Miracle-dz/UniFAR-FTUs huggingface dataset:
Motor Speed Signature Analysis for Local Bearing Fault Detection With Noise Cancellation Based on Improved Drive Algorithm

Motor speed signature analysis provides a noninvasive method for bearing fault detection. However, for the vector-controlled ac motors, periodic speed ripples related to fundamental frequency <inline-formula><tex-math notation="LaTeX">$f_{e}$</tex-math></inline-formula> and its twice harmonic <inline-formula><tex-math notation="LaTeX">$2f_{e}$</tex-math></inline-formula>, which are caused by current measurement errors, are difficult to be attenuated by motor inertia or bandwidth of speed loop under low-speed conditions. The unwanted components would reduce the signal-to-noise ratio of motor speed and increase the difficulty of bearing fault detection. To solve the problem, this paper proposes a new noise cancellation strategy, which applies the improved drive algorithm instead of conventional signal processing schemes to cancel out the noise component before the data acquisition. Specifically, resonance controllers are introduced and set in parallel with the existed proportional–integral controller to suppress the speed ripples. Moreover, the envelope spectrum analysis is carried out to detect fault characteristic. The effectiveness of the proposed method is validated through simulation and experimental tests. Besides, its superiority under low-speed conditions is also demonstrated, compared with the spectral kurtosis of speed signal and three current-based methods.
A SIMPLE EFFICIENT APPROACH FOR THE D-MINCUT PROBLEM

A simple and ecient algorithm is presented and compared with other related algorithms. The time complexity of the proposed algorithm is shown to be O(n 2 m p mp ) which is less than O(mp 2 ), the time complexity of the best existing algorithm. Moreover, the proposed algorithm is more convenient for implementation.
Using Variable Conflict Granularity to Improve the Performance of Transactional Memory Support for Games

Using Variable Conflict Granularity to Improve the Performance of Transactional Memory Support for Games Mihai Burcea Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2015 Transactional memory (TM) is a promising parallel programming paradigm for generic applications on large scale parallel architectures. In spite of significant research work in this area in the past decade, adoption by the parallel programming community has been slow due to two reasons. First, performance of most TM support for applications has been disappointing. Second, efforts towards using TM to parallelize realistic applications or highly popular benchmarks for TM have been rare. It is widely known that two factors can have a drastic impact on the performance of parallel applications with Transactional Memory (TM) support: i) contention among execution threads, particularly contention due to false sharing, and ii) the software TM runtime conflict tracking overhead. We propose techniques to address application contention and reduce TM conflict tracking overheads by varying the memory access tracking granularity in the TM. To further optimize performance of TM support, we leverage a software transactional memory platform called HyTM based on Intel’s TM hardware support, Transactional Synchronization Extensions (TSX) in Haswell. We build on existing efforts towards developing a gaming benchmark for TM, by characterizing and optimizing the contention patterns in a realistic application, SynQuake, developed based on the open source Quake 3 code. We port SynQuake to HyTM and design, prototype and evaluate adaptive techniques for varying the conflict tracking granularity of the TM, on the fly, for SynQuake. For regions of the application space causing high degrees of contention, we reduce the tracking granularity while, conversely, we allow a coarse tracking granularity to be used for application regions with low degrees of contention, in order to decrease tracking overhead and transaction size. Our prototype implements our variable granularity adaptations either by using application-specific knowledge, or, completely transparently, by relying primarily on the support provided by a TM library. Our evaluation shows that our techniques improve performance by 16.7%, on average, compared to a range of configurations using static parameter settings. More importantly, we show that our techniques are lightweight and can provide statistics that allow both application and user to understand application contention patterns at the cost of negligible runtime overhead.
Application of Morphological Filtering in Image Preprocessing and Implementation Based on FPGA

The application of mathematical morphology in grayscale image processing was studied.An architecture based on Field Programmable Gate Array(FPGA) was designed for implementing the image preprocessing.The advantage of this architecture is that it enables automatic generation of given length structuring element for a morphological filter.Simulation was made for the application of mathematical morphology in grayscale image processing.The results indicated that the method has fine real-time performance,can improve the SNR of image while suppressing the interferential information.It is also beneficial for miniaturization of the system.
Application of Stochastic Analysis, Modeling and Simulation (SAMS) to Selected Hydrologic Data in the Middle East

Water resources in the Middle East are very scarce and the management of these resources is a challenge. In this paper, the use of stochastic analysis, modeling, and simulation (SAMS) software package to selected hydrologic data in the Middle East (namely Jordan and Saudi Arabia) are explored. Modeling and simulation experiments were conducted to test the capabilities of SAMS to be used for stochastic modeling and simulation in the Middle East region. The hydrologic data used in this study consist of historic observed rainfall data of different lengths at various sites in Jordan and Saudi Arabia. The models used in this study include: autoregressive moving average (ARMA) models, periodic autoregressive moving average (PARMA) models, multi-site contemporaneous autoregressive moving average (CARMA) models, and temporal disaggregation models. Results indicate that SAMS can be used as a tool for stochastic modeling and simulation of hydrologic data in Jordan and Saudi Arabia. It is important for managers and decision makers of water resources in these countries to be able to use sophisticated tools such as SAMS while deciding water management policies in these countries.
Mission Assurance: A Review of Continuity of Operations Guidance for Application to Cyber Incident Mission Impact Assessment (CIMIA)

Military organizations have embedded information technology (IT) into their core mission processes as a means to increase operational efficiency, improve decisionmaking quality, and shorten the sensor-to-shooter cycle. This IT-to-mission dependence can place the organizational mission at risk when an information incident (e.g., the loss or manipulation of a critical information resource) occurs. Non-military organizations typically address this type of IT risk through an introspective, enterprise-wide focused risk management program that continuously identifies, prioritizes, and documents risks so an economical set of control measures (e.g., people, processes, technology) can be selected to mitigate the risks to an acceptable level. The explicit valuation of information resources in terms of their ability to support the organizational mission objectives provides transparency and enables the creation of a continuity of operations plan and an incident recovery plan. While this type of planning has proven successful in static environments, military missions often involve dynamically changing, time-sensitive, complex, coordinated operations involving multiple organizational entities. As a consequence, risk mitigation efforts tend to be localized to each organizational entity making the enterprise-wide risk management approach to mission assurance infeasible. This thesis investigates the concept of mission assurance and presents a content analysis of existing continuity of operations elements within military and non-military guidance to assess the current policy landscape to highlight best practices and identify policy gaps in an effort to further enhance mission assurance by improving the timeliness and relevance of notification following an information incident.
A Novel Feature Extraction Technique for Offline Handwritten Gurmukhi Character Recognition

Abstract A novel feature extraction technique is presented in this paper for an offline handwritten Gurmukhi character recognition system. Handwritten character recognition is a complex task because of various writing styles of different individuals. To select a set of features is an important step for implementing a handwriting recognition system. In this work, we have extracted various topological features, namely, peak-extent features, shadow features and centroid features. A new feature set is also proposed by using horizontal peak extent features and the vertical peak extent features. For classification, we have used k-NN and Linear-SVM classifiers. In view of learning and simplification capabilities of multi layer perceptrons (MLPs), MLPs based pattern classifier is also used for classification. In the present work, we have taken 7,000 samples of offline handwritten Gurmukhi characters for training and testing. Proposed system achieves a maximum recognition accuracy of 95.62% using SVM with linear kernel classifier. By using k-NN and MLPs, a maximum recognition accuracy of 95.48% and 94.74%, respectively, has been achieved with five-fold cross validation.
Lexical transfer selection using annotated parallel corpora

This paper addresses the problem of bilingual lexicon extraction and lexical transfer selection, in the framework of computer-aided and machine translation. The method relies on parallel corpora, annotated at part of speech and lemma level. We first extract a bilingual lexicon using unsupervised statistical techniques. For each word with more than one translation candidates we build context vectors, based on the annotated parallel corpus information, in order to aid the selection of the contextually correct translation equivalent. The method achieves an overall precision of ca. 85% while the maximum recall reaches 75%.
Effects of Design Parameters on the Multiphysics Performance of High-Speed Permanent Magnet Machines

In the design of high-speed permanent magnet machines (HSPMMs), comprehensive effects of design parameters on the various properties of electromagnetic loss, rotor stress, rotor critical speed, and temperature distribution must be analyzed to obtain the optimal design. The aim of this paper is to examine the effects of design parameters, such as the pole number, stator slots number, number of conductors per slot, permanent magnet (PM) thickness, PM pole arc coefficient, air gap length, rotor diameter, core length, and sleeve thickness, on multiphysics performance based on a high-power HSPMM. A reasonable design is determined according to the abovementioned analysis that satisfies all specified multiphysics constraints. The theoretical results are confirmed by the experimental results on a prototype in terms of electromagnetic, mechanical, and thermal characteristics such as back electromotive force, no-load loss, full-load current, overspeed experiment, and temperature distribution.
Optimal operational scheduling and reconfiguration coordination in smart grids for extreme weather condition

With the increasing in frequency, intensity, and side effects of grid outages caused by weather based events, the importance of resiliency is also increased. This study presents a novel multi-objective optimization problem for improving the resilience of smart grid against natural disasters in the presence of a considerable amount of line outages occurring due to the weather conditions. The proposed model considers the management of distributed energy resources (DERs), energy storages (ESs), and dynamic topology reconfiguration of the distribution network. The main goal is based on a dynamic resilient operational planning using prediction of weather condition. The objectives in the proposed approach are defined as: to minimise operational cost of distribution network from the system operator's viewpoint, to minimise the penalty costs for energy not supplied and side effects of outages, and to maximise benefits of DER-ES owners by using flexible reconfiguration and dynamic operational planning considering weather conditions. A hybrid optimization algorithm based on genetic algorithm and ɛ-constraint method using fuzzy decision maker is employed to choose the best solution from a provided Pareto optimal set. The outcome of the Pareto solution specifies the best scheduling for DERs, ESs and switching of lines during the scheduling horizon.
Image Processing and Machine Learning Techniques for Facial Expression Recognition

The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression recognition. A comprehensive review of recently proposed methods is provided along with an analysis of the advantages and the shortcomings of existing systems. Moreover, an example for the automatic identification of basic emotions is presented: Active Shape Models are used to identify prominent features of the face; Gabor filters are used to represent facial geometry at selected locations of fiducial points and Artificial Neural Networks are used for the classification into the basic emotions (anger, surprise, fear, happiness, sadness, disgust, neutral); and finally, the future trends towards automatic facial expression recognition are described.
Evaluation of a Communication-Assisted Overcurrent Protection Scheme for Photovoltaic-Based DC Microgrid

Integration of distributed generations units in distribution systems affects the amplitude and the direction of fault currents. On the other hand, dc fault currents have large amplitude and fast transients. Thus, the setting and coordination of overcurrent relays in dc microgrids will be a challenging issue. This paper proposes a new communication-assisted overcurrent protection scheme for photovoltaic (PV)-based dc microgrids. The proposed scheme has lower cost than the schemes based on the differential protection and also provides the backup protection for the relays. First, possibility of using the overcurrent-based protection is evaluated using a comprehensive strategy for every line section of the system. The strategy determines which relays have to be set for protection of the microgrid in the grid-connected and islanded operation modes. Then, the selectivity and coordination problems of the overcurrent relays in the presence of the PV units are solved by implementing blocking and intertripping schemes using communication infrastructures of the smart grids. Finally, the proposed protection scheme is validated by simulation results on a dc microgrid test system.
Ranking new comers to work based on commitment propensity through hybrid method of AHP and TOPSIS

Received: 5 November 2017 Abstract Accepted: 15 September 2018 This research provides a tool to select and prioritize new comers to work based on their preentry organizational commitment propensity through examining links between the big five personality factors: extroversion, agreeableness, conscientiousness, neuroticism, openness; and three component model of organizational commitment: affective commitment, continues commitment, normative commitment. Findings show that extroversion and openness respectively have positive and negative effects on all three components of organizational commitment. Results gained by Structured Equation Modelling (SEM) indicate neuroticism is negatively related to affective and continues commitment and positively to conscientiousness effects on continues commitment. In the second part of the study, the received results are applied to extract the general equations that enables to estimate new comer’s pre-entry organizational commitment and to rank them using TOPSIS and AHP. The AHP is used to determine the relative weights of commitment criteria and TOPSIS is employed for the final ranking of new comers based on these criteria’s.
A motion detection scheme for video surveillance

A scheme for motion detection in compressed code stream for video surveillance is presented in this paper. The scheme reads out signs of the discrete cosine transform (DCT) coefficients from the compressed video code stream and detects motion by counting the number of DCT coefficients that change their signs in a pair of DCT blocks corresponding to consecutive frames. A probability model and nonlinear filtering is used for eliminating the disturbance of noise in the background and luminance change, respectively. Experiments on various types of video sequences show the proposed scheme can perform motion detection correctly with low computational cost.
Collision detection method for industrial robot based on envelope-like lines

Purpose Successful sensorless collision detection by a robot depends on the accuracy with which the external force/torque can be estimated. Compared with collaborative robots, industrial robots often have larger parameter values of their dynamic models and larger errors in parameter identification. In addition, the friction inside a reducer affects the accuracy of external force estimation. The purpose of this paper is to propose a collision detection method for industrial robots. The proposed method does not require additional equipment, such as sensors, and enables highly sensitive collision detection while guaranteeing a zero false alarm rate. Design/methodology/approach The error on the calculated torque for a robot in stable motion is analyzed, and a typical torque error curve is presented. The variational characteristics of the joint torque error during a collision are analyzed, and collisions are classified into two types: hard and soft. A pair of envelope-like lines with an effect similar to that of the true envelope lines is designed. By using these envelope-like lines, some components of the torque calculation error can be eliminated, and the sensitivity of collision detection can be improved. Findings The proposed collision detection method based on envelope-like lines can detect hard and soft collisions during the motion of industrial robots. In repeated experiments without collisions, the false alarm rate was 0 per cent, and in repeated experiments with collisions, the rate of successful detection was 100 per cent. Compared with collision detection method based on symmetric thresholds, the proposed method has a smaller detection delay and the same detection sensitivity for different joint rotation directions. Originality/value A collision detection method for industrial robots based on envelope-like lines is proposed in this paper. The proposed method does not require additional equipment or complex algorithms, and highly sensitive collision detection can be achieved with zero false alarms. The proposed method is low in cost and highly practical and can be widely used in applications involving industrial robots.
Estimating Vocal Fold Contact Pressure from Raw Laryngeal High-Speed Videoendoscopy Using a Hertz Contact Model

The development of trauma-induced lesions of the vocal folds (VFs) has been linked to a high collision pressure on the VF surface. However, there are no direct methods for the clinical assessment of VF collision, thus limiting the objective assessment of these disorders. In this study, we develop a video processing technique to directly quantify the mechanical impact of the VFs using solely laryngeal kinematic data. The technique is based on an edge tracking framework that estimates the kinematic sequence of each VF edge with a Kalman filter approach and a Hertzian impact model to predict the contact force during the collision. The proposed formulation overcomes several limitations of prior efforts since it uses a more relevant VF contact geometry, it does not require calibrated physical dimensions, it is normalized by the tissue properties, and it applies a correction factor for using a superior view only. The proposed approach is validated against numerical models, silicone vocal fold models, and prior studies. A case study with high-speed videoendoscopy recordings provides initial insights between the sound pressure level and contact pressure. Thus, the proposed method has a high potential in clinical practice and could also be adapted to operate with laryngeal stroboscopic systems.
A Review of Deep Learning Models for Time Series Prediction

In order to approximate the underlying process of temporal data, time series prediction has been a hot research topic for decades. Developing predictive models plays an important role in interpreting complex real-world elements. With the sharp increase in the quantity and dimensionality of data, new challenges, such as extracting deep features and recognizing deep latent patterns, have emerged, demanding novel approaches and effective solutions. Deep learning, composed of multiple processing layers to learn with multiple levels of abstraction, is, now, commonly deployed for overcoming the newly arisen difficulties. This paper reviews the state-of-the-art developments in deep learning for time series prediction. Based on modeling for the perspective of conditional or joint probability, we categorize them into discriminative, generative, and hybrids models. Experiments are implemented on both benchmarks and real-world data to elaborate the performance of the representative deep learning-based prediction methods. Finally, we conclude with comments on possible future perspectives and ongoing challenges with time series prediction.
IMPACT OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN EDUCATION (ICT)

Information and communication technologies (ICT) can be defined as the use of hardware and software for efficient management of information. ICTs can empower teachers and learners, transforming teaching and learning processes from being highly teacher-dominated to student-centered, and that this transformation will result in increased learning gains for students, creating and allowing for opportunities for learners to develop their creativity, problem-solving abilities, informational reasoning skills, communication skills, and other higher-order thinking skills . In this study the performance of the students were found considerably improved by the use of ICT. The results indicate that the technical resources for using ICT at school are very good. The research was a descriptive survey. A considerable improvement of 18.4% was achieved. An increase in student's achievements especially in terms of knowledge, comprehension, practical skill and presentation skill in subject areas such as mathematics, science and social study was clearly visible. Through ICT, images can easily be used in teaching and improving the retentive memory of students, teachers can easily explain complex instructions and ensure students comprehension and teachers are able to create interactive classes and make the lessons more enjoyable, which could improve student attendance and concentration. The integration of ICT into education highly rewarding as it may be, but can face a number of challenges which need to be addressed in an effective way.