Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling, Analysis and Design of a Magnetically Integrated LCL Filter for Grid-Tied Inverters

LCL-type filters are widely used in grid-connected inverter systems due to their good capability of harmonic suppression. But in traditional design, the components of LCL-filters are placed discretely, which would waste some volume and is adverse to the compact design of the inverter. For addressing this issue, in this work, to reduce the volume occupied by the inductors of LCL-filters, we proposed a magnetic integration method of the two inductors, which finished the magnetic integrated design on one EE-type core. By designing air-gaps in both the side-legs of the EE-type core, the magnetic decoupling effect between the two windings is mitigated to a low span. Then, the influence of magnetic integration on the ability of harmonic depression is modeled and analyzed and the feasibility and superiority of the magnetic integration method proposed in this paper are verified by simulation.
Extremum Seeking Regulator for a Class of Nonlinear Systems with Unknown Control Direction

Nussbaum function techniques are commonly used to investigate output regulation problems for various systems subject to unknown control direction. However, their implementation often leads to large overshoots when the initial estimates of the control direction are wrong, which yields systems with poor transient performance. This study proposes an extremum-seeking control approach to overcome the need for Nussbaum-type functions. The approach yields control laws that can handle the robust practical output regulation problem for a class of nonlinear systems subject to an unknown time-varying control direction. The stability of the design is proven using a Lie bracket averaging technique. It is shown that uniform ultimate boundedness of the closed-loop signals is guaranteed. Finally, a simulation study is performed involving a chaotic control problem for the generalized Lorenz system with an unknown time-varying coefficient to illustrate the validity of the theoretical results.
Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes

Many powered prosthetic devices use load cells to detect ground interaction forces and gait events. These sensors introduce additional weight and cost in the device. Recent proprioceptive actuators enable an algebraic relationship between actuator torques and ground contact forces. This paper presents a proprioceptive force sensing paradigm which estimates ground reaction forces as a solution to detect gait events without a load cell. A floating body dynamic model is obtained with constraints at the center of pressure representing foot-ground interaction. Constraint forces are derived to estimate ground reaction forces and subsequently timing of gait events. A treadmill experiment is conducted with a powered knee-ankle prosthesis used by an able-bodied subject walking at various speeds and slopes. Results show accurate gait event timing, with pooled data showing heel strike detection lagging by only 6.7 ± 7.2 ms and toe off detection leading by 30.4 ± 11.0 ms compared to values obtained from the load cell. These results establish proof of concept for predicting gait events without a load cell in powered prostheses with proprioceptive actuators.
Modern Farming Using IoT-Enabled Sensors For The Improvement Of Crop Selection

As per the various research statistics it has been observed that global population will rise to approximately 10 billion by 2050 and to feed this massive population in terms of food, agriculture industry is shifting towards modern farming system using Internet of Things (IoT). It has been observed that with the advent of IoT in agriculture sector, there are many significant changes has been observed like exponential increase in farming efficiency, increased food production, smart farming with latest sensors equipped devices, integrated crop prediction, reduced fuel utilization, etc. These Internet of things (IoT) based devices would bring revolutionary changes in Agro based industry and further helps to establish a sustainable agriculture with conserving human resources, protecting land from chemicals and increasing productivity of crop. The various critical factors which are necessary for the selection of crop are also discussed. In this paper the author has also done a comparative analysis between Traditional Farming devices and IoT (Internet of things) devices based on the parameters like Data Collection, Efficiency, Accuracy, Maintenance, etc which will provide clear understanding that how IoT devices can revolutionized the way we approach farming by providing real-time data collection and analysis. The wide uses of Internet of things (IoT) sensor devices which will be helpful in agriculture industry like Electrochemical Sensors, Airflow Sensors, Mechanical Sensors, Dielectric Soil Moisture Sensors etc. are discussed. In this paper the authors had formulated few research questions like IoT devices for finding moisture in soil, IoT devices for selecting temperature, IoT devices for rainfall prediction, etc. and shown how the selection of crop could be improved with the help of IoT sensors.
An Untraceable Monitoring for Patients Using Wireless Medical Sensor Networks

Medical sensor network consists of an architecture in which medical sensor devices are implanted in human body to collect different types of sensitive data and share it on a network. These networks are connected with each other via wireless technology. Wireless medical sensor network (WMSN) came into existence with these medical sensor networks. Transmitted data from these WMSN’s is then further processed in remotely located medical units. In the meanwhile, a registered medical professional of this network can access this information to rectify the patient’s conditions via internet. This transmitted information is critical for the patient. It should be secured and private. It is very important to provide secure and private access to patient’s data. Internet has an open infrastructure for every one as well as hackers whom can intrude such kind of sensitive information for their own benefits. It is a challenging issue for WMSNs to resist such kind of abrupt activity. A smaller number of protocols are being introduced in the literature, about WMSN, providing security and user authentication. Some of the protocols regarding WMSN are not standardized enough to provide user anonymity. This study is proposed to provide a protocol with potential to preserve user anonymity as well as mutual authentication for the mobile users. Precisely, this protocol is not only providing sufficient complexity but also robustness to security against unwanted threats. Moreover, it is providing a login phase, mutual authentication characteristics, and a phase to change the old password into a new one.
An equivalent two section method for calculating the workspace of multi-segment continuum robots

Obtaining the shape and size of a robot's workspace is essential for both its design and control. However, determining the accurate workspace of a multi-segment continuum robot by graphic or analytical methods is a challenging task due to its inherent flexibility and complex structure. Existing numerical methods have limitations when applied to a continuum robot. This paper presents an Equivalent Two Section (ETS) method for calculating the workspace of multi-segment continuum robots. This method is based on the forward kinematics and a piecewise constant curvature (PCC) model to determine the boundaries of the workspace. In order to verify the proposed method, simulation experiments are conducted using six different maximum bending angles and seven different number of segments. Results of the ETS method are compared to the true workspaces of these configurations estimated by an exhaustive approach. The results show that the proposed ETS method is both efficient and accurate, and has small estimation errors. Discussions on the advantages and limitations of the proposed ETS method are also presented.
Effective Combination of Vertical, Longitudinal and Lateral Data for Vehicle Mass Estimation

Real-time knowledge of the vehicle mass is valuable for several applications, mainly: active safety systems design and energy consumption optimization. This work describes a novel strategy for mass estimation in static and dynamic conditions. First, when the vehicle is powered-up, an initial estimation is given by observing the variations of one suspension deflection sensor mounted on the rear. Then, the estimation is refined based on conditioned and filtered longitudinal and lateral motions. In this study, we suggest using these extracted events on two different algorithms, namely: the recursive least squares and the prior-recursive Bayesian inference. That is to express the results in a deterministic and statistical sense. Both simulations and experimental tests show that our approach encompasses the benefits of various works in the literature, preeminently, robustness to resistive loads, fast convergence, and minimal instrumentation.
Trajectory Tracking Control of an Autonomous Vehicle using Model Predictive Control and PID Controller

Over the years, there has been a substantial increase in the number of vehicular traffic, which has led to vital problems like car crashes and congestion. More than 90 percent of collisions are the result of human error. Technology that allows for autonomous driving has the potential to enhance traffic efficiency and safety. Based on knowledge about the nearby traffic, an autonomous vehicle can create a trajectory and follow it using control algorithms. A significant technology in the study and implementation of autonomous vehicles is trajectory tracking control. Paths are a series of instructions that provide directional directives to get to a specific location, whereas a trajectory includes the schedule of velocity and higher order words, such as acceleration in terms of the body’s longitudinal and lateral motion, that are necessary to reach there. In this study, PID controllers and model predictive controllers (MPC) are used to govern the trajectory of an autonomous vehicle. The performance of the autonomous vehicle using both the controllers are then compared. The work is validated using simulations on MATLAB simulink.
A New Non-Separable Kernel for Spatio-Temporal Gaussian Process Regression

We adopt spatio-temporal Gaussian process regression (GPR) for prediction of PM2.5 concentrations at various spatial locations across India, and we compare its performance to purely temporal GPR. We design a new, non-separable spatiotemporal (ST) covariance kernel which can express dependence (i.e. coupling) between the spatial and temporal covariance structures. Specifically, we replace the separable weighted Euclidean norm—commonly used to achieve automatic relevance determination (ARD)—with a non-separable Mahalanobis norm in the squared exponential kernel function of the GP. We find that the spatio-temporal GPR (STGPR) task achieves better predictive performance at hold-out locations than purely temporal GPR at those locations. We also find that the new, non-separable ST exponential kernel significantly outperforms the separable ST kernel in this application.
Enhancement of Low-Quality Diatom Images using Integrated Automatic Background Removal (IABR) Method from Digital Microscopic Image

Most diatom images scanned from digital microscopes suffer from low contrast, noise, and contain unwanted floating particles and debris in a single image. Moreover, the active movement of diatom along with poor lens focusing produces a blurred image. Thus, in this paper, we introduce a new integrated automatic background removal technique (IABR) to enhance low-quality microscopic diatom images. This paper describes a two-step process of microscopic diatom image for image smoothing. First, haze removal technique is applied to the low light image to enhance and removes the image from haze and noise. Second, the background removal technique extracts the diatom cell from the background image and improves the image contrast. The output results show that the proposed IABR method has successfully enhanced and smooths low-quality diatom images by removing the image background and improving image contrast.
A Bearing Remaining Useful Life Prediction Method based on Residual Convolutional Network and LSTM

Rolling bearing is an important component for equipments, and failure caused by bearings may cause heavy casualties and realistic losses. Therefore, remaining useful life prediction for bearings has important practical significance. The degraded vibration signals is taken as the research object, and the end-to-end life prediction under different working conditions is taken as application background. In order to improve the prediction accuracy of remaining useful life, the prediction method based on residual convolutional network and long short-term memory is proposed. This method makes training of deeper convolutional network more effective by introducing skip connections in the network to construct different residual unit modules. It can avoid the disappearance of gradient or the degradation of network caused by too many layers, and effectively extract deep-level features of data. In view of time series features representation for degradation process, the deep long short-term memory network is used to construct the trend features of bearing degradation signal. Finally, simulation results indicate the superiority in life prediction.
Complex Network Based Virtual Power Plant CPS Reliability Analysis

In view of the problem that the scale of cascading failures may expand due to the failure of key nodes in cyber network of virtual power plant (VPP) cyber-physical system (CPS), this paper establishes a VPP CPS architecture network model. Small world network and scale-free network are used to simulate the cyber network as well as power network in CPS respectively. In view of the fact that VPP CPS can't resist deliberate attacks well, a protection strategy for key nodes of VPP cyber network is proposed to enhance the protection of key nodes and reduce the impact of cascading failures. The number of surviving nodes and clusters are adopted as indicators to evaluate the reliability of VPP-CPS network. Through the analysis of the complex network characteristic parameters and cascading failure process in VPP CPS network, protecting key nodes can improve the reliability of VPP network effectively.
Periodic Event-triggered Boundary Control of a Class of Reaction-Diffusion PDEs

This paper provides a novel periodic event-triggered boundary control (PETBC) strategy for a class of reaction-diffusion PDEs with Robin actuation using infinite-dimensional backstepping boundary control. We propose a method for converting a certain class of continuous-time dynamic event-triggers that require continuous monitoring to periodic event-triggers that require only periodic evaluation. We achieve this by finding an upper bound on the underlying continuous-time event-trigger between two successive periodic evaluations. We provide an explicit criterion for choosing a sampling period for periodically evaluating the event-trigger. The control input is updated only at events indicated by the periodic event-trigger and is applied in a Zero-Order-Hold fashion between two events. We prove that the closed-loop system well-posedness and global L2-exponential convergence to zero under continuous-time event-triggered boundary control (CETBC) are preserved under the proposed PETBC. We provide simulation results to validate the theoretical claims.
Robust H∞ Consensus for Homogeneous Multi-agent Systems with Parametric Uncertainties

This paper addresses the problem of robust consensus of an undirected network of homogeneous multi-agent systems with uncertain agent dynamics and system noise. We consider uncertain time-varying input matrices that are arbitrary up to a known bound for the singular values. We also assume that each agent’s controller is able to access the neighbors’ relative states. We focus on the design of a linear controller gain that is to be identical across all agents. We provide sufficient conditions for the control gains to achieve both consensus in the noiseless setting and a transfer function with a given bounded H∞ norm in the setting with noise. More specifically, we show that this is achieved if a set of linear matrix inequalities containing the non-zero eigenvalues of the Laplacian are satisfied. In a numerical simulation, we illustrate these theoretical results and show that our method outperforms a consensus region-based approach.
Feature Management for Machine Learning Operation Pipelines in AI Native Networks

Artificial intelligence (AI) has emerged as a key technology in various domains, including telecommunications. The use of AI in cellular networks has the potential to revolutionize the way we communicate by enabling new capabilities, improving network efficiency, and providing better user experiences. However, domain specific data transformations, pre-processing and management in modern data driven pipelines is yet to be addressed. In this paper we perform a discuss Machine Learning Operations automation for with focus on feature management and feature store selection. The design of features and their subsequent management by specialized feature stores are one of the key components of successful infrastructure automation. We also study existing feature stores qualitatively and quantitatively.
Time Delay based Neural Network Control for Systems with State-Dependent Nonlinearity

This paper tackles the problem of control for a class of nonlinear systems with state-dependent nonlinearities. A new control algorithm combining Time Delay Control and Neural Networks is proposed for such systems. Assuming all state variables are available, the proposed control algorithm is shown to learn the nonlinearity online, provide closed loop stability and achieve tracking performance better than that of time delay control. The performance of the proposed control algorithm is evaluated and compared to that of Time Delay Control and traditional PI control through simulation studies of an Interior type Permanent Magnet Synchronous Motor (IPMSM).
Optimized Distributed Filtering for Time-Varying Saturated Stochastic Systems With Energy Harvesting Sensors Over Sensor Networks

This paper addresses the distributed filtering (DF) problem for time-varying saturated stochastic systems subject to energy harvesting (EH) sensors and time delay through sensor networks. The sufficient energy is a prerequisite for normal data transmission, so the EH technique is considered in the communication network, which can be regarded as an explicit decision, i.e., the sensors have the ability to harvest energy from surrounding environment. Particularly, the data information can be transmitted only when the sensors store nonzero units of energy, and vice versa. The specific probability distribution of EH level for individual sensor node can be computed iteratively at each sampling time by virtue of rigorous theoretical derivations. The focus is on the design of a novel DF scheme such that an optimized upper bound matrix on the filtering error covariance is obtained. Furthermore, the boundedness analysis with regard to the proposed filtering error dynamics is discussed with the help of some detailed mathematical computations. Finally, some comparative experiments are used to illustrate the validity of the developed variance-constrained optimized DF scheme under EH strategy.
Evaluation of Pulse Rate from Radial Artery Using Fibre Bragg Grating Arrays

Pulse wave signals (PWS) can be utilized for the evaluation of various physiological parameters such as Pulse Rate, Respiratory Rate, Heart Rate Variability, and many more. In the present study, Fibre Bragg Grating Arrays (FBGA) have been employed to evaluate the pulse rate from the acquired signal. Enhanced sensing range, local cross-referencing, and noise reduction through data averaging are some of the enhanced features provided by the arrays as compared to the FBGs. Focusing on the radial region of the artery, pulse wave signals were acquired and were tested for similarity using coherence. Affirming a higher degree of similarity between individual signals and with averaged signals, heart rate was evaluated with an error of 2.85 percent.
Research on Key Technologies of pilot assistant system

In the future complex and dangerous combat environment, in view of the pilot’s difficulty in processing massive/incomplete information quickly, the lack of flight control ability, the conventional flight is difficult to deal with the battlefield environment and other problems, intelligent design ideas are introduced and the pilot intelligent assistant system is proposed. This paper first introduces the development history of pilot association system (PAS) at home and abroad, and puts forward the current design requirements. Finally, it systematically presents the challenges and key technologies in the overall architecture design, decision-making technology, human-computer interaction, autonomous learning and test verification.
Small-Gain Theorem and ℒ2-gain Computation in Large using Koopman Spectrum

The paper is about ${\mathcal{L}_2}$-gain computation and the small-gain theorem for nonlinear input-output systems. We show that the Koopman operator’s spectrum can provide conditions for ${\mathcal{L}_2}$-gain guarantees and small-gain theorem-based stability of interconnection over a large region of the state space. The large region in the state space can be characterized in terms of the region where Koopman eigenfunctions and the solution of the Hamilton Jacobi equation are well defined. The connection of system ${\mathcal{L}_2}$-gain to the spectral properties of the Koopman operator has led to a novel approach, based on the approximation of the Koopman spectrum, for the computation of the ${\mathcal{L}_2}$-gain and stability verification of the interconnected system. We present simulation results including application of the developed framework to a power system example.
Short-Term Stochastic Real-Time Schedule of Power System with Hybrid BESs

The high proportion of new energy penetration brings a series of challenges to the operation and schedule of the power grid, and the real-time operation status of the power grid requires more accurate and efficient prediction and management. This paper proposes a short-term real-time schedule method with hybrid energy storage for the power system stochastic operation. This method takes the minimum total cost of system operation as the optimization goal, integrates the operating constraints of thermal power, hybrid energy storage system, electric vehicle and wind farm, and compares the economy of the system under different energy storage configuration schemes. In addition, the Model Predictive Control (MPC) is applied to deal with the uncertainties of wind generation and load. Finally, case studies demonstrate the advantage of hybrid energy storage in flexibility enhancement and the effectiveness of MPC in uncertainty handling for power system scheduling.