Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Reaching Law Based Sliding Mode Controller and Conventional PI Controller when Implemented on a Vector Controlled Induction Motor

Among the various ac machine drives available, induction motors have gained wide popularity due to its simple and rugged structure, excellent control capabilities, high performance four quadrant operations and high reliability. Until recently, various control approaches have been studied for performance improvement of Induction motor. Conventional PI controllers still find universally accepted solution for current regulators in industrial applications due to their dynamic performance nature. Recently sliding mode controllers have gained global acceptance due to its insensitivity to parameter variations, external disturbance rejection, robustness and fast dynamic response. The main objective of this paper is to propose a comparative study of conventional PI controller and the robust sliding mode controller applied on a vector control based induction motor drive platform.
Estimation of Load Margin by Machine Learning based on Synchrophasor data

In the modern power systems, precise estimation of load margin limits becomes an important guiding the system operator to determine the operation ranges before reaching the instability. This paper proposes a model of machine learning based method to improve the load margin estimator performance. The methodology is based on Support Vector Regression (SVR) which uses the same principles as Support Vector Machines (SVM). The input data are voltage, amplitude and phase angles obtained in real-time from phasor measurement units (PMU), while the target output is load margins at the operating point and critical point. The results reveal the effectiveness of the proposed method for fast estimating of load margin in a power system.
Vehicle License Plate Localization based on Local Binary Pattern Features

The importance of Intelligent Transportation System (ITS) is increasing because of increasing the number of vehicles on the roads. Automatic License Plate Detection (ALPD) is still a challenging task based on weather, occlusion, dirty License Plates (LP) and some other factors. In this paper Local Binary Pattern (LBP) feature is proposed for the detection of LP region. Cascaded Adaboost classifier is used for the classification of candidate region. The input images are chosen with different conditions such as occlusion, low contrast, dirty LP images and poor illumination. The proposed technique is tested on 600 images. This method achieves 96.8% detection rate.
Book Recommender System for Wikipedia Article Readers in a University Library

Wikipedia has emerged as an important source of information for university students. It has been reported that university students tend to read and do research with Wikipedia articles more than they do with books, even when within a library. To encourage students to read library books as a more reliable source of information, a library system was developed for recommending library books to Wikipedia readers within a particular university library. The proposed system assigns a Nippon Decimal Classification (NDC) category to each Wikipedia article and recommends library books in the same NDC category to readers of the article. In the test implementation of the system, the precision of assigning NDC categories to Wikipedia articles using a convolutional neural network was as high as 87.4%, while the precision of selecting books for recommendation using a support vector machine reached 99.8%.
An ECG-Based Authentication Scheme for Body Area Networks

Biometrics is the field of identifying subjects based on physiological and behavioral characteristics. Over the past few years, the electrocardiogram signal has become a hot topic in human identity recognition, mainly due to its robustness to attacks, universality and liveness detection. This paper makes use of these properties to generate ECG-based hash codes for inter-sensor authentication in body area networks. Mutual authentication prevents unauthorized access to sensors and actuators. The solution is lightweight and does not require a high computing power, making it perfect for small, wearable or body implanted sensors. Moreover, since only the hash code is transmitted, no sensitive-health related data is exposed. Finally, since the ECG signals used for hash generation are collected from different parts of the same subject, the mechanism is not vulnerable to errors due to physiological and psychological stress.
Reactive Support Polygon Adaptation for the Hybrid Legged-Wheeled CENTAURO Robot

The need for robots operating in the real-world has sparked interest in hybrid locomotion that combines the versatile legged mobility with a simpler wheeled motion. The use of legged-wheeled robots in complex real-world scenarios requires controllers that will capitalise on this flexibility. In this work, a reactive control scheme that exploits wheels steering and robot articulated legs is proposed to continuously adjust the robot support polygon (SP) in response to unknown disturbances. The designed cartesian-space control is expressed in the joint-space to account for the hardware limits. To tackle the non-holonomy in the joint-space model, the linear velocity/acceleration-based model is developed for the general legged-wheeled platform and applied to resolve the SP adaptation of a platform with steerable wheels. The proposed control is experimentally verified on the CENTAURO robot, demonstrating the SP adjustment when external disturbances are applied.
Deep Learning-Based Driving Maneuver Prediction System

Many of today's vehicles come equipped with Advanced Driver Assistance Systems (ADAS). Proactive ADAS have the ability to predict short term driving situations. This provides drivers more time to take adequate actions to avoid or mitigate driving risks. In this work, we address the question of predicting drivers’ imminent maneuvers before they perform an actual steering operation. The proposed system uses deep recurrent neural networks to fuse the information regarding driver observation actions and the driving environment. With new data labeling methods and effective sequential modeling approaches, the system is able to predict with high accuracy driving maneuvers shortly before the actual steering operations. A set of experiments show that the proposed approach anticipates lane change maneuvers 1.50 seconds before cars start to yaw with an accuracy improved to 90.52% and anticipates turn maneuvers at intersections with green lights 2.53 seconds before cars start to yaw with an accuracy improved to 78.59%. We also show in this work how the system can be adapted for driving proficiency assessment.
Efficient Delay-Based Internet-Wide Scanning Method for IoT Devices in Wireless LAN

Recently, Internet-wide scanning has emerged as an important element of detecting the security vulnerabilities of the Internet of Things (IoT) devices. However, Internet-wide scanning induces network congestion when sending a huge number of port scanning packets in a short time. The wireless networks are particularly subject to congestion from scanning traffic. Therefore, the scan rate should be low to reduce network congestion. However, as the number of IoT devices connected to a WLAN increases, a higher scan rate is required due to the excessive time consumption of low rate scanning. In this article, we propose a method for setting an optimal scan rate which is as high as possible without imposing congestion on a WLAN. To address this problem, we construct a model for quantifying the impact of scanning traffic on IoT data communication in a target network and demonstrate how scanning packet delay elicits the congestion of the target network. The validity of our model is evaluated and is corroborated by numerical analysis. In addition, numerical analysis results demonstrate the effectiveness of the proposed scan rate optimizing method in terms of low network congestion and a high scanning rate.
Search-Based Testing using EFSMs

Extended finite state machines are widely used in model based testing. Since testing is such a critical step from the software development process, it is important to have many different test suites, generated on different criteria. This paper presents a test suite generation algorithm for EFSMs. The algorithm produces a set of feasible transition paths that cover all transitions using a modified NSGA-II and a NSGA-III Algorithm (based on different feasibility optimization) and a Hybrid Genetic Algorithm to generate input values that trigger all the paths.
Application of LSTM neural network for consumer electronics stock market

Machine Learning and Artificial Intelligence are few of the technologies revolutionizing financial markets of consumer electronics. In this paper, we explore LSTM neural network implementation on consumer electronic stocks for prediction of future prices based on identification of contextual dependencies present that results in better performance as compared to a general recurrent neural network. We also present the working of LSTM cell using TensorFlow library.
Improving Massive MIMO Message Passing Detectors With Deep Neural Network

In this paper, deep neural network (DNN) is utilized to improve message passing detectors (MPDs) for massive multiple-input multiple-output (MIMO) systems. A general framework to construct DNN architecture for MIMO detection is first introduced by unfolding iterative MPDs. DNN MIMO detectors are then proposed based on modified MPDs including damped belief propagation (BP), max-sum (MS) BP, and simplified channel hardening-exploiting message passing (CHEMP). The correction factors are optimized via deep learning for better performance. Numerical results demonstrate that, compared with state-of-the-art (SOA) detectors including minimum mean-squared error (MMSE), BP, and CHEMP, the proposed DNN detectors can achieve better bit-error-rate (BER) and improve robustness against various antenna and channel conditions with similar complexity. The DNN is required to be trained only once and can be reused for multiple detections, which assures its high efficiency. The corresponding hardware architecture is also proposed. Implementation results with 65 nm CMOS technology approve the efficiency and flexibility of the proposed DNN framework.
Energy Collaboration for Non-Homogeneous Energy Harvesting in Cooperative Wireless Sensor Networks

In wireless sensor networks, energy harvesting is developed as an effective way to solve energy drain problem of nodes which powered by battery, just like that cooperative communication is used to improve transmission reliability. In some energy harvesting networks, the nodes’ energy harvested from ambient environment is disequilibrium. For example, nodes exposed to sunlight collect more solar energy than those shaded. Impoverished energy will reduce transmission reliability because some nodes have not enough energy to send data or are forced to decrease transmission power to save energy. In this paper, energy collaboration is taken into consideration for non-homogeneous energy harvesting in cooperative wireless sensor networks, where all sensor nodes harvest solar energy and then store it in rechargeable battery. And an energy cooperative protocol is proposed for energy inhomogeneity to enhance transmission reliability among relay nodes through radio frequency, where the nodes with abundant energy share excess power to other nodes which undertake the forwarding task but store insufficient energy. By exploiting ACK/NACK frame fed back from the destination node and No Enough Energy (NEE) frame advertised by the forwarding relays in the active set, each candidate node forms its energy supply set when all of them are lack of energy for transmitting. For the relay selection, three strategies are presented for choosing the pair of the best forwarding relay and the optimal energy supply relay, which are respectively named Best Channel Strategy, Nearest Distance Strategy and Minimum Energy Sharing Strategy. Furthermore, the outage probability of cooperative transmission under energy collaboration is derived. Simulation results show that energy collaboration for cooperative wireless sensor networks can significantly reduce the outage probability of the system and improve transmission reliability of the network.
Sensor Fault Diagnosis via Deterministic Learning

In this paper, an approach for the sensor faults detection of a class of nonlinear systems is proposed which can response sensor faults quickly. Firstly, through adaptive observer, high gain observer and deterministic learning (DL) theory, unknown sensor fault function can be approximated locally. The stability of the system and the convergence of the neural network are proved by using Lyapunov method and the lemma of uniformly completely observable. The states of system are stored in constant value neural network which can be reused by establish a set of dynamical estimators. If the residual norms of one fault estimator is less than the preset threshold in a finite time, the occurrence of the fault can be detected. The main contribution of this paper is that the convergence of the adaptive observer is proved without constructing a multi-delayed system. Simulation studies are included to demonstrate the effectiveness of this approach.
Eudragit FS 30D as a potential polymer for use in the technology of preparing matrix tablets contain metronidazole – an experimental and mathematical modeling study

Abstract The aim of this study was to examine the usefulness of a pH-dependent copolymer - Eudragit FS - for employment in the technology of preparing modified release metronidazole matrix tablets. In addition, in our work, Eudragit RL and Eudragit RS were included in the composition of some formulations, as well as sodium lauryl sulfate and polysorbate 80. As part of the study of the dissolution test, the similarity coefficient (f2) for the obtained profiles was calculated, and mathematic models were used to estimate the kinetics and mechanism of active substance release. In our work, it was observed that the inclusion of polymer Eudragit FS alone in the tablet composition ensured a modified release of the active substance for 10 h. After this time period, the amount of metronidazole determined in the acceptor fluid was 71% - 81% of the declared dose. Modification of the composition by the addition of surfactants resulted in an increased release of the active substance of up to 98%. This effect was dependent on the type of surfactant and its quantitative ratio to the Eudragit FS. Similar release profiles were obtained for tablets containing Eudragit RS and sodium lauryl sulfate, as well as Eudragit RS and polysorbate 80. Depending on the composition of tablets, metronidazole release proceeded in accordance with either first or second-order kinetics. We calculated as well, that the differing masses of Eudragit FS in the studied formulations correlates with the order of release kinetics (p < 0.002). Such an effect was validated using the Weibull model, wherein, in all the studied formulations, the release rate was seen as a decreasing function of time. An analysis of data according to the Ritger-Peppas model and the Peppas-Sahlin model for some formulations, indicated that the mechanism of active substance release from matrix tablets is diffusion.
Modified Higher-Order Sliding Mode Observer-Based Super-Twisting Controller for Perturbed Overhead Cranes

The designs of feedback control and automation systems for overhead cranes require precise information about the crane states. Unfortunately, cranes are usually not equipped with velocity sensors. The current study deals with the problem of finite-time exact states estimation and control of perturbed overhead cranes subject to a dynamic disturbance. To address the state availability problem, a modified higher-order sliding mode observer (HOSMO) is proposed and a HOSMO based second-order super-twisting sliding mode control (STC-HOSMO) is proposed to control the system. Control applications and error analyses based on IAE and ITAE criteria showed the effectiveness of the proposed STC-HOSMO scheme. The results also showed that tracking/regulation and estimation error trajectories asymptotically converged to equilibrium. Additionally, attenuated chattering of the control effort is observed and second-order sliding mode (2-SM) is achieved.
Method for Output Reconstruction of Relay Protection Simulation Signal Based on Wavelet Theory

Power system transient simulation software such as PSCAD and MATLAB is used as a signal source for a new generation of relay protection signal generators. In the simulation process, the external errors of the system model caused by different simulation algorithms will be generated, so the output signal has larger harmonics distortion. This phenomenon affects the actual value of the data and the accuracy of the experiment. Therefore, a signal detection method based on wavelet theory is proposed. Combined with the total harmonic distortion rate index of voltage and current, a complete detection and reconstruction method of output waveform is established to eliminate the non-source error caused by different algorithms in transient simulation. Tests were performed using simulated signals and the results show the proposed methodology to be feasible.
On-Chip Trainable 1.4M 6T2R PCM Synaptic Array with 1.6K Stochastic LIF Neurons for Spiking RBM

A fully silicon-integrated restricted Boltzmann machine (RBM) with event-driven contrastive divergence (eCD) algorithm is implemented using novel stochastic leaky integrate-and-fire (LIF) neuron circuits and 6-transistor/2- PCM-resistor (6T2R) unit cells on 90-nm CMOS technology. A bidirectional asynchronous spiking signaling scheme over an analog-weighted phase change memory (PCM) crossbar enables spike-timing-dependent plasticity (STDP) as a local weight update rule. This results in concurrent massively- parallel neuronal computation for low-power on-chip training and inference. Experimental image classification using 100 handwritten digit images from the MNIST database demonstrates 92% training accuracy. SPICE simulation abstracted from the fabricated design indicates 8.95 power. A projection to 28-nm technology gives 5.39 pJ per synaptic operation.
Bayesian Anytime m-top Exploration

We introduce Boundary Focused Thompson sampling (BFTS), a new Bayesian algorithm to solve the anytime m-top exploration problem, where the objective is to identify the m best arms in a multi-armed bandit. First, we consider a set of existing benchmark problems that consider sub-Gaussian reward distributions (i.e., Gaussian with fixed variance and categorical reward). Next, we introduce a new environment inspired by a real world decision problem concerning insect control for organic agriculture. This new environment encodes a Poisson rewards distribution. For all these benchmarks, we experimentally show that BFTS consistently outperforms AT-LUCB, the current state of the art algorithm.
An Adaptive Flow-Aware Packet Scheduling Algorithm for Multipath Tunnelling

This paper proposes AFMT, a packet scheduling algorithm to achieve adaptive flow-aware multipath tunnelling. AFMT has two unique properties. Firstly, it implements robust adaptive traffic splitting for the subtunnels. Secondly, it detects and schedules bursts of packets cohesively, a scheme that already enabled traffic splitting for load balancing with little to no packet reordering.Several NS-3 experiments over different network topologies show that AFMT successfully deals with changing path characteristics due to background traffic while increasing throughput and reliability.
Soft-target Model Predictive Control for PMSG-based Wind Generator

Among various renewable energy technologies, wind power is one of the most popular one due to its multiple benefits. Wind power is produced by wind generators, permanent magnet synchronous generator (PMSG)-based wind generator is popular for its advantages such as gear-less, high efficiency, and low cost. However, because of the stochastic nature of wind, the effective control of PMSG-based wind generator is a challenging task. In order to address this issue, in this paper we propose an optimal control method for control of PMSG-based wind generator. In this method, model predictive control (MPC) approach is utilized, which enable the optimal control of control of PMSG-based wind generator. In addition, considering the highly uncertainty in wind speed, we introduce the concept of soft-target control, which makes the proposed control method more practical. Simulation results validate the performance of the proposed control method.
Single-fed Single-patch Dual-wideband Dual-radiation Pattern Microstrip Patch Antenna

A single-fed single-patch linear polarization (LP) dual-band dual-radiation pattern patch antenna is presented by using hexagon-star-shaped patch and U-slot. The impedance bandwidth $(\vert \mathrm{S}11\vert < -10\ \mathrm{dB})$ of the antenna are 19.6% (5.1-6.2 GHz) and 17.2% (6.9-8.2GHz), respectively. The lower and higher bands can be achieved conical-beam and single-beam radiation patterns. The wireless power transmission band (5.745-5.825GHz) and wireless communication band (7.1-7.3 GHz) can be completely covered. The advantages of proposed antenna are dual-wideband performance, low weight and simple structure. Good simulated result is obtained by a design example.
Optimal Design Methodology for High-Power Interleaved Bidirectional Buck-Boost Converters for Supercapacitors in Vehicular Applications

Electric vehicle (EV) power sources with a wide variable DC output voltage, such as supercapacitors, cannot be connected directly to the motor drive of the EV power train. Therefore, the power train requires a high-efficient bidirectional dc/dc converter between the power source and the motor drive. There are many dc/dc converters that accomplish that function, however in EVs, simple topologies are commonly used due to its reliability and ease of control. This paper is focused on the design of interleaved bidirectional buck-boost converters which provide energy with high-efficiency, robustness, ease to control and high specific power transfer (W/kg). High specific power transfer is important in vehicular applications to make them lighter, faster and more efficient. The paper analyses a design methodology for converters in the range from 1 kW to 10 kW power transfers for different weight constraints. Based on that analysis, a model was extracted that gives an approximation of the efficiency and the optimal number of interleaved converters as a function of the power transfer (W) and weight (kg). This approximation is extremely useful for designers in order to obtain the maximum profit of the power source (supercapacitors).
Modelling of Biological Lower Extremity

The work done in this paper is an effort to study the leg movements during walking in order to motivate the design of biological leg efficiently and modelling of biological leg. Hypothesize that the leg comprising of two links represents femur and shank. For modelling, the biological leg is treated as a serial two link manipulator. The leg modelling is done using inverse dynamics with Lagrangian formulation approach and merged some virtual work concept to incorporate the ground reaction forces occurs in the gait cycle. For the development of the parameters, the data acquired is processed through mathematical equations using MATLAB/SIMULINK tool for precision and reliability. Leg model is constructed using different kinematic and kinetic input from gait experiments to provide output in the form of knee and hip torque. Complete and simplified model is introduced and determined torque is validated with experimental data.