Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven MIMO discrete-time predictive model-free adaptive integral terminal sliding mode controller design for robotic manipulators driven by pneumatic artificial muscles

In this paper, a data-driven multi-input multi-output discrete-time predictive model-free adaptive integral terminal sliding mode controller is proposed for the robotic manipulators driven by pneumatic artificial muscles with unknown models and external disturbances. First, using the concept of compact-form dynamic linearization technique, dynamics of the robotic manipulator are represented in discrete-time linear data-model form. Second, a time-delay based estimation algorithm along with an adaptive law is investigated to estimate external disturbances and pseudo-Jacobian matrix, respectively. Eventually, the proposed controller is derived based on a data-driven discrete-time integral terminal sliding function and by utilizing the concept of model-predictive controllers. The simulation results obviously clarify the efficacy of the proposed method.
Design of a New Fault-Tolerant Permanent Magnet Machine With Optimized Salient Ratio and Reluctance Torque Ratio

This article proposes a new fault-tolerant permanent magnet (FTPM) machine with the improved salient ratio and reluctance torque ratio. The key of the proposed machine is the new slot–pole combination (20-slot/14-pole), which breaks the traditional rule which stipulates that the difference between slot number and pole number in FTPM machines is equal to 2. After the selection of slot–pole combination, the influence of the ratio of armature diameter to length and the injected current on salient ratio and reluctance torque ratio is evaluated, indicating that the relationship between the salient ratio and the reluctance torque ratio is contradictory in the FTPM machine. Therefore, the multiobjective method is employed to obtain optimal rotor parameters. In order to confirm the superiority and the uniqueness of the proposed FTPM machine, the comparison between the proposed FTPM machine and the two traditional FTPM machines (20-slot/18-pole) is analyzed. From the finite-element method and experimental results, it clearly depicts that the designed 20-slot/14-pole FTPM machine exhibits good fault-tolerant capacity, the improved salient ratio and reluctance torque ratio.
A Review: Predicting the Performance of Students Using Machine learning Classification Techniques

The most important issue concerning the country's development is education. By taking into consideration of different attributes used by various researchers, the main focus of this paper is to present a review in an organised way of different classification techniques in predicting the performance of students (POS). This review will be helpful for the teachers of the university or college to find out the weak students, which will help in increasing their performance and enhance their learning, as well as this growth of the student, also reflects on the student background.
Enhanced Solution of the Disjoint Paths Set Calculation for Secure QoS Routing

In this paper, the enhanced solution of the disjoint paths set calculation for Secure QoS Routing in the infocommunication network is presented. The task stated was reduced to solving the optimization problem of integer linear programming with maximization of the number of paths and their bandwidth along with minimization of the probability of their compromising in the presence of linear constraints since the routing variables are Boolean, and variables that determine the number of routes used take only integer values. The presented solution is proposed for using in SDN based networks.
A proposed Crypto-Ransomware Early Detection(CRED) Model using an Integrated Deep Learning and Vector Space Model Approach

Crypto-ransomware is a malware category that targets user-related files to encrypt them and hold them to ransom. The irreversible effect of crypto-ransomware attacks entails early detection before it starts encrypting the files. Although several works have been proposed to detect such attacks at the pre-encryption phase before the encryption takes place, the main limitation of these works is the way in which they define the boundaries of the pre-encryption phase. That is, these studies determine the pre-encryption boundaries based on tracking the first call of any cryptography-related Application Programming Interface (API). However, relying on the first call of cryptography-related APIs to delineate the pre-encryption boundaries is not accurate as these APIs might be related to other (normal) tasks done by the crypto-ransomware, such as unpacking and/or decrypting the metamorphic payload, before the ransomware starts the malicious activities. In that case, the collected pre-encryption data lack many relevant pre-encryption attack patterns that come after the mistakenly-identified pre-encryption boundary. Such data insufficiency adversely affects the accuracy of the detection model and increases the rate of false alarms. To overcome such limitations, this paper proposes an early detection model (CRED) that can determine the pre-encryption boundaries and collect the data related to this phase more accurately. Unlike the extant research, the CRED model employs data-centric and process-centric detection approaches to combine both IRP and API data. These data will then be used to train a deep learning-based model. The CRED model will be evaluated using a data-benchmark collected by executing real-world crypto-ransomware samples downloaded from a widely-used repository. The performance of the detection model will be validated using the k-fold cross validation and compared against the models proposed by the existing works.
Optimal Summation of Natural Power Distributed Resources in Grid Connected MicroGrid

To keep our globe green, natural power resources like wind and solar power plants are achieving extreme attention. These non-polluting and sustainable natural resources have an accountable effect on the microgrid. It is very crucial to locate and manage these renewable distributed sources with their stochastic behavior. Appropriate integration of renewable Distributed Generator is a key factor for the qualitative operation of a microgrid. This paper presents the optimal placement of solar-microturbine and wind-microturbine power plant with minimum network losses and a reliable power supply. The output power of these intermittent distributed generators has been computed on probability-based distribution. Beta probability distribution and Weibull probability distribution have been used to compute probable power generation of these Natural power Resources. The heuristics methods: Particle swarm optimization has been proposed for minimization of microgrid losses and substation dependency factor of a microgrid. Optimal placement and size have been determined having node voltage limit and forward-reverse feeder current flow limit. The proposed method has been applied to the 13-bus radial distribution system. IEEE-RTS load model has been used for the result analysis. The result shows a significant reduction in energy losses and substation dependency factor
Maximum Power Point Tracking Algorithm for Laser Power Beaming Based on Neural Networks

With the high voltage intelligent substation developing at a pretty high speed, more and more artificial intelligent techniques have been incorporated into the power devices to meet the automation needs. For the sake of the line maintenance staff's safety, the high voltage isolating switch draws great attention among the most important power devices because of its capability of connecting and disconnecting the high voltage circuit. However, due to high-voltage isolating switch's working environment, the power supply system of the surveillance devices could suffer from great electromagnetic interference. Laser power beaming exhibits its merits in such situation because it can provide steady power from a distance despite the day or the night. Then the energy conversion efficiency arises as a new concern. To make full use of the laser power, this paper mainly focuses on extracting the maximum power from the photovoltaic (PV) panel. And a neural network based algorithm is proposed which combines both the intrinsic and the extrinsic features of the PV panel with the proportion of the voltage of the maximum power point (MPP) to the open circuit voltage of the PV panel. Simulations and experiments are carried out to verify the validness and feasibility of the algorithm.
Video Streaming using Scalable Video Coding over Opportunistic Networks

Opportunistic networks provide delay-tolerant communication in the absence of network infrastructure. Video streaming is challenging for such networks since real-time feedback is not available and the network is highly distributed and disconnected. This paper proposes an algorithm to adaptively transfer video over such networks according to available network resources. Scalable Video Coding is used to ensure that video of minimal quality gets delivered. If resources allow, the algorithm transmits the video at higher quality. We present a simple implementation for Android devices as a proof-of-concept. For evaluating the performance impact of variants in the algorithm, we also present the simulation results.
On ℒ2, the set of Lipschitz continuous operators is a set of first category in the set of uniformly continuous operators

In this paper, we prove that the set of Lipschitz continuous nonlinear operators defined from ℒ2 into ℒ2 is dense on the set of uniformly continuous operators and is furthermore a set of first category in the same set. It is the extension of a well-known result in the case of continuous real-valued functions to the case of the (nonlinear) operators. This result is important for the control community due to the interest of Lipschitz continuous operators and the associated incremental approach for the development of nonlinear control.
Identifying nonlinear dynamics of CoP during upright standing using muscle activation pattem

Postural balance plays an important role in daily activities, optimal performance and injury prevention. Recent studies suggest that identifying nonlinear dynamics of center of pressure (CoP) can be useful in evaluating postural balance. Thus, the purpose of this study is to propose a model that estimates the CoP signal at any moment using ankle muscle activation pattern. Electromyogram signals of two muscles (Tibialis Anterior and Medial Gastrocnemius) and CoP of healthy subjects during upright standing were recorded simultaneously. In this research, a discrete nonlinear and linear time series models derived from an ARX (autoregressive with exogeneous input) model are proposed to determine the nonlinear dynamics of the CoP. The inputs and outputs of model are ankle muscle activation pattern and CoP, respectively. The root mean square error (RMSE), R-squared (R2), Pearson correlation coefficient (Pea) and variance accounted for (VAF) were used for model evaluation. Variance accounted for is above 99%. Moreover, the correlation coefficient and R-squared are more than 0.99 and the root mean square error is also very low. The results show that these proposed models have a good performance and can directly identify the pressure center dynamics.
Parameter Identifiability in a Novel Kinetic Adsorption Isotherm for Multi-Modal Chromatography

Chromatography plays a significant role in the separation of proteins in the production of biopharmaceuticals. Multi-modal chromatography emerged as a powerful tool for this task due to its high selectivity. The interest in mechanistic models for this type of chromatography is growing; yet, such models are still rare. We derive a novel kinetic isotherm for the description of the adsorption behavior in multi-modal chromatography where the pH is modeled as an additional component. Based on artificially generated experimental data we examine the developed isotherm with regard to the identifiability of its model parameters. We discretize the emerging nonlinear least squares problem constrained by a partial differential equation with the method of lines and multiple shooting approach. We employ a generalized Gauss–Newton method on the resulting high-dimensional nonlinear least squares problem and describe a structure exploiting method that reduces the problem dimension to the number of unknown model parameters. Our computational experiments show that this approach reliably determines all component-dependent isotherm parameters identifiable from the experimental setup.
Reinforcement Learning for Decentralized Stochastic Control

In the study of stochastic dynamic team problems, analytical methods for finding optimal policies are often inapplicable due to lack of prior knowledge of the cost function or the state dynamics. Reinforcement learning offers a possible solution to such coordination problems. Existing learning methods for coordinating play either rely on control sharing among controllers or otherwise, in general, do not guarantee convergence to optimal policies. In a recent paper, we provided a decentralized algorithm for finding equilibrium policies in weakly acyclic stochastic dynamic games, which contain stochastic teams as an important special case. However, stochastic dynamic teams can in general possess suboptimal equilibrium policies whose cost can be arbitrarily higher than a team optimal policy’s cost. In this paper, we present a reinforcement learning algorithm and its refinements, and provide probabilistic guarantees for convergence to globally optimal policies. The algorithms presented here are strictly decentralized in that they require only access to local information such as cost realizations, previous local actions, and state transitions.
Sliding Mode Control of Ball-on-Wheel System

To study non-linear systems, Ball-on-Wheel (BOW) is an ideal set-up. It is also underactuated as the ball can not be held at any arbitrary position on the wheel. This system poses a control challenge to balance the ball on top of the wheel by rotating the wheel in either direction. Such a system is mathematically simulated and a sliding mode control law is developed to overcome the disturbance in the wheel velocity. The control law shows almost zero overshoot and quickly brings the ball to its equilibrium position. Robustness of this controller is also checked by calculating the integral of absolute error for parametric uncertainty in wheel’s moment of inertia. The results are found to be satisfactory.
Lung Nodule Detection and Classification using Random Forest: A Review

Lung nodule is an abnormal growth of tissues in the lung that can be an onset for lung cancer. Fast detection for those nodules and classifying them will ensure better chances for treatments. Random Forest (RF) is a powerful machine learning algorithm and a state-of-the-art technology that proved to give rewarding results in helping radiologies diagnosing lung pathologies. The paper presents a survey on recent researches made for lung nodule detection and classification using RF. Wide range of datasets can be used for lung nodule detection are listed. Different models with the used features and their results are discussed in this review.
Context Classification in Dialog-Based Interaction

A difficulty in processing of the natural language is recognizing the context of a statement. However, since this contains implicit knowledge which we unconsciously use in our formulations, it is necessary to assign the context for correct interpretation. A context in our system is managed by a service that interprets and processes input and provides feedback to the end user. In this paper, we present a solution how an end user input can be assigned to such a service. For this purpose, we score the end user inputs by the system with an ensemble of 6 different classifiers that consider semantics as well as syntax. The system learns the user's input at run time and adapts his enunciation step by step. During the evaluation, the system was able to classified 87% of the user statements to the correct service. Far from perfect, this research might lead to fundamental changes in computer use.
A Framework for Privacy Preserving, Distributed Search Engine Using Topology of DLT and Onion Routing

Risks regarding Internet privacy and security have been identified as issues for both new and experienced users of Internet technology. This paper explores the privacy and security threats in centralized search engines and proposes a decentralized search system. The proposed decentralized search system provides privacy and security for its users. The decentralized nature also provides transparency, distributed search and community-driven decisions in the proposed framework. We compare the proposed framework with the existing centralized approach. Our analysis shows that (1) there is no significant difference in search time between the centralized and proposed framework, and (2) the proposed framework is more efficient by providing search results with fewer search resources compared to the centralized approach.
Gaussian Filter Based Data-Driven Cepstral Features for Robust Speaker Verification System

In different speech-based applications, the speechsignal-based frequency cepstral coefficient (SFCC) based feature extraction method has been used successfully. The conventional method for extraction of such feature uses triangular filter banks similar to mel frequency cepstral coefficient (MFCC) features. In this work, we first present Gaussian filter based speech-signalbased frequency cepstral coefficient (GSFCC) based feature extraction technique derived from the speech-signal-based scale. Next, we use spectral entropy modulated speech-signal-based scale along with Gaussian filter to derive new cepstral feature for ASV task. We find improved performance of proposed features over baseline method in both clean and noisy conditions. The experiments were carried on NIST SRE 2001 database with simulated noise. In addition, a more recent real-world, noisy database VoxCeleb1 is used in the study. Finally, we do score level fusion of auditory filter based cepstral feature with datadriven filter based cepstral features. The performance obtained by the proposed features gives up to 15.71% and 15.37% relative improvement in equal error rate (EER) over the baseline method in NIST SRE and VoxCeleb1 databases, respectively.
Performance Evaluation of Pest Detection Techniques via Image Processing in Agriculture

In India 75% of population is dependent on agriculture. It provides not only food but also playing a key role in the economy of a country. Due to the attacks of bio aggressors or pests, agricultural byproduct lost heavily in every year. This paper is based on image processing methods to detect pest. Some of the segmentation techniques like region of interest (ROI), relative difference in intensities (RDI) and k-mean (KMEAN) clustering are implemented and tested for the detection of pests and extracting from its background. Performance of three above techniques can be analyzed by image quality assessment parameters such as structural content (SC), normalized absolute error (NAE), normalized correlation coefficient (NCC), average differences (AD) and peak signal to noise ratio (PSNR). ROI achieves better performance among all the techniques. This algorithms was developed and implemented by MATLAB 9.1 2017a version.
Introduction to Computational Thinking for K-12 Educators through Distance Learning

This Research-to-Practice Full Paper evaluates an online programming course for K-12 educators that aimed to teach Computational Thinking (CT). CT is widely defined as a set of skills, such as problem solving and systems design, that rely on Computer Science principles. As these skills are increasingly demanded in modern society, it is critical to include CT in K-12 education to boost not only computing courses, but various other disciplines. However, most educators do not yet know the basics of CT and Computer Science fundamentals, particularly in Brazil. In this context, we designed and implemented an online programming course for K-12 educators to provide qualification in CT and Programming basics, as well as to assess learners’ attitudes towards Computing, how their motivation are expressed during the course, and the correlation between motivation and attitudes. The results indicate that participants’ attitudes towards Computing has become more positive after the course, primarily their comprehension of CT and confidence to learn Computer Science. The outcomes also suggest a high level of learners’ motivation over the course, and reveal a high correlation between the relevance expressed in the course with their interest and confidence towards Computing.
Local stability analysis and estimation of domains of attraction for nonlinear systems via Takagi-Sugeno fuzzy modeling*

This paper addresses the problem of nonquadratic stability and estimation of domains of attraction for continuoustime nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy models. The T-S fuzzy model is obtained by the sector nonlinearity approach and represents exactly the nonlinear system in a compact set of the state-space. Sufficient linear matrix inequality (LMI) conditions to access local stability are obtained using homogeneous polynomially parameter-dependent Lyapunov functions and slack variables introduced through the Finsler’s Lemma. The time-derivatives of the membership functions are handled by means of a polytopic representation of the gradient of the membership functions. As a technical novelty, whenever possible, the nonlinear terms of the gradient are handled through a Taylor series expansion, avoiding the definition of new (generally overbounded) uncertain parameters that increase the numerical complexity. Finally, a numerical example is presented to show the superiority of the approach in terms of estimating larger domains of attraction when compared with previous methods from the literature.
Recursive Elimination Method for Moving Horizon Estimation of Discrete-Time Polynomial Systems

This paper proposes an algebraic method to solve optimal filtering problems for discrete-time polynomial systems. Most of the computation in the proposed method can be performed off-line using symbolic computation based on mathematical tools from algebraic geometry. Therefore, the computational time required in the on-line part of this method is significantly reduced; this property is suitable for moving horizon estimation, where a certain optimal filtering problem is solved at each time step for different observed outputs. A numerical example is provided to show the efficiency of the proposed method.
Three dimensional simulation of the microneedle penetration process in the skin by finite element method

Microneedles are a type of micron-sized needle that have been considered in recent years in various fields including, drug release and rejuvenation. Simulation of the penetration of the microneedle into the skin is useful for examining the strength of the microneedle and its effect on the skin during penetration. In this study, the penetration of microneedles into the skin was simulated in Abaqus software. It is intended for double layer skin and an Ogden model is defined for each of them. The path of failure is marked by cohesive elements. According to various applications of microneedle, the penetration depth is calculated for different times and speeds. At a constant velocity of 0.6 mm / s, for penetrating the epidermis only 0.5 second and to penetrate the dermis only 2.5 seconds is needed. Also, by decreasing the tip diameter microneedle, the reaction force applied to the microneedle decreased while the maximum stress of the skin increased. This simulation can be useful for medical biopsy sampling, drug release systems as well as for stress assessment in rejuvenation.