Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Off-Policy Iterative Learning Control

Reinforcement learning has emerged as a powerful paradigm to learn control policies while making few assumptions about the environment. However, this lack of assumptions in popular RL algorithms also leads to sample inefficiency. Furthermore, we often have access to a simulator that can provide approximate gradients for the rewards and dynamics of the environment. Iterative learning control (ILC) approaches have been shown to be very efficient at learning policies by us-ing approximate simulator gradients to speed up optimization. However, they lack the generality of reinforcement learning approaches. In this paper, we take inspiration from ILC and propose an update equation for the value-function gradients (computed using the dynamics Jacobians and reward gradient obtained from an approximate simulator) to speed up value-function and policy optimization. We add this update to an off-the-shelf off-policy reinforcement learning algorithm and demonstrate that using the value-gradient update leads to a significant improvement in sample efficiency (and sometimes better performance) both when learning from scratch in a new environment and while fine-tuning a pre-trained policy in a new environment. Moreover, we observe that policies pretrained in the simulator using the simulator jacobians obtain better zero-shot transfer performance and adapt much faster in a new environment.
Time-Incremental Learning of Temporal Logic Classifiers Using Decision Trees

Real-time and human-interpretable decision-making in autonomous systems is a significant but challenging task, which usually requires predictions of possible future events from limited data. While machine learning techniques have achieved promising results in this field, they lack inter-pretability and the ability to make online predictions for sequential behaviors. In this paper, we introduce a time-incremental learning framework to predict the labels of time-series signals that are received incrementally over time, referred to as prefix signals. These signals are being observed as they are generated, and their time lengths are shorter than their corresponding time horizons. We present a novel decision tree-based approach to learn a finite number of Signal Temporal Logic (STL) specifications from a given dataset and construct a predictor based on them. Each STL specification serves as a binary classifier of the time-series data and captures a specific part of the dataset’s temporal properties over time. The predictor is built by assigning time-variant weights to the STL formulas, which represent their classification impacts. The weights are learned using neural networks to minimize the misclassification rate of classifying prefix signals with different time lengths. The predictor is then used to predict the labels of prefix signals by computing the weighted sum of their robustnesses with respect to the STL formulas. The effectiveness and classification performance of our algorithm is evaluated on urban-driving and naval-surveillance case studies.
Application of Benjamin-Feir equations to Tornadoes' rogue waves modulational instability in oceans

Pilots put their plane in the ideal conditions for the atmosphere to suck it and allow a successful launch; similarly some portions of the ocean surface put a Troposphere column placed above them in ideal temperature and humidity training tornadoes and primary gravity waves that accompany them. To study the behavior of rogue waves triggered by tornadoes in terms of their space and time evolution, that is, their motion and also in terms of mechanical transformations that these systems may suffer in their dealings with other systems, we use Benjamin-Feir equations on gravity waves modulational instability. Spectacular images available on tornadoes when impacting on land allow each of us to have an opinion on the magnitude of the pressure forces deployed by that space weather phenomenon. This study on atmosphere-oceans interactions is unique and based on the effectiveness of Mbanes’ fluid dynamic balance model which provides relevant knowledge on the vertical profile of winds triggered by tornadoes. Key words: Tornadoes’ gravity waves, modulational instability, Benjamin-Feir equations.
Irregularity of Optimal Trajectories in a Control Problem for a Car-like Robot

We study the problem to find (a) shortest plane curve(s) joining two given points with given tangent angles and curvatures. The tangent angle and the curvature of the path are continuous and the derivative of the curvature is bounded by $2$. At a regular (i.e. of the class $C^3$) point such a curve must be locally a piece of a clothoid or a line segment (up to isometry a clothoid is given by Fresnel's integrals $x(t)=\int _0^t\cos \tau ^2d\tau $, $y(t)=\int _0^t\sin \tau ^2d\tau $). We prove that if the distance between the initial and final points is greater than $320\sqrt{\pi }$, then a generic shortest curve contains infinitely many switching points.
Recent changes in winter Arctic clouds and their relationships with sea ice and atmospheric conditions

Changes in Arctic clouds during boreal winter (December through February) and their relationship with sea ice and atmospheric conditions in recent decades have been examined using satellite and reanalysis data, and they are compared with output data from atmospheric general circulation model (AGCM) experiments. All the datasets used in this study consistently show that cloud amount over the Arctic Ocean (north of 67°N) decreased until the late 1990s but rapidly increased thereafter. Cloud increase in recent decade was a salient feature in the lower troposphere over a large part of the Arctic Sea, in association with obvious increase of lower tropospheric temperature and moisture. The comparison between the two periods before and after 1997 indicates that interannual covariability of Arctic clouds and lower tropospheric temperature and moisture was significantly enhanced after the late 1990s. Large reduction of sea ice cover during boreal winter decreased lower tropospheric static stability and deepened the planetary boundary layer. These changes led to an enhanced upward moisture transport and cloud formation, which led to considerable longwave radiative forcing and, as a result, strengthened the cloud–moisture–temperature relationship in the lower troposphere. AGCM experiments under reduced sea ice conditions support those results obtained by satellite and reanalysis datasets reproducing the increases in cloud amount and lower tropospheric temperature and their enhanced covariability.
Trajectory Simulation of a Spinning Projectile Based on Variable Step Size CFD/RBD Method

The unsteady Navier-Stokes equation and rigid body equations of 6-DOF motion are solved using variable-step coupling computation method to simulate and analyze the flight trajectory of U.S. Army Research Laboratory spinning projectile test model. At the initial state, the spinning projectile has a high revolving speed. During the free-flight process, the revolving speed will gradually decrease, due to the effect of aerodynamic resistance. Accordingly, the unsteady aerodynamic effect caused by spinning will gradually diminish. Based on the above characteristics, at the starting stage of the simulation, a smaller time step-size will be chosen for describing the intense unsteady aerodynamic effect. Then the time step-size will gradually increase with the declining revolving speed to save the computing time. The unsteady aerodynamic computations uses an in-house hybrid unstructured Navier-Stokes flow solver (HUNS3D). Time marching technique uses a non-uniform step-size dual time-stepping method. The rigid body equations are solved using Variable-step-size Adams prediction-correction method. Both variable-step-size and fixed-step-size methods are applied to solve the CFD/RBD coupling system and predict the trajectory of the test model. Computed results are found to be generally in good agreement with experiment data and numerical results extracted from reference. For the same initial step-size, the computational time for the variable-step-size method is reduced nearly by half of that of the fixed-step-size method.
A Novel Positive Buoyancy Underwater Glider: Dynamic Characteristics and Optimal Glide Speed Range

The novel underwater glider maintains the positive buoyancy during the whole profile motion and the diving process is realized by the propulsion system. Since there is no variable buoyancy system, the glider is simpler in structure and has more internal space to carry energy or payload. In addition, the positive buoyancy glider can realize attitude adjustment more neatly without oil pumping process, which is more suitable for shallow-water survey and provides a direction for the design of large gliders. In this paper, through the analysis of the dynamic characteristics of the positive buoyancy glider, the advisable motion parameter space is given. Taking the energy consumption per meter as an indicator of sailing efficiency, the total energy consumption model including thrust energy consumption and internal subsystems energy consumption is established and studied. The result shows that there is an optimal glide speed to minimize the total energy consumption. Compared with the traditional glider, the positive buoyancy glider also has a higher sailing efficiency when gliding within a desirable speed range, which verifies its energy advantage and lays a theoretical foundation for the motion parameter design.
Analysis of the Sit-to-Stand Movement Based on Dynamic Stability

Sit-to-Stand (STS) is one of the most frequent movements in people’s daily life. For patients with muscle weakness or paraplegia, exoskeleton assistance is an effective way to resume their ability to STS. And STS is a dynamic process of movement, so the torque of exoskeleton is gradually constructed. Therefore, analysis of the STS process is an important foundation for exoskeleton assist human to stand. In previous studies, most used methods based on mode recognition to determine the phase of the STS. However, pattern recognition methods are poorly interpretable and are affected by training samples and human motion variability. In this paper, based on the dynamic stability analysis of STS movement, we proposed a phase estimation algorithm that considers velocity variation. Six healthy volunteers were recruited in the experiment. The effectiveness of the method mentioned was verified by comparing the changes of XCOM and COM. This model-based method has better interpretability and provides a new means to analyze STS movement.
Characterization of the event-related potentials during GAN-based generation of EEG signals and their data augmented subject classification

One of the most crucial challenges in exploring deep neural networks for signal modeling and classification tasks is the availability of a large quantity of domain-specific data. The generative adversarial network (GAN) has emerged as an effective artificial data generation method. Motivated by that, we explore the generation of auditory event-related electroencephalographic (EEG) signals and their task-specific authentication in this work. For synthesizing subject-specific EEG signals, a conditional deep convolutional GAN is used. Apart from measuring the usual signal similarity, we also computed the correlation between the subject-wise event-related potentials (ERPs) corresponding to the real and synthetic EEG data. The characterization of the ERPs highlights that the GAN is not only able to learn the distribution of real EEG signals but also can preserve their temporal characteristics. Further, EEG biometrics experiments are also performed to verify the effectiveness of the synthesized EEG signals in data augmentation. It is noted that the averaged classification accuracy improves by augmenting the real data set with synthetic EEG signals.
Research on Vector Control of Permanent Magnet Synchronous Motor Based on Active Disturbance Rejection

To address the problem that the traditional control method of permanent magnet synchronous traction motor for rail vehicles can hardly meet the requirements of high performance control of the system, this paper adopts the second-order active disturbance rejection controller instead of the traditional PI regulator for the research and design of the system. The proposed strategy is simulated by building a vector control system for a permanent magnet synchronous motor in MATLAB/Simulink simulation environment. The results show that the control strategy proposed in this paper can improve the control performance of the permanent magnet synchronous motor and realize the fast response and smooth conversion of the motor.
A Comparative Performance Evaluation of Machine Learning Approaches for Spectrogram-based Music Genre Classification

The market for various music styles has expanded along with the company’s steadily expanding consumer base. It is crucial to categorize music according to genres in order to suit people’s needs. It is the responsibility of the listener to manually rank the music because it is a tedious and time-consuming operation. The music signals in this work are first transformed into the relevant spectrograms. The classifier then receives these spectrogram features as input. The work mainly uses a convolutional neural network. Different machine learning models are compared and validated against the neural network. The models will be trained and compared on GTZAN, which is a public dataset consisting of thousands of audio files comprising ten genres. The goal is to create a machine-learning model that categorizes music into its appropriate genre, assess the accuracy of this model against other classifiers, and derive the appropriate conclusions. The accuracies for K Nearest Neighbor, Multi-Layer Perceptron, Support Vector Machine, and Naive Bayes classifiers are 88.8%, 84.0%, 84.0%, and 51.8%, respectively. The accuracy that has been obtained using CNN is 94.87%, with a validation accuracy of 89.03% and a 0.84 f-score.
Multireservoir System Optimization Using Multi-Objective Genetic Algorithms

Multireservoir system consists of several reservoirs which are connected serially or parallel in the same basin. To optimize such a complex multireservoir system, the dynamic programming (DP), linear programming (LP) and non-linear programming (NLP) have been widely applied to real problems. However, when DP is applied to multireservoir system it has a major problem, so called `the curse of dimensionality' and LP and NLP have essential approximation problems dealing with discontinuous, nondifferentiable, non-convex, or multi-modal objective functions. Recently, there has been an increasing interest in a biologically motivated adaptive system for solving optimization problems. The genetic algorithms (GAs) are one of the most promising techniques in natural adaptive system field and receiving many attentions because of their flexibility and effectiveness for optimizing complex systems. Optimization of multireservoir system is to solve multi-dimensional and multi-objective problems and GAs are appropriate optimization methods to multireservoir system. GAs are not restricted by a number of dimensions because computer memory increases by dimensions linearly, not exponentially. Thus, there is no `curse of dimensionality'. Especially classical optimization methods such as DP, LP, and NLP are not proper to multi-objective optimization because these methods use a point-by-point approach, in which the outcome of classical optimization methods is a single optimal solution. However, GAs use a population of solutions in each iteration instead of a single solution, so they are called as population-based approaches. This is one of the most striking differences between classical optimization methods and GAs.
An Open-Source Static Timing Analysis EDA Tool for Digital System Design

Static timing analysis (STA) is an EDA tool used for Digital System Design to compute the expected delay/timing of a synchronous digital circuit without requiring a simulation of the full circuit. High-performance VLSI designs have traditionally been characterized by the clock frequency at which they operate. Estimation of a circuit to operate at the specified speed requires an ability to measure, during the design process, its delay at numerous abstraction levels. Moreover, delay calculation must be incorporated into the inner loop of timing optimizations at various steps of design, such as logic synthesis, layout design, PNR (placement and routing), and in in-place optimizations performed late in the design cycle. There are high-end Industrial EDA tools available, but this paper addresses the need of a low-cost solution for STA. The paper proposes a novel aspect of an open-source EDA tool to find the critical path of Digital Design. The tool is developed using Python Language. The netlist format used for this tool is a modified version of gate level ISCAS netlist format which included the information on circuit structure and the gate and net delays can be provided by the user. The developed tool provides a visual impression of the design in the form of a directed acyclic graph (DAG) and highlights the critical path. The tool aims to provide assistance in digital designing for STA and help the designer provide modification in case of a timing violation (Setup or Hold).
Deep Learning Framework for Spectral Efficient Intelligent Hybrid Beamforming

Next-generation wireless networks’ attractive use cases call for more extensive coverage and highly dependable connectivity. A promising candidate that considerably helps to fulfil these requirements is beamforming. In massive Multiple-Input-Multiple-Output (MIMO) systems, the conventional digital beamforming method results in significant costs and hardware complexity. By using fewer RF chains than the conventional digital beamforming method, hybrid beamforming lowers the hardware needed. However, due to the restrictions on hardware consumption, it is difficult to arrive at the open optimal solution for joint optimization problems. We suggest a hybrid beamformer that learns to maximize spectral efficiency using deep learning as its foundation. To achieve the optimal beamforming weights, the channel state information (CSI) is supplied into the deep learning model. Both perfect and imperfect CSI are used to validate the proposed hybrid beamforming scheme. Simulation results reveal that the proposed method outperforms the current statistical approaches while lowering cost and hardware complexity. It is also more robust to poor CSI.
Design and Implementation of an Improved Finite-State Predictive Direct Torque Control for Induction Motor With New Weighting Factor Elimination

The performance enhancement of the Induction Motor (IM) is a crucial and strenuous task. The Direct Torque Control (DTC) method is a common control strategy for boosting the IM drives and wildly implemented due to its merits including insensibility to motor parameters and fast response. However, the main shortage of the traditional DTC method is that it may lead to large flux and torque ripples. In this regard, this paper proposes an efficient control method called a Finite-State Predictive Torque Control (FS-PTC) which is based on multi objective optimization technique to overcome the shortage in the conventional DTC method. In addition to that a ranking approach is presented to assign the weighting factor in the cost function which directly affects the performance of system. The proposed ranking approach is based on elimination the weighting factor by replacing the single cost function with two different cost functions. The proposed FS-PTC is applied and tested using the dSPACE environment to validate its effectiveness and efficacy. From the experimental simulations, the performance of system is enhanced considerably where the torque and flux ripples are reduced with maintaining a constant switching frequency compared to the conventional DTC.
Inductance Calculation for the Power Net Area Fill of Packages and PCBs Based on Plane-Pair PEEC

The inductance of the power/ground planes is an integral contributor to the input impedance of a power delivery network for high-speed printed circuit boards (PCBs) and packages. Conventionally, differential-equation (DE) circuit and cavity type models have been applied to compute the inductive behavior of the plane-to-plane inductance. However, these methods are not suitable for the case where the structures are perforated or involve other uneven structures. In this article, a new partial-element-equivalent-circuit (PEEC)-based method is presented to compute the inductance of parallel plate-like planes and other structures. Examples are given to show that the new method can efficiently compute inductances for multiple integrated circuit power vias, power/ground planes, and multiple decoupling capacitors. The proposed model is validated with both full-wave CEM simulations as well as with measurements. Further, the speed and the accuracy for real PCB and package designs are presented to validate the efficiency as well as the accuracy of the proposed approach. An important aspect of any approach is the limitations for solving real life problems. In this article, we consider important issues related of plane-pair PEEC to power distribution evaluations. Specifically, we show that large holes in planes can accurately be modeled. This is a difficult issue for DE methods. Another surprising practical issue is the accuracy obtained even if the planes are not of the same size. We also consider the speedup, which can be obtained in comparison to solutions for other approaches. This is due to the sparsity of the coupling for the rapid coupling decrease with distance. This short-distance coupling also increases the maximum frequency for which the method can be applied.
Assessing the impact of climate change and weather conditions endangering inland waterway navigation

Inland water transportation has been long-recognized as an environmentally friendly and efficient transportation mode. The problem is that the availability and efficiency of inland water transportation are heavily influenced by droughts, floods, and extreme weather conditions, and climate change will worsen it. The influence of these factors on inland waterways can be mitigated by soft measurements, such as providing information services. This study aims to reveal the most influential weather conditions and relevant information service functions covering shipment, as well as loading and storage at ports. Hence, deep interviews and an online questionnaire were conducted with relevant stakeholders. The most influential weather conditions were low water for shipment, intense precipitation and storm for loading, and high water for storage. The transportation planning could be improved by more than 1-week forecast on low water, a 5-day forecast on high water, floods, and heatwaves, and a 2-day forecast on intense precipitation, wind, and fog. The results highlight the main development areas and functions for information service developers.