Miracle-dz/UniFAR-FTUs huggingface dataset:
Method for left atrial appendage segmentation using heart CT images

In this study, we propose a method to automatically extract the details of the left atrial appendage region from heart CT images in order to facilitate the preoperative planning of Left Atrial Appendage Occlusion. Generally, it is difficult to automatically classify the left atrial appendage region in a heart CT image because the heart is a very complicated organ. Therefore, in addition to the segmentation method using fully convolutional neural networks, we performed an automatic extraction of only the left atrial appendage region using mini-batch and adversarial training. This method was applied to heart CT images made with a contrast medium. With this method, it becomes possible to automatically obtain information necessary for preoperative planning support of left atrial appendage closure from heart CT images.
Electrical treeing in silicone gel under repetitive voltage impulses

Silicone gel is widely used to encapsulate power electronic modules. The weakness of the electrical insulation is surface discharges initiated at the gel-substrate-electrode triple junction and the subsequent formation of cavities; so called electrical trees. The propagation characteristics of cavities under high frequency and fast rise time voltages, which are typical waveforms formed in power electronic modules, have not been fully understood. Thus, in this research, the influence of frequency, rise time, and polarity on electrical treeing under repetitive voltage impulses are investigated. The results show that discharge tracks under positive impulses have filamentary shapes, and discharge tracks under negative impulses have spindle-shapes. The cavity length increases with the frequency and is especially long when the rise time is short under high frequency. This indicates the importance of understanding the cavity propagation characteristics under actual voltage waveforms in power electronics modules.
Exploring area and total wirelength using a cell merging technique

The industry of Integrated Circuits (ICs) has been making increasingly complex chips with up to billions of transistors in a single die. As we cannot do the design flow by hand, the leading adopted solution to deal with this challenge has been to use a pre-designed library of standard cells and using EDA tools to automate the process. Nevertheless, the resulting netlist is not as efficient in terms of the number of transistors as a handmade design, possibly reflecting in the overall area, power, and delay of the circuit. To generate cells on-demand is a way to improve this inherent limitation, as previous works demonstrate. In this paper, we investigate a netlist optimization methodology based on gate merging and its impacts regarding area and wire-length when applied to the Nagate’s Open Cell Library for 45nm. We obtained a reduction in area and total wire-length of 3.5% and 4.2% on average, respectively.
Prioritizing Prosumers in the energy trading mechanism: A Game Theoretic approach

This paper presents an energy trading mechanism in the smart grid where different entities interact with each other’s (i.e., the sellers, the buyers and the grid). Prosumers can sell their extra energy to the buyers. Then, the latter can interact with the grid if any remaining needed energy. In the existing literature, most studies use the assumption that there is no priority for the buyers participating in the energy management system. All of them pay the same price for the energy suppliers (providers or sellers) without any distinction. In our case, our aim is to divide the deficient of energy in an optimal way between the sellers and the grid while integrating a priority for the buyers. We propose a game theoretical approach to model the different interactions. It is shown analytically that our proposed approach satisfies the prosumers by reducing their bills in comparison to other works.
Multi-Objective Optimization of an Air-Cored Axial Flux Permanent Magnet Synchronous Machine with Segmented PMs based on Support Vector Machine and Genetic Algorithm

This paper focuses on the multi-objective optimization of an air-cored axial flux permanent magnet synchronous machine (AFPMSM) with segmented PMs. Support vector machine based on radial basis kernel function is combined with orthogonal design to create the surrogate model between output performance and design parameters of a coreless AFPMSM with segmented PMs efficiently, and genetic algorithm is used to obtain the global optimal solutions to decrease the torque ripple and increase the output torque. The effectiveness of the proposed method is verified by finite element analysis.
Electromagnetic Analysis of Compound Excitation High Speed Permanent Magnet Synchronous Motor

To suppress the rotor eddy current loss of the conventional high speed permanent magnet synchronous motor (HSPMSM), a compound excitation HSPMSM (CEHSPMSM) excited by both rare earth permanent magnet and ferrite permanent magnet on rotor is proposed in this paper. Firstly, the new compound rotor structure is introduced and its equivalent magnetic circuit model (EMCM) is established. And then the magnetic fields are analyzed and an in-depth study of eddy current losses are calculated by finite element method (FEM) in detail. Finally, a CEHSPMSM and a conventional HSPMSM constrained with the same stator structure and effective volume are designed for performance comparison and analysis. The results show that the CEHSPMSM can effectively reduce rotor eddy current loss without decreasing the motor performance compared with that of the conventional HSPMSM.
Deep Learning for Dropout Prediction in MOOCs

In recent years, the rapid rise of massive open online courses (MOOCs) has aroused great attention. Dropout prediction or identifying students at risk of dropping out of a course is an open problem for MOOC researchers and providers. This paper formulates the dropout prediction problem as predicting how much content in the whole course syllabus can be completed by the student. A dropout rate prediction model is based on a recurrent neural network (RNN), and an URL embedding layer is proposed to solve this problem. The results show that the prediction accuracy of the model is significantly higher than that of the traditional machine learning model.
Inductance Parameter Identification Method of Permanent Magnet Synchronous Motor Based on the HF Rotating Square Wave Voltage Injection

Aiming at the shortcomings of the traditional inductance parameter identification methods of permanent magnet synchronous motor, such as operate complexly, response slowly and only adapt to single condition. This paper proposes a new method which can be applied to both rotor stationary offline identification and rotor on-load operating online identification. The method injects high-frequency (HF) square wave voltages to two-phase rotating coordinate system, extracts the HF response currents and demodulates the signals to calculate the d-axis and q-axis inductance. Offline identification is independent of the initial position of the rotor and no external force or DC current is required to ensure the rotor is stationary. No additional hardware circuitry is required on the basis of the vector control system. The calculation amount of the method is small， is easy to implement and be applied to engineering. Finally, simulations and experiments on a 1.5kW interior permanent magnet synchronous motor demonstrate the feasibility and reliability of the method.
The role of exercise training on oxidative stress and inflammation in vascular diseases

Sickle cell disease (SCD) and atherosclerosis are two very different and distinct diseases thatshare similar underlying characteristics. Sickle cell disease is a hemoglobinopathycharacterized by a genetic mutation which causes the normal blood cells to become rigid andweak. The resulting pathophysiological effects, including sickling, vaso-occlusion, andadhesion, involve the production of oxidative stress and inflammation. Atherosclerosis is achronic inflammatory disease that is characterized by plaque buildup within the vessel walls.An initial step in the pathogenesis of this disease involves the oxidation of lipids, which notonly produces inflammation, but more oxidative stress as well. We sought to determine howthe control of oxidative stress and inflammation could ameliorate complications stemmingfrom the disease.Exercise training is an important mechanism for the beneficial modulation oxidative stressand inflammation through several adaptive pathways: antioxidants, shear stress, vasodilation,and anti-inflammatory cytokines. The purpose of this thesis was to determine if thesebeneficial effects of exercise training could improve oxidative stress and consequentlyinflammation in sickle cell trait (SCT) and atherosclerosis.
A 12-Bit 33-mW and 96-MHz Discrete-Time Sigma Delta ADC in 130 nm CMOS Technology

The paper presents a 12-bit discrete-time third-order Sigma Delta analog-to-digital converter (ADC) for Sub-GHz transceiver applications. In the designed ADC, a sigma delta modulator with 74.76 dB SNR was implemented. The modulator consumes approximate 33 mW from a 3.3 V supply and operates at sampling frequency of 96 MHz with an oversampling ratio of 64. Digital decimation filter including CIC filter and FIR filter is integrated with the analog sigma delta modulator for the full ADC operation. The filter achieves a cut-off frequency of 750kHz and operates at a data rate of 12 Msps or 6 Msps. The ADC circuit has been implemented using 130 nm CMOS technology. At a supply voltage of 3.3 V, the maximum ENOB is simulated to be 12.12 bit which corresponds to a resolution of 12 bit. The simulations on each corner confirm that the modulator satisfies the specifications even in the worst case.
Cables Configuration Analysis for a planar CDPR, based on the Lowest Kinematic Energy for a Rotation Movement

In the field of robotics, energy consumption is a very important factor that must be considered. Plenty of research focuses on optimizing the systems design in order to maximize energy efficiency. In cable driven parallel robots, the energy required for picking up or releasing is directly related to the geometric position of the links between the cables and the final effector. This article evaluates the configuration of a cable driven parallel robot with three degrees of freedom, and a rectangular final effector. For this reason the kinematics are analyzed with a Jacobian matrix using Screws theory considering different linkage points. With the kinetic energy known it is possible to find a proper location guarantying the lowest energy consumption. All calculations are done in a MATLAB and the results are corroborated with the Adams View 2017.2 simulation software.
Enhancing Metagenome-based Disease Prediction by Unsupervised Binning Approaches

Metagenomic data from human microbiome is a novel data source to improve diagnosis and prognosis for human diseases. Nevertheless, since the number of considered features is much higher than the number of samples, we meet numerous challenges to perform a prediction task based on individual bacteria data. In addition, we face difficulties related to the very high complexity of different diseases. Deep Learning (DL) has been obtaining great success on major metagenomics problems related to Operational Taxonomic Unit (OTU)- clustering, and gene prediction, comparative metagenomics, assignment and binning of taxonomic. In this study, we introduce one-dimensional (1D) representations based on the unsupervised binning approaches and scaling algorithms to enhance the prediction performance for metagenome-based diseases using artificial neural networks. The proposed method is evaluated on seven microbial datasets related to six different diseases including Liver Cirrhosis, Colorectal Cancer, Inflammatory Bowel Disease (IBD), Type 2 Diabetes, Obesity and HIV with 2 types of data consisting of species abundance and read counts at the genus level. As shown from the results, the proposed method can improve the performance of Metagenome-based Disease Prediction.
A Design of a Programmable Impedance Scaling Circuit for Low Frequency Applications

A large capacitor is needed for low-frequency active filters to process biomedical signals. The large capacitor is implemented by an impedance scaling circuit. However, the apparent capacitance of the conventional impedance scaling circuit cannot be tuned because that the scaling factor of the impedance scaling circuit is designed by the size of transistors of a negative current feedback circuit. This paper presents a digitally programmable impedance scaling circuit using electrical switches. The proposed circuit is realized by controlling the number of parallel MOS devices which compose the negative current feedback circuit using electrical switches. The validity of the proposed method is confirmed by simulations using HSPICE. The frequency characteristics of the proposed impedance scaling circuits using ideal switches are not deteriorated as compared with one of the conventional impedance scaling circuit. If the capacitor varies from −22% to +49.5%, the tuning error of the proposed impedance scaling circuit is within 0.5%. The proposed circuit can be applied to a 2nd order Butterworth filter whose cutoff frequency is 100 Hz.
A Framework for the Conversion of Textual BigData into 2D Architectural Floor Plan

In the recent past, due to the boost in digitization a huge volume of data (BigData) related to building and architectures has been accumulated. The sources of such data are interviews, blogs, websites, etc. These descriptions has details about the interior of a building. For humans it is easy to interpret and imagine its structure and arrangements of furniture. Automatic synthesis of real-world images from text descriptions has been explored in the computer vision community. However, there is no such attempt in the area of document images, like floor plans. Even though, Floor plan synthesis from sketches, as well as data-driven models, were proposed earlier, this is the first attempt to automatically render building floor plan images from textual description. Here, the input is a natural language description of the internal structure and furniture arrangements within a house, and the output is the 2D floor plan image of the same. We have experimented on publicly available benchmark floor plan datasets. We were able to render realistic synthesized floor plan images from the description written in English.
Attitude Control of Staring-Imaging Satellite Using Permanent Magnet Momentum Exchange Sphere

The attitude control of staring-imaging satellite using permanent magnet momentum exchange sphere (PMMES) is studied in this paper. The desired attitude and desired angular velocity of the satellite are analyzed firstly. The PMMES is applied to meet the requirement of miniaturization for staring-imaging satellite. In order to overcome the drawbacks of the control law based on the Lagrange dynamics model, the Kane method is applied to the PMMES for the first time, and the relationship between the control torque and generalized angular velocity of the PMMES is established directly. Then the dynamic model of staring-imaging satellite with the PMMES is established, and the kinematics model of satellite attitude is deduced. In addition, a PD controller based on disturbance observer is designed to achieve satellite staring task, and the sliding mode controller of the PMMES is designed to suppress the parameter perturbation and the external disturbance. Finally, the simulation results show the validity of the designed control law.
Extraction of Equivalent Mechanical Properties for Power ICs Metallization

A finite element method analysis using the concepts of representative volume element and periodic boundary conditions is carried out in order to determine the equivalent linear-elastic properties for a multi-level routing metallization typically found in power integrated circuits. Some theoretical aspects regarding homogenization are discussed, a typical metallization structure is presented, a substructure is chosen as a representative volume element and then mechanical loads are applied to it through adequate use of periodic boundary conditions in a stationary mechanical simulation. The resulting stress and strain distributions are analysed and their volume averages are used to calculate the anisotropic linear-elastic properties of the routing metallization. The procedure is repeated at a larger scale to validate the method. Conclusions are drawn regarding the accuracy and the advantages of the method.
Voltage Segment Coordinated Control Strategy for Isolated DC Microgrid with Multiple Energy Storage Units

This paper investigates the voltage segment coordinated control scheme of wind/pv hybrid isolated dc microgrid with multiple energy storage units. Firstly, the ring topology is adopted with more flexibility and higher reliability, compared with radiant topology. Then three different operation modes are proposed, that is energy storage voltage regulation mode, wind/pv power reduction mode, and load shedding mode. According to the regulation of dc voltage operating range, the voltage segment coordinated control strategy is proposed, to ensure battery energy storage units, wind/pv hybrid power generation unit and loads are all involved in the coordinated control. These terminals can adjust the dc voltage automatically according to their priorities. For multiple energy storage units, droop coefficients of droop control are adjusted dynamically based on loads power share and converters capacity. In addition, control methods of various side converters in each voltage segment are studied to meet different operation modes. Finally, simulation results show that the dc microgrid power balance is maintained automatically under different conditions such as variations of wind power generation, photovoltaic power generation, loads and energy storage capacity. The control strategy can be extended to other distributed power-based dc microgrids. Finally, the proposed control is validated by simulations using MATLAB/Simulink. The effectiveness and feasibility of the voltage segment control strategy for the proposed wind/pv hybrid isolated dc microgrid are verified.
Are You Paying Attention? Detecting Distracted Driving in Real-Time

Each year, millions of people lose their lives to fatal road accidents. An ever increasing proportion of these accidents is due to distracted driving caused by co-passengers and mobile devices. Thus, there is a growing need for a system which detects the distracted driver in real-time and raises a warning alarm. To this end, we present a simple and robust architecture using foreground extraction and a Convolutional Neural Network (CNN). Our ConvNet model has significantly fewer parameters (0.5M) than state-of-the-art models. Detailed experimental evaluation on two publicly available datasets, the State Farm Distracted Driver Detection dataset (SFD3) and the AUC Distracted Driver dataset (AUCD2), confirm that our model either outperforms or compares with the models proposed so far on both the datasets, with a test accuracy of 98.48% and 95.64%, respectively. Our experiments also suggest that incorporating additional features such as the posture of a driver through foreground extraction using GrabCut substantially improves the performance of our model. Furthermore, our ConvNet is capable of detecting real-time distractions without any parallel processing.
Statistical Lock-in Thermography to Improve Contrast and Detectivity of ICs Thermal Maps

Lock-in thermography is a well-known technique used in the detection of small thermal sources. For years, it has been applied to the investigation of integrated circuits, in particular for failure investigation. This paper investigates ways to improve the quality and the readability of lock-in thermal maps. Most of the time, the identification of thermal sources relies on human interpretation, thus leading to subjective conclusions. This work proposes an automated signal detection feature based on statistical analysis of thermal measurements. Notably, variance properties of lock-in phase measurements allow applying statistical tests in order to highlight areas of thermal activity. By doing so, the readability and the detection of thermal hot spots are improved.
Multi-node Mode Decomposition Based Deep Learning Model for Road Section Traffic Prediction

Road traffic prediction is a vital role of real-time traffic management in the intelligent transportation system (ITS). Many of existing prediction models are established and achieved good results. However, most of them ignored the intrinsic characteristics of traffic parameters data and also not considered on the spatiotemporal effects of the road section which can reflect the situation of the whole road section traffic. Therefore, multi-node road section traffic prediction is still an open problem. This paper, the empirical mode decomposition (EMD) technique is employed to decompose the traffic parameters information into many intrinsic mode function (IMF) components, which represent the original road traffic information in periodic sequence and random sequence. Then, by considering the superiority of convolution neural network (CNN) in multi-dimensional data processing which could handle the spatiotemporal effects, a prediction model based on CNN is used to achieve the prediction of periodic sequence and random sequence. Finally, two parts of the prediction results are combined to get the final prediction results. The dataset from Caltrans Performance Measurement System is used for building the model and compared with several well-known models, such as PCA-BP, Lasso-BP, and standard CNN. The results show that the proposed prediction model achieves higher accuracy with smaller prediction error.
Magnetic Circuit Model and Finite Element Analyze of Stator Excitation Transverse Flux High Speed Permanent Magnet Synchronous Machine

In order to solve the problem that the rotor protection of traditional High Speed with Surface-mount Permanent Magnet Synchronous Machine (HSPMSM) and the permanent magnet are easy to demagnetize under the high temperature. This paper propose a Stator Excited Transverse Flux High Speed Permanent Magnet Synchronous Machine (SE - TFHSPMSM), which has a simpler rotor structure that makes it easier to protect the rotor. In addition, this new structure protects the permanent magnet from the threat of centrifugal force and high temperature and enhances the mechanical strength of the rotor. The paper establishes the equivalent magnetic circuit model of electric machine and gets the calculation formula of key part reluctance. The correctness of the theory and the feasibility of the structure are proved by finite element simulation analysis (FEA).
Automatic Discovery and Cleansing of Numerical Metamorphic Relations

Metamorphic relations (MRs) describe the invariant relationships between program inputs and outputs. By checking for violations of MRs, faults in programs can be detected. Identifying MRs manually is a tedious and error-prone task. In this paper, we propose AutoMR, a novel method for systematically inferring and cleansing MRs. AutoMR can discover various types of equality and inequality MRs through a search method (particle swarm optimization). It also employs matrix singular-value decomposition and constraint solving techniques to remove the redundant MRs in the search results. Our experiments on 37 numerical programs from two popular open source packages show that AutoMR can effectively infer a set of accurate and succinct MRs and outperform the state-of-the-art method. Furthermore, we show that the discovered MRs have high fault detection ability in mutation testing and differential testing.