Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Vehicle Dynamic Plant Model and Embedded Co-Simulation with ARM Platform

In this paper, a Vehicle dynamic 14 Degree of Freedom(DoF) plant model is developed, the performance of the developed model is tested in MATLAB simulation and validated using CarSim software. The objective of the vehicle model is to assist and assess new or modified light motor vehicles (LMVs) while they are in the developing stage. The architecture is to increase modeling flexibility and allow for quick adaptation to new vehicle designs. Minimized inter dependencies exist between model subsystems such as Steering model, Wheel model, Suspension model, Roll model, and tire model. This a longitudinal and lateral model, further tested for straight breaking, Inferential torque measurement on real LMV model. Additionally, the developed model is implemented on the Embedded ARM (STM32F746) Platform by creating, C-code to solve the model in order to assess the feasibility of hardware implementation. Furthermore, the model is also tested on vehicle test-bench developed by VRDE.
Volt/VAR Support and Demand Response Co-Optimization in Distribution Systems with Adaptive Droop Control of Inverters

This paper proposes a hierarchically-coordinated Volt/VAR support scheme to cooperatively minimize operation costs and voltage deviations. The scheme uses the two-step droop control of smart inverters as well as scheduling of slow response capacitor banks and transformer tap changers. The peak shaving objective is also combined with the Volt/VAR support model to reduce dependency on traditional voltage regulators while minimizing operating costs. The problem is formulated as two-stage stochastic programming subject to day-ahead Volt/VAR device technical constraints. The results are used in a short-time probabilistic model for voltage deviation minimization by sub-hourly updating the forecasting signals. Finally, the competence of the proposed Volt/VAR support framework enabling the utilities to optimize the characteristic of inverters effectively and use the advantages of demand response (DR) and legacy devices is verified on three-phase distribution feeders. The results show that the proposed method leads to less voltage deviation and loss minimization compared to traditional strategies.
Mechanical Noise Affects Rambling and Trembling Trajectories During Quiet Standing

Falling is a serious worldwide public health issue, leading to heavy interest in the development of interventions to reduce fall risk. Mechanical noise has been shown to be a promising candidate in this regard - the application of mechanical noise to the foot sole has been shown to improve the sensitivity of the foot sole to pressure stimuli, thereby improving postural control and reducing fall risk. However, the mechanism by which mechanical noise affects the neural drive responsible for postural control is yet unknown. A single-blind randomized controlled study was conducted on the bodily sway of 19 healthy young subjects during quiet standing balance on a firm and compliant surface with and without introduced mechanical noise. The trajectory of the center of pressure (COP) of the subjects during balance was decomposed into its constituent rambling and trembling components, which reflected supraspinal and reflexive neural control respectively. On the compliant surface, the introduction of mechanical noise significantly reduced sway compared to control in the overall COP trajectory as well as the rambling and trembling trajectories. These findings indicate that mechanical noise can improve neural control of balance through both the supraspinal and reflexive neural pathways, leading to tighter bodily control and reduced bodily sway. These findings are particularly promising for the development of targeted noise-based assistive devices to reduce fall risk in demographics with neurological issues impairing motor control and balance.Clinical Relevance– This study demonstrates that mechanical vibratory noise introduced to the foot sole reduces both rambling and trembling of the COP during postural control, thus improving balance, with a wider bandwidth of mechanical noise having a larger effect. This has practical applications in the development of noise-based wearable devices for fall risk reduction.
Dynamic Transmission and Distribution Analysis for Asymmetrical Networks in the Presence of Distribution-Connected DER Units

This paper investigates the dynamic impacts of distributed energy resources (DERs) by utilization of symmetrical and asymmetrical analyses on the power system. In the proposed transmission and distribution (T&D) co-simulation framework, the transmission network is modeled using sequence networks, while the distribution network is modeled in a full three-phase manner. Also, the North American Electric Reliability Corporation DER model (NERC-2018) is utilized to model the dynamics of the DER-A control scheme. DER-A mechanism is modeled downstream of the distribution substation. Here, the transmission system is modeled using the IEEE 118-bus test system.In this paper, the impacts of the DER-A control parameters on the DER operation and the dynamics of the transmission system are examined in the balanced and unbalanced power systems. It is shown that the DER-A control mechanism injects more reactive power during a fault in an asymmetrical system than in a symmetrical one initiating voltage overshoot.
A Comprehensive Study on Routing Protocol for IoT-enabled WSNs using Deep Reinforcement Learning Strategy

Internet of Things enabled Wireless Sensor Networks (IoT-enabled WSNs) rely heavily on routing protocols because of the importance of various system performance parameters, such as end-to-end delay, system capacity, data delivery rate, and energy efficiency. As a result of this, sensor nodes may have a detrimental effect on the routing protocol's reliability and power tolerance when compared to other nodes in the network. As a result, the IoT-enabled WSNs' wide-field applications necessitate a self-driven energy intelligent routing protocol. Reinforcement Learning (RL) strategy has recently been used to support the development of an intelligent routing protocol that has a high potential for energy conservation while also increasing system performance above the typically achieved target. Deep Reinforcement Learning (DRL) routing protocols for IoT-enabled WSNs have been studied in this paper, and the current state-of-the-art algorithms have been compared. It is the purpose of this study to evaluate the operational characteristics and key features of the current DRL algorithms. In addition, the practical difficulties of routing protocol design and implementation were discussed.
The warm absorber of the quasar MR2251–178

Abstract MR 2251–178 was the first quasar initially discovered in X-rays, and the first one found to host a warm absorber. The quasar has a high ratio of X-ray to optical luminosity, is surrounded by a giant HII gas envelope, and is located in the outskirts of a cluster of galaxies. Here, we present results from an analysis of the X-ray spectral and variability properties of this source based on deep ROSAT observations. In particular, the warm absorber is modeled in detail.
Coating Damage Detection of Vessels Using Machine Learning-Based Underwater Electric Field

There has been extensive research on corrosion electric fields because of their special characteristics. There are apparent distribution characteristics corresponding to different coating damage regions of a vessel. To accurately detect where the coating damage region is located on vessels using underwater electric field signature, in this study, a method called “refined composite multivariate multiscale fluctuation reverse dispersion entropy (RCMMFRDE)” was proposed. This method effectively extracted the multichannel signal in a complex environment of poor stability and high noise. By combining RCMMFRDE and hierarchical prototype (HP), a novel coating damage detection method was developed. First, the accumulated differential value was calculated to determine the side on which the vessel had the damaged coating. Second, RCMMFRDE was used to extract the features of electric field signals. Then, feature vector dimensionality reduction was carried out using pair-wise feature proximity (PWFP). The low-dimensional features were given as input to the HP classifier to detect the damaged region. Finally, a series of numerical and physical scale experiments were conducted to validate the feasibility of the proposed method. The damaged region was efficiently predicted, achieving satisfactory accuracy of 96.67% and 92.00% in numerical and measurement data respectively; thus, it is a suitable alternative to conventional methods, especially when there is a lack of previous environmental information.
Abnormal Traffic Detection Technology of Power IOT Terminal Based on PCA and OCSVM

The abnormal traffic detection technology based on the access boundary of the power Internet of Things (IOT) is a common method for detection and identification of terminal attack risk. Aiming at the problem that it is difficult to obtain attack samples and lack of effective prior knowledge in the abnormal traffic detection of power IOT terminals, the abnormal traffic detection technology of power IOT terminals based on PCA and OCSVM is proposed. Considering that the normal network traffic of each type of terminal has regular characteristics in the actual operation environment, model aggregation training can be conducted based on the normal operation traffic of the terminal. The technology extracts the key features of the traffic through PCA features, removes redundant information, and then constructs a detection model for the normal traffic behavior of the power IOT terminal based on OCSVM to detect and identify the abnormal traffic of the terminal. The simulation results show that the technology can effectively balance the classification accuracy of normal and abnormal samples of terminal traffic, and has better detection accuracy than some machine learning algorithms.
A Compact SiC Power Module Gate Driver for High-voltage EV Application with SPI Initialization, Fault monitoring and Diagnostics

This paper proposes the design of a Gate Driver for a full-bridge Silicon Carbide (SiC) Power Module for Electric Vehicle (EV) applications. The detailed design includes the protection schemes, isolated power supply, Serial Peripheral Interface (SPI) based configuration, and product standards complied gate driver PCB layout. The SPI master code was developed in the VHDL environment and verified with experimental results. The SPI-based programming of the gate driver is implemented that gives flexibility and enables fault monitoring and diagnostics capability, which is required for achieving functional safety with Automotive Safety Integrity Level (ASIL)-D. Finally, the developed gate driver board has been tested on the Double pulse test (DPT) setup using a $1.2\ kV$ SiC power module with Desaturation (DESAT) and shoot-through protections.
A New Bridge-less Power Factor Correction IPOS Luo Converter Driven BLDCM Drive

The paper presents a bridge-less power factor correction (BL-PFC) input parallel output series (IPOS) type Luo converter for brushless DC motor (BLDCM) drive. A theoretical analysis of the IPOS Luo converter is conducted on the basis of its possible operating modes, and then the converter is designed to drive an electronically commutated BLDCM. Afterwards, a cost-effective single sensor-based voltage follower control technique is used for the BL-PFC IPOS Luo converter to achieve unity power factor operation and regulate output DC bus voltage. Additionally, the controlled output voltage is used to power a three-phase inverter of the BLDCM drive, and inverter switches are driven by PWM signals derived from rotor position hall effect sensors. With this control approach, switching losses can be minimized and drive can efficiently controlled. Moreover, the MATLAB/Simulink platform is used to evaluate the steady-state and dynamic performance of the drive. In conclusion, the obtained results indicate that the BL-PFC IPOS Luo converter based BLDCM drive is suitable for low power, low cost applications where torque limits are not as significant.
An On-Board Integrated Fast Charger Based on Multiphase Machine

This paper studies an onboard integrated fast battery charger based on multiphase machines. Multiphase machines give an extra degree of freedom as compared to their three-phase counterparts; this extra degree of freedom is utilized to get zero developed torque during charging mode. Propulsion circuit components remain in an idle state during the charging of the electric vehicle, so they are incorporated into the charging process to form an onboard integrated charger. During charging mode, the inverter operates as a unity power factor (UPF) converter, and stator windings self-inductance is utilized as an inductive filter between the UPF converter and grid. The charger is non-isolated from the grid and a dc-dc converter is used after the dc link to get the desired output voltage. During traction, the machine works as a symmetrical six-phase motor. The proposed onboard integrated charger is a three-phase bidirectional type. The validity of theoretical analysis is verified with the help of simulation results.
Intervention of Autism Spectrum Disorder (ASD) in a New Perspective: A Review on the Deployment of Adaptive Human-Robot Interaction (HRI) System in Enhancing Social Skill Impairments

Research in the area of Human-robot Interaction (HRI) has gained momentum in recent years. The robot-based intervention has now spread its wings to help the less fortunate specifically children who suffer from Autism Spectrum Disorder (ASD). These robot-based intervention studies utilized HRI in improving impaired skills, such as, social skills, motor skills, and behavior. Recently, robot-based therapies have shown encouraging outcomes in improving the social skills of Autism Spectrum Disorder Children (ASDC). Herein, this paper aims to review the studies of the use of adaptive HRI in improving social skills of ASDC. Due to rapid advancement in HRI studies, this paper limits the review from the past 10 years. The steps in the procedure, such as the choice of study participants and outcome measurements, are described in this paper. According to the analysis, all the studies deployed NAO robots as their HRI system. There was variability within the parameters measured to evaluate the efficacy of the HRI, however, the analysis discovered that robot-based interventions were practically helpful in enhancing the social skills for ASDC.
Computational study of cooling strategies in a high frequency compact transformer

High frequency transformers are electrical devices which are capable of handling high frequency AC supply as per the requirements of a circuit. These are mainly used in switch mode power supply circuits. The recent fast charging requirement of devices like smart phones, electric vehicle chargers and other electronic devices have placed the high frequency transformers more and more in demand. These transformers suffer from losses like core loss, copper loss etc. In the present study, the losses are estimated and are used to perform computations using commercial ANSYS Icepak solver. Use of heat sinks and potting material are studied so as to identify the best possible mode of cooling. This paper aims to identify the thermal distribution in such a high frequency transformer used in a charging circuit and to deduce a cooling technique to keep the transformer under optimal operating conditions.
A FastDTW Based Lightweight Limb Motion Function Assessment Method

Stroke is a severe cerebrovascular disease caused by the disruption of blood supply to the brain. To provide Inhome rehabilitation, it is essential to solve the problem of high rehabilitation cost and improve the rehabilitation efficacy. The training quality assessment is the core part of an in-home based training system as it provides important feedback which can be used by both the doctors and the patients. Furthermore, patients can conduct training in a comfortable, familiar environment. In this paper, a fast-training motion quality evaluation algorithm is proposed for the motion data captured by an accelerometer-based sensor network. The experiment results show that the evaluation result from the proposed system is within a satisfactory error range compared with that obtained by using the commercial X-sens system. The presented fast and efficient quantitative assessment method could be used for implementing a networked regional in-home rehabilitation system covering multiple users. Clinical Relevance– A fast and efficient upper limb assessment framework is proposed to support the development of a massive telerehabilitation system. Clinical Relevance – A fast and efficient upper limb assessment framework is proposed to support the development of a massive telerehabilitation system.
Improving quality of laser scanning data acquisition through calibrated amplitude and pulse deviation measurement

Newest developments in laser scanner technologies put surveyors in the position to comply with the ever increasing demand of high-speed, high-accuracy, and highly reliable data acquisition from terrestrial, mobile, and airborne platforms. Echo digitization in pulsed time-of-flight laser ranging has demonstrated its superior performance in the field of bathymetry and airborne laser scanning for more than a decade, however at the cost of somewhat time consuming off line post processing. State-of-the-art online waveform processing as implemented in RIEGL's V-Line not only saves users post-processing time to obtain true 3D point clouds, it also adds the assets of calibrated amplitude and reflectance measurement for data classification and pulse deviation determination for effective and reliable data validation. We present results from data acquisitions in different complex target situations.
A Coordinated Energy Management Framework for DNO Ensuring Financial Benefits of MGOs

The distributed energy resources (DERs) operation in the distribution network and microgrid (MG) requires a coordinated framework for optimal power sharing due to their different ownership. Specifically, the DERs present within MG should operate to ensure the financial benefit of the respective MG operators (MGOs). This paper proposes a coordinated energy management framework for a distribution network operator (DNO) and set of MGOs. Indeed, it provides opportunities for the MGOs to participate in the power-sharing framework established by the DNO and minimize their power-sharing costs with the DNO. The proposed framework is modeled as the mixed integer linear programming problem and solved using the Gurobi solver. The comparative results are illustrated for a modified IEEE-33 bus distribution network connected to three MGs. MGOs net power share cost reduction is observed when the DERs within MGs are economically scheduled.
A Neural Network Approach to Estimate Lower Extremity Muscle Activity during Walking

Gait analysis is the study of human locomotion. It plays an essential role in the diagnosis and rehabilitation of gait abnormalities, the study of physiological changes associated with ageing, and the treatment of injuries. Muscle activity is an important gait parameter that controls joint function during walking and provides valuable information about the gait quality. However, current techniques to measure muscle activity, such as electromyogram (EMG) and musculoskeletal modelling tools, have drawbacks. This study develops an artificial neural network (ANN) method to estimate eight lower extremity muscle activities using pelvis, hip, knee and ankle joint angles. It uses an online gait database that contains kinematic and kinetic gait parameters and lower limb EMG. Four training algorithms were explored and investigated. Despite the noticeable differences between the actual and the estimated muscle activities, e.g. gluteus maximus and bicep femoris, the results demonstrate the feasibility of the proposed method in determining the muscle behaviour during walking. The study also shows the potentials of machine learning to compensate for the lack of modality and to provide an insight on the dynamics of muscles in gait. Clinical Relevance- Gait analysis is important in clinical and rehabilitation settings. The proposed method has the potential in reducing the dependency on EMGs and can be an alternative to the musculoskeletal modelling tools in diagnosing, treating, and rehabilitating gait abnormalities.
Steady axisymmetric creeping plumes above a planar boundary. Part 1. A point source

Asymptotic solutions are obtained for the rise of an axisymmetric hot plume from a localized source at the base of a half-space filled with very viscous fluid. We consider an effectively point source, generating a prescribed buoyancy flux $B$, and show that the length scale of the plume base is $z_0 \,{=}\, (32\upi \kappa^2\nu/B)$, where $\nu$ and $\kappa$ are the kinematic viscosity and thermal diffusivity. The internal structure of the plume for $z \,{\gg}\, z_0$ is found using stretched coordinates, and this is matched to a slender-body expansion for the external Stokes flow. Solutions are presented for both rigid (no-slip) and free-slip (no tangential stress) conditions on the lower boundary. In both cases we find that the typical vertical velocity in the plume increases slowly with height as $(B/\nu)^{1/2} [\ln (z/z_0)]^{1/2}$, and the plume radius increases as $(zz_0)^{1/2} [\ln (z/z_0)]^{-1/4}$.
Cross-well Lithology Identification based on Dynamic Adversarial Adaptation

Lithology identification is a fundamental task in geo-logical work such as stratigraphic correlation, reservoir zonation, and sedimentation simulation. Traditional lithology identification methods require too much manual labor and are inefficient. The shifts in data distribution between differ wells make it difficult to apply the same logging lithology identification method from well to well. In this paper, we study cross-well lithology identification by unsupervised domain adaptation. The features of the logging curve in the depth domain are extracted by semantic segmentation to achieve intensive lithology prediction for each depth point. An adversarial learning strategy is introduced to the cross-well lithology classification task to reduce the difference in distribution between two well features. The proposed method outperforms the baseline in four cross-well lithology identification tasks, demonstrating the efficiency of our method for cross-well lithology identification.
Fluctuating lift and drag on a long cylinder of square cross-section in a smooth and in a turbulent stream

The paper presents the results of measurements of fluctuating lift and drag on a long square cylinder. The measurements include the correlation of lift along the cylinder and the distribution of fluctuating pressure on a cross-section. The magnitude of the fluctuating lift was found to be considerably greater than that for a circular cross-section and the spanwise correlation much stronger. It was found that the presence of large-scale turbulence in the stream had a marked influence on both the steady and the fluctuating forces. The most significant changes were at small angles of attack (%alpha; < 10°) and included a reduction in base suction and a decrease in fluctuating lift of about 50%.
Design of Miniature Motor for a Hybrid Gyroscope

The Micro-electromechanical Hybrid Gyroscope adopts the principle of traditional Dynamically Tuned Gyroscope and uses micro-machining technology to realize Gyroscopes of small size and mass with comparable performance. The rotor subassembly of the hybrid Gyroscope needs to be driven by an electrical motor at a higher speed to impart the required angular moment. The miniature motor has to operate the rotor subassembly of the Gyroscope at a steady speed irrespective of the bearing friction fluctuations and other external disturbances. This paper presents the design and realization details of a four pole slot-less, easy-to-assemble, radial flux BLDC motor designed for a Micro-electromechanical Hybrid Gyroscope.