Robot for Effective Manipulation

In a robotic-assisted surgical system, fine and precise movement is essential. However, when the user wishes to cover a wider area during tele-operation, the configuration designed for precise motion may restrict the user and/or slow down the system’s operation. This paper proposes a kinematics based method applicable to redundant manipulators to allow the user to make both fast and precise movements and reduce the burden on the user. In the proposed method, different kinematic configurations are selected automatically in real-time to adjust the speed of the robot’s end-effector according to the velocity of the haptic device. The proposed method is tested on a 9 Degree-of-Freedom (DOF) system that is realized by attaching a 3-DOF servo driven surgical instrument to a 6-DOF manipulator through a custom interface. The validity of the proposed method is shown with experiments requiring dexterous manipulation using the 9DOF system. The results indicate that adoption of the proposed method in actual operations can facilitate reduction in surgery time and surgeon’s effort, thereby help reduce the risk of tissue deformations and other complications in the patient.
Nonlinear Disturbance Observer Based T-S Fuzzy Logic Control of Pneumatic Artificial Muscles

As a new type of actuator, pneumatic artificial muscle (PAM) possesses lots of superior characteristics, which make it extensively used in the robotics field, especially in rehabilitation engineering. But the complex dynamics such as time-varying parameters and hysteresis make it difficult to achieve high precision trajectory tracking control. In order to achieve accurate tracking performance and enhance the robustness of the controller, this paper proposes a nonlinear disturbance observer based T-S fuzzy logic controller (NDOTS). Based on the three-element model of PAM, T-S fuzzy modeling is utilized to decompose the nonlinear model into a series of linear models, which make it possible to use the linear system control theory in the controller design. The MATLAB LMI Toolbox is applied to get the feedback gains. A Lyapunov candidate is designed to analyze the stability of the system. The experimental results of attaching loads on the PAM validate the proposed NDOTS controller.
Research on Assistance Evaluation and Online Optimization Method of Lower Limb Exoskeleton Based on Musculoskeletal Model

Human intends play a dominat role in exoskeleton robot control system and frequently need an accurate estimation algorithm. This paper attempts to build a detailded musculoskeletal model of the knee joint based on human anatomy, and then introduces electromyographic(EMG) signals to estimate human body intend from sitting to standing. We identified parameters of musculoskeletal model considering individual differences. Furthermore, an evaluation indicator is proposed that appropriately reflects the comfortable degree of human body and muscle strength. An online optimization algorithm of the evaluation indicator is then investigated to obtain expected exoskeleton forces. The simulation results indicate that the musculoskeletal model is able to correctly estimate msucle torque, and the indicator makes it possible to follow human motion to some extent.
Laterality Coefficient: An EEG parameter related with the functional improvement in stroke patients

Stroke is one of the most prevalent pathologies around the world, with severe effects to the motor and sensory system that hinder the daily living activities. Brain Computer Interface (BCI) systems can help the stroke survivors to relearn the lost movements inducing neuroplastic changes in the affected motor cortex. The event-related synchronization and even-related desynchronization (ERD/ERS) calculated with the brain signals during the motor imagery tasks, could be related with the functional state of the stroke patients. The Laterality Coefficient (LC) is a parameter calculated using the ERD/ERS changes in the mu wave. The goal of this study is to test how useful the LC is for the functional assessment of stroke patients. Fifteen stroke patients with hemiparesis in the upper limbs have been enrolled on this study and performed 25 sessions of BCI therapy. All of them performed assessment sessions before and after the therapy. The results showed significant correlation between the LC and functional scales, like the Fugl-Meyer Assessment (FMA) or Box and Block Test (BBT). The LC could be a good biomarker for the functional assessment in stroke patients.
Iterative Learning Control for Linear Systems with Fading Channels

This paper addresses the iterative learning control (ILC) for linear systems with random fading channels. The fading problem is a type of multiplicative uncertainties, which is generally caused by unreliable communication networks. It is modeled by a random variable with prior statistical information for correcting the received signals. Both output fading case and input fading case are discussed in this paper. The P-type learning algorithms with a constant learning gain are proposed with strict mean square boundedness convergence analysis. Illustrative simulations are provided to verify the theoretical results.
3D Humanoid Robot Multi-gait Switching and Optimization

In RoboCup3D humanoid robot competition, players need to have a better offensive and defensive ability, efficient walking is the basis of collaboration and confrontation. RoboCup3D is a highly simulated complex environment, for different real-time forms of robots need to switch different walking modes, and the speed characteristics of different walking modes and the stability of switching will play an important role in the final competition results. For different interference degree of environment, adopt the method of reinforcement learning to optimize different gait parameters, to ensure the fastest under different disturbance degree walking speed, and use the reduction strategy based on PID control to realize smooth switching of gait can further enhance the efficiency of the walking robot, the intelligence that has more initiative in complex environment. Experimental results demonstrate the effectiveness of this method.