Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Fault Diagnosis of Autonomous Underwater Vehicle Based on LSTM

Autonomous underwater vehicle (AUV) is a complex nonlinear system, and it is difficult to establish its accurate mathematical model and further deal with fault diagnosis problem. Therefore, a novel fault diagnosis method based on a predictive model with using the Long Short-Term Memory Network (LSTM) is proposed in this paper. First, the LSTM network is trained by AUV experimental data, such as acceleration navigation, changing the depth of navigation, changing the direction of navigation and changing the speed of navigation. Then, the trained network can be used to establish the motion model of AUV by fitting the sensor system. Furthermore, the residual generated by comparing the output of the predictive model of the sensors with the actual measured value of the sensors can be used to diagnose sensors fault of AUV, which contains a lot of fault information. Finally, sensor fault diagnosis simulation experiment of the AUV is carried out. The results of the simulation show that the method is effective.
A Precise on Wearable ECG Electrodes for Detection Of Heart Rate and Arrthymia Classification

Many countries are facing burdens on their health care systems due to ageing populations. In this paper, urge the monitoring system of the ECG signal in the convenient way. With respect to heart rate monitoring wearable devices have principally used pulse oximeter sensors, wet silver/silver chloride (Ag/silver chloride) electrodes, optical techniques by shining light through the skin. By inducting this we can monitor the heart rate of the patients using PPG signals. The main aim of this work is to bring out the most convenient system of monitoring ECG signals which is an inventive additional device that can permit hospitals to receive the signals in an advanced sense of confidence and self reliance.
Development of the HALCON Software-based Harness Number Identification

With the wide application of video technology and infrared imaging technology in the mechanical manufacturing industry, some applications are given priority to the use of computer vision technology, and computers replace monitoring personnel for image understanding. At the same time, with the development of electronic technology in recent years, the wire harness is used as the physical carrier of electrical signals mainly for the transmission of energy and signals, and is widely used in aviation, automobiles, electrical appliances and other fields. Among them, the wire harness has a major impact on the operating status of the electrical and mechanical equipment, but in most cases, the background of the detection environment is complex, and the surface material of the wire set has affected the binding, cutting, assembly and other subsequent processes. , especially for the identification of line number caused greater difficulties. This paper is based on the HALCON software development, after the online determination of the wire diameter of different types of the wire harness, according to the characteristics of different the wire harness diameters, the global threshold and the local threshold are divided, the positioning of the numbered area, correction of the line number deflection, and repair of character breaks are performed, and their features are extracted. Through experiments, the recognition of the line number character was successfully achieved, and a higher level of recognition accuracy was achieved, and the production efficiency was improved.
Event-Triggered Distributed Extended Kalman Filtering with Randomly Occurring Deception Attack: A Variance-Constrained Approach

In this paper, a distributed extended Kalman filtering algorithm is developed for a class of discrete-time nonlinear systems subject to stochastic disturbances and randomly occurring deception attacks. In order to utilize the limited communication and computation resources efficiently, the event-triggered communication scheme is introduced such that data transmission is executed only when the predefined condition is violated. Furthermore, a set of independent Bernoulli random variables with known statistical properties is defined to characterize the phenomenon of randomly occurring deception attacks. An upper bound for the estimation error covariance considering the event-triggered meachanism and linearization errors is derived via the variance-constrained approach. The filter gain for each node can be calculated recursively by solving two Raccati-like difference equations to minimize such an upper bound, which is suitable for online application. Finally, an illustrative example is presented to verify the feasibility and effectiveness of the proposed algorithm.
Impact of socioeconomic status on incident heart failure and left ventricular dysfunction: systematic review and meta-analysis

AIMS Socioeconomic status (SES) is recognizably linked with incident heart failure (HF) risk and the association of SES with geography presents a potential target for geographical location of preventive health services. To better inform policy we sought to quantify the independent association between SES and incident HF and investigate differences by type of SES measure. METHODS AND RESULTS MEDLINE and EMBASE were searched up to August 2018. Observational studies and randomized trials reporting adjusted HF incidence by stratified socioeconomic measures were included. Effect sizes reflected HF incidence in the lowest vs. highest SES stratum and were pooled using a random-effects model. Low SES referred to the lowest resource stratum, the definition of which varied across studies: meta-analysis was only performed where strata were comparable. Statistical heterogeneity was assessed using the I2 statistic. Eleven studies comprising 6 308 006 individuals and 104 217 HF events found that low SES was associated with an increase in risk of incident HF ranging between 43% and 87% depending on SES measure, with an overall estimate of 62% [hazard ratio (HR) 1.62, 95% confidence interval (CI) 1.50-1.76]. By individual measure, HRs of 1.66 (95% CI 1.3-2.11), 1.87 (95% CI 1.33-2.62), and 1.54 (95% CI 1.22-1.95) were observed for education, income, and occupation, respectively. For area-level indexes, HRs were 1.43 (95% CI 1.2-1.69) (Carstairs index) and 1.61 (95% CI 1.56-1.65) (index of multiple deprivation). CONCLUSION Low SES assessed by all common measures confers independent risk for incident HF. These findings carry implications for the design and delivery of HF prevention programmes.
Two Stream Neural Networks with Traditional CNN and Gabor CNN for Object Classification

Image classification is still a hot and challenging task in the field of computer vision. With the combination of traditional CNN and Gabor CNN, we designed color-texture convolutional neural networks. We believe that color and texture is the most important feather to describe an object. So the original image, which main consist of color information, and the image processed by Gabor, which contain the texture information, input two stream networks separately is a natural idea. And merging the output of two stream at the end of network to complete the task of classification. We design a simple but effective network structure and test it on Cifar-10, STL-10 and NWPU-RESISC45 dataset. The result shows that the network achieve a well accuracy and proves that our idea is feasible.
Stabilization of Fuzzy Memristive Neural Networks With Mixed Time Delays

In this paper, stabilization for a class of Takagi–Sugeno (T–S) fuzzy memristive neural networks (FMNNs) with mixed time delays is investigated. By virtue of theories of differential equations with discontinuous right-hand sides, inequality techniques, and the comparison method, an algebraic criterion is derived to stabilize the addressed FMNNs with bounded discrete and distributed time delays via a designed fuzzy state feedback controller in Filippov's sense. The result can be reinforced to stabilize FMNNs with unbounded discrete time delays. Meanwhile, exponential stabilization of FMNNs with bounded discrete time delays and unbounded continuously distributed delays is also discussed. FMNNs in this study are general since fuzzy logics and hybrid time delays are all considered, and the obtained conditions enhance and extend some existing ones. Finally, four numerical simulations are carried out to substantiate the efficiency and merits of developed theoretical results.
Multi-class Classification Method Based on Support Vector Machine with Hyper-sphere for Steel surface Defects

In order to obtain better recognition result for steel surface defects, a multi-class classification method based on support vector machine with hyper-sphere is proposed in this paper. On one hand, support vector data description (SVDD) is used to obtain one-class classification hyper-sphere for each type of defect dataset. In order to ensure the representability of sparse boundary samples and overcome the adverse effect of noise, adjustable factor and parameter constraint are introduced. On the other hand, based on the boundary samples, a support vector machine with multiple hyper-spheres (MH-SVM) model is formulated. It not only absorbs the hyper-sphere of SVDD, but also uses one-class classification hyper-sphere to amend and restraint itself. The above two aspects ensure the classification performance of MH-SVM and reduce the sensitivity to noise. Experimental results show that the proposed multi-class classification method has high classification accuracy and efficiency for defect datasets, especially for corrupted datasets.
Equalization in WIMAX System

This paper theoretically explains and demonstrates the MMSE and ZF equalization in WIMAX system for Rayleigh channel model. The ZF equalization performance depends on the . value of the equalization factor, which are varied from -0.1 to 0.1, reflecting the difference in BER value of the WIMAX system. The techniques used for the channel equalization mitigate the effect of ISI with an improvement in BER value compared to the existing technique. The focus of this paper is to corresponding generation of the reference sub- carriers and reducing the channel effect at the receiver by the inverse approximation filter and by the minimization of error signal. The graphs are represented by the computer simulations in this contribution are genuine.
FIP4/Arfophilin‐2 plays overlapping but distinct roles from FIP3/Arfophilin‐1 in neuronal migration during cortical layer formation

The class II Rab11 family‐interacting proteins, FIP3 and FIP4, also termed Arfophilin‐1 and Arfophilin‐2, respectively, are endosomal proteins that function as dual effector proteins for Rab11 and ADP ribosylation factor (Arf) small GTPases. In the present study, we examined the expression and role of FIP4 in neuronal migration during cerebral layer formation. FIP4 mRNA was first weakly detected in post‐mitotic migrating neurons in the upper intermediate zone, and expression was markedly increased in the cortical layer. Exogenously expressed FIP4 protein was localized to subpopulations of EEA1‐ and syntaxin 12‐positive endosomes in migrating neurons, and was partially colocalized with FIP3. Knockdown of FIP4 by in utero electroporation significantly stalled transfected neurons in the lower cortical layer and decreased the speed of neuronal migration in the upper intermediate zone and in the cortical plate compared with control small hairpin RNA (shRNA)‐transfected neurons. Furthermore, co‐transfection of shRNA‐resistant wild‐type FIP4, but not wild type FIP3 or FIP4 mutants lacking the binding region for Rab11 or Arf, significantly improved the disturbed cortical layer formation caused by FIP4 knockdown. Collectively, our findings suggest that FIP4 and FIP3 play overlapping but distinct roles in neuronal migration downstream of Arf and Rab11 during cortical layer formation.
A Filtering Method for Identification of Significant Target mRNAs of Coexpressed and Differentially Expressed MicroRNA Clusters

MicroRNA (miRNA) binding is primarily based on sequence, but structure-specific binding is also possible. Various prediction algorithms have been developed for predicting miRNA target genes; the results, however, have relatively high levels of false positives, and the degree of overlap between predicted targets from different methods is poor or null. We devised a new method for identifying significant miRNA target genes from an extensive list of predicted miRNA target gene relationships using hypergeometric distributions. We evaluated our method in statistical and semantic aspects using a common miRNA cluster from six solid tumors. Our method provides statistically and semantically significant miRNA target genes. Complementing target prediction algorithms with our proposed method may have a significant synergistic effect in finding and evaluating functional annotation and enrichment analysis for miRNA.
Analysis of user-generated content from online social communities to characterise and predict depression degree

The identification of a mental disorder at its early stages is a challenging task because it requires clinical interventions that may not be feasible in many cases. Social media such as online communities and blog posts have shown some promising features to help detect and characterise mental disorder at an early stage. In this work, we make use of user-generated content to identify depression and further characterise its degree of severity. We used the user-generated post contents and its associated mood tag to understand and differentiate the linguistic style and sentiments of the user content. We applied machine learning and statistical analysis methods to discriminate the depressive posts and communities from non-depressive ones. The depression degree of a depressed post is identified using variations of valence values based on the mood tag. The proposed methodology achieved 90%, 95% and 92% accuracy for the classification of depressive posts, depressive communities and depression degree, respectively.
Single Phase Five Level Inverter for Solar-PV Applications

This paper deals with five level inverter topology for solar-PV generation in standalone applications. Desired voltage has been obtained by regulated pulse width modulated (PWM) gating signal fed to gate of bidirectional inverter switches. Perturb and Observe (P&O) based maximum power point tracking (MPPT) tracks maximum generated power. DC-DC converter has been established to regulate power to rated values. The results has been established and verified in MATLAB/Simulink platform.
Mechanical and Warpage Characterization of Glass Carrier in Fan-out Technology Development

Carrier is considered a good tool for process warpage control in wafer/panel level packaging technology. Glass is the most common carrier material. Its rigid, strong property makes it have the high potential for recycle in assembly. And its adjustable CTE (coefficient of thermal expansion) also have the advantage for warpage control. Thus glass strength and warpage must be investigated for the developing advanced technology, such as panel level fan-out package. Glass is tested by 4PB (4-point bending) which follows ASTM C158-02 to come out strength. Two kinds of standard glass sample (around 50×50 mm2) are chosen to compare with glass supplier’s datasheet for benchmark. The standard sample has the same material, dimension and test condition with supplier. 3 types of standard glass sample with different CTE are tested for strength. Those samples have similar average strength and strength range. Two kinds of panel glass (300×300 mm2) with different CTE are tested by 4PB to benchmark with standard sample using the same material. The panel glass strength is within the database of standard sample. For 12″ wafer glass, there is no equation to transfer breaking load into glass strength. A standard glass is chosen as the base to build modeling, so that the wafer load can be converted into strength by simulation. And the simulation by finite element method result shows a liner relationship between load and strength which is the same with strength equation from ASTM. Three dimensional digital image correlation (3D-DIC) theory developed equipment named aMA (advanced metrology analyzer) is used for warpage measurement. For standard sample, its warpage is very small, approximately 6 um. But warpage of wafer, panel glass are much bigger than standard sample due to those large sizes. 12″ wafer glass samples, CTE ranges from 3.0 to 8.7, have similar warpage behavior, and the CTE can also be calculated by aMA. The CTE from aMA is similar with that from datasheet. For A-supplier 12″ wafer glass, it shows a saddle warpage shape if the glass is attached a PI film. For B-supplier 12″ wafer glass, the warpage becomes much large if the glass CTE is large than 9.6. And the warpage shape behaves a concentric circle. To control process warpage, glass is added some material such as PI film and jig. The glass with PI film or jig also shows a concentric circle warpage shape at 30°C.
A Novel Task Coordinate Frame Reduced- Dimension 3-D Contouring Control

In typical contour-following applications such as computer numerical control (CNC) machining, contouring error characterizes the surface quality of final workpieces. The traditional task coordinate frame (TCF)-based approach transforms the contouring control problem into a 3-D regulation problem along the axes of the local Frenet frame. The contouring error is then controlled indirectly by two decoupled regulation systems. In order to achieve a satisfactory contouring error performance, two sets of control parameters must be tuned for the two systems, respectively. In this paper, a novel TCF (nTCF) is proposed. Given the nearest position from the actual position to the desired (or approximated) contour, one axis is set along the line passing the actual position and the nearest position and another axis is along the advancing direction. The system dynamics in the world Cartesian coordinate frame is transformed into nTCF. The contouring control problem for 3-D contours can be reduced and it locally becomes a 2-D regulation problem, one in contouring direction and the other in advancing direction. By implementing the computed-torque control, two proportional-derivative controllers are integrated to regulate the advancing error and the contouring error, respectively. The contouring error is directly regulated by tuning a single set of control parameter, which becomes much simpler than that in the TCF-based approach, where two sets of control parameters are needed to tune. Experiments on an industrial three-axis glass engraving computer numerical control (CNC) machine show the validity of the proposed nTCF-based approach with two typical 3-D contours. Note to Practitioners—In machining applications, the contouring error is a crucial index with respect to the surface quality of machined parts. In this paper, a novel task coordinate frame (nTCF) is proposed to deal with the 3-D contouring control issue. By transforming the system dynamics from the world Cartesian coordinate frame to nTCF in real-time control, a 3-D contouring control problem is locally reduced and transformed into a 2-D error dynamics regulation problem. In the nTCF, the contouring performance and advancing performance can be decoupled and separately regulated by only two parameters in 3-D contouring control applications. The contouring error can be directly controlled. Moreover, existing contouring error calculation (estimation) techniques, such as linear approximation, circular approximation, or analytical methods, can be readily integrated into the nTCF-based approach. Both analysis and experiments show that the nTCF-based approach is effective and much simpler in parameter tuning for 3-D contouring control.
Source Localization and Network Topology Discovery in Infection Networks

Determining the network topology is typically a challenging problem due to the number of nodes and connection between them. Complexity is added whenever this identification problem relies solely on a subset of the outputs of some dynamical system or distributed algorithm running on those nodes. In this paper, we focus on both the source identification and network topology discovery problems in the context of infection networks where a subset of the nodes are elected as observers. The solution consists in writing the binary constraints associated with the problem. Convex relaxations are also proposed and investigated through simulations where a pattern emerges that placing observers in high-degree nodes increases the accuracy of the method.
Channel selection in multi-channel surface electromyogram based hand activity classifier

Surface electromyogram (sEMG) is being extensively studied for development of limb prosthesis and for designing human-machine interfaces. To achieve acceptable accuracy in classification of hand activities using signals acquired from muti-channel sEMG system, the utility of various features and algorithms for reducing the size of the feature set have been reported extensively in literature. In this paper, an approach to select the channels that may be better suited for classification is proposed. The selection of channels is based on the feature selection algorithm namely minimal redundancy maximal relevance method. The proposed algorithm is applied on actual sEMG signals for two set of hand activities to assess the utility of the channels in activity classification. The results are validated by determining the extent of degradation in classification accuracies provided by the support vector machine classifier, when certain sEMG channels are excluded from classification.
Historic reconstruction of reservoir topography using contour line interpolation and structure from motion photogrammetry

ABSTRACT The geometry of impounded surfaces is a key tool to reservoir storage management and projection. Yet topographic data and bathymetric surveys of average-aged reservoirs may be absent for many regions worldwide. This paper examines the potential of contour line interpolation (TOPO) and Structure from Motion (SfM) photogrammetry to reconstruct the topography of existing reservoirs prior to dam closure. The study centres on the Paso de las Piedras reservoir, Argentina, and assesses the accuracy and reliability of TOPO- and SfM- derived digital elevation models (DEMs) using different grid resolutions. All DEMs were of acceptable quality. However, different interpolation techniques produced different types of error, which increased (or decreased) with increasing (or decreasing) grid resolution as a function of their nature, and relative to the terrain complexity. In terms of DEM reliability to reproduce area–elevation relationships, processing-related disagreements between DEMs were markedly influenced by topography. Even though they produce intrinsic errors, it is concluded that both TOPO and SfM techniques hold great potential to reconstruct the bathymetry of existing reservoirs. For areas exhibiting similar terrain complexity, the implementation of one or another technique will depend ultimately on the need for preserving accurate elevation (TOPO) or topographic detail (SfM).
Stereo Visual Inertial Odometry Using Incremental Smoothing

Visual inertial odometry (VIO) is a technique which estimates the motion of a mobile platform using the measurements from visual and inertial sensors. Recently, these sensors are made available with higher accuracy, smaller size, but lower cost thanks to the developing manufacturing of chips and micro-electromechanical systems (MEMS) devices. However, it is very challenging for real-time visual inertial odometry to obtain robust and accurate localization and mapping with the limit of computational resources. In this paper, we present a stereo visual inertial odometry which pre-integrates IMU measurements to reduce the variables to be optimized and avoid repeated IMU integration during optimization. In addition, incremental smoothing is employed to obtain Maximum A Posteriori (MAP) estimates. The proposed method is shown to achieve high accuracy and efficiency in simulation results.