Miracle-dz/UniFAR-FTUs huggingface dataset:
Breakdown Field Strength Variations and Energy Density Limits of Nanoparticle Composite Materials

In response to the growing demand for renewable energy storage technologies, current research into nanoparticle (NP) composite materials seeks to push the boundaries of energy density for solid-state capacitive energy storage. To understand the limits of these composites, it is important to understand the electric field patterns and breakdown events that prevent such devices from storing more energy per unit volume. To this end, a novel breakdown path detection algorithm is designed to mimic breakdown events by using the internal electric fields to determine the breakdown voltage of simulated composite materials. This breakdown path detection model is used to calculate the energy density for over 1200 different geometries for BaTiO<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> NPs embedded in a P(VDF- HFP) host matrix. These simulations reveal a large normalized variation in the volumetric energy density of up to 54%. Furthermore, these P(VDF-HFP)/BaTiO<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> composites are found to have only a modest improvement in energy density of 1.08x compared to the pure P(VDF-HFP) host; however, alternative hypothetical NP compositions composed of SrTiO<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula>, SiO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula>, and HfO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> NP inclusions showed increases in energy density of up to 5.64x for SrTiO<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> (volume fraction of 0.48), 8.87x for SiO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> (volume fraction of 0.34), and 2.89x for HfO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> (volume fraction of 0.38).
OpenThread Based Mesh Enabled IoT Smart Device Cluster for Health Monitoring of the Elderly in Old Age Homes

In this paper the primary motivation is to continuously monitor the health vitals of elderly in old age homes. In old age homes, the elderly residents generally have health issues, and it is critical to monitor their vitals in real time. In this regard, the development of an OpenThread based mesh system was carried out. The OpenThread mesh system of connected devices in which nodes were represented using Particle Xenon and Particle Argon which also doubled up as a capillary gateway/access point. All the nodes continuously monitored the bpm/pulse of the person and the data was communicated to the cloud client directly via capillary gateway. If a particular node was not in direct range of the capillary gateway, then the data which would hop over other nodes until it reached the capillary gateway. An android application was developed to fetch the data from the cloud database and display it in real-time. This application was developed such that an abnormal bpm value would trigger the application to notify an alert Message. A medication reminder is also implemented in the application to remind the residents when to take their medication.
LiDAR Data Classification Based on Improved Conditional Generative Adversarial Networks

Light detection and ranging (LiDAR) data contains the height of different objects and records the elevation information of ground objects, so it plays an important role in land classification. In recent years, deep learning has been widely used in LiDAR data classification due to its strong ability to extract features. However, deep learning methods usually need sufficient training data to achieve better classification results. In order to solve this problem, a new classification method combined conditional generative adversarial network (CGAN) with residual unit and DropBlock, is proposed here for the classification of LiDAR data, called as RDB-CGAN. CGAN expands the generated samples to training data to improve the classification performance when the training samples are relatively small. Residual unit increases the network depth of the generator to improve its generation capability and utilizes shortcut connection to transfer the input information directly to the output to solve degradation caused by increased network depth. DropBlock improved the generalization of the network by dropping a whole area with spatial information correlation so that the network can learn the remaining features. The experimental results on two different LiDAR datasets show that RDB-CGAN significantly improved the classification performance of LiDAR data compared to several state-of-the-art classification methods.
Closed-Loop Single-Stage Coordinated Frequency Control of a Stand-Alone Microgrid

The key to driving demand growth and sustainability are renewable energies and, in particular, solar power. Off-grid systems are convenient and more favorable for the distribution of electricity to remote areas, which are isolated from the grid. In this paper, a single-stage conversion strategy is shown that converts inherent DC PV voltage to the single-phase AC voltage involving MPPT to ensure optimal power output under varying irradiances. The system depends on environmental circumstances and the number of powered consumers. The operational frequency is maintained by coordinated control of frequency. The MATLAB/Simulink results demonstrate, clearly, that in distant areas where the utility grid is economically impractical, the suggested system is anticipated to be a tenable alternative.
Determination and emulation of motor-like flux conditions for loss characterization by means of a single tooth geometry

High quantities and a demand on low costs in automotive drives result in new production methods of electrical machines. Besides, the electric drive train efficiency is improved to offer long ranges. Referring to this relationship the loss models of electrical machines are improved more and more. Focusing on iron losses, remarkable influences on the loss characteristics are attributed to the manufacturing processes. In this publication, a new approach of measuring the losses of a single stator tooth of an electrical machine considering motor-like flux conditions is introduced. Derivation of motor-like flux conditions is described, transfer to the test bench is defined and measurements are shown - concluding with a comparison of simulation and measurement as well as the identified tooth losses of the investigated machine. This gives the possibility to improve iron loss models in case of additional losses due to manufacturing influences.
Investigation of Maximum Torque per Ampere and Maximum Efficiency Control Strategies of a Transverse Flux Machine

This paper investigates a transverse flux machine (TFM) with regard to the simulation of the occurring iron loss over the full operating area and to the applied Maximum Torque per Ampere (MTPA) control strategy. Furthermore, a way to determine different torque-speed maps, which are used for the design process of a TFM or for the implementation of the control strategy in form of lookup tables, is presented. The simulated results are compared to measurements. Moreover, the potential of a Maximum Efficiency (ME) control strategy applied to the investigated TFM is estimated. It can be shown, that a combination of different loss calculation methods for stator and rotor domain is suitable. Furthermore, for the investigated machine, using the ME control strategy provides only slight improvements compared to the MTPA control strategy. Also, the expected torque-speed characteristics which are determined with the presented method are verified by measurements.
A Very Low SEF Neural Amplifier by Utilizing a High Swing Current-Reuse Amplifier

Although current-reuse amplifier has been widely used in biomedical applications because of their low input-referred thermal noise, they don’t have high output swing and their gain is limited. In this article, a rail-to-rail current-reuse amplifier with a 92 dB open-loop gain is introduced while its power and noise increment is just 7%. The proposed structure is a two stage amplifier which doesn’t need further compensation since all nodes are diode connected except for the output node. In order to show the merit of the proposed structure, the NEF, PEF and SEF of the proposed amplifier in a capacitively-coupled neural amplifier structure is compared to the state-of-the-art neural amplifiers. The amplifier is designed and simulated in a commercially available 0.18 µm CMOS technology. The midband gain of the neural amplifier is 40 dB in the bandwidth between 0.6 Hz and 5 kHz. The proposed structure consumes 1.07 µA current from a 1.2 V supply voltage. The NEF, PEF and SEF of proposed structure are 1.68, 3.4, 0.05, respectively. The total area consumption of the neural amplifier is 0.03 mm2 without pads.
Exploring the possibility for early detection of Alzheimer's disease with spatial-domain neural images

Mild cognitive impairment (MCI) is an intermediate stage leading to Alzheimer's disease (AD). Diagnosis for MCI patients at an early stage can reduce the chances of developing into a severe condition for cognition. This study aims to identify the healthy control (HC) and MCI through the neural images in the specific time points during the mental tasks by the convolutional neural network (CNN). The signals were acquired by the functional near-infrared spectroscopy (fNIRS). 15 MCI patients and 9 HC subjects are employed in the experiment to perform the N-back task, Stroop task, and verbal fluency task (VFT), respectively. The neural images were generated by the brain map in the specific time points (i.e., 5 sec, 10 sec, 15 sec, 20 sec, 25 sec, 30 sec, 35 sec, 40 sec, 45 sec, 50 sec, 55 sec, 60 sec, and 65 sec). Four layers CNN were applied to classify the neural images of the different time points for three mental tasks (i.e., N-back, Stroop, and VFT). For evaluating the performance of the classifier, we utilized a 5-fold cross-validation method. The CNN results indicated that all the mental tasks obtained a great performance (i.e., averaged accuracy of N-back: 82.59%, Stroop: 85.03%, and VFT: 82.20%). Especially, the highest accuracy of the Stroop task in the 60-sec time point is 98.57%. Thus, these findings demonstrate that the neural images can be useful for the identification of the MCI. The fNIRS could be a next promising non-invasive neural imaging tool for early detection of AD in the clinical field.
A New Hand Exoskeleton Framework for Rehabilitation of Fingers

Objective: This study investigates a new hand exoskeleton control framework for stroke patients using a brain-computer interface (BCI) technique. Methods: Hand rehabilitation exoskeletons are required to improve critical features, including portability, low cost, safe human-robotic interaction, and intelligent control. The new hand exoskeleton works on brain-computer interface techniques to present a feasibility human-control framework, further to evaluate stroke patients in rehabilitation action research. A visualization design software is utilized to construct the mechatronic system of the hand exoskeleton. A subscript process in the software sets motion parameters of torque, angular displacement, and angular velocity to present a feasibility aim in extension and flexion motion. Main results: The simulation results showed that the proposed hand exoskeleton could generate enough range in finger extension and flexion motion without any interruptions, and real motion experiments by the 3D product also support the feasibility result. Significant: The hand exoskeleton can be used to explore stroke patients in cortex hemodynamic changes using a brain imaging modality, such as function near-infrared spectroscopy.
Electrodermal Activity Smart Sensor Integration in a Wearable Affective Computing System

Nowadays, the integration of smart sensors in wearable affective computing systems aims to improve and create applications based on conventional sensing technology. The inclusion of this type of sensors is key in high sensing-demanding applications, such as those based on affective computing. This sensing integration must be quantitatively supported by metrics that directly affect performance, such as processing time, memory usage, and measurement accuracy. This paper presents a comprehensive analysis of a specific smart sensor integration in a wearable constrained device from an embedded perspective. The authors performed this implementation by considering BINDI, which is a wearable device developed by the UC3M4Safety research group to prevent gender violence situations. BINDI monitors user physiological and physical variables to detect fear through artificial intelligence algorithms. Along with other sensors, this device incorporates an electroDermal activity (EDA) sensor. This type of sensor was proven to be a valuable source of information directly related to the autonomous nervous system (ANS) activation, which regulates physiological responses in stress and relaxed affective states. In the initial version of BINDI, which was validated in different experiments with different volunteers, the EDA sensor is based on a DC measurement schema. However, AC measurement EDA circuitry is known to provide a higher amount of information. Thus, this work presents a detailed comparison between the current EDA sensor and a specific AC smart EDA sensor for a future version of BINDI.
Efficient Application Deployment in Fog-enabled Infrastructures

Fog computing is a paradigm that extends cloud computing services to the edge of the network in order to support delay-sensitive Internet of Things (IoT) services. One of the most promising use-cases of fog computing is Smart City scenarios. Fog computing can substantially improve the quality of citywide services by reducing response delays. Owing to geographically distributed and resource-constrained fog nodes and a multitude of IoT devices in Smart Cities, efficient service deployment and end device traffic routing are quite challenging. Therefore, in this paper, we present an Integer Linear Programming (ILP) formulation for the Joint Application Component Placement and Traffic Routing (JAcPTR) problem in which users’ delay requirements and the limited traffic processing capacity of application instances are considered. Besides, the JAcPTR enables users and infrastructure managers to easily enforce their locality and management requirements in the deployment of application instances. To cope with the considerably high execution time in large instances of the JAcPTR problem, we propose a fast polynomial-time heuristic to efficiently solve the problem. The performance of the proposed heuristic has been evaluated through extensive simulation. Results show that in large instances of the problem, while the state-of-the-art Mixed Integer Linear Programming (MILP) solver fails to obtain a solution in 50% of the simulation runs in 300 seconds, our proposed heuristic can obtain a near-optimal solution in less than one second.
NetMarSyS - A Tool for the Simulation and Visualization of Distributed Autonomous Marine Robotic Systems

The past few years have seen a steady rise of public interest in ocean science and technology as evident through the United Nations proclaiming the coming decade as the United Nations Decade of Ocean Science for Sustainable Development. To appropriately address this challenge, it is essential to possess the right set of information dissemination tools. In this paper, we present a steady effort towards one such tool, called the Networked Marine Systems Simulator or NetMarSyS. Here, we describe a platform to model and visualize the behaviour of marine vehicles in three-dimensional space for surface and subsurface applications. The simulator is suitable for a wide range of applications, from a single-vehicle lawnmower survey to a complex multi-vehicle cooperative mission scenario. We use Robot Operating System, Unity3D game engine and WebSocket server technology to create a distributed multi-agent networked system where in multi-vehicle simulation is carried out. This allow us to support Hardware-in-the-Loop, Software-in-the-Loop simulations and real-time mission visualization. We highlight the competence of the visualizing software using an reference underwater-docking mission comprising of 2 vehicles, and one underwater structure.
An Island Remote Sensing Image Segmentation Algorithm Based on FC_U-Net Network

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, for remote sensing images with rich details and different target scales, there are still some problems such as insufficient segmentation accuracy. To handle these problems, we propose an island remote sensing image segmentation algorithm based on FC_U-Net network. In this network, U-Net and FCN (Fully Convolutional Network) are improved respectively to form two new modules, and then the semantic features and detail features from the two modules are fused by a fusion module. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of FC_U-Net are better than other three state-of-the-art methods, U-Net, FCN (Fully Convolutional Network), SegNet, which indicates the effectiveness of our method.
Tendon-Driven Continuum Robot Systems with only A Single Motor and A Radius-Changing Pulley

Continuum robots are utilized in various fields, such as surgical catheters, and used to assist human muscular strength. To make continuum robots more practical, it is essential to miniaturize or reduce their weights. For tendon-driven robots, the weight of motors is the most important factor to be concerned. Accordingly, we suggest a method to control the tendon-driven continuum robot with a single motor to lighten the weight, which needed more than two motors. However, it is difficult to control the tendon-driven robots with one motor to track the desired trajectory as the lengths of two or more tendons change when the shape of the tendon-driven robot changes. To overcome this issue, we designed a radius-variable pulley, using which the required lengths for respective tendons can be achieved when only a single motor is operating.
Robustness Analysis of Long-Horizon Direct Model Predictive Control: Induction Motor Drives

Model predictive control (MPC) requires an accurate system model to achieve favorable performance. Thus, in presence of disturbances, model uncertainties and mismatches, MPC needs tools that provide high degree of robustness to them. Since MPC is, essentially, a proportional control technique, an effective method to deal with the aforementioned issues is the addition of an integrating element to the control scheme. This paper presents a prediction model that introduces an integrator to the control strategy without increasing the size of the optimization problem. To examine its effectiveness, the sensitivity of the classical and the proposed MPC to parameter deviations are discussed and analyzed, considering a wide range of switching frequencies as well as prediction horizon lengths. The robustness examination is performed based on an industrial case study, namely a medium voltage induction motor drive.
Research on The Price-Volume Relationship of CSI 300 Stock Index Futures

The yield and volatility of financial assets are dominant factors in risk management. In addition, the price-volume relationship of financial assets is also a hot research topic. This paper investigates the price volatility and the price-volume relationship in the stock market using financial high-frequency data. More specifically, we first divide the open interest and trading volume into the expected and unanticipated parts, and divide the price fluctuation into continuous and jumping parts. Then, we build a price-volume model to and analyze the stock index futures market on a deeper level. The results indicate that the predictable and unpredictable trading volume are consistent with the impact of price volatility, which is positive correlated with the price. On the opposite, the effect of predictable and unpredictable open interest on price volatility is different, and the expected predictable is positive correlated with the price while unanticipated parts are negatively correlated with the price.
System Identification Methods for Refined Fault Detection in LVDC-Microgrids

A specific refinement of fault detection in LVDC-microgrids requires model-based knowledge about the system due to possible variations in topology, component parameters and line lengths. In order to gain this information, passive system identification methods based on current and voltage measurement data are applied. Further using machine learning tools for parameter validation, refined and fast model-based fault identification methods can be set up.
Recursive Inverse Kinematics Analysis for Teaching Human Motion to a Humanoid Social Robot using a Depth Camera

Teaching human motion to a humanoid social robot is important because it enables a humanoid social robot to interact with humans more naturally and in a friendly manner. Recently, a depth camera-based human motion teaching has been developed. However, inverse kinematic analysis using human motion skeletal data obtained from the depth camera is not general but rather robot structure-dependent. Thus, in this paper, we present a more general approach to compute joint angles from the skeletal data obtained from a depth camera using a recursive inverse kinematic algorithm. With a known base body orientation, each joint angle is recursively computed from the base body (torso) to the tree end body (robot hand). To validate the proposed recursive inverse kinematic algorithm, simulations of a humanoid robot model have been carried out using the RecurDyn multibody analysis software. The virtual humanoid robot consists of two 6 DOF arms and a 2DOF waist. The human upper body motion when walking has been captured using a Microsoft Azure Kinect depth camera. The hand motion from the depth camera and that from the simulation have been compared to investigate the effectiveness of the proposed algorithm.
Industrial Grade Adaptive Control Scheme for a Micro-Grid Integrated Dual Active Bridge Driven Battery Storage System

In this paper, an industrial grade adaptive control scheme is proposed for a micro-grid integrated dual active bridge driven battery management system (DIBMS). A benchmark industrial grade adaptive control scheme depends on two factors namely robustness and computational resource utilization when such controllers are implemented over processors. The mathematical model of DIBMS system is nonlinear, thus for desired response, non-linear controllers based on sliding mode variable structure control theory suits it well for the state regulation problem of DIBMS, however such controllers utilize high computational resources when practically implemented over processors. Keeping in view the above performance indices, this paper proposes an industrial grade computationally efficient and finite time adaptive robust convergent control for DIBMS system. A proportional integral (PI) scheme is used as central control unit and Hebbian algorithm with double integration of the state error is introduced for online tuning the gains of central control unit. The robustness and computational resource efficiency of the proposed control paradigm is validated using a laboratory scale test bench through TI Launchpad (TMS320F28379D). The superiority of the proposed AI based PI control paradigm is compared with classical PI, integer order sliding mode control (SMC), and fractional order SMC (FOSMC) in terms of computational resource utilization and robustness under all test conditions.