Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous positioning of omnidirectional mobile robot based on visual inertial navigation

With the development of artificial intelligence, robot intelligence has gradually become a hot research field. Autonomous positioning of robot is an important part of realizing its intelligence. SLAM technology can effectively solve this problem. The monocular camera is cheap and easy to install, but the monocular vision SLAM has the problem of scale uncertainty, which reduces its practicability. Based on the ORB-SLAM2 algorithm, this paper uses the monocular camera as the sensor, uses the integrated IMU inside the omni-directional mobile unmanned car to provide scale information, and carries out information fusion in a tightly coupled way. Finally, the experiment on the autonomous positioning of the actual omni-directional mobile unmanned car platform verifies the feasibility and practicability of the ORB-SLAM2 algorithm with monocular camera as sensor combined with inertial element (IMU) in the autonomous positioning of mobile robot.
Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios

Heterogeneous systems are present from powerful supercomputers, to mobile devices, including desktop computers, thanks to their excellent performance and energy consumption. The ubiquity of these architectures in both desktop systems and medium-sized service servers allow enough variability to exploit a wide range of problems, such as multimedia workloads, video encoding, image filtering and inference in machine learning. Due to the heterogeneity, some efforts have been done to reduce the programming effort and preserve performance portability, but these systems include a set of challenges. The context in which applications offload the workload along with the management overheads introduced when doing co-execution, penalize the performance gains under time-constrained scenarios. Therefore, this paper proposes optimizations for the EngineCL runtime to reduce the penalization when co-executing in commodity systems, as well as algorithmic improvements when load balancing. An exhaustive experimental evaluation is performed, showing optimization improvements of 7.5% and 17.4% for binary and ROI-based offloading modes, respectively. Thanks to all the optimizations, the new load balancing algorithm is always the most efficient scheduling configuration, achieving an average efficiency of 0.84 under a pessimistic scenario.
Adaptive Neural Control of Uncertain MIMO Nonlinear Systems with Time-Varying Output Constraints

In this paper, the problem of adaptive neural control is discussed for multi-input multi-output (MIMO) strict feedback nonlinear systems with time-varying output constraints and unmodeled dynamics. Using the composite function of hyperbolic tangent function as a nonlinear mapping, the original system is transformed into a novel unconstrained MIMO strict-feedback system with uncertain terms. A auxiliary dynamic signal is designed to estimate the dynamical uncertainties in the novel system. Based on the transformed system, adaptive neural controller is constructed by using modified dynamic surface control(DSC) technique. Using the defined compact set in stability analysis and Lyapunov function method, all signals in the closed-loop system are proved to be the semi-global uniform ultimate boundedness (SGUUB). Each element of the output vector can strictly abide by the time-varying constraint conditions.
Simultaneous dynamic controller and anti-windup synthesis for systems with sector-bounded and slope restrictions

This paper presents new techniques for construction of dynamic output feedback controllers with a static anti-windup compensator for a class of saturated systems with sector-bounded and slope-restricted nonlinearities. To handle the slope restriction, a so-called differential compensator is incorporated into the traditional nonlinear controller. Considering its sector-bounded property and convex-hull representation, respectively, two LMI-based synthesis methods are proposed to address the problems of the estimates of the region of attraction (ROA) and ${{{\mathcal{L}}}_2}$ performance analysis of the closed-loop system. A numerical example illustrates the effectiveness of the proposed approaches.
A Power System Fault Alarm Processing Method Based on ANN and FSM

Combined with Artificial Neural Network (ANN) and Finite State Machine (FSM), the substation alarm data is processed. Firstly, to reduce the complexity of ANN model construction, the alarm sequence is simplified by the signal processing method of homology and complementary events merging. Secondly, the ANN weight matrix model and learning algorithm are constructed, and the logic reasoning and knowledge expression of system fault and abnormalities of four types of circuit breaker, line, bus and transformer, are acquired by training and testing data samples. Thirdly, carry out correlation analysis of fault set and build FSM model for alarm process recording, and finally form comprehensive analysis results. The results show that the method has the characteristics of fast, fault tolerance and strong learning ability, and it is of great significance to solve the online fault diagnosis problem of large-scale power system.
An Interpolation Method of Particle Distribution Reconstruction in Liquid-Solid Two-Phase Flow using Ultrasonic Diffraction Tomography

In liquid-solid two-phase flow, the solid-phase particles distribution is important for further analysis of two-phase flow parameters. For the small particles, such as 1 mm - 3 mm in size, which the particle size is close to the ultrasonic wavelength, the ultrasonic diffraction phenomenon occurs. Ultrasonic phased array probes can be used to collect ultrasonic diffraction information to reconstruct the particle distribution. Since ultrasonic diffraction tomography (UDT) is immature in practical applications, small errors in the interpolation steps during the reconstruction process will have a great impact on the results. Therefore, a natural neighbor interpolation (NNI) method is introduced to minimize the error. A series of simulation experiments were designed, and the reconstruction results of the NNI method were compared with the reconstruction results of the bilinear interpolation (BLI) method and bicubic interpolation (BCI) method, which verified the feasibility and effectiveness of the NNI method for UDT.
An improved Kalman filter based on neural network for turbofan engine gas-path health estimation

The Kalman filter is the most commonly used state estimation method for turbofan engine health monitoring. It achieves the state estimation on condition that the number of the available measurement sensors is more than the number of the health parameters to be estimate. However, it is hard to hold this assumption in the turbofan engine gas-path health monitoring application. Thus, in this paper, an improved Kalman filter based on neural network is proposed to improve the filter estimation accuracy. The improved Kalman filter consist of a master filter and a neural network based estimator. During each sampling period, the estimation result of the neural network based estimator is integrated to the master filter as a penalty term to update posterior state, which completes a better trade-off between the estimation accuracy and computational efforts. Moreover, a mind evolutionary algorithm is adopted to optimize both the weights and thresholds of the estimator. The simulation results of a turbofan engine health monitoring application in the flight envelope show that the proposed method yields a significant improvement of the estimation accuracy and robustness, it achieves better trade-off between the estimation accuracy and computational efforts.
A Badging System for Reproducibility and Replicability in Remote Sensing Research

Remote Sensing is both an active research area and the source of valuable information for decision-making. Many actors play a fundamental role in Remote Sensing, from industry (public or private) to large or small research groups. From that intensive activity, methods, algorithms, and techniques are continuously published or broadcasted through papers, conference presentations, repositories, patents, standards, and other means. The consumers of that information need it to be readily available and dependable. Reproducible research can handle those needs. In this article, we discuss two concepts: reproducibility and replicability in the context of Remote Sensing research. We propose a badging system suited to the specifics of the Remote Sensing community. Such a system aims at both recognizing the level of the reproducibility of the research, and to help increase its visibility. We show examples of reproducible research and provide clues to make easier the transition to the inevitable new times that embrace contemporary science and technology.
P300 Recognition Based on Ensemble of SVMs : - BCI Controlled Robot Contest of 2019 World Robot Conference

P300 is a typical event related potential (ERP), which has been applied in the brain-computer interface (BCI) and attracted the attention of researchers for nearly thirty years. Until now, the most challenge of P300-based BCI is to detect P300 in a minimum of repeats, which is a balance between recognition accuracy and a number of repeats. Previous studies showed that the shallow learning model such as support vector machine (SVM), relevance vector machine (RVM) and linear discriminant analysis (LDA) had been successfully used for P300 classification. In this study, we proposed a P300 recognition algorithm based on ensemble of SVMs. Firstly, we intercept 600 ms data after the visual stimulation. Secondly, the extracted segment electroencephalogram (EEG) was averaged by iteration to enhance the signal-to-noise ratio. Thirdly, these obtained signals were fed to the ensemble of SVMs for target character is recognition. Finally, an output regulation mechanism was proposed for adjusting the outputs of ensemble of SVMs. The experimental results show that the highest accuracy (80%) and the information transmission rate (17.21 bits/min) are observed in the repeat number of only five. As an application and verification, this algorithm won the third prize in the BCI Controlled Robot Contest of 2019 World Robot Conference.
Nonlinear Predictive Path Following Control for a Stratospheric Airship

A novel planar path following control strategy for a stratospheric airship is presented based on velocity allocation and an improved nonlinear predictive control approach containing control energy optimization. Firstly, the airship system equations is obtained from simplified planar kinematic and dynamic equations. Secondly, based on the position error equation and mixing velocity allocation, the desired forward speed and yaw angle speed is deduced. Thirdly, a nonlinear predictive control method with energy optimization improvement is utilized to design controller for tracking the desired speeds, and stability of the closed loop is also analyzed. Finally, simulation results for circle path following show the effectiveness of proposed method and its capability to reduce control energy.
Full-stage Reentry Trajectory Optimization for Reusable Launch Vehicle

In this paper, full-stage reentry trajectory optimization for the reusable launch vehicle from 120km to ground is investigated. The full-stage trajectory is optimized stage by stage with the sufficient consideration of properties in different phases. The proposed trajectory optimization strategy solves the problem of state discontinuous at multi-segment trajectory switching points. For the initial reentry phase, a sequential convex programming with variable trust region method is developed to improve the convergence efficiency of the algorithm. For the terminal area energy management phase, a reference dynamic pressure profile is designed to solve the attack angle, while the bank angle is determined by tracking the ground trajectory. For the approach and landing phase, we proposed a method which can generate reference geometry based on quasi-equilibrium glide and exponential descent subphase, respectively. The secant line method is used to determine the height of the contact points and ensure the smooth transition between the two subphases. Finally, numerical simulations are provided to show the efficiency of the proposed method.
Studies on the determination of enzymatic active for papain

OBJECTIVE To develop the national drug standards for papain.METHOD Base on determination of enzymatic active for papain in USP ⅩⅩⅢ. The analytical conditions were selected by orthogonal tests.RESULT The method acquired enzymatic active was a little high than USPXX Ⅲ acquired.CONCLUSION This method is feasible for the determination of enzymatic active for papain.
An improved Few-Shot Learning for insulator classification in railway 2C system

Abstract: Deep learning has attracted increasing attentions due to its critical role in feature representation and image classification. Conventional deep learning takes training and test data from the same class. However, deep learning requires abundant labeled data in the field of image classification and it is challenging to classify unseen samples. In this paper we introduce a novel Few-Shot Learning (FSL) for insulator classification of overhead catenary system (OCS) in Railway 2C system. We learn a super-resolution reconstruction generator for each image as expanding the receptive field and enhancing the resolution. After extracting features from images based on Convolutional Neural Network (CNN), the relation network trained with few examples distinguishes the different images to be classified.The proposed method is evaluated on three datasets: two public datasets and one OCS dataset. Extensive experiments on these datasets demonstrate that proposed approach provides a more accurate approach to the classification with few samples.
An improved convolutional neural network-based approach for short-term wind speed forecast

Wind power is taking more and more important proportion in new energy power system, where wind speed forecast (WSF) plays a key role in maintaining safe and smooth operation of wind power generation system. However, with wind speed influenced by complex meteorological and topographic factors, the current models based on raw historical data have encountered many problems. Therefore, the accurate WSF has been a great challenge in practice. To solve this problem, a novel WSF method based on convolutional neural network (CNN) is proposed in this paper. By utilizing the big data collected from the running log of a real wind farm, the convolution layers of CNN can automatically extract the deep features from the historical data. The characteristics of sparse connection and weight sharing contributes to a faster training speed of CNN. In order to find the optimal CNN structure suitable for WSF, the parameters are determined one by one through experimental analysis. The CNN model is compared with the traditional multi-layer perception (MLP) network to show the advantages in WSF. In the full connection layer, the improved Biased Dropout method is applied to effectively reduce the number of parameters and reduce the complexity of the model. The performance of the modified CNN is evaluated through a WSF test in every ten minutes, which verifies that the training speed is accelerated in comparison with that of the typical CNN.
Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

Object detection methods based on Convolutional Neural Networks (CNNs) require a large number of images with annotation information to train. In aircraft detection from remote sensing images (RSIs), aircraft targets are usually small and the cost of manual annotation is very high. In this article, we tackle the problem of weakly supervised aircraft detection from RSIs, which aims to learn detectors with only image-level annotations, i.e., without bounding-box labeled data during the training stage. Based on the fact that the feature maps learned from the CNN network are localizable, we propose a simple yet efficient aircraft detection algorithm called Weakly Supervised Learning in AlexNet (AlexNet-WSL). In AlexNet-WSL, we utilize the AlexNet CNN as backbone network, but replace the last two fully connected layers with a Global Average Pooling (GAP) and two convolutional layers. Based on the class activation maps, we generate heat maps via reverse weighting for locating the target object. Unlike object detection methods that require object location data for training, our proposal only needs image-level labelled data. We furthermore build a set of remote sensing aircraft images, the Weakly Supervised Aircraft Detection Dataset (WSADD) for algorithm benchmarking. The experimental results on the WSADD show that AlexNet-WSL effectively detects the aircraft and achieves a detection effect equivalent to the Faster R-CNN method and the YOLOv3 method, which both require bounding-box labelled data for training, with a lower false alarm rate and a shorter training time.
Proposal and Analysis of a Novel Class of PUFs Based on Galois Ring Oscillators

In this article, the possibility of using Galois ring oscillators to construct physically unclonable functions (PUFs) has been studied. The idea is to use novel PUF architectures, similar as the ring oscillator PUFs that, instead of comparing frequencies, compare the statistical bias of pairs of oscillators implemented in different locations. To study the viability of these systems, three different Galois oscillators have been implemented in several locations in several FPGAs and we have studied the main properties of their bias: repeatability, variability with the location, variability with the FPGA and spatial autocorrelation. Based on this study, we have determined that the bias of these oscillators meet the requirements that are needed to be used to construct a PUF. Finally, a PUF based on comparing the bias of neighboring 7-LUT Galois ring oscillators have been implemented and analyzed. The experimental results show that this PUF generates uniform responses that are highly reproducible and unique, making this PUF suitable for being used in identification applications.
Dynamic Movement Primitives based Parametric Gait Model for Lower Limb Exoskeleton

Lower limb exoskeleton robot is widely used in clinical rehabilitation training and daily walking for paraplegia patients. However, most of lower extremity exoskeleton products use fixed gait and speed, making them limited in use. Therefore, this paper proposes a parametric gait model for lower limb exoskeleton gait planning, which is based on the dynamic movement primitives method. By learning the characteristics of the sample curves of hip and knee joints at different velocities, the model can output the curve of hip and knee joints under continuous velocity variation. Thus, the lower limb exoskeleton is given an anthropomorphic variable gait which would enhance its dynamic performance and broaden the application scenarios.
An Improved LADRC Algorithm for Quadrotors

Inthis paper, a quadrotor’s tracking control problem is investigated in which the feedback loop has a time delay caused by transmission. First, the relative motion model is established. Then, an improved Linear Active Disturbance Rejection Controller(LADRC) algorithm is proposed to deal with the time delay in feedback. Finally, simulation and experimental results show the effectiveness of the proposed algorithm.
Study of Cognitive Fatigue using EEG Entropy Analysis

Cognitive fatigue may be induced by prolonged periods of continuous and repeated activity. The subsequent drop in alertness levels may be detected and analyzed by the use of physiological signals such as Electroencephalogram(EEG), ocular features, speech etc. The present paper employs entropy of EEG signals as a marker of cognitive fatigue and validates the results through performance of subjects in a set of standard neuropsychological tests. Correspondence observed in the trends may be considered indicative of gradual increase in fatigue levels with increase in cognitive loading.
A PID Gain Adjustment Scheme Based on Reinforcement Learning Algorithm for a Quadrotor

In this paper, a PID gain adjustment scheme with the basis on Reinforcement Learning Algorithm is proposed, the validity of the scheme is demonstrated with the application to the control of a quadrotor. Specifically, the PPO algorithm of reinforcement learning is utilized in the scheme to adjust a PID controller gains. The procedure and details of the scheme are presented. The experiments prove that the control strategy with this scheme can quickly make the controlled system converge and stabilize. The scheme, compared with a traditional PID controller, has a good performance in terms of control stability, anti-interference stability, and aircraft altitude stability.
Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

Edge computing can be widely used in unmanned aerial vehicle (UAV) inspection, field operation control, power consumption information collection and other businesses in the power Internet of Things scene. Edge computing offloads functions such as data processing and applications to network edge nodes near the terminals to provide low-latency services and ensure service quality. However, with the explosive growth of business terminals, the capacity of single edge node is limited and it is difficult to meet all business requirements at the same time. Therefore, this article proposes a task allocation mechanism based on cooperative edge computing. Firstly, a task allocation model based on cooperation of two edge nodes is established to minimize the average task completion delay while meeting business requirements. Secondly, the Two-edge-node Cooperative-task Allocation based on Improved Particle Swarm Optimization (TCA-IPSO) algorithm is proposed, which applies the crossover and mutation strategy in genetic algorithm to improve the particle swarm optimization algorithm, and solves the problem that the task allocation scheme in cooperation is prone to fall into a local optimum. Finally the simulation results show that the proposed TCA-IPSO algorithm reduces the average task completion delay by 53.8% and 36.0% compared to the benchmark and QoS-based Task Distribution (QBTD) algorithm.