Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Lateral Stability Region for Lightweight Electric Vehicle Using Phase Plane Approach

Vehicle dynamics control systems play an important role in improving vehicle handling stability performance and passenger safety. Analysis of lateral stability region can further help to arouse the potential of the dynamics control system under critical maneuvers. This paper investigates lateral stability region for lightweight electric vehicle via phase plane approach. Based on the improved cubic formula tire derived from Pacejka model, the nonlinear vehicle dynamic analysis model is built and the particular state-space representation for vehicle phase plane analysis is developed. The lateral stability region is analysed using the band-shaped stability region related to two symmetric saddle points. And the influence of important factors such as road friction coefficient, steering wheel angle and vehicle velocity on the lateral stability of vehicle yaw motion is also discussed and presented, which may provide assist for vehicle dynamics control design.
Guaranteed Cost Control for Delayed Discrete-Time Linear Systems: A Lyapunov Matrix-Based Method

This paper is concerned with the guaranteed cost control problem for delayed discrete-time linear systems. Firstly, the concept of Lyapunov matrix is employed to construct a complete-type Lyapunov–Krasovskii functional. Secondly, solvable conditions of the problems under consideration are proposed. These conditions are formulated as several simple linear matrix inequalities, which can be solved by using the toolbox YALMIP of MATLAB. Thirdly, the precise form of the controller is proposed, which guarantees that the resultant closed-loop system is asymptotically stable and has a guaranteed cost. Finally, an example is given to illustrate the validity of the theoretical results.
A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation

Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symmetry in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations11This material is based upon work supported by the National Science Foundation under Grant Number 1657260. Research reported in this publication was also supported by the National Institute On Minority Health And Health Disparities of the National Institutes of Health under Award Number U54MD012388.
On Mainlobe Orientation of the First- and Second-Order Differential Microphone Arrays

Due to the increased sensitivity to microphone mismatches, lower-order differential microphone arrays (DMAs) are usually employed in practice, especially the first and second-order DMAs. It is known that the mainlobe orientation of the typical first- and second-order DMAs is both along the fixed endfire direction. This may be no longer true, however, in the presence of microphone mismatchs. This paper studies the fundamental problem of how microphone mismatches affect mainlobe orientation of the first- and second-order DMAs. Some insights into the effects of microphone mismatches on the mainlobe orientation of the two types of DMAs are revealed. In addition, the property on mainlobe orientation of the second-order DMA under ideal condition, which is yet to be known, is also studied. Moreover, tolerance analysis of microphone mismatches to ensure correct mainlobe orientation of the first- and second-order DMAs are performed in order to offer a useful guidance for practical design. Numerical examples are shown to verify the theoretical findings.
PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

Automatic digital pressure gauge calibration is challenging due to various unconstrained conditions. Although existing CNN-RNN based methods have been almost perfect on scene text recognition, they fail to perform well on digital pressure gauge calibration that requires to be extremely computation-efficient and accurate. In this paper, we propose a light weight fully convolutional sequence recognition network for fast and accurate digital Pressure Gauge Calibration (PGC-Net). PGC-Net integrates feature extraction, sequence modelling and transcription into a unified framework. Experimental results show that PGC-Net runs 28 fps on CPU with 97.41% accuracy. Compared with previous methods, PGC-Net achieves better or comparable performance at lower inference time. Without bells and whistles, PGC-Net is capable of recognizing decimal points that usually appear in pressure gauge images, which evidently verifies the feasibility of PGC-Net. We collected a dataset that contains 17, 240 gauge images with annotated labels for automatic digital pressure gauge calibration. The dataset has been public for future research.
Design of Sewage Treatment Monitoring System Based on Internet of Things

In order to improve the efficiency of industrial wastewater treatment system and the immature operation management experience, an IoT-based sewage treatment monitoring system was designed. The system consists of an application layer, a network layer, and a sensing layer. The sensing layer collects the parameters of the plant wastewater through a variety of sensors and equipment components. The collected data is uploaded to the network layer and connected by the GPRS technology through the gateway, and then the data is processed and stored by using the Baidu cloud platform, and finally applied to the actual, and can be viewed by the client at any time. It provides a complete solution for the operation and management of sewage treatment plants and has high reliability in practical applications.
Amphibious Wheels with a Passive Slip Mechanism for Transformation*

In a variety of missions, small sized robots are preferred due to their capability to maneuver and operate in tight spaces and voids. However, scaling down the size of robots impacts adversely on their ability to surmount larger obstacles on their path. A miniature robot operating in an unprepared environment has to face different kinds of terrains. Also, a fixed structure for locomotion does not allow for mobility across all surfaces. We propose a passive transformable wheel design that offers a multimodal (wheel-paddle-leg) design to adapt to operations in both land and water.The appendages were designed to act like paddles in water as well as allow the option to choose between legged or wheeled motion through a passive mechanism. A prototype of the wheel has been built and deployed on a testbed robot to validate the concept. The design has been shown to be feasible, albeit with refinement required in the design and manufacturing process for actual deployment. The results show that the wheel is capable of operating both on land and water with the passive mechanism successful in transitioning the wheel from legged motion to wheeled motion and vice versa.
An Enhanced Neural Network Based on Deep Metric Learning for Skin Lesion Segmentation

Accurate segmentation of skin lesion is a very important step in computer-aided diagnosis (CAD). However, this task is full of challenges due to the significant variations of lesion appearances across different patients. Aiming at solving the inaccuracy of segmentation, we propose a deep metric learning enhanced neural network (DMLEN) to promote the lesion segmentation result from existing method. We make full use of the relationships of pixels between the lesion area and the non-lesion area, and change the problem of pixels classification into the problem of distance measurement of pixels. In addition, an iterative enhancement scheme is proposed that gradually revises the lesion segmentation and results in a better result. Experimental results on two datasets show that the DMLEN decreases MAE 3% at least than previous methods.
Parameter Estimation Method for Random Ocean Waves with Mixed Spectrum Model

To overcome the deficiencies of the continuous spectrum model in the description of the physical meaning of the wave spectrum, we propose an innovative method to model random ocean waves based on the mixed spectral theory. In this paper, the ARIMA model is proposed to fit the continuous terms in the mixed spectrum, and the feasibility of the mixed spectrum model is verified with simulation data. Finally, this paper obtains the mixed spectrum expression of random ocean waves model, which provides a useful tool for interpreting the physical meaning of the composition of the wave spectrum.
Design of a class of nonlinear observers for global robust parameter estimation of biased multi-sinusoidal signals

This paper addresses design and robustness analysis of a class of nonlinear functional observers as dynamical estimators for achieving global robust parameter estimation of a biased multi-sinusoidal signal. We show that when the measurement noise is absent, the global exponential parameter estimation can be done. Otherwise, we elaborate an interesting robustness property in a sense of both integral input-to-state stability (iISS) and local input-to-state stability (LISS) with respect to the external measurement noise. Particularly, the relevant LISS threshold can be made arbitrarily large by selection of suitable design parameters.
Face Recognition Based on the Improved Sparse Representation

In recent years, face recognition is a hot research topic in the field of biometrics, and sparse representation is a hot topic in this field. In order to solve the problem that the sparse representation algorithm does not have good recognition effect in the face image training samples and test samples including both expression changes and illumination condition changes, a face recognition algorithm based on sparse coefficient and residual fusion is proposed. The algorithm combines the sparse coefficient into the residual classification decision, considering that the sparse coefficient is the fidelity condition that discriminates the correlation between the feature correlation and the class in the training sample class, and the feature correlation between the training sample and the test sample class. The new residual is defined as the classification standard, and the improved residual fully exploits the correlation information of the sparse coefficient. At the same time, the algorithm in this article is combined with the PCA feature extraction method to effectively reduce the data dimension while ensuring the recognition rate of face images. Several experiments were carried out in the standard face database to verify the effectiveness of the proposed method, and it is proved that the algorithm is robust.
Dynamic Modeling and Verification of Continuum Robot Based on Cable Drive

With infinite degrees of freedom, continuum robot can achieve continuous bending deformation, and has a flexible application prospects. A kind of modular continuum robot based on Polyurethane (PU) is proposed. At the same time, a 3D geometric model of the robot is established. For this configuration, the absolute node coordinate method is used to calculate the element mass matrix, element stiffness matrix and element elasticity matrix, etc. The system matrix is integrated and substituted into the multi-body system dynamics equation to obtain the dynamic equation of the robot in this configuration. In the same working condition, the fourth-order Runge-Kutta algorithm and symplectic algorithm are respectively used to solve the system dynamics equation. By comparison, it can be found that the final results of the two algorithms are the same, and the symplectic algorithm takes less time to calculate. Therefore, the symplectic algorithm is used to solve problems in this paper. In the static experiment, the error between the experimental results and the theoretical calculation results is compared to verify the correctness of the quasi-static system model. In the dynamic experiment, photogrammetric technique is used to obtain the dynamic experimental data of measuring points. By comparing the experimental data with the theoretical research numerical calculation, the correctness of dynamic modeling is verified.
Analysis identification of hub airport of Flight Training Network

Identifying the most important hub airport of complex network formed during the long- time flight training possesses effective solution and obvious potential to alleviate resource tensionǃimprove training ability and strengthen safety management. The flight training network(FTN) was innovatively constructed employing the airports and training course via the complex network framework, and its fundamental scale-free network characteristic was analyzed. The FTN data was simulated based on the classic network topological node analysis and an improved method based on the key assessment index correlation. The main results are as follows: degree-value or betweenness-value can effectively identify the hub airport while clustering-value showing no obvious rules, moreover, the correction between node’s betweenness and degree can identify the complete list of hub airports. The simulation results show high consistence with actual situation, which can serve future guidance and direction in hub airport application in actual flight training.
Pose Estimation for Multicopters Based on Off-Board Multiple Cameras

Pose estimation is a crucial issue for the control of multicopters. Based on a process model specially designed for multicopters and a generic camera model, an extended Kalman filter (EKF) method is proposed to estimate the pose of multicopters by using off-board multiple cameras in this paper. It is appropriate not only for multiple conventional/fisheye cameras but also for a single conventional/fish-eye camera. Simulation experiments show the proposed method performs more robustly in the case of marker missing than the commonlyused method, while giving comparable accuracy.
Millimeter Wave Time-Varying Channel Estimation via Exploiting Block-Sparse and Low-Rank Structures

The acquisition of channel state information is crucial in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, the previous studies for mmWave channel estimation only focus on the conventional static channel model without considering the Doppler shifts in a time-varying scenario. Since the variations of angles are much shorter than that of path gains, the mmWave time-varying channel has block-sparse and low-rank characteristics. In this paper, we show that the block sparsity, along with the low-rank structure, can be utilized to extract the Doppler shifts and other channel parameters. Specially, to effectively exploit the block-sparse and low-rank structures, a two-stage method is proposed for mmWave time-varying channel estimation. In the first stage, we formulate a block-sparse signal recovery problem for AoAs/AoDs estimation, and we develop a block orthogonal matching pursuit (BOMP) algorithm to estimate the AoAs/AoDs. In the second stage, we formulate a low-rank tensor due to the low-rank structure of time-varying channels, and based on the results of the first stage, a CANDECOMP/PARAFAC (CP) decomposition-based algorithm is proposed to estimate the Doppler shifts and path gains. In addition, in order to compare with conventional tensor decomposition-based algorithms, two tensor decomposition-based time-varying channel estimation algorithms are proposed. Simulation results demonstrate that the proposed channel estimation algorithm outperforms the conventional compressed sensing-based algorithms and the tensor decomposition-based algorithms, and the proposed algorithm remains close to the Cramér-Rao Lower Bound (CRLB) even in the low SNR region with the priori knowledge of AoAs/AoDs.
Personalized Control of Indoor Air Temperature Based on Deep Learning

Currently, in order to achieve the optimal thermal comfort for a given occupant, the optimal indoor air temperature should be set by the occupant himself/herself and then the air conditioning is used to control the actual indoor air temperature to converge to the optimal temperature. In this paper, we develop a method to determine the optimal indoor air temperature with which the air conditioning can be adjusted automatically without the involvement of the occupant. We first borrow the predicted mean vote(PMV) model to describe the relationship between the indoor air temperature and the occupant’s thermal comfort. We then adopt the deep learning method to obtain a classification deep neural network model which is used to capture the relationship between the indoor air temperature and the occupant’s thermal comfort. We test the model and the result shows that its mean average error is about 0.1°C, which satisfies the accuracy requirements of practical systems.
Environment-Based Railway Transportation Safety Assessment Using Hybrid Belief Rule Base

For the high-speed line and the existing line in the Cheng-Yu region, an environment-based railway transportation safety assessment approach using hybrid belief rule base (H-BRB) is proposed. Indicators of building the H-BRB models include rain, wind, fog, frost, disasters and so on. Model data are from January 1, 2017, to August 30, 2018. The case study shows that high-speed line is relatively safer than the existing line. The results of the assessment are compared with the historical records of accidents and failures to verify the effectiveness of the method. The H-BRB model contains both conjunctive and disjunctive rules, which can reduce the number of rules and thus avoid the combination explosion problem.
Event-triggered Adaptive Attitude Tracking Control of a Quadrotor UAV

This paper presents a novel attitude controller based on event-triggered mechanism for the uncertain quadrotor unmanned aerial vehicle (UAV). Due to the uncertainties contained, it is difficult to check the assumption of the ISS with respect to the measurement errors. Therefore, an adaptive controller and an event-triggered mechanism are to be employed such that the ISS assumption is no more needed. Two different strategies, i.e. the fixed threshold strategy and the relative threshold strategy are both established in an event-triggered mechanism, with which all the closed-loop signals are globally bounded and the tracking errors converge towards a small compact set. It is obvious that the updating frequency of the control signal has been reduced and the communication burden of the network is decreased therefore.
Study of bypass diodes configuration on PV modules with partial shaded

Light intensity is a very important factor in photovoltaic (PV) power generation, its efficiency will be greatly reduced in generating electricity when they are in partial shading and shadow effecting. In order to solve the maximum power point tracking (MPPT) problem of centralized PV arrays under complicated illumination environment, this study proposes reconstructing the by-pass diode, it can be effectively solved to keep the unobstructed power units working through this method, the power units of PV are judged by measuring the voltage of PV and they are connected the by-pass diode switches and parallel diode switches, so that the rest unshielded cells can continue to work, the whole PV will output electric power under the partially blocked condition. The test results show that the part of the shadow of the effective control of the output power with refactoring bypass diode of solar battery power, the output power has obvious improvement, the output can reach 126.9% by using refactoring’s diode layout of PV by compared with ordinary diode layout.
Instantaneous Velocity Optimization Strategy of Electric Vehicle Considering Varying Road Slopes

Economic cruising strategies of vehicles on terrain have been studied for years to reduce energy consumption by optimizing the profile of driving velocity. However, most controllers require prediction of future terrain information, with heavy computing load, which prevent them applying in real-time implementation. This paper presents an instantaneous energy optimization cruising strategy for pure electric vehicles (EVs), named normalized energy consumption minimization strategy (NCMS) to improve its cruising energy efficiency on roads with varying slopes. By converting the vehicle kinetic energy into equivalent energy consumption, the total energy consumption is minimized instantaneously to optimize the traction or braking torques. In addition, a velocity-related factor is designed to regulate the vehicle speed within a given cruising speed range. Since NCMS does not require the knowledge of future road conditions, it calculates the control rule within one millisecond, making it suitable for real-time control. Finally, energy saving potential of NCMS is explored by comparing to constant speed (CS) cruising strategy.
A Novel Control Method of A Soft Exosuit with Plantar Pressure Sensors

Soft Exosuit is a kind of Lower-limb wearable robots to augment and assist the wearer’s performance. Recently, our team developed a cable-driven wearable robot named SIAT Soft Exosuit (SSEX) which can transmit assistive force to wearer’s body. Natural gait feature extraction is the key to control a Soft Exosuit. In this paper, we propose a method to obtain the gait feature by plantar pressure sensors which measure the force load on a shoe insole. The system can detect the gait event accurately and reliably in real time which is the onset time of assistance for hip extension. Then we use IMUs to get the hip joint angle during hip extension which can help us calculate the variation of cable length and motor position profile. There are also two load cells in our system to make a feedback and get us desired assistant force. To achieve a desired active force, the control system adapt a way based on learning iterative control.
Running State Prediction and Evaluation of Power Transformers

Power transformers are the core of energy conversion and transmission in power grid. Once an accident occurs, it may cause huge losses such as equipment assets and power outages, and even have serious social impact. Therefore, effective state prediction and evaluation of power transformers can guide the operation and maintenance, which has important theoretical and practical significance. In this paper, the large data analysis method is used to predict and evaluate the running state of the transformer. Firstly, the running state index of the transformer is predicted based on long-short term memory (LSTM). Then, the current and future running state of the transformer is evaluated by decision tree classification algorithm. The experimental data comes from the running data of 630 kVA transformers in a certain area. The results show that the method used in the paper can effectively predict the future running state of the transformer and evaluate the abnormal state with high accuracy, which can provide reference for actual operation and maintenance.