Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Variable DC-Bus Voltage Control on the Inverter Lifetime in Electric Vehicle Applications

The reliability of the power converters in electric vehicle applications has been a great concern. As most power converters are not designed with redundancy, a malfunction of any component in the converter may prevent the vehicle to operate. Thermal cycling of the power devices is one of the main causes of failure in the power converter. Since the temperature and power losses of a power device are proportional, thermal cycling can be reduced by controlling one of the loss-related variables, such as the dc-bus voltage. For instance, variable dc-bus voltage control which adjusts the dc-bus voltage to the required motor stator voltage, has been widely investigated in the literature as an effective approach to reduce inverter power losses. In this paper, the effectiveness of this strategy in comparison with constant dc-bus voltage control is analyzed in terms of lifetime improvement. The analysis is carried out under Artemis urban and US06 driving cycles, which are used to obtain the thermal loading of the inverter power devices. Then, a lifetime model that explores the link between the thermal loading and lifetime consumption is presented. The performance analysis is validated through MATLAB Simulink which reveals that, variable dc-bus voltage control has the ability to improve the lifetime of the inverter by a factor of 5.06 and 3.43 under Artemis urban and US06 driving cycles, respectively.
An iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance to minimize total flowtime

In recent years, the distributed permutation flowshop scheduling problem (DPFSP) has been widely studied. In this paper, we extended the DPFSP by considering preventive maintenance (PM) operation to prevent machines from breaking down after the long process. An iterated greedy (IG) algorithm is developed to minimize total flowtime. A heuristic with swapping operator is proposed to initialize the IG. After that, the destruction phase and construction phase are modified to fit our problem. A local search is then applied to further improve the solution generated in the construction stage. At last, a simple simulated annealing-like acceptance criterion is used to prevent local optimal situations. Comparison with three state-of-the art algorithms in the recent literature based on 225 instances shows the high performance of our IG algorithm for solving the DPFSP with PM operation.
Short-term forecasting method for dynamic traffic flow based on stochastic forest algorithm

In order to overcome the problems of low accuracy and long time-consuming in traditional short-term forecasting methods for dynamic traffic flow, a short-term forecasting method for dynamic traffic flow based on stochastic forest algorithm is proposed in this paper. This method chooses short-term forecasting equipment for dynamic traffic flow, eliminates invalid data from the collected data, and normalizes the available data to complete data preprocessing before traffic flow forecasting. A combined forecasting model is established to optimize the output of the pretreatment results and complete the dynamic traffic flow rate forecasting. On this basis, the stochastic forest algorithm is introduced to train the sampling set of flow rate decision tree and generate short-term flow decision tree to realize short-term forecasting of dynamic traffic flow. The experimental results show that the forecasting time of the proposed method is short, always less than 0.5 s, and the forecasting accuracy is high, with more than 97%, so it is feasible.
Ultra-wideband and Visual Odometry Based Relative Localization for Multi-UAV System

This paper proposes a relative localization algorithm for Multi-UAV system, in which each agent is able to estimate the relative position of its neighbor based on Ultra-wideband measurement and visual odometry information exchanged. The relative localization algorithm consists of two parts. Visual odometry pre-process part fuses the IMU data and radar altimeter data to correct visual odometry error and reach a higher update rate to ensure flight smoothly and a better state estimation. Using the UWB measurement and information exchanged, each agent constructs a graph to solve relative localization problem in sliding window. Finally, experimental results are presented to corroborate the effectiveness of our proposed relative localization algorithm.
Model Identification of The Skid-steered Wheeled Vehicle at Operating Point

Since it is usually difficult to determine the mechanism model of the skid-steered wheeled vehicle, this paper mainly studies the system model identification technology of the vehicles. Because of the non-linearity of the skid-steered wheeled vehicle, an innovative concept of operating point is introduced. The entire system is divided into several operating points in terms of the motion state of the system. Then relying on the input and output identification data of the system, the Hankel matrix identification method based on Markov coefficients is used to get the linearized system model at each operating point. The comparison of the identification model with the mechanism model and real output of system shows that the identification model can characterize the system at the operating point.
A CNN-based Vocal Recognition Algorithm for IARC Mission 8

In this paper, a vocal command recognition problem is investigated to meet the rule of the International Aerial Robot Competition (IARC) Mission 8, which requires the participant to interact with UAVs through non-electrical methods. First, the audio signal description is given, and the preprocessing of the data is provided. Then, a CNN-based vocal recognition algorithm with model of residual connection is proposed in which feature extraction with preprocessing method is applied instead of taking the raw data as the input. Moreover, the proposed algorithm is implemented on an android platform. Then, simulation experiments are conducted to show the effectiveness of the proposed algorithm. Besides, comparisons and analysis are given for different methods of data processing.
Modeling and Kinematics Analysis of a Novel 5-DOF Upper Limb Exoskeleton Rehabilitation Robot

It has become increasingly popular that robot is applied to assist patients with central nervous system injury in rehabilitation exercise. This paper introduces a novel 5-DOF upper limb exoskeleton rehabilitation robot. In MATLAB, the forward kinematics expression of the robot is established by using the D-H method and the workspace of the robot is analyzed by Monte Carlo method. Considering the physiological characteristics of the human body and the practical requirement of rehabilitation movement, this paper uses the "Swivel Angle" to solve the redundancy solution problem of inverse kinematics of the robot on line. In order to verify the correctness of the kinematics model of the robot, a simulation experiment based on the minimum jerk trajectory planning is designed. This work lays the foundation for the future studies on dynamics, control and human-computer interaction strategy of the robot.
Why applications are coupled to I/O and how the scenery can be changed

The versatility and I/O demanding nature of modern applications, compel them to be operable across various storage platforms. As the platforms may exhibit different characteristics, the applications need to incorporate platform-specific code paths. More demanding applications that require concurrent use of platforms must also implement transactions, parallel streams, error handling routines and data reconstruction strategies. Building this logic is a complicated task that requires I/O expertise, but more importantly, it must be re-implemented for each application. To that end, the application decouples from the platform but is bounded to I/O related code paths. This paper presents a framework (TrIO) to decouple the I/O logic from the application source code. The framework exhibits a domain specific language, that allows the developers to model the I/O logic as a graph of components, without any implementation concerns. The runtime engine takes as input the graph and generates the respective intransit data processing network with transactional and parallel data delivery guarantees. As a proof-of-concept, a distributed filesystem prototype built atop the engine, with graphs that imitate RedHat GlusterFS volumes to define its behavior. Conducted tests show that for volumes that rely on parallelization, our prototype can outperform GlusterFS by a factor of 2.
Simulating Industrial Alarm Systems by Extending the Public Model of A Vinyl Acetate Monomer Process

The research area of industrial alarm monitoring and management has been attracting increasing studies in the past decade. A variety of advanced alarm management techniques have been proposed. However, the validation of the results were mostly based on real industrial data while such data was not always accessible to all researchers. Even if obtained, the privacy policies of industrial corporations may lead to difficulty in publishing new results. Thus, there is a great demand to construct a large scale simulated alarm system and create standard alarm & event data for the test of new methods. This paper proposes the design of a simulated alarm system by extending the public model of a Vinyl Acetate Monomer (VAM) process and collects the alarm & event data through a long term alarm monitoring process. An example alarm & event dataset is created and analyzed to illustrate the usability of the simulated alarm system.
Research on brake pad surface defects detection based on deep learning

In order to solve the problem of feature extraction of brake pad surface defects in traditional machine learning, an automatic defect detection system based on deep learning is designed. Firstly, the image acquisition system is designed and the brake pad image dataset is constructed. Then, a kind of defect detection method based on convolutional neural network (CNN) is proposed. The dataset is input into the CNN model for training, and the appropriate defect classification model of brake pad is selected for defect detection and classification. In addition, in order to further improve the performance of the detection system, a defect detection method based on full convolution network (FCN) is also proposed. Finally, the two methods mentioned above are tested. The results show that both methods are suitable for the detection of brake pad defects and can realize the recognition and classification of brake pad defects. In comparison, the method based on CNN has higher detection efficiency than the one based on FCN, but the method based on FCN has better recognition accuracy and stronger performance than the one based on CCN.
Adaptive Distributed State and Input Estimation Using Retrospective-Cost-Based Information Filter

In this paper, the problem of distributed state and input estimation using sensor networks for linear system is investigated. First, the retrospective-cost-based information filter (RCIF) is proposed to estimate the state and input simultaneously, by combining the retrospective cost input estimator subsystem and information filter state estimator subsystem. Next, the retrospective-cost adaptive input estimator subsystem is formulated, which utilizes retrospective cost optimization and recursive minimum mean square estimation to drive the estimated input to approximate the actual input without prior information. Then, the consensus algorithm is used to extend the RCIF to distributed estimation, and to improve the convergence rate, the adaptive update law of consensus weights is presented. Finally, a simulation example is illustrated to validate the effectiveness and feasibility of the proposed algorithm.
Behavior Assessment and Trajectory Planning for Autonomous Vehicles on Structural Urban Roads

For autonomous vehicles driving on structural urban roads, we should not only concentrate on passing efficiency but also address the interaction among vehicles, especially the potential risks from the ambient obstacles. In order to find a more secure trajectory, a hierarchical framework combining behavior planner and trajectory planner is proposed in this paper. Probabilistic risk assessment associated with acceleration is used to obtain the optimal behavior with low risk. The spatiotemporal trajectories are generated using quintic polynomial and model predictive method under the guidance of the adopted behavior. The effectiveness of the proposed method is verified in a simulated scenario.
Image Fusion based on Variational Method for Maintenance of Gradient and Intensity

Infrared and visible image fusion technology helps to improve the spatial resolution. It mainly preserves the features and details of the source images and generates a fusion image with visual enhancement. In this paper, based on the gradient features and intensity information of the source images, an optimization model for image fusion is built. Firstly, the pre-fused gradient of the source images is obtained by combining the structure tensor and the proposed local gradient similarity, where local gradient similarity is used to make the fused gradient direction more accurately. Secondly, the source images are reconstructed into salient and non-salient images according to the comparison of the pixel intensity. A weight map before the non-salient image in the optimization model makes the effective details preserved, so that the pre-fused images consist of the salient image and the non-salient image with a weight map. Finally, an optimization model is constructed to constrain the gradient and intensity of the final fused image close to the pre-fused gradient and the pre-fused images. The final fused image is obtained from solving the optimization model by use of the variational method. The experimental results are evaluated from subjective and objective assessments, which show the effectiveness of the proposed algorithm.
Stability Analysis for Networked Power Systems with LFC and Event-Triggered Communication

This paper is dedicated to investigate the stability problem of multi-area networked power systems with load frequency control (LFC) and event-triggered communication. To save limited network resources and improve the data transmission efficiency, an ACE-dependent time-delay model is constructed to describe the multi-area networked power system via event-triggering mechanism. By utilizing the Lyapunov theory, a delay-dependent stability criterion for networked power systems is presented in terms of linear matrix inequality. Finally, a three-area power system based on MATLAB is provided to show the superiority and effectiveness of the proposed method.
Verifying Computation Tree Logic of Knowledge via the Similar Reachability Graphs of Knowledge-oriented Petri Nets

Computation Tree Logic of Knowledge (CTLK) can specify many requirements of privacy or security of multi-agent systems (MAS). In our previous paper, we defined Knowledge-oriented Petri Nets (KPN) to model MAS. A KPN is a special Petri net in which some places are used to represent knowledge. Consequently, KPN can formally represent not only the interaction/collaboration process of multiple agents but also their epistemic evolutions. These epistemic evolutions are closely related to privacy and security of MAS. Then we defined the similar reachability graphs of KPN and constructed the equivalence classes of knowledge for each agent. We considered an epistemic operator $\mathcal{K}$ and designed its model checking algorithm based on similar reachability graph and equivalence class. In this paper, we design the model checking algorithm for another epistemic operator $\mathcal{C}$. Additionally, we develop a related tool. Examples of privacy protocols illustrate the usefulness of our model and method.
Robust Adaptive Finite-Time Fault-Tolerant Tracking Control for Uncertain Systems

The adaptive finite-time fault-tolerant tracking issue of a class of uncertain systems is taken into consideration in this paper. The unknown time-varying actuator faults and uncertainties of both the system matrix and the input matrix are considered simultaneously when designing the control scheme. The unknown control parameters and gains caused by the actuator faults and model uncertainties are estimated by the designed adaptive laws to construct the fault-tolerant controller. Lyapunov stability analysis shows that the finite-time convergence property of the closed-loop system can be accomplished with great robustness to actuator faults and uncertainties guaranteed. Simulation results of an application to an aircraft model are illustrated to highlight the effectiveness of the proposed methodology.
SINR-based multi-channel power control under DoS attacks: A Stackelberg game approach

In this paper, the optimal power control problem is investigated for the wireless communication network under denial-of-service (DoS) attacks modelled by signal-to-interference-plus-noise ratio (SINR). Different from the Nash Equilibrium (NE), a Stackelberg game is used to describe the process that the defender and the attacker make their decisions sequentially. Moreover, a Lagrangian multiplier approach is utilized to design the optimal strategy for the attacker, and Adaptive Penalty Function (ADF)-based Genetic Algorithm (GA) is employed to calculate the best strategy for the defender. Two different types of power constraints are studied, respectively, i.e., equality and inequality constraints. Then, the optimal solutions for both players under the two different constraints are analyzed. Finally, two examples are provided to illustrate the main results, and GA, Differential Evolution (DE) and Immune Algorithm (IA) are used to solve the non-convex optimization problem.
An output regulation approach for swing-up and tracking control of double pendulum on a cart

It is well known that the control problem of the double pendulum on a cart (DPC) system is challenging since it is a complicated underactuated mechanical system with high nonlinearity. This paper first studies a swing-up and tracking control problem of the DPC system with time-varying reference signal. A novel switched swing-up and tracking controller is proposed, which can be carried out in two steps. In step one, the energy-based swing-up controller is used to drive the double pendulum to the neighborhood of the up-up equilibrium point. In step two, based on approximate nonlinear output regulation theory, the polynomial approximation based tracking controller is used to achieve high precision position tracking control. Finally, the simulation results illustrate the effectiveness of our design.
Band Selection Using Combined Divergence–Correlation Index and Sparse Loadings Representation for Hyperspectral Image Classification

Band selection and feature extraction are the two paradigms of hyperspectral dimensionality reduction. While feature extraction methods have many desirable properties, band selection methods keep the actual reflectances intact, which results in better interpretation of the behavior of the materials. Keeping this in mind, several feature extraction-based band selection methods have been proposed in the past, which can bridge the gap between these two approaches and combine their salient advantages. In this article, a feature extraction-based, clustering-ranking type band selection method is proposed in which the band selection is performed in two stages. In the clustering stage, a new and improved representation of bands using their component loadings for the top principal components is introduced, which is employed to cluster similar bands into groups using sparse subspace clustering. In the ranking stage, a new metric called combined divergence–correlation index is introduced to elect the most discriminative as well as least correlated bands as cluster representatives. Experimental results indicate that the proposed method can proficiently select a set of distinct and discriminative bands, which can help in effective hyperspectral classification. Additionally, it was also shown that the selected bands show rich information content, low interband correlation and high-class separabilities.
Airport Connectivity Optimization for 5G Ultra-Dense Networks

The rapid increase of air traffic demand and complexity of radio access network motivate developing scalable wireless communications by adopting system intelligence. The lack of adaptive reconfiguration in radio transmission systems may cause dramatic impacts on the traffic management concerning congestion and demand-capacity imbalances. This has driven the industry to jointly access licensed and unlicensed bands for improved airport connectivity. Therefore, intelligent system is embedded into fifth generation (5G) ultra-dense networks (UDNs) to provision dense and irregular deployments that support extended coverage and improved energy-efficiency for the entire airport network at higher throughputs. To define the technical aspects of the proposed solution, this paper addresses new intelligent technique that configures the coverage and capacity factors of radio access network considering the changes in air traffic demands. This technique is analysed through mathematical models that employ power consumption constraints to support dynamic traffic control requirements to improve the overall network capacity. The presented problem is formulated for medium or large airport air transportation network. The power optimization problem is solved using linear programming with careful consideration to latency and energy efficiency factors. Specifically, an intelligent pilot power method is adopted to maintain the connectivity throughout multi-interface technologies by assuming minimum power requirements. Numerical and system-level analysis are conducted to validate the performance of the proposed schemes for both licenced macrocell New Radio (NR) and unlicensed wireless fidelity (WiFi) topologies. Finally, the insights of problem modelling with intelligent techniques provide significant advantages at reasonable complexity and brings the great opportunity to improve the airport network capacity.
Ascent Guidance for Launch Vehicle Based on Receding Horizon Control

For the difficulties and challenges caused by the unknown disturbance during the ascent stage of the launch vehicle, a trajectory tracking strategy using particle swarm optimization (PSO) algorithm in a receding horizon control (RHC) framework is investigated in this paper. Since the RHC solves the online optimization problem at each step, the calculations can be time-consuming. To speed up the computation of RHC, the online optimization problem is transformed into a one-dimensional variable optimal problem and the control actions are obtained by PSO. Providing solve times in milliseconds, which guarantees the feasibility of the RHC controller. Simulation results are provided to illustrate that this strategy can not only satisfy the fast calculation speed, but also can realize better performance with the existence of aerodynamic uncertainty, thrust uncertainty, and process constraint.
Studies on Chromosomal Karyotype in Ramie

Ramie [Boehmeria nivea (Linn.)! Gaudich. cv. Heipidou] was used as a sample to study the number and karyotype with the F-BSG method. The results showed that somatic chromosomes of Heipidou were 2n=28, and its karyotype was 2n=28=28st (2SAT). The satellites of ramie were often located in the short arms of No.3 and No.13 chromosomes. The karyotype of Heipidou belonged to 4B type, and the chromosomes were asymmetry.