Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Adaptive Hybrid Compensation for Actuator-Sensor Compound Faults of Non-Gaussian Uncertain Systems

This paper presents a hybrid fault-tolerant control (FTC) method to handle actuator-sensor compound faults in non-Gaussian uncertain stochastic systems with approximation errors. Considering that multi-source nonlinearities of a class of stochastic systems cannot be approximated by a unified method, fuzzy logic is used to linearize nonlinear parameters, and the Lipschitz condition helps to proof the stability of FTC systems with nonlinear sensing functions. Moreover, to handle compound faults, an adaptive hybrid fault compensation scheme is devised. When only a sensor fault occurs, the feedback contains the fault information, and a feedback error composite function in the controller direct compensates the fault passively. When multiple actuator faults and a sensor fault occur, an adaptive fusion observer simultaneously implements sensor fault masking and actuator fault estimation, and then an active-passive hybrid FTC algorithm uses a compensation function and fault estimation to perform both passive compensation of the sensor fault and active FTC of actuator faults. Furthermore, an adaptive algorithm that resembles the animal predation behavior makes the controller more sensitive to incipient fault deviations. Lyapunov functions prove the robust stability of the proposed fault tolerant systems with approximation errors, and simulation experiment verifies the performance compared to a state-of-the-art method.
Improving Record Linkage for Counter-Threat Finance Intelligence with Dynamic Jaro-Winkler Thresholds

Counter-Threat Finance Intelligence (CTFI) is a discipline within the U.S. intelligence enterprise that illuminates and prosecutes terrorist financiers and their supporting networks. Relying on voluminous, disparate financial data, efficient and accurate record linkage is critical to CTFI, as successful prosecutions and asset seizures hinge on the association of designated, nefarious entities with financial transactions falling under U.S. jurisdiction. The Jaro-Winkler (J-W) algorithm is a well-known, widely used string comparator that is often employed in these record linkage problems. Despite J-W’s popularity, there is no academic consensus on the threshold score at which strings should be considered likely matches. In practice, J-W thresholds are selected somewhat arbitrarily or with little justification. This paper uses a simulative approach to identify optimal J-W thresholds based on an entity pair’s string lengths, thereby improving the lead-discovery process for CTFI practitioners.
Community Detection Framework based on Multi-Strengthening Approach

Community detection is one of the research topics recently gain interest of researchers in the area of network science. Finding the community, or groups of having stronger connections within its group members is important as to provide the underlying information hidden within the network. It is a challenge to ensure the community found represent the factual meaning of the community. This paper proposes a conceptual framework that embedded multi-strengthening of group member as another approach allowing comprehensive community found in the complex network. The framework and its merging phases are presented in this study.
Performance Evaluation of Single-Junction Indoor Photovoltaic Devices for Different Absorber Bandgaps Under Spectrally Varying White Light-Emitting Diodes

In this article, we present a detailed theoretical study to predict performance characteristics of single-junction indoor photovoltaic (PV) devices operated under white light emitting diodes (LEDs) having different spectral characteristics. Efficiency limits of both ideal and practical PV converters have been evaluated considering illumination by commercially available white LEDs. The obtained results have been generalized for white LED sources having a wide range of correlated color temperatures (CCTs) and fraction of blue in their corresponding spectrum. Depending on bandgap of the absorber material, both positive and negative correlations are observed between photon conversion efficiency of PV devices and CCT values of the white LED sources. For material bandgaps of ∼1.5 eV or lower, higher photon conversion efficiencies are obtained for warm glow white LEDs. On the contrary, white LEDs characterized to emit cool light are found to be more conducive for PV devices having absorber layer bandgaps of ∼2 eV or higher. The observed characteristics have been explained in terms of linewidth of the main emission peak and relative intensity of blue emission peak of the irradiating white LED spectrum. Based on the analysis of photon yield, three distinct bandgap ranges of the PV absorber material have also been identified, each of which represents different dependence of PV device performances on the white LED spectral characteristics. These results in effect provide the necessary guidelines for designing homojunction, heterojunction, or tandem PV devices suitable for operation under different practical white LED sources.
Topic Modelling of News Articles for Two Consecutive Elections in South Africa

In election cycles, the political-themed articles published by news providers present a rich source of information about election discourse. Extracting useful themes from a large article corpus manually is infeasible, text mining techniques such as topic modelling provide a mechanism to automatically infer themes from a corpus of text. Exploring the coverage of a single election period uncovers topical discourse that is relevant to current affairs in that election period. Analysing two consecutive election periods allows one to analyse the evolution of discourse from one period to another. Articles published by News24 were sourced to conduct the analysis and answer the research questions set forth. The articles were cleaned and topic models were built to identify 20 latent topics. The articles are classified with their topic before a pairwise cosine similarity comparison is applied on topic corpora to identify similar topics between election periods. The results of this study provide important insights relating to the two election periods, some of these include: coverage of corruption- related content is consistent between the two election periods and most political-themed articles in this corpus address problematic themes.
A Hybrid Modelling Approach Using Forecasting and Real-Time Simulation to Prevent Emergency Department Overcrowding

Emergency Department (ED) overcrowding is a pervasive problem worldwide, which impacts on both performance and safety. Staff are required to react and adapt to changes in demand in real-time, while continuing to treat patients. These decisions and actions may be supported by enhanced system knowledge. This is an application of a hybrid modelling approach for short-term decision support in urgent and emergency healthcare. It uses seasonal ARIMA time-series forecasting to predict ED overcrowding in a near-future moving-window (1-4 hours) using data downloaded from a digital platform (NHSquicker). NHSquicker delivers real-time wait-times from multiple centers of urgent care in the South-West of England. Alongside historical distributions, this data loads the operational state of a real-time discrete-event simulation model at initialization. The ARIMA forecasts trigger simulation experimentation of ED scenarios including proactive diversion of low-acuity patients to alternative facilities in the urgent-care network, supporting short-term decision-making toward reducing overcrowding in near real-time.
Classification of Oryza Sativa using Digital Image Analysis

Rice grains with high percentage of chalk are prone to breakage and making them undesirable that affects their market value. Moreover, in relation to health issues; people who consume rice with high chalkiness value are prone to type two diabetes since chalk I the rice grain contains a lot of starch. This research describes an algorithm for analyzing the chalkiness percentage of different types of white rice variety using different image processing techniques. The rice grains will be put in the black background and scanned with 1200 dots per inch. The scanned image will be converted into a grayscale image, the next step is binarization of image using thresholding, the next phase is morphological transformation; opening to remove the islands and unnecessary parts. The result image for both the whole rice area and chalk area will be a binary image in which all white pixels present on the whole rice or chalk area that are desired pixels and are counted to measure the chalkiness. Different types and classes of rice grain varieties are considered in the study for getting the chalkiness level. The results about accuracy of the system are presented in details by different tables.
Fog Computing: An Enhanced Performance Analysis Emulation Framework for IoT with Load Balancing Smart Gateway Architecture

With the exponential progress of IoT applications in last few years, a huge amount of data is being collected and stored every day and thus an enhanced and mature solution is very much necessary to knob these data. Usually cloud is utilized as the processing and storing unit of an IoT system. Cloud is deliberated to be the best solution for emerging IoT models with its diversified facilities. To tackle the massy amount of data that is being pumped everyday throughout the network, Fog computing is an effective solution which can trim and pre-process the data before forwarding it to cloud where Fog resides near the IoT application. Unlike cloud, Fog may not have high computing facilities but it can perform better in case of time sensitive applications with moderate processing. Based on the recent trend smart gateway-based Fog-Cloud solutions are gaining more popularities compared with the other related solutions. In this paper, we have extensively discussed about a load balancing based smart gateway with Fog and Cloud environment where an analysis of load balancing environment with regards to processing delay and network delay is performed. Subsequently we have compared different scenarios with and without load balancing algorithm in the projected architecture.
Epilepsy Detection by Processing of EEG signals using LabVIEW Simulation

Epileptic seizure or epilepsy, commonly known as fits, have been occurring one in a 100 in day to day life and the causes to this are many. The objective of the work presented over here is that the epilepsy occurrence appearing physically can be detected a priori via a use of the LabVIEW software. This is accomplished by capturing and monitoring the Electroencephalograph (EEG) signals from the patients, distinguishing the signals with respect to their frequencies and then comparing it with the threshold with respect to their amplitudes. Crossing of amplitudes beyond certain threshold indicates the occurrence or initiation of the Epilepsy and thus it can be indicated by the occurrence of an alarm or blinking of a LED specified for the particular frequency. The proposed detection mechanism is implemented and simulated using LabVIEW Simulation Software by feeding in the EEG signal samples as input.
Advanced Assistance Services Using Hybrid Ambulance (AASHA) System

According to a report by the World Health Organization (WHO) in 2018, 1.35 million people die each year due to road accidents globally. Post-Accident care plays a very crucial role in reducing fatalities. In a country like India, it is becoming increasingly difficult to provide post-accident services on time with an increase in congestion. In this paper, we propose a system which decreases the post-accident response time of Emergency Medical Services (EMS) in India by adding another layer of the patient transport vehicle. The paper discusses a new system design with simulation model and algorithm. Further, when compared with the traditional system, it provides an overall time reduction of approximately 3 minutes with a 97% survival rate.
Research of heuristic approaches for determining the tonality of text messages in natural language processing problems

Determining the tonality of the text is a difficult task, the solution of which essentially depends on the context, the field of study and the amount of text data. The analysis shows that the authors in their works do not jointly use the full range of possible transformations on the data and their combinations. The article explores a generalized approach, which consists in sequentially passing through the stages of intelligence analysis, obtaining a basic solution, vectorization, preprocessing, tuning hyperparameters and modeling. The experiments carried out by iterative application of these stages give a positive increase in quality for classical machine learning algorithms and a significant increase for deep learning.
Detect Malicious IP Addresses using Cross-Protocol Analysis

From the fundamentals of the domain name system (DNS) system, to the websites we browse, the files we download, and emails we receive, every aspect of our online lives involves connections to internet resources. As a result, the Internet protocol (IP) Address is a pivotal component for risk assessment of online exchanges. Our goal in this study is to develop large- scale classification of malicious IPs that leverages cross-protocol telemetry to produce accurate and context-aware risk assessment. We developed an IP reputation system for generic IP addresses based on real-world data. We added interpretability to our machine learning solution to infer a malicious IP address. Our results show that the cross-protocol analysis achieves exceptional testing performance and is effective in real-world application to detect malicious IP addresses.
Systolic and Diastolic Multiclass Classification of PPG Signals using Neural Network with Random Weight

Photoplethysmography (PPG) signals can be defined as a type of signal which obtained through a noninvasive optical method that contains information of the cardiovascular system such as arterial blood pressure, tissue perfusion, heart rate and respiratory rate. Due to its noninvasive characteristics and independent capability to identify blood pressure, many researches focused on identifying blood pressure using PPG signals. As false peak and point detection can affect estimation of blood pressure significantly, the important first step of identifying blood pressure which is accurate detection of systolic peaks and diastolic points is necessary. However, only peak detection of systolic for Neural network with random weight (NNRW) classifier has been done. Therefore, this research project aims to classify systolic peak and diastolic point through multiclass classification of PPG Signals using NNRW Classifier. The total of 20 features separated evenly for systolic and diastolic act as the input for the classifier and multiclass that act as output are True Systolic peak (TS), False Systolic Peak (FS), True Diastolic point (TD) and False Diastolic point (FD). The generation of confusion matrix aids the evaluation of performance by the classifier. The findings exhibit the convincing overall accuracy and ࡳ࢓ࢋࢇ࢔with 99.67 and 99.68 percent, respectively. In addition, the performance of classification on true systolic peak and true diastolic point also obtained 99.72 and 99.95 percent, respectively. The results extracted from experiments show the capability of the proposed approach to identify systolic peaks and diastolic points of PPG Signals.
Using Simulation to Study the Last to Enter Service Delay Announcement in Multiserver Queues with Abandonment

The Last to Enter Service (LES) delay announcement is one of the most commonly used delay announcements in queueing theory because it is quite simple to implement. Recent research has shown that using a convex combination of LES and the conditional mean delay are optimal under the mean squared error and the optimal value depends on the correlation between LES and the virtual waiting time. To this end, we show using simulation that it is important to be careful when using finite queue sizes, especially in a heavy traffic setting. Using simulation we demonstrate that the correlation between LES and the virtual waiting time can differ from heavy traffic results and can therefore have a large impact on the optimal announcement. Finally, we use simulation to assess the value of giving future information in computing correlations with virtual waiting times and show that future information is helpful in some settings.
Complex Systems Modeling and Analysis

Undesired or unexpected properties are frequent as large-scale, complex systems with non-linear interactions are being designed and implemented to answer real-life scenarios. Modeling these behaviors in complex systems, as well as analysing the large amounts of data generated in order to determine the effects of specific behaviors remains an open problem. In this tutorial, we explore three main complex systems properties and how they can be modelled in well known scenarios.
Stability Analysis of Linear, Time-Invariant Systems with Time Delay

The paper considers the relative stability of the linear, time-invariant systems with time delay and multiple poles. The proposed method is based on the usage of the Lambert W function. The suggested algorithm determines the Gain margin, Phase margin, Delay margin, as well as the gain and phase crossover frequencies. This method can be used with systems without time delay as well. The results are illustrated with examples.
An Efficient Facial Feature Extraction Method Based Supervised Classification Model for Human Facial Emotion Identification

Certain situations require human intervention to identify the emotional condition of the subject. In order to automate this process, the paper explores the idea of applying the Fisher face method to build an accurate, reliable and automatic system accompanied by supervised classification methods such as K-nearest neighbor and artificial neural network for human emotion recognition as depicted in still images. The experimental analysis is performed on the popular Japanese female facial expression (JAFFE) database. We have explored the efficacy of the Fisher face model in classifying human emotion based on content obtained from facial images. We have used basic prototypic emotions as defined by Paul Ekman - happiness (H), sadness (S), surprise (SU), and neutral (N), as our four classes. The technique used for feature extraction of facial expression detection is local fisher discriminant analysis (LFDA) which performs dimensionality reduction. The supervised classification task is carried out by K-nearest neighbor (KNN) and artificial neural network (ANN) separately. It is observed that ANN performance is better in case of emotions like happiness and surprise while neutral and sad emotions are recognized better by KNN technique.
Preventive & Predictive Maintenance of Telecommunication Equipment - A Review

Maintenance is a process for which the objective is to keep the equipment in a working, efficient and cost-effective condition. There are different techniques used in maintenance, such as corrective, preventive and predictive maintenance. This paper is a review about preventive & predictive maintenance to learn more about the benefits of each type and how the implementation process should be for the telecommunication equipment. In addition, the role of the original equipment manufacturer has been summarized. Also, the link between stock of equipment and maintenance for an optimized solution discussed due to its high importance in the maintenance field.
A Control Scheme With a Novel DMP-Robot Coupling Achieving Compliance and Tracking Accuracy Under Unknown Task Dynamics and Model Uncertainties

A control scheme consisting of a novel coupling of a DMP based virtual reference with a low stiffness controlled robot is proposed. The overall system is proved to achieve superior tracking of a DMP encoded trajectory and accurate target reaching with respect to the conventional scheme under the presence of constant and periodic disturbances owing to unknown task dynamics and robot model uncertainties. It further preserves the desired compliance under contact forces that may arise in human interventions and collisions. Results in simulations and experiments validate the theoretical findings.
Circularly polarized two-element array for DSRC Application

Design of a novel microstrip patch antenna followed by a two-element linear array has been presented in this paper. The proposed antenna as well as array both are circularly polarized and operating in Dedicated Short-Range Communications band (DSRC IEEE 802.11 $p$) with 5.9 GHz resonating frequency. The design is obtained by implementation of Koch snowflake first iteration of the motif along with additionally created diagonal slots. Wilkinson uniform power divider is incorporated to design the proposed array antenna. The gain of single element and the array are 6.5 dB and 9.49 dB respectively. A lower value of the axial ratio $(< 3\ \mathbf{dB})$ ensures about the circular polarization property of the proposed antenna and antenna array.
Design of Low Power and High Speed VLSI Domino Logic Circuit

The power dissipation of the circuit is reduced in normal mode of operation compared to test mode for any low power VLSI designs. This intern reduces the battery life of the device. Therefore, reducing power dissipation during normal operation has become a critical objective in today’s any VLSI circuit designs. The power dissipation of the circuit can be reduced at different stages of the circuit by the designers. For any CMOS circuits, power dissipation may be dynamic or static. The dynamic power dissipation exists in CMOS circuit due to switching activates between the test patterns. But the static power dissipation due to leakage in the device and it can be neglected. The domino circuits are used in various circuits, especially in memory, multiplexor, comparator and arithmetic circuit and also used in full adders that are the most important part of a CPU. Additionally, the effective implementation of domino logic in arithmetic unit and floating point units plays important role in the application circuit like microprocessor and Digital signal processor. Various design approaches had been investigated for realizing domino CMOS. The Extensive use of high speed domino circuits attracts many researchers in this field. There are various issues related to domino circuits, such as power consumption, speed and noise immunity.
Improving Fundamental Frequency Generation in EMG-to-Speech Conversion Using a Quantization Approach

We present a novel approach to generating fundamental frequency (intonation and voicing) trajectories in an EMG-to-Speech conversion Silent Speech Interface, based on quantizing the EMG-to-F0 mappings target values and thus turning a regression problem into a recognition problem. We present this method and evaluate its performance with regard to the accuracy of the voicing information obtained as well as the performance in generating plausible intonation trajectories within voiced sections of the signal. To this end, we also present a new measure for overall F0 trajectory plausibility, the trajectory-label accuracy (TLAcc), and compare it with human evaluations. Our new F0 generation method achieves a significantly better performance than a baseline approach in terms of voicing accuracy, correlation of voiced sections, trajectory-label accuracy and, most importantly, human evaluations.