Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multivariate Logistic Regression Model with Over Sampling for Bronchopulmonary Dysplasia Associated with Pulmonary Hypertension in Very Preterm Infants: A Multi-Center Retrospective Study

Bronchopulmonary dysplasia associated with pulmonary hypertension (BPD-PH) is considered as one of the most serious complications in premature infants, and its early detection and intervention can help improve the patients' prognosis. This study aims to apply machine learning technique to predict very premature infants with BPD-PH. 761 clinical records were collected from the neonatology department of four public hospitals in China. After data preprocessing, feature selection and model selection, 5 of 47 features, including invasive ventilator time, BPD index, ventilator-associated pneumonia, pulmonary hemorrhage, and early diagnosis of PH are used to build a multivariate logistic regression model. To work around the unbalancing issue of the dataset, oversampling algorithms have been applied to improve the model and achieve 94% recall, 88% accuracy, and 0.94 area under the receiver operating characteristic curve, which is sufficient to support clinicians to make early diagnosis and form better treatment plan for patients with BPD-PH.
Modeling and Analysis of Concrete-air Cross-media Wireless Power Transfer Systems

Aiming at the influence of concrete addition on the transmission performance of concrete-air cross-media wireless power transfer system, this paper uses theoretical calculation combined with numerical simulation to analyze, first establishes the system model after concrete addition based on equivalent circuit theory, and uses field theory to derive the change of mutual inductance of key parameters of the system. Then, COMSOL simulation was used to compare the magnetic field changes of the system before and after concrete was added under different external conditions, and the relevant electrical parameters were extracted. Finally, Simulink simulation was used to verify the influence of concrete addition on the transmission performance of the system. The results show that the addition of concrete medium will affect the self-inductance of the resonant coil and the mutual inductance between the coils, and ultimately lead to the degradation of the transmission performance of the system.
Switchable Fuel Cell Electrolysis System For Grid-Related Purposes Using Real Bus Depot Data

The combination of gas-to-power and power-to-gas plants in a multi-energy system leads to an increase in the resilience of the participating grids and sectors through the resulting combination of electricity and hydrogen as energy carrier. Especially with regard to the electrical energy supply, the fluctuating feed-in of renewable energy generation plants such as wind and photovoltaic plants can be counteracted in this way. This article shows possible applications of a novel switchable fuel cell electrolysis system for grid-related purposes and its potential for stabilizing electrical energy systems. The primary use case of the system investigated herein is an emergency power supply and its main advantages compared to conventional emergency power supplies are i) the ability to be operated with green hydrogen, ii) that the system is nearly maintenance-free due to the permanent operation, and iii) potential for cost reduction for the operator. The technical description and simulative investigation of the application cases peak shaving and power factor correction are presented in this article. By choosing different time intervals in the combined simulation environments of the hydrogen and electrical domains, the overall simulation time is reduced. It is further concluded that the system presented here is very well suited for peak shaving and reactive power compensation.
Dynamic Modeling and Simulation of a Switched Reluctance Drive in Electric Locomotive

The results of switched reluctance drive with linear load speed control. In order to investigate the operational behaviour as well as special operating states of the entire drive system, a simulation model has been developed with the program SIMPLORER. A structural diagram of the drive with an intermediate direct current link is proposed. The possibility of using a DC–DC converter with a two–circuit control system is considered. Transient responses plots and mechanical characteristics electric locomotive are given.
DiffSDF: Learning Implicit Surface from Noisy Point Clouds

Computing surface properties such as surface normals, curvatures, and reconstructing surfaces from noisy point clouds are a few key tasks in 3D point cloud processing. UUsing theSigned Distance Function (SDF) to represent the implicit surface of a point cloud is an efficient way to accomplish these tasks at the same time. Recently, it has been shown that SDF can be effectively learned by an MLP neural network. However, existing works use the distance values as the supervised signal only, which cannot guarantee the learned to be a valid sign distance function. Some methods assume all the input points are on the zero level, hence they cannot deal with noisy point clouds. In this paper, we propose a novel approach, namely DiffSDF, which takes a noisy point cloud as input and estimates a continuous SDF that implicitly represents the underlying shape for the input data. In addition, we supervise the gradient of the SDF, calculated from differentiating the MLP network with respect to the input. Through experiments, we show that our approach achieves superior results in multiple raw point cloud processing tasks.
Flexible System Architecture of Stand-alone PV Connected System with Energy Storage

This article proposed a flexible system architecture of a stand-alone photovoltaic connected system (SPVS) with energy storage. An SPVS with energy storage requires power management architecture to account for various operating modes. A co-ordinate controller is often required to manage the change in control architecture depending on the operating mode. This architecture contains a boost converter (BC), and a bidirectional switched quasi-Z-source DC-DC converter (BSQZSDC). The BC is used for two purposes. The first is to extract the maximum power from the PV by using the MPPT technique. The second is to control the DC link voltage when the SPVS is not in MPPT mode. This BSQZSDC is used for energy management and regulates DC link voltage when SPVS is operating in MPPT mode. To regulate the DC bus voltage and battery current in different modes of operation, BSQZSDC makes use of an effective control approach, the Integral Sliding Mode Control discussed in this paper. To prove that the controller is effective in fulfilling the control objectives, simulations were run.
Investigating the Influence of Background Music on the Performance of a cVEP-Based BCI

Brain-computer interfaces (BCIs) like e.g. different types of EEG-based BCI spellers (up to date the most common BCI applications) allow new methods of control and interaction with computers and machines. The impact of background music and noise on user's performance (information transfer rate - ITR and accuracy) has already been investigated for different types of spellers, and usually led to a distractive, decreasing effect on the BCI performance, though there is always room for improving the subjective user experience. In this study, 11 participants used a cVEP-based BCI to perform two spelling tasks “BCI_AND_MUSIC” and “CONTRARY”, while listening via headphones to a standard instrumental song, a self-chosen instrumental song (“own music”), or to noise conditions: “no music” and “white noise”. Objective factors such as the blood pressure and heart rate were measured after each spelling task, and questionnaires were answered. The BCI accuracy was close to 100% in most cases, while the lowest accuracy was reached while listening to the individually selected, self-chosen music (“own music”). Similarly, the ITR was the highest in case of “no music” and lowest in case of “own music” (which, on the other hand, resulted in a better mood and higher excitement of the participants). Thus, while the general condition of “no music” could be recommended for further BCI experiments, additional research regarding individual factors like musicality or listening habits of BCI participants should be considered.
Real Time Speech Recognition Method for Online Complaints from Power Grid Customers Based on Improved Residual Network

Online complaints may be subject to various types of sound interference, such as noise and echo, which can affect the quality and clarity of the speech signal, thereby affecting the accuracy of speech recognition. To address this issue, this paper proposes an electric power customer online complaint realtime speech recognition method based on an improved residual network. The white noise and partial discharge pulse signals of the electric power customer online complaint realtime speech are extracted, and the deep residual network is improved for waveform reconstruction of the speech signal. Linear prediction parameters and linear prediction cepstral coefficients are calculated to complete the speech recognition. Experimental results show that the proposed method can effectively recognize electric power customer online complaint speech in different signal-to-noise ratio areas, and has good speech signal-to-noise ratio recognition and positioning performance. The recognition rate is over 96%, and the recognition time fluctuates between 5ms and 25s. Moreover, the proposed method can achieve higher speech signal-to-noise ratios.
Mask R-CNN Transfer Learning Variants for Multi-Organ Medical Image Segmentation

Medical abdomen image segmentation is a challenging task owing to discernible characteristics of the tumour against other organs. As an effective image segmenter, Mask R-CNN has been employed in many medical imaging applications, e.g. for segmenting nucleus from cytoplasm for leukaemia diagnosis and skin lesion segmentation. Motivated by such existing studies, this research takes advantage of the strengths of Mask R-CNN in leveraging on pre-trained CNN architectures such as ResNet and proposes three variants of Mask R-CNN for multi-organ medical image segmentation. Specifically, we propose three variants of the Mask R-CNN transfer learning model successively, each with a set of configurations modified from the one preceding. To be specific, the three variants are (1) the traditional transfer learning with customized loss functions with comparatively more weightage on the segmentation performance, (2) transfer learning based on Mask R-CNN with deepened re-trained layers instead of only the last two/three layers as in traditional transfer learning, and (3) the fine-tuning of Mask R-CNN with expansion of the Region of Interest pooling sizes. Evaluating using Beyond-the-Cranial-Vault (BTCV) abdominal dataset, a well-established benchmark for multi-organ medical image segmentation, the three proposed variants of Mask R-CNN obtain promising performances. In particular, the empirical results indicate the effectiveness of the proposed adapted loss functions, the deepened transfer learning process, as well as the expansion of the RoI pooling sizes. Such variations account for the great efficiency of the proposed transfer learning variant schemes for undertaking multi-organ image segmentation tasks.
Control of distributed energy storage systems for minimum reverse flow in a distribution grid with high share of photovoltaic

Due to the increasing numbers of photovoltaic (PV) systems installed at the low-voltage (LV) level, reverse power flow (RPF) between the $L V$ and the medium-voltage (MV) grid occurs more frequently and with higher magnitudes. This poses a threat to grid security as RPF can cause overvoltage and thereby potentially damage system equipment. This paper proposes a method of reducing RPF and increasing the local consumption of renewable energies (RES) by collectively controlling distributed residential battery energy storage systems (BESS) in a LV grid. For this purpose, the charging and discharging powers of the BESS are set correlating to the expected RPF and power demand at the transformer level by using a linear optimization algorithm and a forecast of the power flow at the transformer. This method aims at RPF shaving by charging the BESS and at covering the power demand by discharging. Comparisons with BESS controlled individually at the household level show, that the proposed method leads to a more effective reduction of reverse power peaks.
Cytotoxic Compounds from the Seeds of Sophora alopecuroides

Chemical purification of the ethyl acetate extract from the seeds of Sophora alopecuroides yielded a new natural product, ethyl 2-(4-hydroxybenzyl) malic acid (1), along with 7 known compounds, methyl eucomate (2), eucomic acid (3), piscidic acid (4) and ethyl 2-(4-hydroxybenzyl) tartrate (5), butein-4'-O-β-D-glucopyranoside (6), iso-liquiritigenin (7), and butein (8). The structure of 1 was determined by spectroscopic analyses including extensive 1D (1H NMR, 13C NMR) and 2D NMR spectra (COSY, HSQC and HMBC) as well as MS data. All compounds were evaluated for their cytotoxic activities against hepato-cellular carcinoma (HCC) cell lines, HepG2 andHep3B. Compound 8 significantly inhibited the cancer cell growth in a concentration-dependent manner.
Pulsed X-rays Induced Single Event Effects in Si, SiC and GaN Technologies

Alternative SEE test methods are increasingly sought after. This work is a contribution to the use of a pulsed X-rays beam. The main objective was to evaluate the parameters leading to the correlation between pulsed X-rays, broadbeam heavy ions, and laser pulses on wide bandgap component technologies (SiC and GaN). Indeed, these technologies can present preparation issues and pulsed X-rays can be an interesting solution to overcome them. Comparisons were also performed to study non-destructive and destructive events with different levels of package thinning of the devices allowing to rule on the capacity of pulsed X-rays to cross these packages.
Design and construction of substation monitoring system based on Niagara

By studying and analyzing the development trend and practical application of substation, this paper designs and builds a substation monitoring system based on Niagara software. The system can realize remote control substation switching operation, intelligent control of substation lighting and air conditioning system, transformer oil temperature monitoring, control and alarm functions. The 3D model of the substation is built in the system, which can visually display the connection mode of the entire substation equipment, the operation status and status monitoring of the substation, and realize real-time control.
Enhancing Seizure Detection from EEG Signals-Optimization Driven Feature Selection and Classification using Artificial Neural Networks

EEG seizure detection is critical in enabling prompt intervention and therapy for people with epilepsy. Using Artificial Bee Colony (ABC) optimization with an Artificial Neural Network (ANN) to serve as classifier, a novel seizure detection method is given in this research. The objective is to increase the efficacy of EEG signal-based seizure detection. First, significant elements from the EEG signals are retrieved, which include statistical metrics, frequency-domain properties, and time-frequency representations. The ABC optimization approach is then used to pick the most informative and discriminative features, with the goal of reducing dimensionality and improving classification performance. The selected characteristics are then sent into the ANN classifier to identify seizures. Extensive tests were carried out using a publicly accessible EEG dataset to assess the efficacy of our suggested technique. The proposed technique attained a high accuracy of 90% in seizure detection, according to the data. The combination of ABC optimization for feature selection and ANN classification showed successful in distinguishing seizure and non-seizure EEG segments.
Medical Image Fusion Using Discrete Wavelet Transform: In view of Deep Learning

Medical image fusion technique plays a major role in various clinical applications by deriving the multi modal information of medical images. This paper describes an approach for wavelet based medical image fusion of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images. Both the types of images are decomposed using wavelets. Various wavelet families are explored and nine different combinations of approximation coefficients and detail coefficients of CT and MRI images are used to get the fused image. These generated fused images are obtained from deep learning implementation point of view. After obtaining the results, it is observed that the max-max combination of approximation coefficients and detail coefficients is producing the better result in comparison with other type of combinations.
Improved YOLOv7 Based on Transformer for Object Detection in UAV-Captured Images

As the drone captures image targets at different flying altitudes, their scales may vary significantly, which can pose challenges for the object detection model to accurately detect them. Additionally, tiny objects in the image contain minimal information, making them difficult to distinguish from the background. To overcome these two challenges, we proposed a network architecture that aims to improve the accuracy of tiny object detection in drone images. Specially, we designed a tiny object detector(TOD) that can effectively extract features of tiny objects and distinguish between tiny object features and image background. Furthermore, this TOD module contains a Convolutional Visual Attention Network (CVAN) to better focus on the regions of tiny objects. Experimental results demonstrate that the proposed method achieves mAP@. 5 accuracy of 53.9% on the VisDrone2021-test-dev dataset and improves by 2.8 % compared to YOLOv7.
Comparison of leaf area density measured by laser range finder and stratified clipping method

Leaf area density (LAD) have an important role in biomass productivity models, radiative transfer studies, remotely sensed data and so on. In this study, the vertical profile of leaf area density (LAD) was measured by the laser range finder, which is the instrument to measure three dimensional coordinate. LAD was calculated by the method of NORMAN & CAMPBELL 1989 from gap fraction which is the probability of light passing through the canopy. In order to measure the vertical profile of LAD, gap fraction in horizontal layer divided into the canopy can be measured by the laser range finder. The vertical profile of LAD was compared with LAD measured by the stratified clipping method in two different-aged Japaneses larch forests, one is 5-year-old and another is 47-year-old. In 5-year-old forest, high correlation was seen in LAD between both methods (R 2 =0.94) and slope of regression equation is about 1.00. In the vertical profile of LAD, both method was shown that the maximum LAD in the layer from 1.0 m to 2.0 m and LAD decreased toward the upper and lower layer. In 47-year-old forest, LAD measured by laser range finder was underestimated by LAD measured by the stratified clipping method. In the profile of LAD, both methods were shown that the maximum LAD in the layer from 11.3 m to 13.3 m, but high LAD in the lower layer than 5m height was seen in the method of laser range finder. The cause of this difference is considered that only larch was measured by the stratified clipping method, on the other hand, low trees or shrubs were measured by the laser range finder. Consequenty, by using the laser range finder, it was possible to measure the vertical profile of LAD at the understory of forest.
A Multi-Task Water Surface Visual Perception Network for Unmanned Surface Vehicles

In recent years, the significance of unmanned surface vehicles (USVs) has grown substantially across a wide range of applications. The monocular camera, as the most common perception sensor deployed in USV, offers the inherent advantages of cost and can also provide rich semantic information. To harness the perceptual capabilities of a monocular camera for USVs, we propose a multitask water surface visual perception network which can accomplish various perception tasks, including boat detection, drivable area segmentation, and camera quality classification in water surface scenes. Moreover, the proposed method demonstrates significant accuracy and high interface speed compared to baseline methods and can be effectively deployed to low-power embedded platforms in USVs.
Research on Feature Extraction and Classification Recognition of Underwater Small Target Active Sonar

In addressing the detection challenge of small targets with low echo intensity and high noise intensity in underwater environments with active sonar and random reverberation, we propose a target detection method that integrates wavelet packet decomposition with an improved convolutional neural network. This approach employs interframe accumulation processing to suppress and remove strong echoes and clutter interference, such as static reverberation and random burst noise, from the sonar background. Leveraging the characteristics of sonar echo decomposition through wavelet packet analysis, we process simulated underwater acoustic signals from four categories, including divers, underwater mines, metal plates, and oceanic noise interference, after interframe accumulation noise reduction. We then statistically analyze the energy distribution of echoes in various frequency bands based on wavelet packet decomposition. We extract the energy features from different frequency bands to enable the improved CNN to detect targets even at low SNRs. The results validate the feasibility and effectiveness of this algorithm.
Surrogate-Assisted Evolutionary Optimization Based on Interpretable Convolution Network*

When performing evolutionary optimization for computationally expensive objective, surrogate-assisted evolutionary algorithm(SAEA) is an effective approach. However, due to the limited availability of data in these scenarios, it can be challenging to create a highly accurate surrogate model, leading to reduced optimization effectiveness. To address this issue, we propose an Interpretable Convolution Network(ICN) for offline surrogate-assited evolutionary optimization. ICN retains the non-linear expression ability of traditional neural networks, while possessing the advantages of clear physical structure and the ability to incorporate prior knowledge during network parameter design and training process. We compare ICN-SAEA with tri-training method(TT-DDEA) and model-ensemble method(DDEA-SA) in several benchmark problems. Experimental results show that ICN-SAEA is better in searching optimal solution than compared algorithms.
A High Energy Efficiency Discrete-Time Σ Δ Modulator Based on Floating Inverter Amplifier

The development of Internet of Things (IoT) has put forward more demands for energy-efficient ADCs. In high-precision applications with SNDR greater than 90dB, over-sampled ADC is the most important selection. Traditional $\Sigma\quad \Delta$ ADC is based on operational transconductance amplifier (OTA) with high power consumption. This limits the improvement in the energy efficiency of $\Sigma\quad \Delta$ ADC. This work designs a discrete-time sigma-delta modulator (DTSDM) based on floating inverter amplifier (FIA). FIA builds integrator instead of traditional OTA, eliminating static power consumption. The tradeoff between gain, speed and reservoir capacitance is analyzed to further improve energy efficiency. Cascode and improved two-stage FIA balance the DC gain with stability. An energy-efficient successive approximation register (SAR) ADC, coupled with a simplified Digital Weight Algorithm (DWA), is used in the proposed 3-bit quantization module. The power consumption is only 85. 2µW and the figure of merit (FoM) is up to 182.6dB under a high precision quantization level of 98.9dB SNDR.