Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Decentralized Wide-Area Damping Controller to Tolerate Permanent Communication Failures

Damping controllers that use data from Phasor Measurement Units (PMUs) such as Wide-Area Damping Controllers (WADCs) are effective in damping power system oscillation modes, especially inter-area modes. However, communication failures or cyber attacks on the WADC channels can compromise its operation. This work presents a new method of designing a decentralized WADC capable of increasing the damping ratios of the closed-loop control system modes and ensuring the stability of the system in case a communication failure occurs in the data transmission of PMUs throughout the operation of the WADC. The new design procedure is based on a new optimization model and two different metaheuristics from the area of swarm intelligence are applied and evaluated separately: Crow Search Algorithm and Particle Swarm Optimization. The IEEE 68-bus sistem is used as a test system for the analysis of the proposed method through a set of studies of both modal analyzes and time domain analyzes under contingencies. The obtained results show a good performance of the closed-loop control system with a simpler control structure and with few parameters to be tuned by the proposed method. Furthermore, the different metaheuristics evaluated presented relatively different performances in terms of control objectives.
Robust Load Frequency Control of Two-Area Interconnected Power System Using Fuzzy-I Controller

The paper proposes a Fuzzy-I controller for optimal load frequency control (LFC) in a two-area interconnected power system. The proposed controller is composed of a fuzzy logic controller (FLC) and an integral (I) controller. To achieve a better dynamic response for the LFC, the integral controller and frequency bias parameters of the secondary loop are tuned using the Particle Swarm Optimization (PSO) technique. To boost the efficiency of the optimization, a proposed objective function using the integral of squared error (ISE) and settling time is used. A comparative study and analysis are conducted between the base system controller, optimized I controller, and the proposed controller under three different scenarios of load disturbances. The robustness of the proposed controller is shown by performing a sensitivity analysis for various system parameters and operating conditions. Moreover, the performance of the proposed controller was compared with the previous LFC approaches published in the literature. The simulation outcomes demonstrate that the proposed method has an outstanding performance. The work results are presented as time-domain simulations using MATLAB/SIMULINK tool.
Some Study on Multifinger Robotic Gripper

The robotic gripper plays an important role in the robot functioning. It’s the end effector which does the end job for the robot, like pick-place, manipulating and grasping. The dexterity and manipulation depend upon the number of fingers, joints, and degree of freedom (DOF). Multi-finger robotic gripper mimics the human hand design and function. They are used in industries and as well as prosthetics to give rehabilitation to the person with disability. The components of the multi fingered robotic hand consists of palm, actuator, link, joint, tendon, controller, and sensor. It works like the human hand. Several robotic hands have been developed with three, four and five fingers. They have been tested on various objects to determine their efficiency and accordingly modifications have been done. There are various actuation methods and number of actuators that determine the DOF in the robotic gripper. This review article primarily focuses on the robotic hand/gripper along with their design and applications.
NATURAL TRANSFORMATION MODELS IN MOLECULAR BIOLOGY

Molecular models in terms of Categories, Functors and Natural Transformations are introduced for unimolecular chemical transformations, multi-molecular chemical and biochemical transformations. Novel approaches to realization of Relational Biology Models of Complex System Biology are introduced in terms of Natural Transformations between Functors of Molecular Categories. Several applications of such natural transformations are then presented to protein biosynthesis, embryogenesis and nuclear transplant experiments. Other possible realizations in Molecular Biology and Relational Biology of Organisms are also suggested. Future developments will include: Fuzzy Relations in Biology; Categories of Lukasiewicz Logic Algebras and Intuitionistic Logic Algebras for Modeling of Complex Neural Network Processes; Stochastic, Genetic Networks in Lukn-Algebras, and Relational Biology Models of Complex Hormonal Controls.
Electric Vehicle Fast Charging Integrated with Hybrid Renewable Sources for V2G and G2V Operation

In this article, the infrastructure required for the seamless and effective operation of Electric Vehicles (EVs) will be developing because electric vehicles are increasing exponentially on the road. One of them is vehicle-to-grid (V2G) technology. While a vehicle is parked or not in use, a V2G system transfers the battery's energy to the grid. In addition to helping the grid, V2G will also improve the efficiency of vehicles. The EV has been charged and discharged using a bi-directional DC-DC (BDDC) converter. The PI controller is used for BDDC and grid side inverter control. The electricity from the DC microgrid (DCMG) is utilized for EV battery charging in grid-to-vehicle (G2V) operation. DCMG has grid, solar PV, wind, and battery energy storage system (BESS) technologies. The fully loaded condition of the EV battery is taken into account. The modeling and verification of the strategy are performed in the MATLAB Simulink environment.
Anti-Synchronization of Discrete-Time Fuzzy Memristive Neural Networks via Impulse Sampled-Data Communication

This work is concerned with the anti-synchronization (A-S) of drive–response (D-R) memristive neural networks (MNNs) based on fuzzy rules. A novel impulsive sampled-data communication mechanism is proposed by considering information security of the MNNs, in which the random response delay of sensors caused by the impulse signal is also investigated. As the state of MNNs cannot be outputted accurately and transmitted persistently, the state observers of the D-R MNNs are established, which is beneficial to design the A-S controller. By analyzing the stability of the augmented error system (AES) based on the fuzzy-based Lyapunov–Krasovskii functional (FLKF), sufficient conditions of the A-S between D-R MNNs are derived. An illustrative example is given to verify the effectiveness of the proposed A-S strategies.
Single Contact Wireless Power Transfer through the Conductive Surface Without Grounding

In this paper, the single wireless power transfer (SWPT) system without any grounding at high frequency is developed. The experimental setup of SWPT has been developed to extract the power. The designed coil is very compact due to its high frequency, which is helpful in reducing the size as well as the cost of the SWPT system. The maximum power is obtained around 10 W with this SWPT system. The variation of output and input power has been analyzed at different input voltages. The efficiency of the SWPT system has also been discussed and it has been observed that the maximum efficiency is around 40 %.
Sample-Based Passively Compliant Manipulation for Senserless Contact-Rich Scenarios with Uncertainties

In this paper, we present a novel incremental approach to addressing uncertainty in robotic manipulation tasks for contact-rich environments. Traditional methods in contact-rich environments often rely on continuous perception, force/torque sensors and high-frequency controllers. The key idea of our approach is leveraging the passive deformation of flexible components as a feedback mechanism to generate passively adaptable trajectories that can cope with uncertainty. Our algorithm focused on planning the high-level trajectory and introduced the path planning problem in the deviation-fusion process. Our approach doesn’t require high-frequency controllers, force/torque sensors, and high-precision simulation. Instead, we incorporate errors into uncertainties to ensure the validity of our common planned trajectories even if the environment’s state is not perfectly obtained.
Systematic Design of Discrete-Time Control Barrier Functions Using Maximal Output Admissible Sets

This letter introduces a novel discrete-time control barrier function (DCBF) that stems directly from discrete-time set invariance theory. The proposed DCBF provides necessary and sufficient conditions for certifying control invariance and can be used to synthesize a constrained control policy. Moreover, the DCBF can be constructed for arbitrary sets of state and input constraints by taking advantage of maximal output admissible set theory. The resulting DCBF-based controller is proven to be safe and recursively feasible. Numerical examples showcase the effectiveness of the scheme by comparing it to existing constrained control approaches.
Physical Layer Security of Dual-Hop Hybrid FSO-mmWave Systems

Hybrid free-space optical (FSO) and millimeter wave (mmWave) systems have been proposed as an efficient solution to improve spectral efficiency and maintain link reliability. The comparable supported data rate and the complementary characteristics in different weather conditions of both links, i.e., FSO and mmWave) have attracted research efforts towards analyzing the different performance aspects of hybrid FSO-mmWave systems. Following this direction, in this article, the physical-layer security analysis of the dual-hop hybrid FSO-mmWave systems is addressed. In the considered system, both FSO and mmWave links are simultaneously used to carry data at each hop, while an eavesdropper is assumed to present to wiretap either one or both links. The analysis includes building a mathematical framework to derive different secrecy metrics, such as the average secrecy capacity, secrecy outage probability and effective secrecy throughput metrics, in mathematical expressions. Obtained analytical results are verified using Monte Carlo simulations where exact matching are achieved. Moreover, the presented results include investigation of the impact of the different operational parameters on the overall secrecy performance.
Reply to Rejoinder by Plümper and Troeger

The remarks in my paper, "Fixed Effects Vector Decomposition: A Magical Solution to the Problem of Time Invariant Variables in Fixed Effects Models? ," were a comment on the econometric methods pro posed in Pliimper and Troeger, "Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects," Political Analysis, 15, 2007, which I will refer to as PT in the following. The remarks below will respond to the authors' comments published in this issue, which I will refer to as P&T. PT's Equation (1) and the explanatory text define the model they intend to analyze:
Advance PLL based PV-UPQC under Adverse Grid Conditions

This paper presents a cascaded delayed signal cancellation(CDSC) based phase locked loop (PLL) employed for Photovoltaic unified power quality conditioner (PV-UPQC) to compensate grid voltage disturbance. A PV-UPQC is utilized for the purpose of mitigating power quality problems that arise from current and voltage variations in addition to the active power injection from PV. The important function of PLL is continously and precisely tracking grid voltage angle and feeding it to the PV-UPQC control. Synchronous reference frame phase-locked loop (SRF-PLL) less suited for mitigating grid voltage harmonics due to its ability to track and synchronize with the grid voltage fundamental frequency component. This paper proposes a modified PLL named CDSC PLL which mitigate this grid voltage problem. The assessment of the system’s dynamic performance is carried out by Simulating a nonlinear load in Matlab/Simulink.
Intrusion Detection System for SDN based VANETs Using A Deep Belief Network, Decision Tree, and ToN -IoT Dataset

SDN-based VANETs are vehicular ad hoc networks that utilize the software-defined networking (SDN) paradigm for network management and control. The use of SDN in VANETs offers several benefits over traditional VANETs. For example, it allows for a more flexible and dynamic network management, which can respond quickly to changing traffic conditions and network requirements. It also provides a more scalable and efficient mechanism for managing network traffic, which can improve the overall network performance. However, SDN-based VANET has challenges, such as attacks on SDN controllers and injection attacks in the network due to the nature of the SDN network. In this paper, the proposed intrusion detection system (IDS) is based on a deep belief network (DBN) and decision tree (DT) to improve the security level in the network. For the simulation, we used the ToN-IoT dataset to test the proposed work. The proposed work achieves better results than existing works.
Electroencephalogram Channel Selection using Deep Q-Network

In brain-computer interfaces, electroencephalogram channel selection picks the most informative channels. To speed up the model training and improve accuracy by selecting a small number of optimal channels. In this study, we trained an agent that automatically learned the policy to choose an optimal channel, from given EEG data, even without hand engineering. We frame the problem of EEG channel selection as a Markov decision process (MDP), offer a productive method for parameterizing it, and then apply deep reinforcement learning (DRL) to solve it. After the agent has been trained, it tries to learn a policy for channel selection that directs it to choose channels sequentially while leveraging EEG signals and previously selected tracks. The study also offers two reward systems for the DRL environment simulation and analyzes them in trials. This is the first work to look at a DRL model for EEG data interpretation, opening up a new field of study and highlighting DRL’s immense potential in the brain-computer interface.
Spreading Factor Optimization for Interference Mitigation in Dense Indoor LoRa Networks

Recent years have seen the development of several distinct Low Power Wide Area Network (LPWAN) technologies. The Long-Range Wide Area Network (LoRaWAN) is one such LPWAN solution designed for IoT communications. LoRa's low power consumption and scalability makes it a highly regarded option for low bandwidth IoT applications involving a large number of geographically dispersed devices. LoRa modulation uses symbol spreading within a constant channel bandwidth to trade off data rate for sensitivity and coverage. In LoRaWAN networks, the collision rate is highly correlated with the Spreading Factor (SF) assigned to individual nodes. The traditional method of distributing SF to devices in a LoRaWAN network reduces network performance owing to interference caused by Co-SF and Inter-SF collisions. In this work, we address this issue by allocating optimal SF to nodes who fail to transfer data with the initially assigned SF. An optimization problem is devised and addressed to improve the probability of successful transmission with specified SF. In addition, we presented an algorithm for allocating optimal SF to nodes. The results of simulation experiments indicate that the proposed method increases packet success rate and decreases collisions by designating the optimal SF.
A Single Phase Grid Integrated VSC With DC-DC LLC Hybrid Technology For Improved Charging Of Electric Vehicle

Voltage difference of energy storage system (ESS) and DC bus is extensively large leads to adoption of galvanic isolation in between two stage power conversion for safety of system. In this paper, a grid-interfaced dual stage electric vehicle (EV) charger with galvanic isolation, power flow application and improved power quality is presented. To effectively control the traction battery voltage and provide the necessary charging current, traction batteries to have an isolated DC/DC converter in series and an AC/DC single phase VSC (Voltage Source Converter) for Power Factor Correction (PFC) in front-end. This electric vehicle charging system distributes their energy to the grid at times of high demand and recharges their batteries during low peak hours. For ACDC conversion, a four-leg VSC built on a (MOSFET) for linked loads, satisfies the requirements for an active shunt compensator at the (PCC) point of common coupling. Soft voltage or soft current switching is compatible in resonant converters as well as reduces phase difference of given current and voltage, which decreases reactive power in system. But a small ZVS, and significant switch off losses are the results of standard conventional converters. LLC converters are capable of high input voltage operation due to resonant switching and improves overall performance. The first harmonic approximation (FHA) tool is used for governing performance and specifying circuit parameters for designing the LLC converter. A unique LLC-type DC-DC resonant converter with enhanced capabilities of wide range ZVS, G2V, V2G voltage gain, and power flow regulation is examined. To determine the effectiveness of the proposed EV system, a charger with a maximum power transfer capability of 1.5 kW is developed and simulated.
Comparative Study and Analysis of Electric and Dielectric Shielding

This project aims to analyze the dielectric and electric shielding properties to ascertain the optimal shielding. Materials that have been applied range from being electrically non-conductive to conductive based on the application of them being either used as a conductor or as an insulator. This correlation had studied and analyzed in COMSOL Multiphysics software and variability of the capacitance and conductance for different blend of materials at different area fractions of the model that depends on the effective shielding of the EM waves respectively in the dielectric and electric shielding. However, electric and dielectric shielding serves for various purposes and are not directly comparable in terms of which is best. Amongst the materials that are available from the Software, a film made of Carbon nanotubes (CNTs) aerosol CVD; (nk 0.250-3.30 um) sized material produces the best shielding results for dielectric shielding and Copper, Nickel (solid, Annealed) blended material produces the best shielding results in case of electric shielding model.
A Bit-Filp Joint Algorithm of Polar-SCMA Systems for mMTC

The conflict between limited spectrum resources and the need for massive machine-type communications (mMTC) is becoming more significant. To address these challenges, Sparse Code Multiple Access (SCMA) has emerged as a promising solution for mMTC in 5G. SCMA allows many users to share time and frequency resources, increasing spectral efficiency and receiving increased attention in the mMTC domain. As a control channel coding scheme in 5G communication systems, polar codes (PC) offer dependable data transmission capabilities. This paper presents a joint algorithm for an uplink PC-SCMA receiver that yields a reasonable block error rate (BLER) performance and an overload rate of 150% for mMTC. Generally, the previous error bit leads to much decoding error propagation in the following information bits for the iterative algorithm. We propose a bit-flip-assisted joint iterative detection decoding (BF-JIDD) algorithm for the error propagation problem in JIDD. Based on the simulation results, it can be concluded that the BF-JIDD algorithm outperforms the JIDD algorithm in terms of BLER performance.
Carriageway Edge Detection for Unmarked Urban Roads using Deep Learning Techniques

Traffic infrastructure segmentation is widely used in for digitization of urban areas for smart city applications. In this paper, we use transfer learning for carriageway edge detection for Indian urban roads where lane edge markings are often invisible. Also, the edge of the pavement often overlaps with unpaved shoulders posing challenges for image-based edge detection. The images obtained from various routes in Chennai, India are annotated and their masks were trained using DeepLabV3with ResNet101 and MobileNetV3 as backbones to predict the classes for each pixel. Two performance metrics, AUROC and F1 score, were used to evaluate the methods. Results show that DeepLabV3 with ResNet101 outperformed DeepLabV3 with MobileNetV3.
An Interleaved Switched Capacitor Single-Phase Inverter with a Modified Control Strategy for a Standalone PV System with Battery Storage

In this paper, an interleaved switched capacitor single-phase inverter for a Standalone PV System with Battery Storage is presented. A control strategy based on the combination of P&O MPPT, interleaved battery charging, and an interleaved inverter is developed for effectively control the PV array, battery, and load voltage in a standalone energy management system for a residential property. The advantage of the proposed system is a smaller number of switching devices compared to conventional inverter systems. The inverter used in this study is controlled by phase shifted unipolar sinusoidal PWM with an RMS feedback loop to modulate the reference signal. A review of published literature was conducted to gather data on the performance of current existing topologies such as SPUC, Switched Capacitor and various multi-level inverters. The proposed modified interleaved inverter in this configuration shows improved harmonic performance compared with other existing methods, the simulation of the proposed inverter system recorded a THD of 1.81% for the in-phase output current and voltage. This can be attributed to the interleaving of the switched capacitors that reduces the output ripple of the circuit. A MATLAB/Simulink based simulation circuit was developed and used to validate the proposed modified interleaved inverter. The simulation results show the proposed method is effective for standalone battery-based PV systems.
Convolutional Neural Networks for Implementing Opposition Control in Turbulent Channel Flows

Opposition control is an innovative technique that has shown promise for reducing turbulence intensity and wall shear stress in turbulent channel flow. The objective of the present work is to develop a Multi Input Multi Output Convolutional Neural Network (CNN) for accurately estimating and controlling the flow in a turbulent channel at a bulk Reynolds number of 3000. We focus on using all three variables, namely, wall pressure, spanwise and streamwise shear-stresses, as inputs to the CNN architecture to predict the wall-normal velocity at different heights of the detection plane (z+ = 10, 15, 20, 25, and 30). The dataset for training the CNN is generated from Direct Numerical Simulation (DNS) of a turbulent channel flow to extract spatial wall information. The correlation coefficients (ρw) between the actual and predicted wall-normal velocities are found to be very high, with values of 0.99, 0.97, 0.94, 0.86, and 0.85 at z+ = 10, 15, 20, 25, and 30, respectively. We also calculated the R2 score, which confirmed the high accuracy of the MIMO-CNN model in predicting wall-normal velocity fluctuations. This indicates the effectiveness of the proposed MIMO-CNN architecture in accurately estimating the flow field in a turbulent channel.