Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-Based N-Step Heuristic Dynamic Programming for Affine Nonlinear Optimal Regulation

This paper introduces n-step heuristic dynamic programming (NSHDP), which combines regular temporal difference (TD) learning with TD(λ) learning, in order to solve optimal control problems. First, the implementation process of the basic value iteration algorithm is proposed. Then, based on the traditional HDP algorithm, the architecture of the NSHDP(λ) algorithm is described. At the same time, the most important thing is that the stability condition of the NSHDP(λ) algorithm is developed. Furthermore, the one-step critic network, the n-step critic network, and the action network are designed, respectively. Finally, the effectiveness of the proposed algorithm is verified by simulation experiment.
Statistical theory of thermal evolution of neutron stars – II. Limitations on direct Urca threshold

We apply our recently suggested statistical approach to thermal evolution of isolated neutron stars and accreting quasistationary neutron stars in X-ray transients for constraining the position and relative broadening alpha of the direct Urca threshold of powerful neutrino emission in neutron star cores. We show that most likely explanation of observations corresponds to alpha = 0.08 - 0.10 and to the neutron star mass, at which the direct Urca process is open, M_D = (1.6 - 1.8) M_sun.
Controller approximation: approaches for preserving H∞ performance

Investigates the design of reduced-order controllers using an H/sub /spl infin// framework. Given a stabilizing controller which satisfies a prespecified level of closed-loop H/sub /spl infin// performance, sufficient conditions are derived for another controller to be stabilizing and satisfy the same level of H/sub /spl infin//, performance. Such controllers are said to be (P,/spl gamma/)-admissible, where P is the model of the plant under consideration and /spl gamma/ is the required level of prespecified H/sub /spl infin// performance. The conditions are expressed as norm bounds on particular frequency-weighted errors, where the weights are selected to make a specific transfer function a contraction. The design of reduced-order (P,/spl gamma/)-admissible controllers is then formulated as a frequency-weighted model reduction problem. It is advantageous for the required weights to be large in some sense. Solutions which minimize either the trace, or the determinant, of the inverse weights are characterized. We show that the procedure for minimizing the determinant of the inverse weights always gives a direction where the weights are the best possible. To conclude, we demonstrate by way of a numerical example, that when used in conjunction with a combined model reduction/convex optimization scheme, the proposed design procedures are effective in substantially reducing controller complexity.
Input-to-state Stability via Event-triggered Intermittent Control for Load Frequency Control Systems

This paper studies the stabilization to input-to-state stability (ISS) for load frequency control (LFC) systems via event-triggered intermittent control (ETIC). There provides two LFC models including single-area LFC and multiple-area LFC systems. And ETIC is designed for both single-area LFC and multiple-area LFC systems. By the designed ETIC, the stabilization to ISS of LFC systems is achieved. And the rate of control for ETIC is also estimated. From the simulations, it is shown that the designed ETIC including i-ETIC has clearly lower rate of control than the intermittent control reported in the literature and thus the control cost of LFC may be improved.
Output transient trajectory shaping control for a class of nonlinear systems

This paper presents a transient trajectory shaping (TTS) control method for the SISO strict feedback nonlinear systems. The TTS control refers to explicitly constraining the system output tracking error transient trajectories within predesigned time‐varying boundaries while they are converging to equilibrium. By this method, the boundaries of system output transient trajectories can be designed a priori according to the system transient control performance requirements in both symmetric and asymmetric ways. With a class of time‐varying boundary functions, control laws can be devised by utilizing the enhanced differential unbounded function techniques. Such control laws can ascertain that the system output tracking errors travel within their respectively predesigned time‐varying boundaries while converging to origin. To handle the control input exaggeration issue in TTS, input constraint control techniques are proposed to effectively reduce the required control input magnitude for second‐order systems. A numerical example is utilized to show the effectiveness of the proposed TTS control methods. Copyright © 2013 John Wiley & Sons, Ltd.
Distributed H∞ filtering for a class of discrete-time nonlinear systems in sensor networks

This article is concerned with the distributed H∞ filtering problem for a class of discrete-time nonlinear systems in sensor networks. The system model is proposed to describe the working mode change in sensor networks. Next, the H∞ filter is designed by exploiting the Lyapunov functional method together with the Lipschitz condition. Correspondingly, some sufficient conditions are established to ensure the augmented system asymptotically stable and the H∞ performance index is satisfied. Then, the desired filter gain matrix are obtained by solving linear matrix inequalities. Finally, the effectiveness of the designed filter is demonstrated by a numerical example with simulations.
Implementation and Performance Analysis of EPLL Based Field Oriented Control Algorithm for Speed Control in Electrified Transportation Systems

The paper presents performance analysis of speed control techniques for Induction motor and BLDC motor using EPLL based field oriented control algorithm. EPLL is an adaptive notch filter used for parameter estimation and synchronizing the control signals in rotating reference frame. It improves the overall efficiency by reducing the computational burden during the estimation of parameters. The simulation for speed control of these ac motors is implemented using Matlab/Simulink and the results demonstrate the effectiveness of the proposed system. The hardware implementation of the speed control strategy for BLDC motor is presented using C2000 DSP microcontrollers and DRV8301 based inverter. The real-time implementation of control algorithm using DSP controller is discussed for speed control of BLDC motor.
Improvement in Performance of Image Classification based on Apache Spark

Apache Spark is a widely used efficient distributed computing framework in the field of Big Data for data processing and analytics at a large scale. There is wide demand from organizations to apply deep learning technologies to their existing big data analysis pipelines which will reduce the cost of maintaining additional computational resources. To classify large scale image data is a hot topic. For image classification, the classic Convolution neural network (CNN) model has been widely used as a standard deep learning algorithm. This paper focuses on implementation and demonstrates the execution of combination of Apache Spark and Convolution neural network algorithm that will provide significant improvement in performance for the image classification model. The paper aims to reduce overheads involved in this approach to provide better performance by the usage of novel opensource frameworks and bring together a unified pipeline for the same. Improvements in various performance metrics that are obtained from our experimental setup are presented in this work accordingly.
IDCS J1433.2+3306: AN INFRARED-SELECTED GALAXY CLUSTER AT z = 1.89

We report the discovery of an IR-selected galaxy cluster in the IRAC Distant Cluster Survey (IDCS). New data from the Hubble Space Telescope spectroscopically confirm IDCS J1433.2+3306 at z = 1.89 with robust spectroscopic redshifts for seven members, two of which are based on the 4000 Å break. Detected emission lines such as [O ii] and Hβ indicate star formation rates of ≳20 M☉ yr−1 for three galaxies within a 500 kpc projected radius of the cluster center. The cluster exhibits a red sequence with a scatter and color indicative of a formation redshift zf ≳ 3.5. The stellar age of the early-type galaxy population is approximately consistent with those of clusters at lower redshift (1 <z < 1.5) suggesting that clusters at these redshifts are experiencing ongoing or increasing star formation.
Identifying Vulnerabilities in Docker Image Code using ML Techniques

A Docker container image can be defined as a lightweight, unattached, executable package of software that includes everything like code, runtime, system tools, system libraries and settings, needed to run an application, because of these features the container images are preferred over virtual machines. With this enormous usage, there is a lot of scope for the security issues arising in the container images. There are many open-source projects like Anchore, Clair that statically scan the container image’s docker file to find the vulnerabilities using databases like CVE, RedHat etc. Static analysis of container image main code is equally necessary to identify any vulnerabilities in the code and not only focus on the vulnerabilities based on OS level, as many malicious activities might take place if code is not scanned for any vulnerabilities. The main aim of the project is to create a static code analysing machine learning model to identify the vulnerable python libraries in container images.
Social Media Data-Driven Sentiment Analysis for COVID-19 and COVID-19 Vaccines

In this paper, we analyze social media data (e.g., tweets) related to coronavirus disease 2019 (COVID-19) and COVID-19 vaccines. The main objective is to explore daily COVID-19 cases and vaccine rates in addition to analyzing sentiments and discussions related to COVID-19 vaccination on social media, e.g., Twitter. During the early days of the pandemic, there were rapid developments of vaccines that can prevent the novel COVID-19. However, the potential hurdles of developing COVID-19 vaccines faster than any other conventional vaccine has made some people apprehensive about taking the COVID-19 vaccine. Since social media keeps individuals connected locally and globally, Twitter as a social networking platform is a great way to collect information on tweets related to the coronavirus vaccine. Specifically, this paper studies various data analytic tools that can help study the changes in users’ opinions and emotions related to coronavirus vaccines, as well as studying the coronavirus cases and vaccine rates globally. Furthermore, this study will enable individuals to get real-time insights into the sentiments of COVID-19 vaccines based on social media tweets.
Linear-quadratic Stochastic Leaders-followers Differential Game with An Incomplete Information Structure

In order to adapt to the hierarchical network architecture of the industrial internet of things (IIoT), a kind of linear-quadratic (LQ) leaders-followers stochastic Stackelberg differential game with an incomplete information structure is studied in this paper, where the term “incomplete” means that at least one agent in games makes decisions based on partial information. The game is divided into two decision-making stages. The feedback Stackelberg-Nash equilibrium point and the corresponding dynamic evolution equations of the game are derived by using the stochastic maximum principle under partial information and the undetermined coefficient method. These results can be extended and applied to the description of the IIoT.
Study of the Impact of Meteorological Phenomen in Flight Management

This study offers particularly interesting contributions regarding the impact of meteorological phenomena, which have a random character, on aircraft on flight missions. Numerous numerical simulations are performed in MATLAB which include the distribution of wind vectors of different types, which act in the take-off and landing processes of aircraft. Turbulence remains an extremely important operational factor for all flight levels, especially over the FL150. The most useful and real information about the atmospheric turbulence is given in the special observations of the pilots of the aircraft flying in the areas affected by these convective meteorological phenomena.
Design and Analysis of LLC Resonant Converter and CCCV Topology for Battery Charging

The LLC resonant converters are widely used in various sectors of the electronics-based industries due to their advantages of high efficiency, high energy density, electrical isolation, low electromagnetic interference (EMI),wide output ranges and high operation frequency. A Good power factor for the LLC resonant tank is required to achieve high efficiency over a wide input voltage range. A unity voltage gain can be accomplished over the whole loading conditions by having switching frequency equal to resonant tank frequency in LLC converter, however it is not suited for constant current – constant voltage (CC-CV) battery applications. The use of a buck converter at the output of an LLC resonant converter to regulate the charging current and voltage for battery charging applications is proposed in this paper. The use of a buck converter allows the output voltage of the LLC converter to be varied, ensuring CCCV charging. The proposed system is analyzed and studied in MATLAB/Simulink.
Global Practical Sampled-data for a Class of Stochastic Nonlinear Systems

In this paper, we mainly study the global tracking problem for stochastic systems by the sampled data state feedback control. Under the condition of lower-triangular, the sampled data state feedback controller is constructed to make the states globally bounded, and the error between the system output and the reference signal is less than the specified allowable value. At the same time stochastic phenomenon is considered in the nonlinear system. The global tracking performance and stochastic stability are both achieved simultaneously.
A Robustness Benchmark for Prognostics and Health Management

With the rise of intelligent manufacturing, prognostics and health management(PHM) has developed rapidly as an important part of intelligent manufacturing. Existing deep learning-based PHM methods are data-dependent. However, sensor data often contains noise and is redundant and high-dimensional, making it difficult for the PHM methods to learn a stable set of model parameters, so the methods are likely to be wrong when disturbed. However, the factory hopes that the PHM methods are robust enough to adapt to various perturbations, so it is necessary to perform robustness evaluation on the existing methods in advance for easy deployment. Although the existing robust theoretical analysis methods for neural networks can obtain tight robust boundaries, they consume a lot of computing resources and are difficult to scale to large neural networks. To slove this problem, We design a benchmark for robustness analysis of large deep learning PHM models, in which we test the model robustness using a variety of perturbations to simulate the actual production environment of the factory. Specifically, Gaussian noise is used to test the robustness of the model to background noise; random mask is used to test the robustness of the model to data loss. We hope that our robustness benchmark can serve as a reference for designing PHM models to improve the robustness of factory PHM models.
WiBS: A Modular and Scalable Wireless Infrastructure in a Cycle-Accurate NoC Simulator

Network-on-Chip (NoC) has become a fundamental building block in modern Chip Multi-Processors (CMPs) and plays a significant role in their performance. Nevertheless, ever-increasing core counts coupled with shrinking CMOS technology increases communication latency in Electrical NoC (ENoC). Radio frequency enabled Wireless NoC (WiNoC) is one of the promising solutions proposed to supplement the ENoC and form a wired-wireless hybrid infrastructure. However, except for Noxim, there is no other open-source simulation infrastructure for WiNoC exploration. This work implements a cycle-accurate WiNoC infrastructure in a popular ENoC simulator. The infrastructure is modular and scalable for any network size. Among other important features, it implements a broadcast-enabled one-hop communication and a credit exchange mechanism for radio hubs to avoid packet re-transmission. It is a work in progress and hence supports easy integration of new features for exploration.
Effective 3D Video Streaming Using 2D Video Encoding System

The amount of video traffic on the internet is expanding at an alarming rate, necessitating the use of a high compression technology. There are several 3D video compression techniques available, such as multi-view coding and depth-image-based rendering. To apply these techniques, an entirely new codec must be introduced to the present system. In this research, a 3D video compression approach is suggested that would provide better compression with decent video quality while leveraging an existing video streaming infrastructure. The proposed method is tested on two test videos using the existing HEVC video encoding technology. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to evaluate the performance of the proposed 3D video compression technique. These values are then compared to the traditional depth-image-based rendering technique. There is a decrease of around 50% in video size with a 2% reduction in PSNR, and a penalty of around 7% drop in SSIM. This signifies that we benefit in terms of video size reduction while compromising a small quality level.
Use of Data mining Tools in Educational Data Mining

Data Mining is the task of discovering the knowledge from huge amount of data. Data Mining algorithms includes classification, clustering, association rules, regression, summarization, and anomaly detection. There are various proprietary as well as free open source data mining tools using which these algorithms can be applied on the given datasets and analyze the same. This paper compares the free open source and the proprietary data mining tool. Here free open source software like Keel, KNIME, RapidMiner, Weka, Tanagra, and Orange are explained and compared. Proprietary tools like SPSS, KXEN, Microsoft Analysis Services, SAS Enterprise Miner, STATISTICA, and Oracle Data Mining are also explained and compared on the basis of operating system, type, licenses, etc. Weka is mostly used tool for analyzing the data. Use of these data mining tools is elucidated with Name of tool, Data Mining Techniques applied, Algorithm used, Performance parameter considered and Dataset used in Educational Data Mining (EDM).
An Improved FastSLAM Using Combined Features and Optimized Observation Likelihood

To enhance the performance of FastSLAM, an improved FastSLAM algorithm based on combined features and optimized observation likelihood is proposed in this paper. Due to the use of only combined features, so other uncertain features will be filtered out including those dynamic targets in the environment. Then an optimized observation likelihood algorithm of laser scanner is presented, and can mitigate the particle degeneration problem, and reduce the influence of the error caused by the feature fitting, and improve the accuracy of pose estimation of mobile robot. Finally, the map update method based on the combined features using EKF is introduced. The experimental results using Victor park and Navlab datasets show the SLAM result is more accurate and reliable using the proposed FastSLAM algorithm.
The Research on Approximating the Real Network Degree Distribution Level Based on DCSBM

Many things in the real world can be simplified as a complex system composed of nodes and the relationships between nodes like a graph. But in real life, the actual graph topology that we can get is usually limited. The traditional stochastic block model (SBM) can build graph networks of different sizes. Since the SBM cannot simulate real network well in degree distribution level, this paper aims to study a degree-corrected stochastic block model called DCSBM. We construct the DCSBM in two ways, the stochastic sequence and genetic algorithm constraint. Based on the DCSBM, the phase transition, which shows the theoretical upper limit of the model's performance, was derived by the belief propagation (BP) algorithm. And we use different graph embedding methods, including NetMF, ProNE and BP algorithm, to make some evaluations. We find the DCSBM approximate real graphs well and the phase transition we infer is correct.
Stability and Optimality of Non-cooperative Stochastic Adaptive Games with Different Estimations

We consider non-cooperative stochastic adaptive games described by a linear discrete-time stochastic system with two players. When the system parameters are unknown, we assume that the players apply the least-square algorithm to estimate the unknowns respectively instead of using the same estimator. According to “certainty equivalence principle”, the players need to design adaptive strategy profile to optimize their one-step-ahead payoff functions. We will show that the resulting adaptive strategy profile can make the closed-loop system globally stable and in some sense, the profile is an asymptotic Nash equilibrium.
Reinforcement-Learning-Based Active Disturbance Rejection Control of Piezoelectric Actuators

Piezoelectric actuators (PEAs) are crutial components in nano-positioning processes due to their high positioning precision. However, the application of PEAs is hindered by their intrinsic rate-dependent hysteretic nonlinearity. This paper proposes a reinforcement-learning-based active disturbance rejection control (RLBADRC) scheme for controling PEAs. Unlike conventional preexisting linear active disturbance rejection control (LADRC) for PEAs, control parameters are adaptively learned with Q learning in the present RLBADRC instead. Moreover, the tracking errors are regarded as control states, whereas different control parameters are regarded as control actions. By this means, a Q table is established offline and parameters are adaptively learned with the well-trained Q table model online. Comparative experiments are conducted to verify the effectiveness of the approach.
Multi-objective semi-supervised clustering algorithm based on constraint set optimization for gene expression data

Gene clustering helps to discover co-expressed genes and infers the functions of unknown genes. Mining effective and noiseless supervised information and applying it to semi-supervised clustering algorithms can improve gene clustering performance. Usually, semi-supervised clustering algorithms based on pairwise constraints use known labels to form pairwise constraints, or mine pairwise constraints from unlabeled data based on some criteria. The former is not suitable for unlabeled gene expression data, and the latter inevitably generates noisy constraints. To solve the problem of noisy constraints, this paper proposes a semi-supervised clustering algorithm for gene expression data based on constraint set optimization. First, density-based informative constraints are mined from unlabeled gene expression data to form an initial constraint set. Secondly, to realize co-optimization of constraint set and semi-supervised clustering in multi-objective optimization process, a hybrid coding scheme that combines constraint selection and cluster centers is proposed. The proposed method can filter the constraint sets with the guidance of clustering validities, which can delete noisy constraints in the initial constraint set and help to achieve the co-evolution of constraint selection and clustering. The experimental results on multiple gene expression datasets show that the proposed algorithm achieves more accurate clustering results.