Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Design and Analysis of Fractional-Slot Concentrated-Winding Spoke-Type PM Machines for Electric Vehicles Traction Applications

Spoke-type PM machine develops higher torque density due to the capacity of the flux concentration. However, the rotor end leakage is serious and the flux weakening capability is limited. This paper proposes U shape and H shape spoke-type fractional-slot concentrated-winding (FSCW) PM machine for electric vehicle (EV) traction applications, which realizes a good linearity of torque-current characteristic and a proper characteristic current. The machine structure is introduced and optimized in the design procedure. The performance comparison between the optimized design and the original topologies is presented to highlight the performance improvement of the optimized design. Finally, an original prototype has been used for the experimental validation. The experimental results, which agree well with the FE-predicted results, confirm the validity of the analysis method used in predictions and the advantages of the optimized design.
A Comparative Assessment of Cascaded Double Voltage Lift Boost Converter

In several power conversion applications, dc-dc boost converters with voltage boost techniques are extensively used in order to meet the growing power demand. The main drawback of conventional dc-dc boost converter is obtaining high DC voltages, when operated at high duty ratio which causes switching losses and decreases overall efficiency because of the switch being used to be in ‘ON’ state for long time and voltage stresses across switch increases. The main objective of proposed converter is to obtain high voltage without extreme duty ratio. When input voltage of 15V DC is given, 201.1V DC output voltage is attained at duty ratio of 0.4 by the cascaded double voltage lift boost converter. To validate the performance of proposed converter, simulation is carried out in LTspice XVII and a comparative assessment of proposed converter with other converters at different duty ratio are realized.
Enabling Partial Offload of Virtualized Network Functions into the Programmable Data Plane

One of the key aspects hindering NFV adoption is the VNF performance, which is well below middleboxes. The offload of parts of a VNF into programmable data planes presents an alternative to mitigate this loss of performance. However, currently offload is only used locally on SmartNICs or in parts of an entire SFC, in a way that there are no platforms capable of offloading parts of a single VNF into programmable devices. In this paper we propose an architecture capable of managing the offload of elements of a VNF into programmable network devices. The architecture is composed of a VNF Platform with offload support, and a offload manager which communicates with the underlying infrastructure in order to allow the process. A prototype of the architecture was developed and evaluated, with results showing performance gains even in a completely virtualized test scenario, demonstrating the benefits provided by a offload platform.
A Method for Calculating the Inductance of Switched Reluctance Motor Based on End Inductance Coefficient Function

This paper presents an end inductance coefficient function Leσ, which aims at solving the problem that the end inductance of switched reluctance motor (SRM) is difficult to calculate. Firstly, a two-dimensional (2-D) and a three-dimensional (3-D) finite element analysis (FEA) model of the motor are built to acquire the inductance difference under different motor structure and working conditions. Secondly, the end inductance coefficient function is obtained according to simulation results and it is used to modify the inductance of 2-D FEA model. Finally, torque characteristics of 3-D FEA model and modified 2-D FEA model are compared by simulation, the correctness of the proposed method is verified. The modified 2-D FEA model simulation is able to make the calculation results more accurate, and avoid the complexity of 3-D modeling with greatly time saving. Experimental verification is carried out on a 40kW four-phase 16/12 pole SRM.
A Fusion of Java Domain Knowledge Base and Siamese Network for Java API Recommendation

APIs play an important role in modern software development. Programmers need to frequently search for the appropriate APIs according to different tasks. With the development of the information industry, API reference documents have become larger and larger. Due to redundant and erroneous information on the Internet, traditional search methods can also cause inconvenience to programmers' queries. At the same time, there is a gap in terms of vocabulary and knowledge between the natural language description of the programming task and the description in the API documentation, so it is difficult to find a suitable API. To solve these problems, this paper proposes a Java API recommendation model by fusing the Java domain knowledge base and the Siamese Network to improve the accuracy of API recommendation. Experiments on the BIKER data set show that our method has better recommendation results than the state-of-art DeepAPI and BIKER model.
Impact of PAD Micro Contact Size and Distribution on the Planarization in CMP

Optimal planarization of the overburdened copper layers is a critical objective in the integrated circuit (IC) field and is mainly realized via chemical mechanical planarization (CMP) technique. As this kind of mechanical related performance is strongly related to the action of pad asperities, understanding the effect of the micro-contact conditions of pad asperities on the planarization process will be meaningful. In this study, a millimeter-sized polishing pad sample with a known contact condition is used and conducted on a copper patterned surface. Based on the real contact area image, the relationships between planarization efficiency and real contact condition are established. The results indicate that high material removal in up areas is necessary but is not a sufficient condition for material removal in down areas. Both contact spot size and distribution have an obvious effect on the planarization process. This study is expected to provide some insights into the planarization process from the microscale.
A Study on the Identification of Warship Type/Class by Measuring Similarity with Virtual Warship

In the defense field, accurate information about surrounding ships is important, and for this purpose, location information about nearby ships is acquired based on radio or radar. However, in a war situation, it is more important to select not only the position of the opponent, but also a high-value unit among them, and for this, an image-based warship type/class identification technology is required. This paper presents a specific method for constructing a detailed warship type/class identification system by estimating the azimuth/altitude from the input warship image, comparing the similarity using part segmentation between virtual warship model. Through the development of the proposed system, reliable warship identification information can be used for tactical applications in the defense field, contributing to strengthening defense capabilities.
Investigating Electrical Drive Performance Employing Model Predictive Control and Active Disturbance Rejection Control Algorithms

Many issues can degrade the electrical drive performance such as cross-coupling, time delay, external disturbances, and parameter variation. The Synchronous Reference Frame (SRF) PI Current Controller (CC) is the most popular control scheme for the motor drive current control due to its simplicity. However, the PI controller does not have an optimal dynamic response due to the reasonably low transient response of the integral parts. Furthermore, the tuning of the PI controller depends heavily on the machine’s parameters. Recently, alternative control schemes such as Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) are studied due to their dynamic performance and disturbance rejection capability, respectively. This paper presents a comparative study between the conventional PI, ADRC, and MPC control schemes applied for Permanent Magnet Synchronous Motor (PMSM) taking into consideration the operational issues of electrical drives.
On the Design of a Substrate Integrated Waveguide Butler Matrix for K-Band Applications

This paper presents a Butler matrix design and construction in the K band using -3 dB and 0 dB hybrid couplers designed in Substrate Integrated Waveguide (SIW) technology that covers the 18 - 22 GHz bandwidth with excellent performance in terms of phase error and amplitude imbalance. The measured results, supported by simulations, show an input coincidence better than 20 dB, transmission and coupling losses of around 8 dB, and isolation better than 15 dB.
A Carrier-Based Fault-Tolerant Control Method for Three-Level Neutral-Point-Clamped Inverter

This paper firstly analyzes the space vectors that can be used when a single switching device of three-level (3-L) neutral-point clamped (NPC) inverter has a short-switch or an open-switch fault. On the premise of avoiding two-level voltage jump and simultaneous action of three-phase switching devices, a fault-tolerant space vector sequence (FTSVS) is designed which can be applied to any fault of a single switching device. On this basis, the equivalent relationship between FTSVS and carrier-based PWM (CBPWM) is deduced, and a carrier-based fault-tolerant control (CBFTC) method is further proposed. It can realize the fault-tolerant operation of 3-L NPC inverter and ensure the balance of the neutral point voltage, and its calculation is simple and implementation is convenient. Simulation results verify the validity of the proposed method.
Broadband Measurement of Substrate Complex Permittivity Using Optimized ABCD Matrix

In this paper, a novel two-transmission-line method based on optimized ABCD matrix for broadband and continuous substrate dielectric characterization is presented. The original single-line algorithm is firstly discussed and simulated, while the measurement shows that the extracted parameters are far from the actual values due to the presence of microwave connectors and transition mismatches. Therefore, a modified two-line algorithm based on the optimized ABCD matrix is proposed for ungrounded coplanar waveguide (UGCPW) configuration, which is very suitable for electroplating circuits with a consistent conductor layer on newly developed substrates. A comprehensive procedure to calculate the total line attenuation, phase number, characteristic impedance, substrate dielectric constant, and dielectric loss tangent is described. Since both conductor and radiation losses are taken into account, the extracted results show consistency within a single-digital percentage with the reference values. The analysis of the measurement uncertainty and the related uncertainty budgets for the derived dielectric results are also presented. The proposed method is expected to be applied to any transmission lines with arbitrary characteristic impedance, without a prior knowledge of substrate dielectric constant, and no additional calibrations are required other than the calibration kit for Vector Network Analyzer (VNA).
A Novel Deep CNN Model for Classification of Brain Tumor from MR Images

the segmentation of brain tumor and its classification in the early stage is very important for the purpose of diagnosis and treatment. This work introduces a new deep neural network model Lu-Net with less layers, less complexity and very efficient for identifying tumors. The work involves classifying brain magnetic resonance (MR) images from a dataset of 253 images of high pixels into two categories of tumors and non-tumors. MR images are initially resized, cropped, preprocessed, and augmented for accurate and rapid training of deep convolutional neural network (CNN) models. The performance of the Lu-Net model has been evaluated using five types of statistical evaluation matrix accuracy, recall, specificity, F-score and accuracy, and its performance also compared with other two types of model Le-Net and VGG-16. CNN models were trained and evaluated on augmented dataset and tested on untrained datasets. The overall accuracy of Le-Net, VGG-16 and the proposed model is 88%, 90% and 98%, respectively, indicating the superiority of the proposed model.
Automatic Generation of Background Computer Graphics by Deep Learning According to User's Preference

In recent years, the development of computer technology has led to the spread of computer graphics, and computer graphics are often used as a background map for movies and video games. However, when background CG is created using conventional CG software, it is necessary to create CG models to be placed, and it takes time and labor. In this study, we propose an automatic background CG generation system based on deep learning.
Low Voltage Time-Resolved Emission (TRE) Measurements of VLSI Circuit

As a process node is getting smaller, the types of failure mechanisms are increasing. New EFA technologies and methods are constantly development. One of the main changes EFA analyses is an enhancement of dynamic EFA in circuit failed in functional test. We propose a technique for advanced Electrical Failure Analysis (EFA) tool with a Picosecond Imaging Circuit Analysis (PICA) detector with enhanced sensitivity for discussing Time Resolved Emission (TRE). The key applications where the time-resolved imaging capability is very effective in reducing the debug time and improving the understanding the failure behaviors of VLSI chip for fault characteristics
Correct Settings of a Joint Unmanned Aerial Vehicle and Infrared Camera System for the Detection of Faulty Photovoltaic Modules

The technological development of photovoltaic (PV) devices (perovskite-based or bifacial modules, etc.) has progressed considerably, but the commercial PV modules do not show analogous improvements and their efficiency is still low. Therefore, a diagnostic tool that can check the current operation of installed PV modules and their aging is important. In this scenario, an unmanned aerial vehicle (UAV), equipped with a camera, infrared (IR) camera, or both, represents a new avenue for the fast evaluation of faults for large or not easily accessible PV plants and the discovery of defective PV modules. Thermography has the advantage of not disconnecting the PV system during the inspection. Nevertheless, some specific parameters, such as the emissivity, the reflected temperature, and the resolution, are fixed during the acquisition, even if their values are dependent on the mutual position (distance and angle) between the IR camera and the PV modules. The movement of the UAV, equipped with an IR camera, modifies the position of the IR camera, and due to this movement, the values of the parameters can be incorrect during acquisition, which also results in errors in the radiometric map. In this article, we explore these issues and provide suggestions to ensure the correct acquisition and reading of thermal images of PV modules.
Design of IMC & IMC Derived PID Controller for Interleaved Boost Converter

This paper gives the design of IMC and IMC derived biphasic Interleaved Boost Converter (IBC) in voltage mode control technique. Small-signal based modelling of IBC in continuous conduction mode (CCM) of operation shows a right half plane zero (RHPZ) in control to output plant transfer function (TF) of the IBC. IBC produces substantially low output voltage ripple compared to boost converter due to the emergence of multiple power switches in the parallel path. Subsequently, the size and output filter losses of IBC can be substantially reduced in comparison to the Boost Converter (BC). Execution of the converter-controller system is validated by SISO tool-based step response analysis. Developed IMC and IMC dependent PID controllers are performing better than highly cited Type III controller-based IBC in line, load regulation, and reference tracking study. They have comparable performance in parametric uncertainty (variation) study, validated using MATLAB/SIMULINK. IMC derived PID is performing better than the IMC controller in line regulation, load regulation, and parametric variations, but in step response checking, they have nearly equal settling time.
An Improved Design of Hybrid Integrated Voltage Regulator Based on DLDO and SCVR

This paper improves a hybrid fully integrated voltage regulator based on digital low-dropout (DLDO) and switched-capacitor voltage regulator (SCVR) by improving the controllers of DLDO and SCVR, respectively. The controller is improved to achieve tunable voltage droop response and larger power efficiency during DLDO mode. Adaptive width scheme is added to increase the power efficiency during SCVR mode. As a result, tunable voltage droop response and 3% peak efficiency rising is achieved during DLDO mode. Using adaptive width scheme, the overall power efficiency is increased by 3% on average and the peak efficiency is increased by 6% during SCVR mode.
Channel Measurement and Modeling for Path Loss Prediction in Vegetated Environment for IEEE 802.11ah Network

The new standard IEEE 802.11ah is ideal targeting large-scale and dense deployments of IoT and M2M solutions due to three specific characteristics: the low operating frequency allowing coverage up to 1km, the ability to simultaneously connect a large number of devices per access point, and low power consumption. Several studies on the characteristics of radio propagation in the bands used by the IEEE standard are being carried out worldwide. In this paper, we evaluate two urban path loss models and four specific path loss models for vegetated environments, based on a measurement campaign performed in an urban environment with a strong presence of vegetation, typical of living areas, squares and urban parks where IoT technologies should be commonly implemented in the coming years. The models proposed by TGah were also evaluated. Based on empirical data, the most accurate models are determined. The characterization of large-scale fading and small-scale fading are presented.
Machine Learning Virtual SEM Metrology

E-beam metrology, both CDSEM metrology and defect scan metrology, have been playing a very critical role in assessing post lithography or post etch patterning quality. SEM images can provide rich visual information for engineers to do qualitative and quantitative analysis. However, the lowe-beam metrology tool throughput makes it impossible to obtain SEM images for very large area. Monte Carlo based SEM image simulations are slow and they also require post lithography or post etch pattern 3D structures as prerequisite. To bridge the gap, we have proposed a Virtual SEM Metrology solution using physics based feature maps and the U-net neural network. With information in aerial image space encoded properly, SEM images of both post lithography and post etch can be predicted accurately enough for practical applications using our proposed Virtual SEM Metrology models.
Apparel Generation via Cluster-Indexed Global and Local Feature Representations

Apparel design requires expertise in aesthetics, which is challenging for non-professional general users. Inspired by the recent advances in data science, in this paper we address the task of apparel generation in a simple way by leveraging a deep neural network model. We propose to generate clothes through three selection steps from the big picture (e.g., type of clothes) to the details (e.g., color) by varying the cluster ID and latent variables. Users can go through these steps to achieve an ideal design. Experiments on a publicly available dateset demonstrate the effectiveness of our method.
An estimation method of calorie intake using photograph and depth image of dishes in medical facilities

In medical and welfare facilities, the staff visually checks the acquired amount of foods to manage resident health. To reduce the load of the task of the staff, we propose a method that automatically estimates the amount of these meal intakes using images of the meal on the tray. To estimate the amount of change in the food on the tray, we used color as well as depth images of the meals. Using color images, we identified 10 types of foods using machine learning. Consequently, we obtained a food identification accuracy of 85%. Furthermore, we estimated the amount of change in the food on the tray before and after eating. To estimate the change amount of foods, we classified the depth images of each food into six categories and identified the food amounts based on these six categories using the ResNet50 network. As a result, we achieved a 90% accuracy of categorizing for each food type. Using the estimated values, we also proposed a calculation method of the acquired number of calories. We also confirmed that the proposed calorie estimation method was acceptable for use in medical institutions.
Automatic Detection of Gender from Face and Palm Images using a 2-CNN Framework

Gender is the most fundamental demographic feature of human beings. Human gender classification using computer vision has become a relevant aspect in a large variety of fields, extending from daily software applications to forensic science, specifically due to the certain surge in consumption of social media and social networking websites. In the past, many attempts have been made for gender classification using conventional models of Convolutional Neural Networks (CNNs) just by mere extraction of features (from the faces only) and classification (CNNs are capable of both). In this paper, we propose a method to classify genders using two different conventional 3-layered CNN models where one uses the facial features and the other uses the palm features of a human for gender classification. This method not only delivers better accuracy than the past single CNN model framework but also achieves the goal of a two-step verification process. We have trained the face model using one publicly available dataset that we have gathered from the online dataset repository Kaggle and we have trained the palm model using another dataset that was constructed by us in consideration of this model. On testing, the proposed framework has shown significant accuracy growth.
Simple Techniques for Designing Passive Power Filter System to Reduced Harmonic Resonance and Improve Power Quality of Electrical Systems with Servo Drive

The proliferation of servo drives (SD) in the industry has led to serious concerns about harmonic resonance and power quality in electrical distribution systems. This paper reported the resonance conditions of electrical systems with motor control using a small servo drive for use in CNC machines. When a large number of servo drives are combined, it results in harmonic resonance occur in the system. Resulting in the destruction of electrical equipment such as power supply, computer, CNC, or servo drive close to each other, etc. The cost of purchasing the above electrical equipment to replace the damaged ones in each month is very high. A simple passive power filter system (PPFs) design is recommended to reduced harmonic resonance. PPFs are applied to the system and the actual installation which is satisfactory and can greatly reduce the damage of electrical equipment. Finally, the factories can save money each month.
An Extension of Stray Voltage Capture for Hard Switching Fault Detection in Power Electronics with Half-Bridge Structure

A short circuit detection based on a Stray Voltage Capture (SVC) cannot robustly detect a Hard Switching Fault (HSF) if an additional stray inductance is present in the short circuit path. Therefore, this paper proposes an extension of the Stray Voltage Capture (ESVC) method to detect a HSF based on the integration of the SVC output. The result shows that a HSF is detected in the MOSFET linear region and can be easily implemented in any converter with a Half-Bridge structure.