Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Simulation of TDR Test for Power Cables Based on ABCD Matrix

Time domain reflectometry (TDR) technology is widely used in fields for cable length measurement and fault location. The typical test circuit mainly consists of signal generating unit, signal acquisition unit, cable under test and the adaptor/connector. Lattice diagram theory is classic tool to analyze multiple reflections in TDR. With this tool, each reflection has to be calculated separately, which is inefficient. In this paper, ABCD dual-port network matrix is used to simulate the complete test system. The simulation method can derive multiple reflections at once. Sensitivity analysis of TDR measurement is performed with the proposed simulation method. It provides an efficient tool for cable system simulation, which can be used for e.g. test setup optimization.
An Analysis of EEG Nonlinear Interdependence for TMS over the Left VLPFC during Emotion Regulation

Studies point out the importance of left ventrolateral prefrontal cortex (VLPFC) in the process of cognitive reappraisal as well as in the functional brain network. In the current study, we investigated how the transcranial magnetic stimulation (TMS) applied over the left VLPFC influences the inter-regional nonlinear interdependence of brain dynamics during cognitive reappraisal. The fifteen participants were required to view or reappraise unpleasant and neutral images while their scalp electroencephalogram (EEG) were recorded. Single-pulse TMS was applied over the left VLPFC at 300ms after each image onset. Our results showed that the nonlinear interdependence between central region and other brain regions under reappraisal was significantly larger than those when negative/neutral images were watched only, which was found when the TMS was applied over the left VLPFC. Nevertheless, no such significant difference was found either when TMS applied over the vertex or without TMS intervention. The results of the current study demonstrate that single-pulse TMS intervention over the left VLPFC promote the brain functional connectivity and facilitate the process of emotion regulation.
Attention Bidirectional LSTM Networks Based Mime Speech Recognition Using sEMG Data

Surface electromyography (sEMG) has been proven competent and reliable to recognize speech musculature movement patterns. In other words, we can understand what a person prepares to say by collecting sEMG signals around the mouth. Therefore, sEMG-based Mime Speech Recognition (MSR) is a potential technique for human-machine interaction within noisy surroundings as well as the application of helping dysarthric patients. In this paper, we introduce multi-layer Bidirectional Long Short-Term Memory (BLSTM) networks with attention mechanism as a classifier for MSR, and verify it in the data set collected by ourselves. Six-channel sEMG signals are firstly acquired from elaborately selected facial muscles. Short-time Fourier Transform (STFT) and Convolutional Neural Networks (CNN) are utilized to extract time-frequency domain feature maps, replacing the handcrafted features in classic methods. The second phase of recognition process lies in the designed classifier. This classification system achieves over 97% accuracy in the four-class MSR task, significantly surpassing simple CNN and LSTM methods. Such result also indicates that excellent MSR results can be achieved without relying on handcrafted signal features.
Etch characteristics of magnetic tunnel junction stack using a high density plasma in a HBr/Ar gas

Etch characteristics of magnetic tunnel junction (MTJ) stack masked with TiN thin films were investigated using an inductively coupled plasma reactive ion etching in HBr/Ar gas for the application of magnetic random access memory. The effect of HBr gas concentration on the etch profile of MTJ stacks was examined. As HBr gas concentration increased, the sidewall angles of etched patterns of MTJ stacks were slightly improved. The effect of etch parameters including coil rf power, dc-bias voltage, and gas pressure on the etch profile of MTJ stack was explored. At low coil rf power, low dc-bias voltage, and high gas pressure, the etching of MTJ stack displayed better etch profile. A high degree of anisotropic etching of MTJ stacks without redeposition was achieved using HBr/Ar gas at the optimized etch conditions. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Evaluation of Driver Drowsiness based on Real-Time Face Analysis

Driving a car is a complex and potentially risky activity in people’s everyday life, and it requires the full involvement of physiological and cognitive resources. Any loss of these resources can cause traffic accidents. For example, drowsy driving affects the ability to adapt, predict and react to unexpected events. A solution to this problem is the adoption of Advanced Driver Assistance Systems (ADAS), which can warn the driver if sleepiness is detected. Thus, they should include a Driver Monitoring System (DMS) to understand, measure and monitor human behaviour in different scenarios. This article is focused on detecting driver drowsiness by using non-intrusive measures such as the behavioural approach, as it is the most promising solution to use in real vehicles. The developed framework allows the extraction of drowsiness-related measures by analysing the driver’s face with a standard camera. First, a face detection stage identifies the driver face in a video frame. Then, a set of facial landmarks locations are identified. These landmark points are used to estimate the head orientation and to detect when a blink occurs. By monitoring properly defined ocular variables, the degree of driver drowsiness is detected through a Fuzzy Inference System (FIS).
Optimization Design of Parallel Double Stator and Outer Mover Linear Rotary Permanent Magnet Machine Used for Drilling Robot

In order to meet the requirement of the drilling robot, a parallel double stator and outer mover linear rotary permanent magnet machine is proposed, which combines an outer rotor vernier machine (ORVM) and a cylindrical outer rotor linear vernier machine (CORLVM) by using a special support mechanism. The electromagnetic and mechanical structure optimization design is analyzed by 3-D finite element method (FEM) in the paper, including the design of ORVM and CORLVM, the stress calculation of support mechanism and the loss calculation. Then the optimized values of structure parameters are obtained. Compared with the results of the initial topology analyzed by 3-D FEM, the torque and thrust are improved and the amplitudes of the cogging torque and detent force are reduced. The combined structure design has the advantages of two different motors, which can provide a reference for the research of this type of motor.
Technological Experiments for the Enhancement of Glycerol Content in High Quality Wines

The glycerol content of grape juices and wines has been determined by gas-chromatographic methods using silyl derivatives. The effect of different storage conditions and yeast cultures on glycerol content has been investigated. Grape juice samples with different starting D-Glucose concentrations have been treated with several additives (HPO42? , S2O52? , NADH and HSO3? ). Significant glycerol concentration enhancement has been detected in the case of the addition of NADH (5.14 g/L), however the presence of HSO3? prevented the excessive glycerol formation (1.21 g/L).
Research on the Influencing Factors of Music Virtual Community Based on Expectation Confirmation Theory

The huge commercial value brought by the development of the music virtual community has attracted widespread attention. This study takes expectation confirmation theory and music virtual community activity as an entry point, and analyzes the impact of different factors on music virtual community activity. The study used SPSS and Amos software to statistically analyze the collected data and found that Expected Confirmation has a positive effect on Satisfaction; Perceived Usefulness has a positive effect on Expected Confirmation; Perceived Usefulness has a positive effect on Perceived Service Quality; Satisfaction is an important factor influencing Activity; Perceived Service Quality has a positive impact on the User Experience of the community. Therefore, in the management of music virtual community, we must pay attention to the management of community user expectations and improve satisfaction; pay attention to user needs and improve the perceived usefulness of community members; seek a differentiated route to improve the perceived service quality Suggestions.
TentNet: Deep Learning Tent Detection Algorithm Using A Synthetic Training Approach

Homelessness is a complex social problem and there have been limited attempts to use machine learning algorithms to understand the various issues that public health agencies would like to solve. For instance, it is important for the policy makers to know where homeless populations live so that they can provide necessary services accordingly. This article presents a satellite image tent-detection solution with three deep learning methods that utilize transfer learning from the ResNetV2, InceptionV3, and MobileNetV2 models, trained on ImageNet, attached to a unique architecture referred to as "TentNet". The performance of these models are first shown in detecting planes and ships within satellite imagery in previously defined datasets as a baseline. Then, a new dataset is created from a compilation of tents from the xView project to use for testing, along with another dataset of synthetic images from the generative adversarial networks StyleGAN2 and DCGAN for training. After training on a dataset containing only synthetic images for the tents class, the ResNetV2 architecture achieved the highest accuracy of 73.68% when testing on the real satellite imagery.
In Silico Assembly of Alzheimer's Aβ16-22 Peptide into β-Sheets

Recent studies suggest that soluble oligomers of amyloid-forming peptides have toxic effects in cell cultures. In this study, the folding of three Alzheimer's A β16-22 peptides have been simulated with the activation−relaxation technique and a generic energy model. Starting from randomly chosen states, the predicted lowest energy structure superposes within 1 A rms deviation from its conformation within the fibrils. This antiparallel structure is found to be in equilibrium with several out-of-register antiparallel β-sheets and mixed parallel−antiparallel β-sheets, indicating that full structural order in the fibrils requires larger aggregates. Folding involves the formation of dimers followed by the addition of a monomer and proceeds through a generalized mechanism between disordered and native alignments of β-strands.
Development of a VR-based Manipulation System for Dual-arm Robots

Following its applications on reception, space exploration, health care, and others, the dual-arm robot is employed for industrial automation more intensively these days. With a human-like two-arm structure of high degrees of freedom, it should be a strong competitor for industrial tasks of high complexity. However, up to date, its deployment is still far behind the traditional single-arm robot manipulator. It may be partially due to its higher price and larger installation space. Meanwhile, the lack of proper methods or devices to deal with task planning and teaching that usually involves robot motions of more than 10 DOF is also influential. Motivated by it, in this paper, we propose a novel manipulation system for the industrial dual-arm robot manipulator using virtual reality (VR). Especially, we propose taking advantage of the similarity between the dual-arm robot and human arm and come up with a human-like path planner, with an intention to incorporate that of human into robot path planning. In addition, we also furnish the system with several assistive tools, including that for physical behavior emulation and self-collision detection. Simulations based on realistic environments are conducted to demonstrate the effectiveness of the proposed manipulation system, along with the questionnaires to evaluate user’s responses.
Skeleton-based motion estimation for Point Cloud Compression

With the rapid development of point cloud acquisition technologies, high-quality human-shape point clouds are more and more used in VR/AR applications and in general in 3D Graphics. To achieve near-realistic quality, such content usually contains an extremely high number of points (over 0.5 million points per 3D object per frame) and associated attributes (such as color). For this reason, disposing of efficient, dedicated 3D Point Cloud Compression (3DPCC) methods becomes mandatory. This requirement is even stronger in the case of dynamic content, where the coordinates and attributes of the 3D points are evolving over time. In this paper, we propose a novel skeleton-based 3DPCC approach, dedicated to the specific case of dynamic point clouds representing humanoid avatars. The method relies on a multi-view 2D human pose estimation of 3D dynamic point clouds. By using the DensePose neural network, we first extract the body parts from projected 2D images. The obtained 2D segmentation information is back-projected and aggregated into the 3D space. This procedure makes it possible to partition the 3D point cloud into a set of 3D body parts. For each part, a 3D affine transform is estimated between every two consecutive frames and used for 3D motion compensation. The proposed approach has been integrated into the Video-based Point Cloud Compression (V-PCC) test model of MPEG. Experimental results show that the proposed method, in the particular case of body motion with small amplitudes, outperforms the V-PCC test mode in the lossy inter-coding condition by up to 83% in terms of bitrate reduction in low bit rate conditions. Meanwhile, the proposed framework holds the potential of supporting various features such as regions of interests and level of details.
Tracking Operator Intent in Tactical Operations

Effective teams coordinate their actions to achieve shared goals. In Human-Agent teams, the Artificial Intelligent Agents (AIAs) struggle to coordinate effectively due to a lack of understanding of their human teammate’s intent. This places a burden on the human teammate to extensively communicate explicitly what goals they are pursuing and how they are pursuing them. To improve the AIAs ability to coordinate, we have proposed Operationalized Intent as a means to explicitly model how an operator qualitatively desires a task to be performed. In this paper, we report the results of a study to track operator intent through a tactical scenario. The focus of this paper is on the dynamics of intent and it’s cohesiveness across operators. The study employed an immersive, advanced research, remotely piloted aircraft (RPA) simulator to study intent in a synthetic task environment. Using operational pilots and sensor operators in realistic scenarios we were able to elicit their intent under naturalistic conditions in the midst of challenging tactical situations to study the real-time dynamics. Analysis indicates that the method models intent which is dynamically responsive to changes in the situation and the data are suitably cohesive across operators to generalize to an operator role. When the intent data is coupled to situated data from the simulator it provides a labeled data source for future AIAs to estimate intent in real-time.
An Automated Approach to the Generation of Structured Building Information Models from Unstructured 3d Point Cloud Scans

In this paper we present and evaluate an approach for the automatic generation of building models in IFC BIM format from unstructured Point Cloud scans, as they result from 3d­laser scans of buildings. While the actual measurement process is relatively fast, 85% of the overall time are spend on the interpretation and transformation of the resulting Point Cloud data into information, which can be used in architectural and engineering design workflows. Our approach to tackle this problem, is in contrast to existing ones which work on the levels of points, based on the detection of building elements, such as walls, ceilings, doors, windows, and spaces and the relation between these. We present use cases with our software prototype, evaluate the results, and discuss future work, that will bring the research further towards the aim to create automatically semantic links between the conception of building design in BIM and simulations with the build environment.
A Robust Driver’s Gaze Zone Classification using a Single Camera for Self-occlusions and Non-aligned Head and Eyes Direction Driving Situations

Distracted driving is one of the most common causes of traffic accidents around the world. Recognizing the driver’s gaze direction during a maneuver could be an essential step for avoiding the matter mentioned above. Thus, we propose a gaze zone classification system that serves as a base of supporting systems for driver’s situation awareness. However, the challenge is to estimate the driver’s gaze inside not ideal scenarios, specifically in this work, scenarios where may occur self-occlusions or non-aligned head and eyes direction of the driver. Firstly, towards solving miss classifications during self-occlusions scenarios, we designed a novel protocol where a 3D full facial geometry reconstruction of the driver from a single 2D image is made using the state-of-the-art method PRNet. To solve the miss classification when the driver’s head and eyes direction are not aligned, eyes and head information are extracted. After this, based on a mix of different data pre-processing and deep learning methods, we achieved a robust classifier in situations where self-occlusions or non-aligned head and eyes direction of the driver occur. Our results from the experiments explicitly measure and show that the proposed method can make an accurate classification for the two before-mentioned problems. Moreover, we demonstrate that our model generalizes new drivers while being a portable and extensible system, making it easy-adaptable for various automobiles.
Descriptive Statistical Analysis of Frequency control-related variables of Nordic Power System

This paper presents a descriptive statistical analysis (DSA) of time-series of electro-mechanical quantities related to the frequency control (e.g. kinetic energy (KE), electrical frequency and power demand) of the Nordic power system (NPS). The idea of the DSA is to identify main observables features and patterns between these variables. Historical data publicly available has been used in this research paper; pre-processing included evaluating and identify missing data, and it filled by using the linear interpolation. The DSA uses descriptive statistical indicators to obtaining observable features. The dispersion analysis is used to observes how affects the KE to the electrical frequency. The data is grouped by weeks, days and hours, and its correlation coefficient was calculated. A correlation analysis between the KE and the power demand was computed, and the linear regression was used to construct a prediction model.
Integration of Vergence, Cyclovergence, and Saccades through Active Efficient Coding

This paper describes a unified computational model for the joint development of early visual representations and the control of three types of eye movements, i.e., vergence, cyclovergence, and saccades. The model is based on the Active Efficient Coding (AEC) framework, an extension of Barlow's efficient coding hypothesis to active perception. AEC describes the joint learning of sensory encoding and behavioral control. The present work relaxes the assumptions made in our previous work by learning vergence, cyclovergence, and saccades all from random initialization. Our results also demonstrate the importance of the interaction between the learning of these eye movements in terms of learning speed and accuracy. Overall, we find that AEC provides a parsimonious framework to account for the simultaneous learning of active vision skills.
Use of a deep convolutional neural network to diagnose disease in the rose by means of a photographic image

The article presents particulars of developing a plant disease detection system based on analysis of photo-graphic images by deep convolutional neural networks. A original lightweight neural network architecture is used (only 13 480 trained parameters) that is tens and hundreds of times more compact than typical solutions. Real-life field data is used for training and testing, with photographs taken in adverse conditions: variation in hardware quality, angles, lighting conditions, scales (from macro shots of individual fragments of leaf and stem to several rose bushes in one picture), and complex disorienting backgrounds. An adaptive decision-making rule is used, based on the Bayes’ theorem and Wald’s sequential probability ratio test, in order to improve reliability of the results. A following example is provided: detection of disease on leaves and stems of rose from images taken in the visible spectrum. The authors were able attain the quality of 90.6% on real-life data (F1 score, one input image, test dataset).
A Decentralized, Privacy-preserving and Crowdsourcing-based Approach to Medical Research

Access to data at large scales expedites the progress of research in medical fields. Nevertheless, accessibility to patients’ data faces significant challenges on regulatory, organizational and technical levels. In light of this, we present a novel approach based on the crowdsourcing paradigm to solve this data scarcity problem. Utilizing the infrastructure that blockchain provides, our decentralized platform enables researchers to solicit contributions to their well-defined research study from a large crowd of volunteers. Furthermore, to overcome the challenge of breach of privacy and mutual trust, we employed the cryptographic primitive of Zero-knowledge Argument of Knowledge (zk-SNARK). This not only allows participants to make contributions without exposing their privacy-sensitive health data, but also provides a means for a distributed network of users to verify the validity of the contributions in an efficient manner. Finally, since without an incentive mechanism in place, the crowdsourcing platform would be rendered ineffective, we incorporated smart contracts to ensure a fair reciprocal exchange of data for reward between patients and researchers.
Development and compensation experiment of HF detection system for GIS gas chamber

In order to realize on-line real-time detection of HF gas in gas chamber of Gas Insulated Switchgear (GIS) of high-voltage composite electric appliances, and to perfect defect diagnosis of high-voltage electrical equipment based on gas chemical composition analysis, HF gas was detected online by tunable diode laser absorption spectroscopy (TDLAS) technology of 1321nm DFB laser. Simulation results show that changes in temperature and pressure will affect the broadened center wavelength and intensity of the HF gas absorption line. In order to make up for the detection error caused by the change of the second harmonic signal with the ambient temperature and pressure, HF/SF6standard gas with volume fraction of $50\times 10^{-6}$ and BP neural network model algorithm were used to compensate the temperature and pressure. Calibration experiments show that the amplitude of the second harmonic signal is proportional to the standard concentration of HF gas, the fitting coefficient $R^{2}$ is 0.9995, and the detection limit is $5\times 10^{-} 6$. The results show that the BP neural network temperature and pressure compensation algorithm can effectively improve the measurement accuracy, anti-interference ability and repeatability of the detection system.