Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Reinforcement Learning Algorithm Based on Counterfactual Experience Replay

A new algorithm based on SARSA is proposed to avoid the overestimation problem in traditional reinforcement learning. Different from traditional methods to overcome this problem, the new algorithm can alleviate overestimation without significantly increasing the algorithm complexity. At the same time, aiming to problems existing in traditional SARSA, such as the weak ability of active exploration and unsatisfactory convergent results, the structure of Experience Memory Replay(EMR) is creatively modified in this paper. The new algorithm proposed in this paper changes the traditional experience playback structure and creatively adds counterfactual experience, which is called DCER(Dynamic Counterfactual Experience Replay) combining on-policy and off-policy. The exploration performance of the algorithm is increased by adding different experiences to EMR from the actual action when sampling. The algorithm was applied in the Gym Cartpole environment and compared with the traditional algorithm in the same environment, proving that the improved algorithm improved the performance of SARSA. Finally, the feasibility of the algorithm in a multi-agent reinforcement learning environment is analyzed.
Ultimate stability analysis of active disturbance rejection control with a high-gain observer

Model uncertainty is an important issue that must be considered when designing or analyzing controllers. So focusing on a class of nonlinear systems with model uncertainties, this paper analyzed the tracking performance of the active disturbance rejection controller with a high-gain observer, through transforming the model uncertainties into the error form, and the closed-loop system was proved to be ultimately bounded using Lyapunov function and Gronwall-Bellman inequality. Finally, numerical simulations demonstrate the results.
Design of 3D reconstruction system on quadrotor Fusing LiDAR and camera

In this paper, we present our design for a system on quadrotor, which equipped with a 3D LiDAR and camera, to explore and reconstruct unknown environments. The pipeline of the system is composed of LiDAR simultaneous localization and mapping (SLAM) and real-time update dense map generation submodule, only driven by 3D LiDAR point clouds and image flow. By processing LiDAR data, we can obtain real-time pose of the quadrotor and rough surrounding geometric information. At the same time, we use camera to texture the LiDAR points for 3D reconstruction. All data processing is completed on the onboard computer and the attitude control of quadrotor will be handled by a professional flight controller. The experimental results demonstrate the system’s ability to reconstruct environment in unknown space.
Time Series Risk Prediction Based on LSTM and a Variant DTW Algorithm: Application of Bed Inventory Overturn Prevention in a Pant-Leg CFB Boiler

The pant-leg design is typical for higher capacity circulating fluidized bed (CFB) boilers because it allows for better secondary air penetration, maintaining good air-coal mixing and efficient combustion. However, the special risk, nominated as bed inventory overturn, remains a big challenge and it hinders the application of pant-leg CFB boilers. For a time series risk, it is critical to do the bed inventory overturn prevention to leave enough time for the adjustment. This paper proposed a new framework combing long short-term memory (LSTM) and dynamic time warping (DTW) methods to do the risk prediction. Pattern matching of data difference discrimination is employed for DTW algorithm, instead of the traditional Euclidean metric. The pattern matching has the merits in reduction of calculation and improvement of the adaptability to variables with different dimensions. After variable processing of the time series data by the variant DTW algorithm, the bed pressure drop prediction model is established based on the LSTM structure in this framework. Compared with some traditional prediction method, the framework in this paper has achieved superior results in the application of bed inventory overturn prevention.
A New Sensor for Simultaneous Measurement of Strain and Temperature

A new cascaded structure based on two long-period fiber gratings (LPFGs) for simultaneous measurement of strain and temperature is proposed and studied. The proposed structure is cascaded by a LPFG and a S-type long-period fiber grating (S-LPFG). The LPFG is fabricated using the CO<sub>2</sub> laser by etched method. The S-LPFG is fabricated using the CO<sub>2</sub> laser by polished method. The grooves of the S-LPFG are staggered in opposite directions, and this special geometry results in high strain sensitivity. The experimental results show that the strain sensitivities of the two resonant dips of the cascaded structure are 16.31 pm/<inline-formula> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula> and −2.42 pm/<inline-formula> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula> with the range of 0-<inline-formula> <tex-math notation="LaTeX">$500~\mu \varepsilon $ </tex-math></inline-formula>, respectively. Strain sensitivity varies greatly between two dips and the resonant wavelength shifts in the opposite direction. The temperature sensitivities of the two resonant dips of the cascaded structure are 59.73 pm/°C and 60.38 pm/°C, respectively. Since the difference between temperature and strain sensitivity, matrix method can be used for simultaneous measurement of two parameters. The feasibility of the cascaded structure has been experimentally demonstrated for the simultaneous measurement. Therefore, the cascaded structure has the potential to measure strain and temperature simultaneously.
Adaptive Selective Harmonic Elimination Model Predictive Control for Three-Level T-Type Inverter

In this work, an adaptive selective harmonic elimination model predictive control (ASHE-MPC) method for a 3L-T type inverter is proposed. This algorithm is based on model predictive control (MPC), introduces adaptive selective harmonic elimination (ASHE) algorithm, and performs digital delay compensation through two-step prediction. Compared with the harmonic suppression strategies based on PI or PR controller, ASHE-MPC significantly improves the current loop bandwidth, speeds up the dynamic response, and greatly increases the harmonic order that can be eliminated. Compared with the harmonic suppression strategies based on the deadbeat model predictive control (DB-MPC), ASHE-MPC can eliminate low-order harmonics caused by multiple factors (including dead-time and grid background harmonics), and can easily eliminate multiple harmonics ( $5^{\mathrm {th}}-31^{\mathrm {th}}$ in this paper). The proposed ASHE-MPC method for the first time achieves adaptive selective harmonic elimination in model predictive control. The ASHE-MPC method is novel, simple and efficient. A three-level T-type grid-connected inverter for dead-time compensation is used as an example to verify the feasibility of this method.
An Optimization Method for Static Output Feedback Stabilization of Linear Control Systems

In this paper, we are concerned with the problem of static output feedback stabilization of linear control systems. A new necessary and sufficient condition for static output feedback stabilization of linear time invariant control systems is derived. Based on the integral performance of state transition matrix of the closed-loop system and the optimization technique, a numerical method is proposed to seek output feedback gain matrix to stabilize the linear systems. The effectiveness of the method is interpreted by a theoretical result and numerical examples.
A decision-making process in a vehicle to grid concept

The paper deals with the decision-making process of the electric vehicles’ owners in vehicle to grid concept (V2G). The paper presents a theoretical basis for the definition of the discrete choice systems. Consequently, it defines mathematical model of the owner’s discrete choice system in the V2G concept and provides an example of the decision-making process of the electric vehicles’ owners.
Exploring Performance Characteristics of ZNS SSDs: Observation and Implication

ZNS SSDs (Zoned NameSpace Solid State Drives) are a new type of SSD that provides various features such as zone concept and host-level flash management. In this paper, we explore how these features affect performance characteristics of ZNS SSDs. To this end, we design an analysis tool based on a real ZNS SSD prototype that allows to evaluate performance under different workloads. Then, we investigate diverse characteristics in terms of parallelism, isolation and predictability. Our observations reveal that 1) requesting I/Os in a large unit is indispensable in ZNS SSDs to obtain parallelism, 2) workloads can be isolated more effectively compared with traditional SSDs, 3) unexpected performance drop is not monitored, and 4) performance differs based on LBAs (Logical Block Addresses), which can be exploited usefully when we devise a new algorithm for ZNS SSDs.
Controllability of uncertain polynomial fuzzy singular systems

This paper considers I-controllability, R-controllability and C-controllability of uncertain nonlinear singular systems. The polynomial fuzzy model is constructed for uncertain to eliminate the upper and lower bounds restriction of states in the system and reduce the calculation quantity. Sufficient conditions, based on a polynomial fuzzy model, are proposed to guarantee the controllability of the system. Finally, an example is performed to verify the merits of the method proposed.
Exponential Stability for Delayed Neural Networks using Extended Reciprocally Convex Matrix Inequality

In this paper, the problem of the exponential stability for delayed neural networks with a time-varying delay is investigated. Firstly, a novel Lyapunov-Krasovskii functional with simple augmented terms is constructed. Then, a less conservative stability criterion is obtained via a newly developed free-matrix-based integral inequality and an extended reciprocally convex matrix inequality. Meanwhile, the complexity of the techniques used is in consideration. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results.
Computer Medical Image Segmentation Based on Neural Network

Image segmentation in medical imaging has long been a problem in radiological image processing. Most of the image segmentation methods in traditional vision algorithms are difficult to achieve high-resolution image segmentation due to the complexity of the algorithm. This article proposes an image segmentation method based on an optimized cellular neural network. This method introduces a non-linear template and data quantization on the basis of a basic network model, which greatly reduces the computational complexity while maintaining the accuracy of image segmentation. We then applied the method to a computer-aided system to classify tumor lesions in mammograms. Finally, we propose an FPGA-based multilevel optimization architecture for energy-efficient cellular neural networks. The optimization scheme includes three levels: system level, module level, and design space. This solution improves computing performance by increasing system parallelism, using data reuse technology to fully utilize loading bandwidth, and using data quantization to reduce computational redundancy. It also introduces pipeline and dual cache structures to optimize memory access, and analyzes the limited resources through the Roofline model. System for best performance. The experimental results show that the FPGA accelerator in this article can improve unit performance by 34% compared with other existing research work. The nonlinear quantified cellular neural network proposed in this article can reduce LUT resource consumption by 74% and energy of 48.2%. Compared with the original network, in the two projection position segmentation results of the mammogram, only 1.5% and 0.6% of the accuracy loss, respectively.
Adaptive Backstepping Sliding Mode Control for Fractional Order Systems

This paper concentrates on the control problem of a class of fractional order nonlinear systems. A new adaptive backstepping sliding mode control is proposed, which inherits both the advantages of backstepping and sliding mode control. Furthermore, a fractional order sliding mode disturbance observer is proposed to estimate unknown external disturbances. Meanwhile, RBF neural network is adopted to approximate unknown nonlinear terms. Finally, an illustrative simulation result based on a practical fractional order permanent magnet synchronous motor model is presented to verify the validity of proposed controller.
Threats and Opportunities: Blockchain meets Quantum Computation

This article considered deficiencies of the flourishing blockchain technology manifested by the development of quantum computation. We show that the future blockchain technology would under constant threats from the following aspects: 1) Speed up the generation of nonces; 2) Faster searching for hash collisions; 3) Break the security of the classical encryption. We also demonstrate that incorporating some quantum properties into blockchain makes it more robust and more efficient. For example people can establish a quantum-security blockchain system that utilizes quantum key distribution (QKD), and quantum synchronization and detectable Byzantine agreement (DBA) can help the blockchain systems achieve faster consensus even if there exist a number of malicious nodes.
Calibration Nested Arrays for Underdetermined DOA Estimation Using Fourth-Order Cumulant

The application of a nonuniform nested array provides a simple way for underdetermined direction of arrival (DOA) estimation, enabling us to develop a suited self-calibration algorithm for perturbed nested array. The key points of this algorithm contain two steps: a first step is to obtain gain-phase errors estimation by utilizing partly calibrated array and properly choosing the elements of fourth-order cumulant matrix; a second step to form a small perturbed model after compensating the gain-phase errors as well as obtain DOA estimation using the sparse total east squares (STLS) technique. The proposed algorithm is very suited for underdetermined DOA estimation in the presence of gain-phase errors. Numerical simulations validate the effectiveness of the proposed algorithm.
New CAD Tools to ConFigure Tree-Based Embedded FPGA

An embedded FPGA (e-FPGA) is an IP which can be integrated in a System on Chip architecture to add more flexibility and reconfigurability to the design. This e-FPGA needs to be designed, optimized and configured differently compared to a classic FPGA chip. In this paper, we propose a full workflow to conFigure tree-based e-FPGA architecture. The workflow includes some existing tools used for mesh architecture. We modified these tools and adapt them accordingly to the proposed e-FPGA constraints. The new workflow reduces the execution runtime by an average of 57 % compared to the academic workflow used for mesh architecture. The e-FPGA area is also reduced by an average of 27% compared to the mesh architecture. This optimization is due to the ability of the CAD tools to manage the different processes of the configuration workflow for the tree-based architecture.
Vision-Based Fixed-Time Platoon Formation Control of Multi-Agent Systems

This paper presents an adaptive fixed-time formation control of nonholonomic mobile robots subject to limited sensing capability. A fixed-time cooperative control method is considered since it can provide the stability of the closed-loop system and guarantee convergence time of system trajectories independently on initial conditions. In addition, performance constraints are introduced in control strategy, imposed on formation tracking errors to guarantee the transient and stead-state performance. Furthermore, connectivity maintenance and collision avoidance are fully taken into account to prevent multi-agent systems deterioration. Consequently, the barrier Lyapunov function is incorporated into formation controller design such that under the proposed control method, the formation tracking errors can converge into a small neighbourhood of zero in fixed settling time even though the feedback signals are only the relative distance and bearing angle. Simulation results demonstrate the effectiveness of the proposed formation controller.
Finite-time Command Filtered Based Adaptive Event-triggered Neural Control for Nonlinear Systems

This paper addresses the problem of adaptive event-triggered neural network control for a class of strict-feedback nonlinear systems. First, the radial basis function neural network is utilized to approximate unknown nonlinear functions and the command filter technique is introduced to address the problem of explosion of complexity in the backstepping design. Then, by using backstepping design, radial basis function neural network and event-triggered mechanism, an adaptive event-triggered tracking controller is designed. The designed controller ensures that the tracking error converges to an arbitrarily small neighborhood of zero and reduces the communication burden. Finally, the numerical example is presented to demonstrate effectiveness of the proposed control scheme.
Nighttime Traffic Light Location Based on Binocular Vision and Electronic Map

Autonomous driving needs to obtain various traffic information promptly to make decisions, and the accurate positioning of traffic light in real time is one of the key points to realize autonomous driving. Up to now, there are many interesting and practical researches related to the positioning. However, most of the scholars are doing researches of daylight traffic light location. At night, Neon lights, street lights, car lights and other factors may have a serious impact on the accurate positioning of traffic lights. How to remove the influence of these factors is a big challenge in autonomous driving. We propose a practical approach to address this challenge. The motivation of this approach is as follows: Firstly, GPS information and electronic map are used to estimate the distance and direction of the current position relative to the traffic light; secondly, binocular vision is applied to get the depth, orientation and other information of each area in the image, excluding the interference area outside the scope of the traffic light; thirdly, use some methods to positioning the traffic light, including brightness value division, geometric features analysis, circular degree detection, and then the status of the traffic light can be recognized through the HSV color space. The experimental result shows that the positioning accuracy of this method is more than 95%, the average of processing time for each image is 301ms, which can meet the requirements of accuracy and real-time.
Neuromorphic Fringe Projection Profilometry

We address the problem of 3-D reconstruction using neuromorphic cameras (also known as event-driven cameras), which are a new class of vision-inspired imaging devices. Neuromorphic cameras are becoming increasingly popular for solving image processing and computer vision problems as they have significantly lower data rates than conventional frame-based cameras. We develop a neuromorphic-camera-based Fringe Projection Profilometry (FPP) system. We use the Dynamic Vision Sensor (DVS) in the DAVIS346 neuromorphic camera for acquiring measurements. Neuromorphic FPP is faster than a single-line-scanning method. Also, unlike frame-based FPP, the efficacy of the proposed method is not limited by the background while acquiring measurements. The working principle of the DVS also allows one to efficiently handle shadows thereby preventing ambiguities during 2-D phase unwrapping.
Online MTPTA and MTPIA Control of Brushless Doubly Fed Induction Motor Drives

This article proposes a nonlinear controller for brushless doubly-fed induction motor (BDFIM) drives on the basis of input–output feedback linearization (IOFL). In the proposed control approach, first, the maximum torque per inverter ampere (MTPIA) control strategy is considered by applying a fifth-order (reduced-order) model of BDFIM. By realizing the MTPIA, the control winding (CW) current magnitude is minimized under the constraint of constant torque. The realization criterion of MTPIA approach is independent of torque level and CW frequency. To fairly share the current between stator windings and minimizing the total current magnitude for a given torque, the MTPIA idea should be extended. Since, the power winding (PW) of BDFIM is not controllable, obtaining this purpose is a challenge. Therefore, to solve this problem, the model-based maximum torque per total ampere (MTPTA) control is introduced for BDFIM drives and the conditions for realizing this strategy are determined. It should be noted that the total amperes refer to the sum of the PW and the CW current magnitudes. In addition, using the fifth-order model of BDFIM, a nonlinear controller is developed based on IOFL technique which is capable to force the deviation from realization criterion of both proposed strategies to zero while the torque is controlled with high accuracy. The overall stability of the proposed controller is proven using the Lyapunov control theory. The validity of the proposed approach is finally verified by simulation and experimental results.
Standard Formation Generation and Keeping of Unmanned Aerial Vehicles Through a Potential Functional Approach

This paper investigates the standard formation generation and keeping problem for multiple fixed-wing unmanned aerial vehicles (UAVs), where a standard formation is defined as the basic shape of UAVs when executing tasks. Firstly, a control law based on the gradient of potential functions is proposed to drive multiple UAVs to form and keep the standard formation, with theoretical analysis showing that the desired collective behaviors can be obtained. The control law is then extended to achieve obstacle avoidance by taking the virtual repulsion strategy into account. Numerical simulations are finally provided, verifying the effectiveness of the proposed strategy.
An Improved Active Disturbance Rejection Controller for Hydraulic Valve-Controlled Hydraulic Motor

Hydraulic valve-controlled hydraulic motor is a commonly used power component. However, there is uncertain perturbation in hydraulic systems and active disturbance rejection control (ADRC) is able to estimate and compensate the disturbance in real-time, but the controller is not satisfying for the hydraulic system due to the buffeting of tracking process. In this paper, an improved ADRC controller is designed for the hydraulic valve-controlled hydraulic motor. The nonlinear function of the extended state observer (ESO) in the general ADRC controller is replaced by arc-hyperbolic sine function to alleviate the effect of buffeting. It can be seen that the improved controller is able to track the setting value without buffeting via simulation. Thus, the conclusion can be drawn that the improved ADRC controller is suitable for the hydraulic valve-controlled hydraulic motor in this paper since it can eliminate the buffeting and reduce the amount of tuning parameters as well.