Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding an Intelligent Tutor into existing Business Software to provide On-the-Job Training

Traditional on-the-job training for software typically involves human tutoring, training videos, manuals and open-ended exploration of the software. This Masters Thesis investigates embedding a constraint-based intelligent tutor into an existing business software system to enhance the training options for new users. Chreos Tutor, developed using the ASPIRE authoring system, is embedded into Chreos business software. It provides opportunities for users to practice two different types of data input tasks in the actual software environment. The student interface in Chreos Tutor is the combination of a new tutoring screen and existing data input screens. The main learning tool of a constraint-based tutor is the feedback on performance errors. In order to evaluate the effectiveness of the feedback provided by Chreos Tutor, the experimental group were given feedback pertaining to individual errors in the submitted solution, whereas the control group received no feedback while working on a task. Analysis of pre-test and post-test results indicated that participants who received feedback while working on a task achieved a higher learning gain than participants who were presented with the ideal solution after submitting their solution. This suggests that Chreos Tutor is effective at teaching data input tasks to new users. Deputy Vice-Chancellor’s Office Postgraduate Office
Adaptive Super-Twisting Control for Mobile Wheeled Inverted Pendulum Systems

Recently, the mobile wheeled inverted pendulum (MWIP) has gained an increasing interest in the field of robotics due to traffic and environmental protection problems. However, the MWIP system is characterized by its nonlinearity, underactuation, time-varying parameters, and natural instability, which make its modeling and control challenging. Traditionally, sliding mode control is a typical method for such systems, but it has the main shortcoming of a “chattering” phenomenon. To solve this problem, a super-twisting algorithm (STA)-based controller is proposed for the self-balancing and velocity tracking control of the MWIP system. Since the STA is essentially a second-order sliding mode control, it not only contains the merits of sliding mode control (SMC) in dealing with the uncertainties and disturbances but can also be effective in chattering elimination. Based on the STA, we develop an adaptive gain that helps to learn the upper bound of the disturbance by applying an adaptive law, called an adaptive super-twisting control algorithm (ASTA). The stability of the closed-loop system is ensured according to the Lyapunov theorem. Both nominal experiments and experiments with uncertainties are conducted to verify the superior performance of the proposed method.
A High-Frequency High Voltage Gain DCM Coupled-Inductor Boost LED Driver Based on Planar Component

A high-frequency high voltage gain discontinuous conduction mode coupled-inductor boost LED driver based on planar component is presented in this paper. GaN switch is adopted with lower conducting resistance and higher switching speed. Zero voltage switching (ZVS) is achieved at the same time to reduce high switching loss caused by high switching frequency. Compared with the previous work, the voltage gain is higher and efficiency is higher because of ZVS. This paper focuses on high efficiency; therefore, resonant gate driver is adopted and efficiency is increased by 0.6%. In terms of the coupled-inductor, planar component is used and parameter optimization is also proposed to realize lower loss, including wingding loss and fringing loss caused by the air gap. A 500 kHz 36 W prototype has been designed to demonstrate theoretical analysis. In addition, comparison of drive loss between the resonant gate driver used in this paper and conventional driver implemented by Si8271 is made, revealing advantages of the resonant driver. Obtained efficiency was up to 92.1% at full load.
Operational quality of a truck in expert's assessment

The article presents the results of expert assessment of the quality of three different branch of trucks in terms of their suitability for long-term rental. The expert assessment is faster and cheaper compared to the analogous evaluation obtained on the basis of operational tests, hence the purpose of this article is to assess the accuracy of the results obtained in expert studies. The reliability characteristics of vehicles, such as: readiness, probability of first failure and distribution of mileage between successive repairs, were used as parameters for empirical quality assessment. Operational tests covered three groups of 30 vehicles. As part of the research, changes in vehicle technical readiness occurring during operation were also assessed. The failures of individual functional systems of the vehicle were also analyzed, which were compared with the results of expert studies. The expert assessment was based on a questionnaire regarding the overall assessment of the reliability and performance of cars. 32 experts - appraisers, with good knowledge of the construction of cars of the tested brands participated in these studies. On the basis of comparisons of the results of the expert assessment with the results of empirical studies, conclusions were drawn regarding the correctness of the assessment made by experts.
Device-to-Device Communication from Control and Frequency Perspective: A Composite Review

ABSTRACT Device-to-device (D2D) communication facilitates two users in close proximity to communicate directly, bypassing the Evolved Node B (eNB). It provides several advantages like increased spectral efficiency, energy efficiency, decreased transmission delay, and traffic offloading from eNBs. Native support of D2D communications in the emerging fifth generation cellular systems raises significant challenges, like interference, mode selection, resource allocation, and security. Ratification of standards and collaborative research works between industries and academia are now focusing to gradually resolve these challenges. In this paper, we present a novel survey of D2D communications. Our survey provides a new classification of major research works in D2D communications on the basis of frequency and control. Subsequently, major D2D architectures, protocols, and relevant applications are discussed. We also make a detailed review of the analysis techniques and point out open research challenges.
An optimized GRNN‐enabled approach for power transformer fault diagnosis

This article presents an innovative approach for fault diagnosis based on an optimized generalized regression neural network (GRNN) by integrating with dissolved gas analysis, cuckoo search algorithm (CSA), and rough set theory (RS). In the proposed method, the high dimensioned data will be simplified and reduced by RS to generate better features or attributes for the GRNN input. Meanwhile, to enhance the network performance, the smoothing factor of GRNN is optimized by CSA with Levy flight, which leads to a good global convergence. As a consequence, CSA can provide a good solution to effectively improve the fault diagnosis performance. To validate and demonstrate the proposed method, we applied it to a real‐world fault diagnosis application, power transformer fault diagnosis, by comparing the results with those of other methods. From the experimental results obtained from the evaluation, it is obvious that the proposed fault diagnosis method enabled with RS‐CSA‐GRNN can provide a useful solution for power transformer fault diagnosis because it outperformed other GRNN‐based methods that deployed different optimizing algorithms such as the particle swarm optimization and genetic algorithm. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting

Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: How can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-the-art methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.
Numerical Simulation of Bubble-Liquid Two-Phase Turbulent Flows in Shallow Bioreactor

An improved second-order moment bubble-liquid two-phase turbulent model is developed to predict the hydrodynamic characteristics of the shallow bioreactor using two height-to-diameter ratios of H/D = 1.4 and H/D = 2.9. The two-phase hydrodynamic parameters, the bubble normal and shear stress, the bubble energy dissipation rate, the bubble turbulent kinetic energy, etc. were numerically simulated. These parameters increased along with flow direction and constituted a threat to cells living at far distance away from the gas jetting inlet regions, rather than a finding of higher cell damage at near the jetting inlet region, as reported by Babosa et al. 2003. A new correlation named the turbulent energy production of bubble-liquid two-phase flow was proposed to successfully verify this experimental observation. A smaller H/D ratio makes more contributions to the generation of lower turbulent energy productions, which are in favor of the alleviation of cell damage. The extremely long and narrow shape of the bioreactor is deteriorative for cell living.
Transcoding SIP gateway

In todays multimedia society more and more communication is becoming digitized. However under a temporary period, digital and analog techniques must coexist. So that every one can communicate with everyone else. For the deaf community, among other things, this means connecting analog text telephones to new internetbased technology. To enable this coexistance the Telecommunication Access Rehabilitation Engineering Research Center of the University of Wisconsin, Trace Center joint with Gallaudet University, and Omnitor AB have presented a idea of how this can be achieved. This report describes a project, which shows how a gateway can be designed and implemented. A gateway that has the features of enabling communication between analog text telephones and internet based clients. Enabling the users to communicate with both voice and text regardless if the users are using analog or digital media. The implementations in this project are done in Java. So the resulting gateway is as operating system independent as it can be. The result of this work was a gateway that can separate vice and analog text telephone signals in a in coming sound stream. The separation is good enough so as not to influence a conversation through the gateway.
Comparison and Design Optimization of a Five-Phase Flux-Switching PM Machine for In-Wheel Traction Applications

A comparative study of five-phase outer-rotor flux-switching permanent magnet (FSPM) machines with different topologies for in-wheel traction applications is presented in this paper. Those topologies include double-layer winding, single-layer winding, C-core, and E-core configurations. The electromagnetic performance in the low-speed region, the flux-weakening capability in the high-speed region, and the fault-tolerance capability are all investigated in detail. The results indicate that the E-core FSPM machine has performance advantages. Furthermore, two kinds of E-core FSPM machines with different stator and rotor pole combinations are optimized, respectively. In order to reduce the computational burden during the large-scale optimization process, a mathematical technique is developed based on the concept of computationally efficient finite-element analysis. While a differential evolution algorithm serves as a global search engine to target optimized designs. Subsequently, multiobjective tradeoffs are presented based on a Pareto-set for 20 000 candidate designs. Finally, an optimal design is prototyped, and some experimental results are given to confirm the validity of the simulation results in this paper.
DiagonalNet: Confidence diagonal lines for the UAV detection

Due to the maneuverability and small size of the Unmanned aerial vehicles (UAVs), the traditional object detection method cannot meet the requirements of detection accuracy and real‐time performance. To address this dilemma, we propose an object detection method by using the improved hourglass network as its backbone network generating the confidence diagonal lines as detection result, and then getting the bounding box through this diagonal line. As a one‐stage method, it has a good real‐time performance. At the same time, due to the good performance of our improved network, its mean average precision performs well. Through experiments with the UAV dataset, compared with faster‐regions‐with‐convolutional‐neural‐network and You only look once (YOLO), we show that the proposed framework can quickly and accurately detect objects. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Design Methodology for Three-Phase Buck-Type and Boost-Type Rectifiers to Comply With the DO-160G Current Distortion Test

The main standard for avionic equipment DO-160G defines a set of tests that require compliance before the employment of a device in aircraft. An important test involves the generator voltage and consequential current distortion of the three-phase load in the variable 360–800 Hz frequency grid. The main purpose of this paper is to present and analyze a systematic design methodology for three-phase boost and buck rectifiers in order to meet with the voltage distortion test requirement. For a given voltage harmonic profile, the current harmonic limits that have to be satisfied are translated into equivalent per-harmonic admittances and a profile of maximum rectifier input admittance versus frequency is obtained. The defined profile, due to normalization, is applicable to arbitrary power level with simple shifting by the base admittance governed by the converter nominal power. It is shown that for boost-type rectifiers there is a tradeoff between current controller bandwidth and input inductor size, while for the buck-type rectifiers main limitation lies in the input LC filter design used for differential electromagnetic interference filtering. The experimental results conducted on an SiC three-phase 10 kW VIENNA rectifier are tightly backing up the proposed theoretical analysis. This paper is accompanied by three videos demonstrating experimental measurement of d-axis open-loop duty cycle to inductor current transfer function, loop-gain of d-axis current controller with 3.8 kHz bandwidth and variable grid frequency 360–800 Hz operation of the converter under 0% and 10% input three-phase voltage total harmonic distortion.
Agent technology recommending personalized information and its evaluation

These days, personalized contents recommending services are attracting world wide attention due to the development of broadcasting technology based on WWW and P2P technology. In this paper, we propose a method of implementing our active autonomous agents that discover and recommend contents matched with the user's preference without the user's guidance. The agents can learn the user preference by observing the user's reaction to contents recommended by the agents. We evaluate the convergence and adaptability of our agents' learning algorithm by simulation with our programmable movie service (PM service), which composes and provides appropriate contents for users.
Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

SUMMARY In the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.
FUTURE PROSPECTS OF THE APPLICATION OF THE INFANT CRY IN THE MEDICINE

Babies cry for the same reason adults talk: to let others know about their needs or problems. The infant cry contains, besides, more information about the baby, particularly, information about the health of the infant ( e.g. airway affections cause different sounds from the original). Thus, we can conclude on diseases from the modified cry signal. In this study 35 infants with hearing disorders and 35 healthy babies are tested. The author compares several attributes of the cry in the time domain between the two groups.
Purpose-Based Privacy Preserving Access Control for Secure Service Provision and Composition

Two main security issues in software as a service (SaaS) delivery model of cloud environments are access control and privacy preserving in basic web services as well as composite services where we require to infer policies through the automatic composition of the policies specified for their constituting basic services. In this paper, we present a privacy preserving access control model and framework for secure service provision and composition. The model is a combination of an attribute based access control model and a proposed purpose-based privacy model. Following this model, an access request for a service is permitted if the requester's attribute certificates and contextual conditions are in compliance with the access control policies specified by the service provider and simultaneously the privacy preferences of the requester is compatible with the privacy policies of the service provider. In the framework proposed in this paper, for secure service composition, possible chains of composite services are ranked according to the users’ preferences and sensitivity level of their data. The security policies of the composite service, established by the chosen chain of services, are inferred by automatic composition of policies specified for the basic services in the chain.
Controllable and durable ionic electroactive polymer actuator based on nanoporous carbon nanotube film electrode

Ionic electroactive polymer (iEAP) actuators have attracted great attention in the fields of micro-electromechanical systems and biomedicine due to their remarkably large strain under low-voltage stimulation. As actuation performance is mainly dominated by the electrochemical and electromechanical processes, the electrode layer and its structure are increasingly crucial to the actuators. In this research, we introduced a nanoporous carbon nanotube film with superior conductivity (1000–2000 S cm−1) as a cost-effective alternative electrode in fabricating iEAP actuators. The actuators were assembled by hot-pressing a Nafion/EMImBF4 electrolyte layer between two electrode films under vacuum conditions. Based on the developed porous hierarchal structure and superior electrical and mechanical properties of the electrode film, the actuators showed highly improved electrochemical and electromechanical properties and can operate controllably and durably under various voltage amplitudes and waveforms with a wide frequency range. The generated strain and stress reach up to 1.26% and 5.27 MPa, respectively, which are greater than the performances of the actuators based on polymer-supporting nano carbon electrodes. Once the voltage increases by 1 V, the strain and stress will increase by 0.336% and 1.407 MPa, respectively. The actuators can perform more than 20 000 cycles with an initial drop less than 20% in the operation stroke, showing a relatively durable and controllable cycle life. The newly developed actuators can be promising candidates for artificial muscles in academic interest and industrial applications.
Capacity Scheduling of Energy Storage and Conventional Generation for Frequency Regulation Based on CPS1

This paper proposes and evaluates a systematic method of scheduling energy storage and conventional generation capacities in a day-ahead frequency regulation market, based on compliance to control performance standard 1 (CPS1), during each hour of the following day. The salient feature of this method is the development of two simple piecewise linear curves that represent the relationship between first, conventional generation and energy storage power capacities, and second, energy storage power and energy capacities, to satisfy CPS1 compliance in the presence of stochastic load variations. These curves are then modeled inside a mixed integer linear program to solve for resource allocations by minimizing total capacity cost. As such, the formulation avoids complex algorithms that are otherwise required to model frequency dynamics inside the optimization problem. To reduce computation time, a scenario reduction algorithm is used to obtain a small set of scenarios to represent the stochastic load. The proposed method is evaluated using comprehensive dynamic simulations of the two-area IEEE reliability test system implemented in PSS/E. The results prove that the proposed method is effective for scheduling capacities to meet CPS1 compliance with minimum cost.
Review assessment support in Open Journal System using TextRank

In this paper, a review assessment support in Open Journal System (OJS) using TextRank is proposed. OJS is an open-source journal management platform that provides a streamlined journal publishing workflow. TextRank is an unsupervised, graph-based ranking model commonly used as extractive auto summarization of text documents. This study applies the TextRank algorithm to summarize 50 article reviews from an OJS-based international journal. The resulting summaries are formed using the most representative sentences extracted from the reviews. The summaries are then used to help OJS editors in assessing a review’s quality.