Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Real-Time Distribution System Testbed Using Co-Simulation

With the increased penetration of power electronics based distributed energy resources (DERs), its control and automation in traditional grid gives rise to more complex operation of power system. It is difficult to realize such a multidisciplinary power system using a single real-time simulator. Co-simulation is a versatile technique to split the large system models into multiple subsystems and components. In case of limited capability of a single simulator, these components can be modeled in more than one simulators and can be solved in parallel. The technique is also useful to interconnect simulators of different make which utilizes propriety software tools. In this paper, a co-simulation based testbed is developed, which can facilitate a platform for validation of various power engineering algorithms. Two different make of real time simulators, Typhoon Hardware-In-Loop (HIL) and Real Time Digital Simulator (RTDS) have been interfaced using peer-to-peer and VILLASnode based UDP/IP socket communication. In this work, an Ideal Transformer model (ITM) based approach is utilized to couple the simulators where the information is exchanged using the dynamic phasors. An illustrative testbed of CIGRE low voltage distribution system is developed using both types of communication methods and the results are compared in terms of latency, jitter and round trip time. In addition, the effects of packet loss, data corruption and duplication on system transient are also presented in this paper. The results demonstrate the capability of the testbed to evaluate the controllers for microgrid, DERs and protective devices in the distribution network at different levels of deployment.
Real-time Apriltag Inertial Fusion Localization for Large Indoor Navigation

Localization in large indoor environment is a challenging problem due to lighting variation and dynamic objects. In this paper, we propose a method for indoor navigation utilizing motion data and Apriltag. The advantage of Apriltag is its invariance to uncertainty in the environment. However, due to the need to minimize the number of Apriltags installed, motion data are required. Hence, in this work, iterative extended Kalman filter(IEKF) method is utilized to fuse visual information and motion data. Furthermore, area detection algorithm is proposed to speed up Apriltag processing speed. In the experiment section, we compared our methods with OpenVSLAM and ground truth Lidar trajectory. In addition, we compared each frame processing time with and without the Apriltag fast detection algorithm.
Resilient Receding Horizon Control of Cyber-physical Systems Subject to DoS Attacks

This work proposes a novel resilient receding horizon control for cyber-physical systems (CPSs) with linear state-space model under malicious denial-of-service (DoS) attacks. DoS attacks can be implemented by launching adversarial jamming signals to the communication channels among control components. Due to the limited energy, the duration of DoS attacks is usually bounded. A novel resilient receding horizon controller is designed to defend against the DoS attacks. To guarantee the feasibility of the optimal control problem, sufficient condition referring to duration of DoS attacks, disturbance bound and prediction horizon is achieved. Furthermore, input-to-state practical stability (ISpS) is achieved for CPSs suffering from DoS attacks. Finally, we carry out simulation to verify the effectiveness of the designed resilient receding horizon controller.
Defect detection of fabrics With Generative Adversarial Network Based flaws modeling

Defect feature extraction is mainly problem of detect detection in fabrics. There are many traditional defect detection methods in it. But deep learning shows many advantages in defect feature extraction of fabric. However, with small unpaired dataset, the recognition rate is always not satisfied. To solve this question, we plan to use Generative Adversarial Network (GAN) to train it in this work. Firstly, we use GAN to evaluate the defect feature distribution on the defect image and generate defect blocks. Secondly, we use these patches sampled above to build paired training data sets with the necessary size. And finally, we use Faster Recurrent Convolutional Neural Networks (Faster R-CNN) for further defect detection with the new data set generated in the second step. The experiment proves the superiority of this method in fabric defect detection.
Research on Kinematics Solution of 7-axis Redundant Robot Based on Self-motion

Aiming at the traditional numerical method based on the Jacobian matrix optimal solution to solve the seven-axis manipulator, there is a large amount of calculation and only a single solution can be solved. Based on the structure of the seven-axis redundant manipulator and its self-motion characteristics, an analytical method for the inverse kinematics calculation of the seven-axis redundant manipulator is proposed. The standard DH method is used to establish the kinematics model of the manipulator, the arm angle is defined as the redundancy parameter and the target pose matrix is 7-dimensional constraints, and the inverse kinematics analytical solution of the seven-axis manipulator is derived; the 3D model of the manipulator is established in matlab The inverse kinematics solution and self-motion simulation are performed to verify the effectiveness of the algorithm.
Research on Complex Robot Manipulation Tasks Based on Hindsight Trust Region Policy Optimization

Deep reinforcement learning (DRL) algorithms have make remarkable progress in robot manipulation task in recent years. However, the success of completing the task relies heavily on the special design of reward function which requires engineering experience or domain-specific knowledge. To avoid complex reward shaping and make robot learning more general, it’s of great essential to study the sparse-reward environments. In this paper, we present two types of challenging goal-conditioned sparse-reward tasks with 7-DoF robot arm, one is a target reaching task with obstacles, and the other is the dynamic object task where the target object moves at a certain speed. Based on the Hindsight Trust Region Policy Optimization (HTRPO) algorithm proposed by our research group, we studied the control performance on the two types of tasks with continuous high-dimensional state space. The results show that HTRPO can achieve more stable strategic performance, higher success rate and sample efficiency compared with its baseline algorithm TRPO and HPG. However, there still remains challenges in solving the tasks with high moving speed.
Rigid Disks and Spheres at High Densities. III

A formal derivation of an asymptotic expansion for the Helmholtz free energy FN of a system of N Λ‐dimensional rigid spheres of diameter Σ is given, which has the form, FN / NkBT ∼ lim τ → 1 ν ln(λ / σ) − ν ln(τ − 1) + C + D(τ − 1) + E(τ − 1)2 + ···, where kB is Boltzmann's constant, λ is the mean thermal de Broglie wavelength, and θ = V / V0 is the reduced volume of a system of volume V, close‐packed volume V0, at temperature T. Formal expressions for the constants C, D, and E are derived, and the cell‐cluster technique is applied to the calculation of C and D for the hexagonal‐close‐packed and face‐centered‐cubic lattices. The results are CHCP = 1.7786846···, CFCC = 1.7795003···, DHCP = 0.54187···, DFCC = 0.557994···. A value for D for the two‐dimensional triangular lattice is found to be DT = 0.098993···. Agreement with computer experiments is generally good.
Narrow molecular weight resins by a free-radical polymerization process

Polystyrene and poly(styrene-co-butadiene) with narrow polydispersity (1.19-1.36) could be obtained by free radical polymerization in solution, bulk or suspension in the presence of 2,2,6,6-tetramethyl-1-piperidinyloxy using benzoyl peroxide as initiator
A Method of lane detection Based on a Hybrid Model in Urban Environment

Lane information is one of the main perceptive objects in the environment awareness module of intelligent vehicle. In this paper, the problem of lane detection in urban environment is considered. In order to improve the performance of lane detection in the complex environment, a novel straight-curve hybrid model is proposed. The proposed method mainly consists of coarse location and extraction of key points of lane. First, a progressive probabilistic Hough transform is used to extract the straight line. A straight-line model of lane is then developed using the color, shape, and gradient information, which can be used to locate the lane roughly. Secondly, a sliding window strategy is used to extract the key points of the curve and a curve model is constructed. Then, the straight-line model and curve model are then combined to form a novel straight-curve hybrid model. Finally, a RANSAC algorithm is used for fitting the lane. The performance of proposed method is verified by the public data set and the real data set. The results of computer simulation show that this method has better adaptability in urban environment than the existing methods
Research on the relationship between rats’ respiration-related potentials in the visual perception area and visual stimulations

Through the analysis of respiration and formation and transmission mechanism of electroencephalogram (EEG), based on the classic Morris-Lecar model, the improved respiration EEG model is built, which uses the differential equations to describe the neurons firing process, thereby generating an analog brain wave. The correlation between EEG frequency and respiratory rate is researched. Variance analysis and wavelet analysis are used to prove the cyclical characteristics of potentials in the visual perception area. EEG signal separation model is built. By power spectrum the frequency range of a stimulus and respiration-related EEG is determined. Hypothesis test of relationship between graphic factors and EEG instantaneous frequency shows that EEG doesn’t contain graphic factors.
High Accuracy Conversational AI Chatbot Using Deep Recurrent Neural Networks Based on BiLSTM Model

In the modern world, chatbot programs are implementations that can be used to store data collected through a question and answer system and then can be applied in the Python program to optimize the results based on highly rated questions asked in a service center. The application of chatbots in the Python program can use various models. Specifically in this program, the BiLSTM model will be applied. The output produced from the chatbot program with the application of the BiLSTM model is in the form of accuracy and also data set that matches the information the program user enters in the chatbot’s input dialog box. The selection of models that can be applied to the program is based on data which can affect program performance, with the objective of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in deciding the selected model. Based on the various considerations that are the requirements for choosing a model of a program, in the end the BiLSTM model is selected will be applied to the program. In addition to model selection, the next step is to determine the method used in the program, in this program the greedy method is a form of implementation of the BiLSTM model with the aim that when running the program, data processing time can be faster, and increase the value of the model selected in program. In addition, supporting attributes such as the seq2seq model are a determining factor in a program that can function to verify whether data processing matches the criteria that can be used as new in data processing. In addition, a program evaluation method is needed that can be used to verify whether the program output matches the data expected by the user. Based on the application of the BiLSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size-layer 512, num-layers 2, embedded-size 256, learning-rate 0.001, batch-size 32, epoch 20) from File 3 is the BiLSTM Chatbot with the avg accuracy value of 0.995217 which uses the BiLSTM model is the best parameter pair.
Effects of the combination of block-shape and face flashing stimuli on a P300 brain computer interface

Brain-computer interface (BCI) provides a new way for the brain to interact with the outside world. One of the common BCI systems is the visual P300 speller. Previous studies have shown that faces can induce strong event-related potentials (ERPs) to achieve high classification accuracy and the block-shaped condition can obtain better performance by increasing the size of the highlighted surface. However, the effects of both blended factors have not yet been studied. Therefore, we proposed a red block-shaped face paradigm (RBFP) combining both block-shape and face. We compared the proposed paradigm with the traditional red face paradigm (RFP) to validate its feasibility and effectiveness. Results indicated that RBFP had better classification accuracy (95.27% vs. 91.11%) and raw bit rate (RBR) (25.84 bit/min vs. 23.43 bit/min) than RFP, and could elicit particularly increased ERPs. Moreover, RBFP obtained a high degree of preference in the subjective assessment. In conclusion, the proposed paradigm combined the advantages of both types of stimuli to obtain a significant improvement in BCI performance, which was meaningful for future BCI studies.
Time-varying feedback stabilization of systems with input saturation actuators in complex mode space

Complex modes are often present when structural damping and gyroscope effects are included in systems. With the presence of unsymmetric matrix in governing equations, states of systems should be decoupled in complex mode space. This paper considers design and analysis of control for systems expressed in complex mode space. In the category of Lyapunov stabilization, sufficient conditions of state feedback controller for such systems are established. Under the condition that actuators suffer from the problem of saturation, a new control law which introduces a time varying coefficient for feedback gain into the nested controller is developed. The coefficient is dependent on the convergence rate of the system and utilizes actuators’ capacity and meanwhile, guarantees the asymptotical stability of the system. Numerical examples of a spinning smart beam structure with single input and multiple inputs are given. It is found that the strategy investigated can be used to control systems with complex modes. And the new low-and-high gain controller which consists of a time varying coefficient for high gain can efficiently reduce the vibration and utilize actuators’ full potential at a high level.
On the control of lower extremity exoskeleton base on the interaction force of torso

This paper presents an ECUST Lower-extremity Exoskeleton (ELE-ROBOT) for augmenting human performance. Most exoskeletons calculate joint torques by using ground reaction forces from foot force sensors. However, this method becomes unreliable in the complex terrain environment. We propose a new control strategy by measuring the human-robot interaction forces using force sensors mounted on the wearer’s torso, and placing only switches on the foot. The exoskeleton joint’s torques are estimated by this measured human-robot interaction forces. The experiments were conducted to verify the feasibility of this control method, the wearer carried 35kg load, and walked on the treadmill at 1.3m/s with exoskeleton powered on or off, then the data was compared to the wearer's natural walking gait. The results show ELE-ROBOT can reduce a portion of the carried load that is exerted on the wearer significantly and trace the joints motion in real time.
Class incremental learning via Multi-hinge distillation

Class incremental learning has been proposed and developed widely to solve the problem in which training data is continuously used to extend the model’s knowledge. It has been applied in many fields, such as pattern recognition and computer vision. However, traditional incremental learning methods facing the challenge of catastrophic forgetting. To this end, we use a generative adversarial network to assist the model’s class incremental learning. Apart from that, some methods employ old classifier output soft targets for generated pseudo-data to help classifier keep knowledge of old classes, but generated samples with soft target and new class samples with the explicit training labels will lead to asymmetric information problem between old and new classes during incremental learning. This paper presents a new knowledge distillation method by using multi-hinge cGAN, we use multi-hinge distillation transfer class-correlation information among all classes from discriminator to the classifier, and help classifier overcomes catastrophic forgetting. Experiments on MNIST and CIFAR10 show that our method is comparable to many state-of-art incremental learning methods.
Multi-Channel 2-D Convolutional Recurrent Neural Networks for Speech Emotion Recognition

Speech emotion recognition (SER) is a challenging task in the field of emotion recognition. The performance of SER largely depends on the emotional features extracted from speech. However, the distribution of different emotional features is uneven and linearly combined, and the sensitivity of different emotional features to emotions is also different, which largely limits the accuracy of emotion recognition. In order to solve this problem, a multi-channel 2-D convolutional recurrent neural network model is proposed, which uses the same channel convolution to map different features to the same dimension, and combines the results of each channel to input to the bidirectional long short-term memory (Bi-LSTM) network extracts global features, and finally uses the attention mechanism to eliminate the influence of silent segments. The experiment is evaluated in two benchmark corpora, and the results show that the designed network has achieved good results in SER, and the average accuracy of IEMOCAP and EMO-DB is 69.51% and 86.42%, respectively.
Clustering and resource allocation strategy for D2D multicast networks with machine learning approaches

In this paper, the clustering and resource allocation problem in device-to-device (D2D) multicast transmission underlay cellular networks are investigated. For the sake of classifying D2D users into different D2D multicast clusters, a hybrid intelligent clustering strategy (HICS) based on unsupervised machine learning is proposed first. By maximizing the total energy efficiency of D2D multicast clusters, a joint resource allocation scheme is then presented. More specifically, the energy efficiency optimization problem is constructed under the quality of service (QoS) constraints. Since the joint optimization problem is non-convex, we transform the original problem into a mixed-integer programming problem according to the Dinkelbach algorithm. Furthermore, to avoid the high computational complexity inherent in the traditional resource allocation problem, a Q-Learning based joint resource allocation and power control algorithm is proposed. Numerical results reveal that the proposed algorithm achieves better energy efficiency in terms of throughput per energy consumption.
The Comparison of Distance Measurement for Optimizing KNN Collaborative Filtering Recommender System

Optimizing a recommender system has complexity and challenging things, such as in collaborative filtering. In collaborative filtering recommendation, the similarities are a critical component. This paper uses predictive KNN to perform Collaborative Filtering, and there have some widespread similarity measurements, such as Euclidean, Minkowski, and generalization from both of them called with Minkowski. We compare them to decide which the best similarity measurement to make better recommendation result by considering the prediction performance and execution time.From the experiment that was performed in this paper, the Minkowski has the best performance but two times slower than Euclidean and Manhattan, since manhattan is trying to find the best p (p=1 or p=2) to decide which p was used. On the other hand, Euclidean performance was approaching the Minkowski, so Euclidean was worth considering.
Research on Stability of Single Machine Infinite Power System with PSS

A single-machine infinite system (SMIS) simulation model with AVR, thyristor high gain exciter and PSS is established by using the mathematical mode and Simulink. Firstly, the dynamic performance simulation of the system is carried out under different disturbance conditions. Then analyze the dynamic performance of the system with different types of power system stabilizer ( PSS ) . The experimental results show that the system with PSS can effectively suppress the low-frequency oscillation and maintain the dynamic stability of the system. And the two-input PSS provides better robustness to the system than a single-input PSS.
A game-theoretic perspective on resource management for large-scale UAV communication networks

As a result of rapid development in electronics and communication technology, large-scale unmanned aerial vehicles (UAVs) are harnessed for various promising applications in a coordinated manner. Although it poses numerous advantages, resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently. Specifically, due to the inherent requirements and future development trend, distributed resource management is suitable. In this article, we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner. By exploring the inherent features, the distinctive challenges are discussed. Then, we explore several game-theoretic models that not only combat the challenges but also have broad application prospects. We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks. Specifically, mean-field game, graphical game, Stackelberg game, coalition game and potential game are included. After that, we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models. Finally, we give some future research directions to shed light on future opportunities and applications.
Research on Ear Recognition Based on SSD_MobileNet_v1 Network

Human ear recognition technology is an emerging technology in biometrics. It has high theoretical research value and market application prospects, and it gradually develops with the development of image processing, pattern recognition and other fields. Aiming at the problem of insufficient accuracy and detection speed of human ear image recognition in practical applications, This paper constructs a human ear image recognition method based on SSD_MobileNet_v1 target detection model. The data set uses the USTB human ear image library of University of Science and Technology Beijing, and the image is enhanced and annotated to eliminate the influence of noise on recognition. Under the TensorFlow platform, MobileNet is used to extract features, and feature regions are generated through RPN region suggestions. And input this characteristic area into the SSD network for training, and apply the trained SSD_MobileNet_v1 model to classify the test image and get the recognition result. Experimental results show that the construction method is accurate in target location, and the recognition accuracy is over 99%, and it has good robustness to images with background interference.