Miracle-dz/UniFAR-FTUs huggingface dataset:
Abnormal Traffic Detection Based on Generative Adversarial Network and Feature Optimization Selection

Complex and multidimensional network traffic features have potential redundancy. When traditional detection methods are used for training samples, the detection accuracy of the supervised classification model is affected due to small data samples. Therefore, a method based on generative adversarial networks (GANs) and feature optimization is proposed. First, the feature correlation and redundancy are analyzed by the potential redundancy of network traffic. The feature optimization selection methodofcollaborativelearningautomataisproposed.Second,theconfrontationinteractivetrainingprincipleofthegenerative confrontationnetworkisadapted,inwhichamodelofthegenerativeconfrontationnetworkisproposedtosolvetheproblem thatsmalltraininglabelsamples.Third,theinterdomaindistanceisminimizedbyusingGANandthemultiplekernelvariantofmaximummeandiscrepancy(MK-MMD). Thesharedfeaturesbetweenthesourcedomainandtargetdomaindistribution arelearnedbyapplyingtheinformationbetweenGANconfrontationtrainingandclassificationnetworksupervisiontraining, improvingthedetectionaccuracy.Forth,randomnoisedataandoriginaltraininglabelsamplesaremixedtoformanewtraining set.Theaccuracyisfurtherimprovedbyadoptinggenerativemodelstocontinuouslygeneratesamples. Thefinalclassification resultsareoutputbythe16-dimensionalSoftmaxclassifier.Themethodhasasmalllossratewhenthedatasetsareusedtotrainbytheexperimentalanalysisofalgorithmparametersandsimulationdata. ThemodeloptimizedbyMK-MMDhasstrong generalizationability.Theaveragedetectionaccuracyratesare91. 673%(two-classification)and91.480%(multiclassification) bycomparingmachinelearningandothershallowneuralnetworks,andarethehighestvaluesamongthecomparedmethods.Moreover,theeffectivenessandsuperiorityoftheproposedmethodareverifiedtobethebestbycomparingtherecallrate,false positiverate(FPR),F-measure,AUC.Whentheinterferenceofothersamplesaremixed,theproposedmethodisalsorobust. © 2021 The Authors . Published by Atlantis Press B.V. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Visualizing distribution of pesticide residues in mulberry leaves using NIR hyperspectral imaging

Nowadays, hyperspectral imaging technique has been abroad and successfully used for quality inspection of food products. In this study, the microstructural changes of mulberry were investigated by scanning electron microscope (SEM). Furthermore, NIR hyperspectral imaging system was used to predict the distribution of Pesticide residues in mulberry leaves with the aid of Gas Chromatography. It is primary to extract the characteristic wavelengths in the prediction and visualization of Pesticide residues in mulberry leaves. Successive projections algorithm (SPA), Stepwise regression(SR) and Regression coefficients (RC) were utilized. MLR was applied to build prediction models based on spectral feature in characteristic wavelengths. The best model SPA-MLR ( Rp=0.859 and RMSEP = 38.789) was implemented into the pesticide residues visualization procedure. This study demonstrated that pesticide residues distribution map clearly showed the distribution of Pesticide residues in mulberry leaves. Practical applications Well understanding the effect of Pesticide residues to biological structure is very important for revelation of novel biological function and mechanism of action of the protein. To facilitate more quickly and effectively detect the types of pesticide residues on the surface of mulberry leaves, the microstructural changes of mulberry were investigated by scanning electron microscope (SEM). Furthermore, NIR hyperspectral imaging system was used to predict the distribution of Pesticide residues in mulberry leaves with the aid of Gas Chromatography.
Composite Load Equivalent Modeling of Traction Power Supply System

In recent years, China's high-speed railway from zero to one, from weak to strong, has achieved a leapfrog development. However, the access of a large number of electrified railways is a further challenge for the safety, stability and economy of the power grid, but the current research on the composite load characteristics of traction substations is relatively weak. In this paper, a detailed simulation model of CRH2 emu is built firstly. Based on the analysis of the operation characteristics and load composition of the traction power supply system, a composite load equivalent model of high-speed railway traction power supply system - the mechanism load model of “constant impedance parallel induction motor” is proposed. Finally, the Matlab/Simulink was used to compare the simulation results of the detailed simulation model with those of the equivalent model, and the results showed that the obtained power curve was highly consistent, which verified the accuracy and practicability of the equivalent load model.
A Decoupling Network for Resonant and Non-Resonant Sub-1 GHz MIMO Mobile Terminal Antennas With Improved Compactness and Efficiency

In this work, the design procedure of a new decoupling network for different antennas is presented for mobile terminals operating in the Sub-1 GHz band (from 0.698 to 0.96 GHz). To evaluate its applicability for different resonant and non-resonant antenna types, two antenna elements have been chosen to be decoupled using the proposed decoupling network: a printed inverted-F antenna and a capacitive coupling element antenna. Simulation and measurement results showed that the decoupling network offer promising improvements in terms of impedance matching, port isolation, envelope correlation coefficient (ECC), and total efficiency. In addition, the antennas are also investigated with the ergodic capacity, multiplexing efficiency, gain, and diversity gain (both calculated using maximal ratio combining technique). Due to the contribution of the decoupling network, the ECC of both types of multiple antennas are less than 0.3, with a 30 % improvement in total efficiency and an inter-element spacing miniaturization of 34 %.
Hybrid Beamforming With Reduced RF Chain Based on PZF and PD-NOMA in mmWave Massive MIMO Systems

Hybrid beamforming (HB) is a solution for reducing radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems. By reducing RF chains, HB can achieve energy efficient transmission compared to fully digital beamforming of massive MIMO system. However, if the resolution of RF precoder is not quantized, the energy consumption of HB may become similar to fully digital beamforming. And, if the resolution of RF precoder is heavily quantized, the spectral efficiency of HB is decreased significantly. Non-orthogonal multiple access (NOMA) is one of the emerging technologies for the fifth generation (5G) or beyond fifth-generation (B5G) wireless communication to support high spectral efficiency. Especially, power domain NOMA (PD-NOMA) can share same resources to transmit signals and divide the signals in power domain by using successive interference cancellation (SIC). In this paper, the adoption of NOMA shows increase of spectral efficiency in a low-resolution RF precoder. To apply NOMA, two users with high channel correlation are selected according to the proposed criterion. Then, the RF precoder and digital precoder that are applied to both users at the same time are determined. Finally, intra-pair user fairness is achieved by power allocation. The ratio of channel power is compared for each user in each pair. Then, users with high channel power are assigned with low power. It is shown that the spectral efficiency of the proposed scheme is higher than the conventional scheme in heavily quantized RF precoder.
View Identification Assisted Fully Convolutional Network for Lung Field Segmentation of Frontal and Lateral Chest Radiographs

Locating lung field is a critical and fundamental processing stage in the automated analysis of chest radiographs (CXRs) for pulmonary disorders. During the routine examination of CXRs, using both frontal and lateral CXRs can benefit clinical diagnosis of cardiothoracic and lung diseases. However, the accurate segmentation of lung fields on both frontal and lateral CXRs is still challenging due to the blurry boundary of the lung field on lateral CXRs and the poor generalization ability of the models. Existing deep learning-based methods focused on lung field segmentation on frontal CXRs, and the generalization ability of these methods on the different type of CXRs (e.g., pediatric CXRs) and new lung diseases (e.g., COVID-19) has not been tested. In this paper, a view identification assisted fully convolutional network (VI-FCN) is proposed for the segmentation of lung fields on frontal and lateral CXRs simultaneously. The VI-FCN consists of an FCN branch for lung field segmentation and a view identification branch for identification of the frontal and lateral CXRs and for enhancing the lung field segmentation. To improve the generalization ability of VI-FCN, six public datasets and our frontal and lateral CXRs (over 2000 CXRs) were collected for training. The segmentation of lung fields on the Japanese Society of Radiological Technology (JSRT) dataset yields mean dice similarity coefficient (DSC) of 0.979 ± 0.008, mean Jaccard index ( $\Omega $ ) of 0.959 ± 0.016, and mean boundary distance (MBD) of 1.023 ± 0.487 mm. Besides, the VI-FCN achieves mean DSC of 0.973 ± 0.010, mean $\Omega $ of 0.947 ± 0.018, and mean MBD of 1.923 ± 0.755 mm for the segmentation of lung fields on our lateral CXRs. The experiments demonstrate the superior performance of the proposed VI-FCN over most of existing state-of-the-art methods. Moreover, the proposed VI-FCN achieves promising results on untrained pediatric CXRs and COVID-19 datasets.
R & D Intensity and Corporate AI Strength Attention: The moderating role of Coercive Pressure and Mimetic Pressure

Based on the resource-based view and institutional theory, this paper used the panel data of 101 listed companies in AI-related stocks from 2011 to 2017 as samples to analyze and test the influences of corporate R & D intensity on corporate AI strength attention as well as the moderating effect of institutional pressures during this process. As shown by resulte, corporate R & D intensity can positively affect the corporate AI strength attention. Coercive pressure can negatively adjust the relationship between corporate R & D intensity and corporate AI strength attention. Mimetic pressure can positively adjust the relationship between corporate R&D intensity and corporate AI strength attention. This paper is theoretically and practically significant to explore the relationship between corporate R&D intensity and corporate social responsibilities.
SAR Image Scene Classification Based on Metric Learning and CNNs

With the development of synthetic aperture radar (SAR) imaging techniques, many high-quality images are acquired for exactly earth observation. As a result of heavy speckle, it is difficult to extract effective representation of different scene images to distinguish them accurately. In this paper, a metric-learning based convolutional neural network (CNN) is proposed to characterize SAR images. Specifically, a regularization term is proposed, which is constrained by the Mahalanobis distance. The regularization term learns a new metric space based on CNN, where the similar images should be close and those of dissimilar images should be separated. Comparing with the other approaches, experimental results of the proposed approach illustrate a superior classification for different scenes.
Nine-Level Inverter Based on Resonant Switched-Capacitor and NPP/NPC Unit

By using a single dc voltage source to feed a 4-level switched-capacitor (SC) unit and a 3-level NPP/NPC unit simultaneously, a new 9-level inverter is developed in this work. The 4-level unit is implemented by cascading an inverting half-bridge with a double- or half-mode SC circuit. It involves four transistors, two diodes and two capacitors with self-balanced voltage. Inrush charging current variation di/dt of capacitors at switching instants is successfully suppressed by inserting a small inductor into the SC unit. Compared with the existing solutions for 9-level inverters, the proposed circuit has the advantages of simpler structure and lower cost per level. These make the proposed inverter very suitable for low voltage applications such as PV and fuel cells. Circuit configuration, operation, system characteristics under POD-PWM modulation are analyzed in detail. Feasibility and dynamic performance are also demonstrated experimentally.
A Novel Disease Prediction Method Based on Inductive Representation Learning

Using rich imaging and non-imaging information to complete the disease prediction task, the model is required to simultaneously represent individual characteristics and data associations between subjects from a potentially large population. The graph provides a potential framework for such tasks. In this study, we extend GCN to inductive representation learning, combine imaging and non-imaging data for population brain image analysis, and propose an inductive representation learning method for disease prediction based on population graph. Specifically, the structure represents the population as a sparse graph, where the vertices are associated with image-based eigenvectors and the edges represent the population phenotypic information. Then the inductive representation learning model was trained on a partial labeled graph to infer the category of unlabeled nodes based on node characteristics and pairwise associations between subjects. The results demonstrate the improvement of our proposed approach on the ABIDE dataset and emphasize the importance of obtaining more implicit information from the diagram structure. This has an obvious impact on the performance of the prediction model, and makes the prediction accuracy of 77.2%, which is significantly better than the standard linear classifier which only considers a single feature and the previous graph representation algorithm.
Analytical Calculation of the Residual ZVS Losses of TCM-Operated Single-Phase PFC Rectifiers

Triangular-current-mode (TCM) modulation guarantees zero-voltage-switching across the mains cycle in AC-DC power converters, eliminating hard-switching with a minor <inline-formula><tex-math notation="LaTeX">${\approx} {30}{\%}$</tex-math></inline-formula> penalty in conduction losses over the conventional continuous current mode (CCM) modulation scheme. TCM-operated converters, however, include a wide variation in both switching frequency and switched current across the mains cycle, complicating an analytical description of the key operating parameters to date. In this work, we derive an analytical description for the semiconductor bridge-leg losses in a TCM AC-DC converter, including the rms current and/or conduction losses, switching frequency, and switching losses. For SiC <sc>mosfet</sc>s, we introduce a new loss model for switching losses under zero-voltage-switching, which we call “residual ZVS losses”. These losses include the constant <inline-formula><tex-math notation="LaTeX">$C_\text{oss}$</tex-math></inline-formula> losses found in previous literature but must also add, we find, turn-off losses that occur at high switched currents. The existence and modeling of these turn-off losses, which are due to currents flowing through the Miller capacitance and raise the inner gate source voltage to the threshold level and accordingly limit the voltage slew rate, are validated on the <italic>IMZA65R027M1H</italic> 650V SiC <sc>mosfet</sc>. The complete loss model – and the promise of TCM for high power density and high efficiency – is validated on a 2.2 kW hardware bridge-leg demonstrator, which achieves a peak 99.6<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> semiconductor efficiency at full load. The proposed, fully-analytical model predicts bridge-leg losses with only 12<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> deviation at the nominal load, accurately including residual ZVS losses across load, modulation index, and external gate resistance.
Modular Policy Evaluation System: A Policy Evaluation Framework Based on Text Mining

Nowadays, in the big data era, data processing technology facilitates us to get the utmost out of sufficient information in the data. However, few scholars apply big data technology to the field of policy evaluation. Therefore, under the Policy Modeling Consistency(PMC) index model framework, this paper thoroughly mines the valuable information in policy texts and proposes a modular policy evaluation system combining text mining and machine learning methods. The system is divided into four processing modules, including data acquisition, data processing, index evaluation construction, and score evaluation. Compared with the traditional policy evaluation methods, the modular policy evaluation system presents the advantages of objectivity, high accuracy, and high efficiency, assisting in government policies' implementation.
A Review on Explainability in Multimodal Deep Neural Nets

Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks. Several topics on multimodal AI and its applications for generic domains have been covered in this paper, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain.
A 2.4-GHz 1.3-mW OQPSK RF Front-End TX Based on an Injection-Locked Power Amplifier

An injection locking power amplifier is presented. The proposed solution, tailored to quadrature phase shift keying (QPSK) modulation for IoT has been realized by exploiting the property of an injection-locked frequency divider to work as a phase rotator. Hence, the divider’s output is directly coupled to the antenna by a transformer to deliver the desired output power. The combination of these two ideas resulted in a high-efficiency, high-bandwidth QPSK RF front-end for IoT, capable of operating at up to 120 Mbit/s and delivering 1.3-mW output power while burning 3.4 mW.
Speech Emotion Analysis of Netizens Based on Bidirectional LSTM and PGCDBN

In recent years, the application of speech emotion recognition (SER) in the supervision of Internet public opinion has received increasing attention. This study proposes a new SER algorithm to analyze the public opinion information of network platforms. Firstly, we extract different spectrum features from speech signals and combine them into frame level speech features. Then, we select conditional deep confidence network (CDBN) which has the ability to learn sequential features as the final classification model. We apply particle swarm optimization (PSO) and genetic algorithm (GA) during the fine-tuning stage of the CDBN to obtain more suitable optimal weights of the whole network, and propose the PSO-GA-CDBN (PGCDBN) model. Compare with the traditional back propagation (BP) algorithm, our training method accelerates the convergence speed of the network and improves the robustness and recognition performance of the network. In our experiment, we used the Chinese Academy of Sciences’ Institute of automation (CASIA) Chinese emotional corpus and self-collected Chinese speech datasets, which were collected from Sina Weibo, Tik tok and other online social media platforms. Compare with the popular emotion classifiers such as support vector machine (SVM), deep residual network (ResNet), long short-term memory (LSTM) neural network, DBN, our proposed PGCDBN achieves the best recognition results from both datasets. In addition, we use bidirectional LSTM before PGCDBN to further process the extracted speech features, and the result of bidirectional LSTM has stronger speech signal expression ability. The average recognition accuracy of this new hybrid deep learning model algorithm in two datasets is 98.67%, which can be used for the supervision of netizens’ opinions.
Research on Recognition of High Frequency Signals of Cable Based on Wavelet Energy Distribution

There may be a variety of high frequency signals on the line when the power cable is in operation. It is of great significance to master the causes of high frequency signals for quickly eliminating cable faults and realizing the operation status monitoring of the cable. In order to distinguish different kinds of high frequency signals on cable lines, a recognition method of high frequency signals was proposed, which is based on wavelet energy distribution and time domain features. The model of 10kV cable transmission system was established in ATP-EMTP, and five kinds of high frequency signals, including short circuit fault, lightning stroke with and without causing fault, switch closing and partial discharge(PD), were simulated and studied. The collected current traveling wave of the high frequency signals were decomposed by the wavelet transform, and the wavelet energy distribution rules of different kinds of high frequency signals were obtained. According to the distribution law and time domain characteristics, five kinds of high frequency signal recognition methods for cable lines were proposed. A large number of simulations verified the correctness of the recognition method.
mm-Wave Mixer-First Receiver With Selective Passive Wideband Low-Pass Filtering

The front-end of a conventional millimeter-wave receiver (RX) consists of a bandpass filter (BPF) and low-noise amplifier (LNA) prior to the frequency down-conversion mixer. In interference-limited wireless channels, it is more footprint and power efficient to remove the BPF and LNA, and realize the mixer as a “passive” switching scheme. The noise figure of this passive mixer-first RX is improved when numerous such RXs are placed in a phased array or multi-input multi-output (MIMO) configuration. The passive switching mixer can be followed with an on-chip selective passive low-pass filter (LPF) with a small footprint given the large channel bandwidth that is typical in mm-wave applications. In this article, system and circuit design aspects of mm-wave passive mixer-first RXs are discussed and validated by a proof-of-concept 65-nm CMOS prototype. The implemented RX operates in the 21–29-GHz frequency range with 0.5-GHz instantaneous single-sideband (SSB) bandwidth, and achieves stopband to passband rejection of >49 dB, an in-band <inline-formula> <tex-math notation="LaTeX">$\rm {ICP_{-1\,dB}}$ </tex-math></inline-formula> of −6 dBm, and out-of-band <inline-formula> <tex-math notation="LaTeX">$\rm {B_{-1\,dB}} > 3.4$ </tex-math></inline-formula> dBm.