Miracle-dz/UniFAR-FTUs huggingface dataset:
A deep partial adversarial transfer learning network for cross-domain fault diagnosis of machinery

Recently, the deep transfer learning-based methods have been widely applied in intelligent fault diagnosis of modern manufacturing equipment in real-industrial scenarios, which are capable of identifying the health conditions of unlabeled target samples under various working conditions. In transfer learning- based intelligent fault diagnosis, the source diagnostic knowledge, which is usually extracted by supervised learning approaches, is transferred and reused in related target fault identification tasks. However, the tremendous success of these transfer learning methods is mainly achieved in the field of close-set cross-domain fault diagnosis. But in practical applications, a partial cross-domain scenario is more common and difficult, where the health conditions of the target domain are less than that of the source domain. To address this issue, a deep partial adversarial transfer learning network (PATLN) based on convolutional neural networks and adversarial training is proposed. Experiments on a public rolling element bearing dataset verify the effectiveness of the PATLN method.
Research on High Degree-of-Freedom WPT Systems Based on Dipole Coil

Wireless power transfer (WPT) technology achieves complete electrical isolation between power supply and electronic load. It has attracted worldwide attention due to its advantages of safety, reliability and flexibility. However, limited transfer distance and low transfer efficiency have always restricted the further application and promotion of WPT technology. In this paper, the WPT systems based on magnetic dipole-coil structure are proposed, the system has a multi-directional wireless power transfer (MD-WPT) ability within 360erange though optimizing design of a long-bar ferrite core. The optimum design method has been provided and equivalent circuit of the MD-WPT is given for better analysis. The electromagnetic properties of ferrite core in the dipole-coil multi-directional WPT systems are also studied. The power transfer efficiency of the dipole-coil MD-WPT systems can be in-creased by more than 75% and the output power is more than 45W when the ratio of transfer distance to coil length is 1, which can greatly improve the power transfer efficiency and the degree of freedom of power supply. The research results can provide valuable guidelines for multi-directional WPT technologies in the fields such as unmanned aerial vehicle, portable devices, and Internet of Things in the future.
Circuit Parameter Identification of Degrading DC-DC Converters Based on Physics-informed Neural Network

Power Electronic Systems (PES) is widely used in energy sectors such as renewable energy and aerospace. It is very important to design a reliable PES health monitoring system. This paper provides a new condition monitoring method based on Physics-informed Neural Network (PINN). Although the actual PES has a complex topology and is in a dynamically changing operating environment, the operation process does not violate the circuit physical models. Considering the charge and discharge process in the DC-DC converter, the physical formula is derived through the state-space average method. Then the physical formula is added to the deep learning model of LSTM as prior knowledge, to estimate the degradation parameters of the DC-DC converter. The uncertainty method is used to determine the weighting coefficients for data fitting and physical information fitting tasks. The PINN method can improve the estimation accuracy and generalization ability of the model in the case of limited data, which is conducive to the realization of the condition monitoring of complex PES. It is significant to improve the reliability of new energy vehicles and military equipment.
Standstill Sensorless Self-Commissioning Strategy of Synchronous Machine Considering Rotor Rotation Reduction Technique

This article proposes a self-commissioning strategy to identify magnetic flux saturation characteristics of synchronous machines at a standstill without rotor locking and the position sensor. The self-commissioning process is required to identify the machine's magnetic flux-linkage saturation characteristics. An inverter feeds the machine, where the induced current and the flux-linkage data are gathered from multi-points. The induced current generates the torque, including the magnetic and reluctance torque, so that the machine rotates. However, if the rotor moves, the data would be distorted since the data is gathered under the assumption that the rotor stands still. The proposed method considers not only the magnet torque but also the reluctance torque, which is not considered in the conventional method. The proposed method maintains the rotor speed and position by canceling torque. The proposed method was tested with an 11 kW IPMSM, and the verification results proved the effectiveness of the proposed methods.
Voltage Quality of an AC Grid Supplying a Railway Power System with Energy Saving Strategy

Railway industry is experiencing a strong need of renewal and smart technology. Hence, advanced approaches are needed to improve efficiency and sustainability of railway operations. In urban contexts, the public railway sector is object of an inevitable process of revamping, which involves expectations of end-users, railway operators, local supply distributors or local authorities. This paper examines an urban railway power system, where a smart scenario is envisaged. Particularly, an Italian AC/DC railway power infrastructure is considered, and an energy efficiency strategy is assumed. Aim of the paper is to evaluate the voltage quality in the supplying distribution network for any operating conditions of the railway system. Also, the energy efficiency strategy is arranged to improve voltage quality. More specifically, the strategy exploits electrical energy storage systems recharged by regenerative braking of rolling stocks in order to increase efficiency of the whole system. Also, the storage system allows shaving the peak load demand aimed at improving the voltage quality. Numerical simulations are carried out, they provide power quality analysis by examining nodal voltages as a function of the efficiency measure. The behavior of the railway power system has been analyzed under different operating conditions.
Multiphysics modelling of manufacturing processes: A review

Numerical modelling is increasingly supporting the analysis and optimization of manufacturing processes in the production industry. Even if being mostly applied to multistep processes, single process steps may be so complex by nature that the needed models to describe them must include multiphysics. On the other hand, processes which inherently may seem multiphysical by nature might sometimes be modelled by considerably simpler models if the problem at hand can be somehow adequately simplified. In the present article, examples of this will be presented. The cases are chosen with the aim of showing the diversity in the field of modelling of manufacturing processes as regards process, materials, generic disciplines as well as length scales: (1) modelling of tape casting for thin ceramic layers, (2) modelling the flow of polymers in extrusion, (3) modelling the deformation process of flexible stamps for nanoimprint lithography, (4) modelling manufacturing of composite parts and (5) modelling the selective laser melting process. For all five examples, the emphasis is on modelling results as well as describing the models in brief mathematical details. Alongside with relevant references to the original work, proper comparison with experiments is given in some examples for model validation.
A Study on Digital Active Gate Driving of DC-DC Converter for Suppressing Switching Surge Voltage

High-speed switching of SiC unipolar power semiconductor devices induces an anomalous switching surge voltage in the switching operation of power conversion systems. An active gate driving can control the voltage/current behaviors of the power semiconductor device. It expects to suppress the switching surge voltage. We have focused on a fully digitalized active gate driver. In this paper, the digital active gate driver is applied to a SiC MOSFET in a boost DC-DC converter to improve its switching operation. The experimental results revealed that the driver successfully suppressed the switching surge voltage of SiC MOSFET in the converter.
sEMG Signal based Hand Gesture Recognition by using Selective Subbands Coefficients and Machine Learning

The hand disability can limit the integration of concerned subjects in daily life activities. The life quality of impaired persons can be augmented by using prosthetic hands. However, an effective realization of prosthetic body parts is challenging. Accurate identification of desired hand gestures is not straightforward. In this paper, an effective multirate solution is proposed for automated identification of six different hand gestures by processing the Surface Electromyogram (sEMG) signals. Firstly, the signal is divided into finite-length segments. Each segment is called as an instance. Secondly, the sEMG signal is conditioned by using the noise removal linear phase filter. Onward it is downsampled and decomposed in subbands by applying the Wavelet Decomposition scheme. Afterward, the subbands are selected based on frequency content and features are mined from the selected subbands. Finally, the extracted features sets are conveyed to the machine learning-based classifiers for automated categorization of hand gestures. The system performance is tested by using a publicly available sEMG dataset. To avoid any biasness in findings the 10-fold cross-validation (10-CV) strategy is followed with multiple evaluation measures. Results show that the devised method secures a 6.26-fold dimension reduction while attaining the average values of 97% accuracy, 98% specificity, and 99% AUC during an automated categorization of the six hand gestures of the considered subject.
Identification and Localization of COVID-19 Abnormalities on Chest Radiographs using Ensembled Deep Neural Networks

With the hit of the global pandemic COVID-19, the chest X-ray domain has gained prominence. It has been recognised as one of the principal methods to learn the presence of infection and its effect on various internal organs like the lungs. Chest radiographs show abnormalities due to COVID-19 that appear similar to the anomalies caused by other viruses and bacteria, thus making it challenging for technicians to detect. Therefore, it becomes almost inevitable to have a computer vision model that identifies and localizes the COVID-19 virus to help doctors provide an immediate and confident diagnosis. The models in computer vision tasks have seen considerable advancements in deep learning, so the proposed model tried to integrate a few of them to come up with a model for classifying and localising the diagnosis of COVID-19 using chest X-rays. This paper ensembles a few state-of-the-art models in classification and object detection to build a model for chest radiograph diagnosis. The proposed ensembled model is found to achieve the mean Average Precision value of 0.627 on SIIM-FISABIO-RSNA COVID-19 dataset.
Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation

The task of image segmentation is to classify each pixel in the image based on the appropriate label. Various deep learning approaches have been proposed for image segmentation that offers high accuracy and deep architecture. However, the deep learning technique uses a pixel-wise loss function for the training process. Using pixel-wise loss neglected the pixel neighbor relationships in the network learning process. The neighboring relationship of the pixels is essential information in the image. Utilizing neighboring pixel information provides an advantage over using only pixel-to-pixel information. This study presents regularizers to give the pixel neighbor relationship information to the learning process. The regularizers are constructed by the graph theory approach and topology approach: By graph theory approach, graph Laplacian is used to utilize the smoothness of segmented images based on output images and ground-truth images. By topology approach, Euler characteristic is used to identify and minimize the number of isolated objects on segmented images. Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network better than the baseline without a regularization term.
Process and Runtime Variation Robustness for Spintronic-Based Neuromorphic Fabric

Neural Networks (NN) can be efficiently accelerated using emerging resistive non-volatile memories (eNVM), such as Spin Transfer Torque Magnetic RAM(STT-MRAM). However, process variations and runtime temperature fluctuations can lead to miss-quantizing the sensed state and in turn, degradation of inference accuracy. We propose a design-time reference current generation method to improve the robustness of the implemented NN under different thermal and process variation scenarios with no additional runtime hardware overhead compared to existing solutions.
Federation Learning for Intrusion Detection Methods by Parse Convolutional Neural Network

Intrusion detection algorithms based on deep learning are currently a hot topic in the field of intrusion detection research, but most of the research focuses on how to improve the algorithms to improve the accuracy of intrusion detection, while ignoring the problem that the limited labeled data generated by a single organization is not enough to train a deep model with high accuracy in practice. The paper proposes a federal learning and convolutional neural network-based intrusion detection method that can expand the data volume by jointly training the model with data sets from multiple participants. The method uses a federation learning framework to design a deep learning-based intrusion detection model. The data dimensionality is first reconstructed by data padding to form two-dimensional data, then the DCNN network is used for feature extraction and learning under the mechanism of federation learning, and finally the model is trained with a softmax classifier for detection. In addition, aiming at the over-fitting problem caused by the compression and over-parameterization of deep neural networks, this paper proposes a model pruning method based on sparse convolutional neural network according to the weight parameters of the convolutional layer and the Batch Normalization layer (BN). This section will introduce the implementation details of this method. The experimental results show that this method largely reduces the training time and maintains a high detection rate. In addition, the model ensures data security and privacy compared to typical intrusion detection models.
A Low-cost Active Reflected Wave Canceller for MMC Motor Drive using SiC Devices

SiC based MMCs have shown great potential for medium voltage motor drives. Although MMC motor drive has smaller dv/dt compared to 2-level motor drive, the rising/falling edge of each voltage step is still high due to fast switching speed of SiC devices, which will lead to reflected wave phenomenon and voltage surge at motor terminal. This paper proposed an active reflected wave canceller featuring low cost for MMC motor drive to solve this issue. A voltage pulse is injected into the system at each rising/falling edge which cancels the reflected voltage. The operation modes are analyzed and different methods with the same principle are also discussed. The simulation results are presented to validate the effectiveness of the proposed method.
Deep Feature Selection for Benign and Malignant Classification appearing as Ground Glass Nodules

Lung cancer is the deadliest diseases, and adenocarcinoma in them accounts for nearly 40% due to high mortality and morbidity. Most patients suffer from the advanced lung cancer rather than the early one which can be cured by medical intervention. Different from the advanced lung cancer, the early lung adenocarcinoma is shown as ground glass nodules (GGNs) in CT images. GGNs are insidious, symptoms, and inconspicuous, making patient be misdiagnosed. In this paper, considering the powerful feature learning capability of convolutional neural network (CNN) and the low similarity between medical images and natural images, we proposed a benign and malignant classification method after improving the existing feature-based transfer learning method, where the use of transfer learning method aims at overcoming the problem of deficient GGN samples. Essentially, we extracted the features of GGNs by determining the network layer one by one from different pre-trained CNN networks. Then the optimal layer with the highest classification accuracy is found compared with the existing feature-based transfer learning method. When transferring the features from the "pool5" layer of AlexNet, the classification accuracy can attain 95.3%, the sensitivity 97.1%, and the specificity 93.3%, respectively. So far, the classification result is best compared with other results in literature.
Energy Management and Trading of a Community Microgrid using Blockchain Approach

The concept of the microgrid is developing due to the increased use of distributed energy resources at the community level. The benefits from microgrid enable the concept of a community microgrid. The community microgrid system helps in the energy management and operation on its own thus making it a decentralized system. Peer-to-Peer (P2P) energy trading is the most common and efficient way of exchanging power within the community, or with the neighbor community, or with the grid thus making the whole system easy. This paper proposes an energy management system of the local community in a decentralized manner and therefore proposes a model for power trading using the blockchain approach. In this paper, we considered all aspects of the systems like using Demand response programs. This results in a cost-effective way by verifying using simulations.
Robust interference suppression of three phase structural uncertainty inverter system based on equivalent input interference method

In the actual filter system of three-phase inverter, there are uncertainties of inductance, resistance and capacitance. The traditional equivalent input disturbance (EID) compensation method can compensate and suppress any external disturbance. The improved EID controller designed based on Lyapunov functional method can achieve robust control of structural uncertainty systems. But the tracking performance based on this method is poor and cannot be used in the actual system of sinusoidal signal tracking. In this paper, a three-phase inverter with uncertain structure is used to inject constant amplitude and frequency current into the power grid or load. Park transform is used to transform tracking current signal and EID controller into rotating DQ coordinate system. Combined with Lyapunov functional method, the EID coupling parameters are designed to make the component uncertainty inverter standby robust.
Physical Layer Security Analysis in Ambient Backscatter Communication With Source and Reader Mobility

With the advent of technology and growing demand for energy-efficient technology in wireless communication, there is a need to ponder over the Security aspect of the Wireless Ambient system. In this paper, we have done an Analysis of Physical Layer Security for Ambient Backscatter Communication having Multiple Tags and Mobile sources using Performance Analysis based on Secrecy Outage Probability. More specifically we have derived an equation for SOP considering source mobile and plotted results with varying relative speed and the number of tags.
Special Session on RF/5G Test

This paper presents an innovative integrated load-pull bench at 160 GHz. The proposed system, which is designed in a 55-nm BiCMOS technology, is tailored to deal with all of the measurement functions including signal generation, shaping, and magnitude and phase measurement. This system will allow precise characterization of Devices-Under-Test (DUTs) (i.e., circuits and/or devices) when integrated together with the DUT. In addition, the proposed system could also be envisioned as a System-on-Chip (SoC) that, when reported into an RF probe, could serve as a test bench for any integrated circuit.
Tendencia de la mortalidad por cáncer en las capitales e interior de Brasil entre 1980 y 2006

OBJETIVO: Analizar la tendencia corregida de la mortalidad general por cancer y principales tipos en las capitales y demas municipios de Brasil entre 1980 a 2006. METODOS: Los datos sobre obitos (n= 2.585.012) originados por cancer entre 1980 y 2006 fueron obtenidos en el Sistema de Informaciones sobre Mortalidad, y los demograficos, en el Instituto Brasileno de Geografia y Estadistica. Las tasas de mortalidad general por cancer y principales tipos fueron corregidas redistribuyendo proporcionalmente 50% de las muertes mal definidas y estandarizandolas por edad segun poblacion patron mundial. Las curvas de tendencia para Brasil y grandes regiones fueron calculadas para capitales y demas municipios segun sexo y evaluadas por medio de regresion linear simple. RESULTADOS: Entre los hombres, las tasas de mortalidad para los canceres de pulmon, prostata y colorrectal fueron ascendentes; declinantes para el de estomago y estables para el de esofago. Entre las mujeres, hubo aumento de la mortalidad por cancer de mama, pulmon y colorrectal; y disminucion de las tasas para los canceres de cuello uterino y de estomago. La evolucion de la mortalidad vario entre las regiones del Pais, con patrones distintos entre las capitales y demas municipios. CONCLUSIONES: La correccion de las tasas de mortalidad con redistribucion de los obitos mal definidos aumento la magnitud de la mortalidad general por cancer en Brasil en cerca de 10% en 1980 y 5% en 2006. En los municipios del interior no se observo tendencia de disminucion o estabilidad como en las capitales. Menor alcance de las acciones de prevencion y la dificultad de acceso a servicios diagnostico y tratamiento para cancer para la poblacion residente fuera de los grandes centros urbanos pueden explicar, en parte, estas diferencias.
A WiFi-based System for Recognizing Fine-grained Multiple-Subject Human Activities

Device-free human activity recognition has become a topic of much interest in recent years. While there is much existing work on course-grained human activity recognition, the recognition of fine-grained human activities is still a research challenge. In this paper, we propose a new approach using CSI and RSSI WiFi data to recognize fine-grained human activities. We selected 4 different fine-grained human activities from a human-to-human interaction dataset and defined some frequency features over CSI and RSSI data to use as input to our classification model. Using some classification methods and the K Nearest Neighbors (KNN) classifier, we achieved 97.5% of accuracy in fine-grained human activity recognition.