Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Multi-Abstraction Computing Tools on Promoting Exploratory Self-learning in Engineering: a Case Study using a Custom Real-Time Operating System for Remote Learning

This manuscript reports the results of an experiment to assess the effectiveness of multi-abstraction computing tools on promoting exploratory self-learning in engineering, using a custom Real-Time Operating System (RTOS) during remote learning. Our goals were to determine whether students respond to guided exploratory learning opportunities during remote learning, and whether these contributed to immediate and further student success.Students in a 3rd year course were given the custom multi-abstraction RTOS. The higher abstraction layer was taught directly, whilst students were directed towards exploratory self-learning among the lower abstraction layer (which includes details beyond prior knowledge or the content of the course). In a survey, students were asked about their perceptions of using higher and lower abstraction layers and how these impacted their learning. Responses were correlated with course grades.Students that engaged with the lower abstraction layer demonstrated higher success (3 percentage points above class average), engagement (22.5% reporting they were more engaged), accuracy of self-assessment (1.3 percentage points above class average), and learning beyond course’s expectations (14% reporting they learned beyond the course’s learning outcomes).
Towards a global and abstract end-to-end architecture for data analysis and transformation with ML/DL Application cases: Medical IoT and IoHT

Nowadays, data is a valuable resource for businesses in all industries. However, getting value out of it requires a sophisticated process resulting in life cycle of data analysis, covering data generation and creation, capture, storage, preprocessing, and the choice of algorithms and techniques to be applied according to many criteria, right up to the cost-effectiveness of results through deployment with end-user. The application of this abstract concept concretely takes the form of a range of architectures and infrastructures for processing, storing, and analysing data from connected objects or data lakes. In this work, we propose a general and abstract model of an end-to-end architecture for data processing and transformation from connected objects and data assets of concerned organizations, This data-oriented workflow allows exploiting on-premises infrastructures and services offered by cloud world with load balancing and bypassing shortcomings and limitations of Cloud. Then we instantiate this model for a healthcare data use case with ML/DL.
Three-phase doubly fed induction generators: an overview

Adjustable speed induction generators, especially the doubly fed induction generators (DFIG), are becoming increasingly popular due to their various advantages over fixed speed generator systems. A DFIG in a wind turbine has the ability to generate maximum power with varying rotational speed, ability to control active and reactive power by the integration of electronic power converters such as the back-to-back converter, low rotor power rating resulting in low cost converter components, and so on. This study presents an extensive literature survey over past 25 years on the different aspects of DFIG.
Point Cloud Registration Algorithm Based on Laplace Mixture Model

Registering point clouds quickly and accurately has always been a challenging task. A lot of research based on Gaussian mixture model is widely used in recent years. However, few people use other models for point cloud matching. Therefore, this paper proposes a point cloud registration algorithm based on the Laplace mixture model. In this paper, sampling variance is used to replace the variance of the likelihood estimation to successfully overcome the nonlinear problem. In addition, the Laplace model has strong robustness, which is very suitable for point cloud matching of 3D laser scanning. In the experiment, compared with several other algorithms, proposed method quickly and accurately registers point clouds.
FDOB-Based Robust Impedance Control of Force Sensor Implemented Force Servo System

Instability which occurs when the robot’s end effector contacts a very stiff environment is a challenge in designing control systems for safe physical interaction and cooperation of robots with environment. One of the reasons for the instability is force disturbances caused by the mechanical factors of the robot system. To this effect, this paper presents the design, analysis, and implementation of a robust impedance controller for a force servo system. To suppress the force disturbances, a force disturbance observer (FDOB) is implemented in the impedance-controlled system. For comparison purposes, impedance control system when the FDOB is not implemented is also designed and analyzed. Further, using the passivity approach, coupled stability conditions of the designed impedance control systems are derived and analyzed to assess the effect of FDOB on passivity and overall control performance. Simulations and experiments are conducted to evaluate performance of the designed impedance control systems and it is found that the FDOB-based control system shows superior performance by improving contact stability compared to direct force sensor feedback control system.
Ground-Based Sensing System for Cotton Nitrogen Status Determination

A ground-based sensing system was developed for determination of nitrogen (N) status in cotton plants. The system consists of a multi-spectral optical sensor, an ultrasonic sensor, and a data-acquisition and processing unit. The optical sensor's light source provides modulated panchromatic illumination of a plant canopy with light-emitting diodes. The sensor measures plant reflectance at four spectral wavebands (400 to 500 nm, 520 to 570 nm, 610 to 710 nm, and 750 to 1100 nm). The ultrasonic sensor is used to determine plant height. The data-acquisition and processing unit is based on a single-board computer that collects data from the multi-spectral optical sensor and the ultrasonic sensor, and spatial information from a Global Positioning System receiver. Field tests of the system over two years involved measuring spectral reflectance and plant height simultaneously in real time in situ. An artificial neural network was developed to predict N status in cotton plants based on data from the sensing system. The network was trained with actual leaf N concentration data that corresponded to sensor spectral data and plant height. Results showed that the spectral information and plant height measured by the sensing system had significant correlation with leaf N concentration of the cotton plants. Trained neural networks were able to predict N status of the cotton plants at 90% accuracy when N status was divided into two categories: deficiency and non-deficiency.
Life cycle design achieving a balance between economic considerations and environmental impact with assembly-disassembly evaluation/design: DAC (design for assembly/disassembly cost-effectiveness)

DAC (design for assembly/disassembly cost-effectiveness) is an assembly/disassembly evaluation and design method developed by Sony. It is a life cycle design tool that allows pinpointing of the complex common and disparate features of assembly and disassembly during product development. This helps achieve cost-effective, easy-to-assemble designs as well as designs enabling easier disassembly, recycling and product servicing.
Distribution Factor Assessment for PEVs Charging Stations with V2G Capability in Power Systems

Increased penetration level of plug-in electrical vehicles (PEVs), necessitates the need for more PEVs charging stations. However, by high penetration of charging stations, distribution power systems may have more challenges, such as high power loss and deteriorated voltage profile. So far, optimal sizing and sitting of charging stations have been utilized to improve the features of the power systems affected by charging stations. While, our studies show that distributed arrangements of charging stations can also play an important role. This role is more highlighted, when vehicle-to-grid (V2G) capability is also considered. To this aim, in this paper, considering total charging and discharging capacities of stations, a factor known as distribution factor is proposed. Then, the effect of different distribution factors, considering grid-to-vehicle (G2V) and V2G modes of PEVs, is discussed. Numerical results verify that distribution factor and hence distributed arrangements of charging stations can affect power loss and voltage profile of the host power system.
Breadth-First Search Leakage Tolerant Commutation Method for Matrix Converters in Three-Phase Solid State Transformers

A leakage tolerant commutation method is proposed that enables the use of matrix converters in Solid State Transformers with three-phase high/medium frequency isolation transformers. Typical commutation methods for matrix converters such as two and four step commutation methods are not suitable in the presence of leakage inductances since they do not allow the recirculation of the leakage energy, this means that when using such methods it is necessary to use snubbers and clamping circuits to mitigate potential overvoltages on the transformer. The proposed commutation method does not require any additional circuitry and is suitable for multiphase converters. A systematic description of the process is provided along with simulation results that validate the proposed commutation method.
Technological Overview of Onboard Chargers for Electrified Automotive Transportation

Range anxiety, lack of charging systems and cost are the main concerns for fast adoption of electric vehicles (EVs). Battery chargers decide the performance and life cycle of the battery packs. Therefore, battery chargers play a vital role to escalate the penetration of the Plug-in EVs (PEVs). PEV battery chargers are classified into onboard and off-board chargers based on its availability inside or outside the vehicle. Onboard chargers do not need any additional infrastructure, which can be easily and directly charged from the ac mains. This paper aims to review the technological status of onboard charging infrastructure including front-end power factor correction (PFC) converters and the battery interface converters. The merits and demerits of all the charger topologies are discussed in detail. Finally, a single-phase 1.4 kW onboard charger with interleaved boost PFC as a front-end converter and phase-shifted full-bridge converter as a battery interface is designed and simulated in the PLECS software platform.
Dynamics of an electromagnetic linear actuator operating in error actuated control system

Purpose – The paper seeks to present an algorithm for a dynamical field‐circuit coupled simulation of an electromagnetic linear actuator operating in automated control systems.Design/methodology/approach – The mathematical model includes: a transient electromagnetic field formulation for a non‐linear conducting and moving medium, equations which describe the electric circuits of the converter and the supply system, the equation of mechanical motion, the equation describing closed‐loop control and models for the sensor and the PID controller. The numerical implementation is based on the finite element method and the step‐by‐step algorithm for time discretization. In order to account for the nonlinearity of the ferromagnetic core the Newton‐Raphson procedure has been applied. The influence of the PID controller settings on the operation of the controlled actuator is shown. Dynamic disturbances, e.g. step change of the set value of mover position or change of loading force, have been analyzed.Findings – The .
Unity Power Factor Control using Interleaved Boost Converter for Induction Motor Drive

This paper presents the analysis of an interleaved boost power factor correction (PFC) converter supplying a voltage source inverter (VSI) fed induction motor drive. The PFC converter is a single-stage interleaved boost converter that uses the average current waveshaping technique to make the non-sinusoidal input current drawn by the motor-drive to nearly sinusoidal waveform. The resultant input current is almost free from harmonics, which follows the IEEE Std 519-2014 current distortion limits for systems rated 120V through 69kV and, the system operates with nearly unity power factor (PF). The power stage and control stage of interleaved boost PFC is designed for V/f open-loop control of 5HP, 415V 3-phase induction motor drive. The PFC converter power stage design and control loop design is done for a 5kW output power rating. The whole system is simulated in the PSIM simulation software tool and results are discussed.
Registration using adaptive particle filter and natural features matching techniques for augmented reality systems

Purpose – The purpose of this paper is to provide a flexible registration method for markerless augmented reality (AR) systems.Design/methodology/approach – The proposed method distinguishes itself as follows: firstly, the method is simple and efficient, as no man‐made markers are needed for both indoor and outdoor AR applications. Secondly, an adaptation method is presented to tune the particle filter dynamically. The result is a system which can achieve tolerance to fast motion and drift during tracking process. Thirdly, the authors use the reduced scale invariant feature transform (SIFT) and scale prediction techniques to match natural features. This method deals easily with the camera pose estimation problem in the case of large illumination and visual angle changes.Findings – Some experiments are provided to validate the performance of the proposed method.Originality/value – The paper proposes a novel camera pose estimation method based on adaptive particle filter and natural features matching techni.
A Review of Offshore Wave Energy Extraction System

Offshore wave energy can be easily predicted and is proved to be much better than other forms of ocean energy such as shoreline wave, near-shore wave, and tides. Research on offshore wave energy extraction has been carried out in many countries to meet the growing demand for clean energy and reduce the impact on natural environment. This paper reviews the development of offshore wave energy extraction systems in the recent decade. Several aspects are introduced, including a global wave energy resource assessment, offshore wave energy extraction technologies, and the interaction between wave and floating buoy as well as linear generators. Although various offshore wave energy extraction systems have been proposed and even tested, it is difficult to decide which is the best one. In fact, design of floating buoy and linear generators plays an important role in the operational efficiency of offshore wave energy extraction system. This review provides some useful guidelines for future studies in this field.
Electric and Physical Chacteritics of a SiC-PiN Diode for High-Power Devices

Electric and Physical Chacteritics of a SiC-PiN Diode for High-Power Devices Nam-Oh Kim and Wan-Ki Min Department of Electricity, Chosun College of Science & Technology, Gwangju 501-759 Kyung-Min Sung Department of Electrical and Electronic Systems Engineering, Ibaraki National College of Technology, Ibaraki 312-8508, Japan Kazuma Suzuki Department of Arti cial System Science, Chiba University, Japan Yasunori Tanaka and Hiromichi Ohashi National Institute of Advanced Industrial Science and Technology Power Electronics Research Center, Japan (Received 26 February 2008) This paper is presented on a 2.5-kV silicon-carbide (SiC) PiN diode with a silicon injection enhancement gate transistor (IEGT) in an inductively loaded chopper circuit. The experimental switching characteristics of the SiC-PiN diode are presented under a DC 1200 V bias voltage and 40 A cuto current. Experimental results show that the reverse recovery time and the IEGT turn-on loss are signi cantly reduced by the combination of the SiC-PiN diode and the IEGT. To reduce switching losses and investigate the limitation of the switching frequency in high power converters, we newly present the design concept of an IEGT combined with a SiC-PiN diode. The proposed design concept is evaluated based on a device simulation. The result of the device simulation yields the possible switching frequency by using the comprehensive total devices loss in the test circuit. PACS numbers: 78.40. F, 78.55. F, 73.40. V
Compressive wideband spectrum sensing based on single channel

The modulated wideband converter (MWC) is an attractive analogue compressed sensing technique that has been proposed in recent years. Regrettably, the MWC has high hardware complexity owing to its parallel channel structure. To reduce the complexity, a novel compressive wideband spectrum sensing scheme based on a single channel for band-limited wide-sense stationary (WSS) signals is proposed. In the proposed scheme, a special periodic waveform in the time domain is designed and a power spectrum sensing model is presented to detect the occupied frequency spectrum of such signals from sub-Nyquist samples of which the sample rate is only MWC's single-channel rate. Simulation results show that the proposed scheme has a perfect performance on wideband WSS signal spectrum sensing.
Implementation of an Autonomous ROS-Based Mobile Robot with AI Depth Estimation

Industry 4.0 is one of the biggest industrial revolutions with the creation of new technology and artificial intelligence solutions. In this context, Autonomous Mobile Robot (AMR) is one of the reasons to expand productivity and logistical flexibility due to its adaptability to new environments. However, only recently the AMR has had greater acceptance, much due to its complexity and cost. In this sense, this article presents a ROS-based mobile platform to evaluate the possibility of navigation only with monocular cameras. The hardware and software stack structure will be detailed evidencing the most critical components for the success of the implementation of omnidirectional AMR. To ensure a reduced cost while maintaining the advanced ability to detect and change the trajectory whenever an object is detected during its path, it is proposed to use an artificial intelligence system to create an estimated depth image from the 2D RGB images. Finally, to ensure its ability to generalize and navigate, the system accuracy and behavior were evaluated and validated in several environments. The main contribution of this work is the assessment and validation of an AMR navigation solution using only Ai-based depth estimation from 2D RGB cameras without performance impact in an embedded computational system.
Dynamic Obstacles Avoidance Using Nonlinear Model Predictive Control

In this paper, a Nonlinear Model Predictive Control (NMPC) has been employed to solve point-stabilization problems with static and dynamic obstacles avoidance. The algorithm was implemented on a mobile robot with two differential drive wheels. In NMPC, a cost function is formulated to minimize an error between the reference and the current state of the system subject to constraints. The major drawback of NMPC is the computation time, which results from predicting the system’s state over a horizon. However, in this work, the resulting optimal control problem is converted to a discrete nonlinear programming problem using a recently developed toolkit. Dynamic obstacles avoidance is incorporated as a time-varying constraint and can be affected by a short prediction horizon. On the other hand, a long prediction horizon affects the computation time. For this, a terminal state penalty is added to the cost function to guarantee the stability of the control using a relatively shorter prediction horizon. The performance of the proposed controller achieving both static and dynamic obstacles avoidance is verified using several simulation scenarios.
Quality monitoring for injection moulding process using a semi-supervised learning approach

In mass production, supervised machine learning schemes may not be applicable directly in many cases due to insufficient labelled dataset, especially for failed samples. In this paper, we investigate the feasibility of a semi-supervised approach in a quality monitoring system for plastic injection moulding process using real-time manufacturing data. Due to randomly sampled quality inspection, the obtained dataset consists of only a limited amount of labelled data (8%) while a major portion of the dataset is with unlabelled production data (92%). The work starts with real-time data collection and feature extraction for raw data. Then self-learning and 2-views co-training are investigated for 2 MLP base classifiers. Totally six different approaches are studied to build the predictive quality monitoring model. Self-training and co-training approaches start with supervised training as the baseline. Then, two different pseudo labelling schemes are considered to evaluate their effectiveness to the model’s performance. Also, the feature selection step is repeated for each iteration to ensure top useful features are included. This training loop continues until all unlabelled data samples are shifted to the labelled dataset or the prediction probability drop below an acceptable threshold. Finally, benchmarking is carried out for all 6 approaches in comparison with the baseline model and the validation results strongly support the use of the Co-trained_Top MLP model for the injection moulding quality monitoring application.
Single Phase Energy Management System (EMS) using IoT

The proposed system gives information through online mode with various energy parameter in accurate fashion according to industry standard. The data measured by the system also indicate whether the machine or appliances working properly or not. This system is developed to reduce the human dependency and up-to-date information in real time. This system is implemented using nodemcu with WIFI facility. The various advantages of this system is the user can understand the usage of electricity and also it helps to reduce the power consumption where ever possible, the user can also find the malfunctioning of the machine by looking into power consumption and also the power consumption bill can be generated without visiting the industry or home in person. This system is highly developed according to the industry standard with proper precautions. Totally this system is highly useful for industry or home in all aspects.
Active Damping Control Strategies for WEC with VSM considering DC-Link Dynamics

The rising share of wind energy conversion systems in the electric energy supply results in growing economical aspects of the assets’ size and maintenance costs as well as in challenges to maintain the stability and robustness of the utility grid. To fulfill requirements such as ancillary grid services, the virtual synchronous machine has been in the focus of research as grid-supporting or grid-forming control for grid-connected power electronic converters. Its application in WEC requires an adaption of the overall control structure compared to a conventional vector control, so that also the widely investigated active damping controls for mechanical torsional oscillations need to be analyzed for the adapted structure with a machine-side DC-voltage control. Therefore, in this paper methods to achieve active damping of torsional oscillations are designed with extended DC-voltage control. A special regard is put on the non-linearity of the intermediate circuit and the variable stator voltages of variable-speed WECs. Simulation results validate the effectiveness of the active damping controls and the stability of DC-link voltage control.