Miracle-dz/UniFAR-FTUs huggingface dataset:
Ablated mass of PTFE nozzle due to high‐current SF6 arc exposure: Formulation of ablated mass

Characteristics of PTFE ablation due to SF6 arc exposure were studied to predict the ablated mass of PTFE nozzle in high‐voltage circuit breaker. From the measurement of the ablated mass, the predominant factor of the PTFE ablation was revealed as a radiation power of SF6 arc. Subsequently, the radiation power absorbed in PTFE was formulated on the basis of the radiation transfer property and the arc physics. The formulation for the prediction of the PTFE ablated mass was established with the formulated radiation power and the measured masses. Finally, the validity of the PTFE ablated mass prediction was shown from the results of the error verification with the measured masses.
Text Filtering and Ranking for Security Bug Report Prediction

Security bug reports can describe security critical vulnerabilities in software products. Bug tracking systems may contain thousands of bug reports, where relatively few of them are security related. Therefore finding unlabelled security bugs among them can be challenging. To help security engineers identify these reports quickly and accurately, text-based prediction models have been proposed. These can often mislabel security bug reports due to a number of reasons such as class imbalance, where the ratio of non-security to security bug reports is very high. More critically, we have observed that the presence of security related keywords in both security and non-security bug reports can lead to the mislabelling of security bug reports. This paper proposes FARSEC, a framework for filtering and ranking bug reports for reducing the presence of security related keywords. Before building prediction models, our framework identifies and removes non-security bug reports with security related keywords. We demonstrate that FARSEC improves the performance of text-based prediction models for security bug reports in 90 percent of cases. Specifically, we evaluate it with 45,940 bug reports from Chromium and four Apache projects. With our framework, we mitigate the class imbalance issue and reduce the number of mislabelled security bug reports by 38 percent.
A Novel Index Method for Write Optimization on Out-of-Core Column-Store Databases

The purpose of this thesis is to extend previous research on write optimization in out-of-core column storage databases. A new type of storage model titled Timestamped Binary Association Table (TBAT) will be explored, a new update entitled Asynchronous Out-of-Core Update (AOC Update) designed to leverage the TBAT will be explained, and a new type of B-Tree titled Offset B Tree (OB-tree) will be examined. The performance of the OB-tree and TBAT when utilized for selection tasks will be demonstrated through experiments comparing TBAT selection with an OB-tree index, TBAT selection without an index, and the traditional method of binary selection on a Binary Association Table (BAT). The selection speed of these three methods will be recorded and conclusions will be drawn.
Pedagogical Return on Interactive Whiteboard Investment: Teachers Beliefs

The blooming and diverse literature on implementation of interactive whiteboards in education, in general, praises their effectiveness in learning and teaching. Despite their deceivingly ordinary appearance, they are reported to facilitate the integration of multimedia in the classroom, boost learner activity, support development of the 21st century literacies, and cater to different learning styles. However, the acquisition of the technology is disproportionate to the adequate training of the teachers on its use and schools‘ systematic plan on its integration. Critical theory of learning technology lays its emphasis on contextualizing the technology and accounting for various factors of its integration, such as the inherent nature of the technology, teacher‘s affective and cognitive attitudes, and students‘ awareness of its benefits. This paper focuses on teachers‘ pedagogical beliefs as the grass roots of any change in learning and teaching. Special attention is paid to resistant adopters as they need the most scaffolded and guided training. Therefore, the aim of this paper is twofold. Primarily, it will present arguments advocating use of IWBs, collected from the reviewed studies. Secondly, it will map attitudes and beliefs of the teaching staff at the Faculty of Philosophy in Kosovska Mitrovica and Business School of Applied Studies in Blace regarding the use of interactive whiteboards in teaching.
Association rule mining to identify critical demographic variables influencing the degree of burnout in a regional teaching hospital

– This study uses apriori algorithm of IBM SPSS Modeler 14.1 on nine questions of emotional exhaustion dimension along with ten demographic variables from a regional teaching hospital in Taiwan in 2014 to identify critical demographic variables that influence the degree of burnout. By setting up the support of 25%, confidence of 80%, and lift of 1.5, twenty nine rules are found. To further refine the rules by their similarities, seven major combinations are summarized. The major characteristics are depicted below. Female medical staffs with college/university education who are not in charge of supervisor/manager with very often direct patient contacts feel much stressful to work with people directly and all day. That is, they have relatively higher degree of burnout. In summary, four demographic variables are found to be the major variables that influence emotional exhaustion, including gender, supervisor/manager, education, and direct patient contact.
Capacitive Instrumentation and Sensor Fusion for High-Bandwidth Nanopositioning

Precision capacitive sensing methods encode the measurement in a high frequency signal, which requires demodulation. To extract the measurement, the signal is observed over many cycles limiting the bandwidth of the sensor and introducing an undesirable phase lag. To address this limitation, this article outlines a design, which fuses the output of a standard modulated capacitive sensor and a charge amplifier, providing an instantaneous capacitive measurement whose bandwidth is only limited by the speed at which the electronics operate.
MOPSO-based predictive control strategy for efficient operation of sensorless vector-controlled fuel cell electric vehicle induction motor drives

This paper introduces an optimal control strategy of model-based predictive control (MPC) based on multiobjective particle swarm optimization (MOPSO) for a sensorless vector control induction motor, which is used in a fuel cell electric vehicle drive system. The proposed MPC-MOPSO algorithm is implemented to tune the weighting parameters of the MPC controller to tackle all the con icting objective functions. The paper handles the following tness functions: minimizing the speed error, minimizing the torque ripple, minimizing the DC-link voltage ripple, and minimizing machine ux ripple. Computer simulations studies have been completed utilizing MATLAB/Simulink with a speci c end goal of assessing the dynamic performance of the proposed MPC-MOPSO optimal controller and comparing it with single-objective particle swarm optimization and traditional PI controllers. The simulation results demonstrate the good dynamic response of the proposed MPC-MOPSO optimal tuning strategy over the traditional PI controllers for more accurate tracking performance through the whole speed range, especially at starting conditions and load change disturbances.
Computer Modeling of the Eddy Current Losses of Metal Fasteners in Rotor Slots of a Large Nuclear Steam Turbine Generator Based on Finite-Element Method and Deep Gaussian Process Regression

Eddy current analysis is a key issue for large turbine generators. The finite-element method (FEM) is a computational tool for obtaining the electromagnetic characteristics of electrical machines. In this article, we propose a computer model of the eddy current losses of metal fasteners in the rotor slots of a large turbine generator. The electromagnetic properties of the rotor fasteners and the outer diameter of the rotor are taken as the input, and the eddy current loss of the rotor fasteners is taken as the output. A prediction model is constructed using the FEM and deep learning. The analysis results show that compared with the independent finite-element analysis, this method reduces the design cycle time and improves the design efficiency for a large-capacity turbine generator. Compared with other machine learning models, the error is smaller and the accuracy is higher. This method provides a new way to accurately predict the eddy current loss of a generator under complex nonlinear conditions.
Fast, reliable and efficient database search motion planner (FREDS-MP) for repetitive manipulator tasks

This thesis presents FREDS-MP, a motion planning framework that leverages state of the art methods for solving a set of practical agricultural manipulator applications. Current methods exhibit unacceptably slow planning and execution times, hence FREDS-MP aims to bridge this gap by speeding up planning times whilst maintaining high reliability and solution efficiency. While only a specific set of applications are explored, FREDS-MP can be adopted for other similar applications seamlessly due to its general interface. FREDS-MP consists of three planning phases: offline, task and online. The offline planner pre-computes trajectories and cost information based on special cases that anticipate the real world. This precomputed information is used by the task planner to compute accurate heuristics for sequencing tasks. The pre-computed trajectories are used as initial seeds by the online planner which utilises state of the art trajectory optimisers to adapt them in real-time to online tasks. Software simulations are performed to validate FREDSMP and compare it to other state of the art planners. Further, the suitability of two commercial manipulators, six-DOF and seven-DOF, are compared for the intended applications. Several unconstrained and constrained tasks, commonly seen in agricultural applications, are tested under diverse obstacle configurations. Statistical results based on planner performance metrics are presented. From these results it was found that FREDS-MP significantly outperformed other state of the art planners when using a seven-DOF manipulator. Hence, an active perception experiment was carried out on a real Rethink Robotics Sawyer robot arm which was tasked to seek out apples on an artificial trellis and inspect them individually. The results from these experiments are presented and validate the practicality of FREDS-MP.
News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter’s algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency–Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency.
A Robust 2D-SLAM Technology With Environmental Variation Adaptability

Simultaneous localization and mapping (SLAM) in complicated indoor/outdoor unknown environments is challenging. With a demand on high mobility and high integrity intelligent robotics, it is desired that the SLAM system should be portable and possibly standalone. To carry out the pose estimation as well as the mapping without relying on the information from other sensors, such as image, inertial measurement unit, rotary encoder of ground vehicle and so on, a single 2D light detection and ranging (LiDAR) is considered in this paper. In order to fulfill a robust 2D SLAM technology in unknown environments, the principal component analysis (PCA) is utilized to evaluate LiDAR scan contours and to carry out a corridor detector. The corridor detector is further extended to achieve adaptive unstable points removal, mapping probability adjustment as well as loop closure. Based on an adaptive grid map segmentation scheme, the cumulative mapping errors can obviously be reduced and a precise 2D map can be eventually carried out. Many experiments are conducted to verify the proposed method. Finally, for comparison, this paper utilizes the scan data and ground truth provided by the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT), to verify the localization precision of the proposed algorithm. Experiment shows that from the scan data in the route up to about 350 m, the maximum error can be as low as about 20 cm.
Mining Public Opinion on Ride-Hailing Service Providers using Aspect-Based Sentiment Analysis

The use of ride-hailing services as a solution to current transportation problems is currently attracting much attention. Their benefits and convenience mean many people use them in their everyday lives and discuss them in the social media. As a result, ride-hailing service providers utilize social media to capture customers’ opinions and to market their services. If these opinions and comments are analyzed, service providers can obtain feedback to evaluate their services in order to achieve customer satisfaction. This study combines the text mining approach, in the form of aspect-based sentiment analysis to identify topics in customer opinions and their sentiments, with scoring of ride-hailing service providers in general, and more specifically based on the topics and sentiments. The study analyzes customers’ opinions on Twitter of three ride-hailing service providers. Text data were classified based on six topics derived from the topic modeling process, along with the sentiments expressed on them. Scoring of the three ride-hailing service providers was based on the number of positive and negative comments in relation to each topic, as well as overall comments. The results of the study can be used as input to evaluate and improve the service in Indonesia, thus the customer satisfaction and loyalty can be maintained and improved.
Simplified Regional Prediction Model of Long-Term Trend for Critical Frequency of Ionospheric F2 Region over East Asia

To improve the accuracy of predictions and simplify the difficulty with the algorithm, a simplified empirical model is proposed in developing a long-term predictive approach in determining the ionosphere’s F2-layer critical frequency (foF2). The main distinctive features introduced in this model are: (1) Its vertical incidence sounding data, which were obtained from 18 ionosonde stations in east Asia between 1949 and 2017, used in reconstructing the model and verification; (2) the use of second-order polynomial and triangle harmonic functions, instead of linear ones, to obtain the relationship between the seasonal vs. solar-cycle variations of foF2 and solar activity parameters; (3) the flux of solar radio waves at 10.7 cm and sunspot number are together introduced in reconstructing the temporal characteristics of foF2; and (4) the use of the geomagnetic dip coordinates rather than geographic coordinates in reconstructing the spatial characteristics of foF2. The statistical results reveal that foF2 values calculated from the proposed model agree well with the trend in the monthly median statistical characteristics obtained from measurements. The results are better than those obtained from the International Reference Ionosphere model using both the CCIR and URSI coefficients. Furthermore, the proposed model has enabled some useful guidelines to be established for a more complete and accurate Asia regional or global model in the future.
Field Electrons Intercepted by Coplanar Gates in Nanoscale Air Channel

Air-channel transistors can exhibit ballistic transport and immunity from carrier scattering if the channel distance is smaller than the mean free path (MFP) of electrons in an ambient environment. This study explored metal-based air-channel transistors with coplanar gates with different air-channel distances. The results indicate that the devices can exhibit field-enhanced features, but will eventually be field-lowered with increasing gate potential because the coplanar gates attract and intercept a portion of electron emission. The air-channel distance determines the device conductivity with or without a gate electric field, whereas the gate-to-gate distance determines the interception of electron emission. These devices may be utilized as inverters for vacuum transistors.
Perceived level of exertion compared to actual grip force exertion: The 50% phenomenon

The purpose of this study was to evaluate perceived exertions for various levels of actual exertion. A total of 40 healthy adults participated. They repeatedly performed the task of exerting a grip force for 5 s, following the target force on a screen. Then, they were asked to evaluate the intensity of each task based on Borg's CR‐10 scale. The results of this study indicated that participants tend to underestimate their grip‐force exertion at low target‐force levels (10–40% maximum voluntary contraction [MVC]), whereas overestimation was found at high target‐force levels (60–80% MVC). Interestingly, perceived exertion was very accurate at a target‐force level of 50% MVC (the 50% phenomenon). Based on these results, regression equations were derived to estimate the actual exertion of grip force from a perceived exertion. The Borg's CR‐10 scale for grip force was revised based on the regression equations.
Impact of Modulation Strategies on the Reliability and Harmonics of Impedance-Source Inverters

Impedance-source inverters, e.g., quasi-Z-source inverter (qZSI), with various modulation strategies have been explored in the literature to improve the inverter performance from different perspectives, such as high voltage gain, minimal inductor current ripple, and high efficiency. The modulation strategies can also significantly affect voltage/current stresses, power losses, thermal loading, and harmonics. Thus, this article presents a detailed analysis of the modulation effect on the reliability and harmonic performance of qZSIs. A comparative evaluation of current stresses, power losses, thermal stresses, number of cycles to failure of the power devices, and the output current harmonics is performed for various modulation strategies. Such comparative analysis enables the researchers to select the appropriate modulation strategy for the qZSI for their applications. Analytical expressions of current stresses and power losses are extracted for selected modulation strategies. Simulations and experiments are carried out to validate the analysis and comparison.
New Expandable Switched-Capacitor/Switched-Inductor High-Voltage Conversion Ratio Bidirectional DC–DC Converter

In this article, a group of high-voltage conversion ratio bidirectional dc–dc converters with low voltage and current stresses on switches is proposed. Then, an expandable k-modules switched-capacitor-inductor converter is resulted. In the different combinations of the proposed converter comparing to the combinations of the conventional bidirectional high-voltage gain switched-capacitor based converter, the higher voltage gain can be achieved by using lower number of switches and capacitors. This important characteristic of the proposed converter is resulted from the overall calculated voltage gain relation for the expandable proposed converter, which has $K$ number of switched capacitor modules in parallel. In this article, a theoretical analysis of the different combinations of the proposed converters is provided and the voltage conversion ratio, voltage stresses on switches, and average currents through the switches and inductors are calculated. To demonstrate the validity of theoretical analysis, an experimental prototype of one combination of the proposed expandable converter is implemented for 490 W and 25 V/700 V voltage.
Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0

The essential role of robot simulation for industrial robots, in particular the collaborative robots is presented in this chapter. We begin by discussing the robot utilization in the industry which includes mobile robots, arm robots, and humanoid robots. The author emphasizes the application of collaborative robots in regard to industry revolution 4.0. Then, we present how the collaborative robot utilization in the industry can be achieved through computer simulation by means of virtual robots in simulated environments. The robot simulation presented here is based on open dynamic engine (ODE) using anyKode Marilou. The author surveys on the use of dynamic simulations in application of collaborative robots toward industry 4.0. Due to the challenging problems which related to humanoid robots for collaborative robots and behavior in human-robot collaboration, the use of robot simulation may open the opportunities in collaborative robotic research in the context of industry 4.0. As developing a real collaborative robot is still expensive and time-consuming, while accessing commercial collaborative robots is relatively limited; thus, the development of robot simulation can be an option for collaborative robotic research and education purposes.
A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area

The grid map, representing area information with the number of cells, is a widely used mapping scheme for mobile robots and simultaneous localization and mapping (SLAM) processes. However, the tremendous amount of cells in a grid map for a detailed map representation results in overheads for memory space and computing paths in mobile robots. Therefore, to overcome the overhead of the grid map, this study proposes a new low overhead mapping scheme which the authors call as the Rmap that represents an area with variable sizes of rectangles instead of the number of cells in the grid map. This mapping scheme also provides an exploration path for obtaining new information for the unknown area. This study evaluated the performance of the Rmap in real environments as well as in simulation environments. The experiment results show that the Rmap can reduce the overhead of a grid map. In one of our experimental environments, the Rmap represented an area with 85% less memory than the grid map. The Rmap also showed better coverage performance compared with other previous algorithms.