Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully Automated Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning*

Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for effective surgical planning and treatment optimization. While radiomics-based methods have been explored for pCR prediction using diffusion-weighted MRI (DWI), they rely on manual tumor segmentation—a laborious and error-prone task. Our study introduces a deep learning model that automates tumor segmentation from DWI, enhancing the accuracy and efficiency of radiomics-based pCR predictions and eliminating the need for manual intervention. We evaluated our approach on the publicly BMMR2 challenge data using a k-fold crossvalidation experimental setup, comparing the radiomics-based pCR predictions from manual and automatic segmentations. Our approach demonstrated a human-level performance for pre-treatment radiomics-based pCR prediction from the DWI data.Clinical relevance: This study presents a fully automated method for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. Our model combines diffusion-weighted imaging (DWI) with deep-learning algorithms for breast lesion segmentation and radiomics-based machine learning for pCR prediction. By fully automating the segmentation process, our approach offers the potential for objective, early pCR prediction based on DWI in breast cancer.
Self-organized Criticality Identification of Power Systems Based on Neural Networks

To mitigate the limitations associated with the arduous and time-consuming identification of conventional self-organized criticality, this paper presents a novel t-SNE-BP-based framework for discerning self-organized criticality within power systems. Firstly, the OPA model is employed to simulate cascading failures and acquire the resultant loss in system load, which subsequently serves as the observed parameter for M-K validation, facilitating the construction of the state dataset. Secondly, harnessing the dimensionality reduction advantages offered by t-SNE and the learning capabilities inherent to a neural network endowed with optimized hyperparameters, an innovative t-SNE-BP-based neural network model is introduced. Lastly, through comprehensive case studies conducted on the IEEE-39 node system, the proposed model is demonstrated to surpass alternative methodologies in terms of heightened accuracy and reduced identification time. These findings effectively corroborate the efficacy and superiority of the model, furnishing a solid theoretical foundation and compelling evidence for averting major power outage incidents.
A Fast Physics-informed Neural Network Based on Extreme Learning Machine for Solving Magnetostatic Problems

The deep learning has been rapidly progressed. Especially, some deep neural networks for solving partial differential equations are aggressively developed. Such deep learning techniques are called the physics-informed neural networks (PINNs). Although PINNs have great potentials to drastically change the current numerical analysis processes, their current drawbacks are heavy computational cost due to the backpropagation learning. In this paper, a fast physics-informed neural network for solving magnetostatic problems is proposed based on the extreme learning machine, which is a kind of neural networks whose computational cost is relatively light because it does not use the backpropagation-based learning. Thus, the proposed PINN makes it possible to realize fast learning. In this work, the proposed approach is applied to the learning of magnetic flux density distribution of an inductor model. It is shown that the magnetic flux density distributions can automatically be obtained without backpropagation learning.
Selection of Bidirectional DC-DC Topology for DC Microgrid Energy Storage Systems

This paper focuses on bidirectional DC/DC converters, which are essential components for bidirectional energy transfer between different voltage levels. Firstly, the paper delves into the detailed study of three non-isolated bidirectional DC/DC converter topologies, including the two-level bidirectional buck/boost converter, the bidirectional four-switch buck-boost converter, and the non-isolated three-level bidirectional DC/DC converter with non-grounded structure. The structures and operational principles of each topology are elaborated, and their control strategies are thoroughly analyzed. Additionally, the paper provides a concise overview of isolated bidirectional DC/DC converters, highlighting the unique characteristics of several isolated converter types. Finally, it summarizes the characteristics of the three non-isolated bidirectional DC/DC converters and offers application scenarios, aiming to contribute to the advancement of power electronics technology.
Current Doubler Rectifier: Analysis and Implementation for DC EV Charger Application

DC EV charging applications employs a DC-DC converter to charge the battery as per the charging requirements of the vehicle. Phase-shifted full bridge converter is an efficient high-frequency isolated DC-DC converter topology used for these types of applications. Diode rectifiers and synchronous rectifiers are commonly used for secondary-side rectification methods. In comparison with these methods, current doubler rectifiers have many advantages such as ripple current cancellation, reduced conduction losses, etc. The application of zero voltage switching and better thermal management of power circuit magnetic components are the other merits of these rectifiers. The paper describes about the operation of current doubler with diodes as well with semiconductor switches. The design of current doubler rectifier and modeling of its control system for DC EV charging application is explained. Hardware implementation with performance evaluation results of the same are also presented.
Low-Complexity and High-Performance Combiners for Over the Air Computing

We consider the design of successive convex approx-imation (SCA)-based mean square error (MSE) minimization combiners for over-the-air-computation (AirComp) schemes op-erating over the uplink of a system with one multiple-antenna access point (AP) and multiple single-antenna edge devices (EDs). Within that context, we contribute with two different initial combiner designs alternative to the state-of-the-art (SotA) combiner based on semi-definite relaxation (SDR). The first is a low-complexity design, whereby the SDR formulation of the MSE minimization problem is replaced by a rank-one Rayleigh quotient (RQ) method, whose closed-form solution is obtained with linear complexity; and the second is a high-performance design obtained by enforcing a low-rank solution of the SDR-based MSE minimization problem. Numerical results demonstrate that the low-complexity RQ design can be up to 1000-times less costly than the SotA uniform-forcing (UF)/SDR scheme; while the high-performance regularized SDR design is found to be about 4-times faster than the latter, at a comparable complexity.
Constructing Application-level GPU Error Rate Model with Neutron Irradiation Experiment

This work explores GPU application factors that dominantly characterize SDC and DUE cross sections obtained by neutron irradiation experiments. We evaluate correlation coefficients between the cross sections and application metrics obtained from profiler outputs and fault injection results. Experimental results with six applications show that “warps per block” is a primary factor for both SDC and DUE. The DUE cross section modeling is improved by the secondary factor of L2 hit rate.
Energy-Efficient Pose-Estimation FPGA-Accelerator for Real-Time Mobile V-SLAM Robot

Visual Simultaneous Localization and Mapping (V-SLAM) refers to the process by which a robot estimates its pose using a camera, enabling localization and mapping. Nonlinear optimization-based SLAM systems exhibit high robustness and are widely applied. Real-time SLAM systems demand high computational speed for front-end visual odometry, requiring the assistance of high-performance platforms. To achieve low power consumption and high-performance real-time visual SLAM systems, we propose a reconfigurable FPGA accelerator for robot pose estimation. In this paper, we present a high-performance parallel linear equation solver and introduce an outlier rejection algorithm to enhance the robustness of the system. The proposed pose estimation accelerator can be employed in various feature-matching-based SLAM systems. We capitalize on the reusability and parallelism specific to SLAM, achieving over 14 times the computational speed and over 40 times the energy efficiency compared to Intel i7-10875H platform. With 75 matched feature points input and 50 optimization iterations, the maximum frame rate can reach up to 229 fps.
On the Effect of Inherent Time Delay in Stability Analysis of a Single Phase H-Bridge Inverter

Detailed fourth order parasitic model of a dc-ac H-bridge inverter has been constructed, where instead of the common assumption of zero delay in control circuit, inherent time delay has been accounted for in analytical model by first order Padé approximation. The objective of this paper is to investigate the effects of inherent time delay on stability analysis by a combination of Filippov method and Floquet theory, wherein stability boundaries in control and power circuit parameter spaces have been plotted for ideal condition without and realistic condition with time delay. Results have revealed that time delay drastically aggravates onset of Hopf bifurcation in parameter spaces. However, increasing time delay causes earlier onset of period-doubling bifurcation only by a meagre amount. Critical delay threshold has also been determined analytically. While qualitative verification of analytical results has been demonstrated by plotting waveforms obtained by numerical simulations; both qualitative and quantitative verification have been achieved by plotting bifurcation diagrams.
Multioutput Surrogate Assisted Evolutionary Algorithm for Expensive Multi-Modal Optimization Problems

Real-world optimization problems are often computationally expensive and feature multi-modal objective functions. Surrogate-assisted evolutionary optimization has proven to be an effective approach for addressing expensive black-box optimization challenges, but the technique has not been adequately studied in multi-modal situations. In this paper, we propose a simple but effective multi-output surrogate-based approach for empowering surrogate-assisted evolutionary optimization to address expensive multi-modal optimization problems. Specifically, our proposed approach employs a multi-output Gaussian process to capture correlations between data collected from different local areas. Experiments on synthetic benchmark test problems demonstrate the effectiveness of our proposed algorithm against five state-of-the-art peer algorithms.
Study on Fault Diagnosis for Submersible Motors Based on LSTM-AE

Submersible motors are widely used in oil fields, and vibration data are commonly employed for motor fault detection. Due to the limited sample size of fault data in submersible motors, traditional machine learning methods exhibit poor performance. In this study, we propose a method for fault diagnosis of submersible motors under small sample conditions. We utilize Long Short-Term Memory (LSTM) networks to handle the time series data and combine them with autoencoders to automatically extract vibration signal features. To address the issue of effective feature extraction in the presence of noise, we improve the loss function of the LSTM autoencoder by incorporating L2,1 norm regularization. Compared to traditional LSTM and AE anomaly detection methods, our approach achieves higher accuracy with a precision of 99.1%.
Mini-YOLOX: A Lightweight Network for Real-Time Embedded Applications

In this paper, we present an efficient real-time object detector designed for embedded system applications. Although increased memory and power consumption pose significant challenges, our network adopts techniques to overcome them. Depthwise and pointwise separable convolutions are used across the backbone, neck, and head of the network to reduce the number of parameters. To compensate for accuracy loss, we employ several techniques including starting with a larger baseline, using depthwise separable convolution mixed with blueprint separable convolution in the backbone, replacing the SiLU activation function with the Mish activation function, and switching from Adam to SGD optimization algorithms. These techniques help to achieve a state-of-the-art object detection network called Mini-YOLOX, which optimizes YOLOX-S from the YOLOX series and reaches only ∽57% of its parameter size and ∽52% of its floating-point operations with better accuracy. Mini-YOLOX achieves impressive results with only 5.08M parameters and 13.95 GFLOPs, resulting in a 40.9 AP score in just 5.71 ms.
EV Battery Charging Converters with Wide Output DC Voltage Range

Electric Vehicles (EVs) are going to be a major contributor in achieving the goal of net-zero greenhouse gas emissions. With the growth of the automotive sector and the burgeoning of EVs, the demand for efficient systems for EV charging and grid support is on the rise. The chargers for EVs can be broadly categorized into on-board and off-board, with unidirectional and bidirectional power flow. While on-board chargers are fitted in the EV with the respective battery, the off-board chargers should be wide range to comply with different battery voltage levels. This paper analyzes the existing AC-DC and DC-DC topologies proposed for wide DC voltage range and aims to classify them based on the way this feature was obtained. The goal of the study is to assess the potential of various approaches for achieving a wide output DC voltage range and highlight possible directions of future developments in this area.
Solar Powered Battery Assisted Water Pumping System with UHGQB converter

A hybrid water pumping system (WPS) is proposed in this paper in which PV acts as the main source while battery is used as the assisting source. The system comprises of a Ultra High Gain Quadratic Boost Converter (UHGQBC). This converter is a dual switch circuit and is mend to increase the output voltage of the PV array to a higher level, thus negating the compulsion of a large PV array. Incremental conductance MPP technique allows the system to trail the maximum power from the PV array. Energy balance is maintained by battery acting both as the supplier and consumer of excess energy appearing in the system. It is operated using a robust control algorithm. The system is tested in MATLAB Simulink environment with a Solar PV array of 640 W, a BLDC motor of 400 W and a 48 V 50Ah battery.
Research on Vehicle Network Analytic System Based on Ethernet Protocol Parsing

The Internet of vehicles plays an essential role in the automobile industry. Existing network parsing tools only can complete the tasks of vehicle data traffic monitoring under certain conditions, but they do not fully support the parsing and analysis of Some/IP or doIP protocol that is common used in vehicle system. In order to solve this problem, this paper proposes and implements a new software solution which can generate vehicle network sending messages to start data monitoring, and define the data model to parse the data stream. In addition, this software uses the thread pool and other methods to optimize the overall performance. The implementation of the software can be used to vehicle hardware communication detection and the transmission monitoring. It can also be applied to vehicle fault diagnosis and network optimization field.
Research and Application of Small Target Detection Algorithm Based on Bipedal Robot

With the rapid development and popularisation of robotics, bipedal robotics is widely used in various fields. The robot platform used in this paper is the NAO bipedal robot, focusing on the small target detection algorithms and applications of bipedal robots. Due to the limitations of the hardware of the bipedal robot, there are certain noise interference and blurring of the picture quality in the target detection, especially in the case of unstable external light intensity. In this paper, three target detection algorithms are compared and experimented to select the algorithm with excellent detection effect. And through the process of image vision pre-processing of the collected images further processing, analysis of the three colour space of the collected images, selected the least affected by the light of the YUV colour space as the NAO bipedal robot target detection of the colour space, and then the implementation of the image segmentation, image noise reduction process improves the ability of bipedal robot target detection.
Influence of Panax notoginseng continuous cropping on seed germination and seedling growth of the plant

A pot experiment was conducted to explore the influence of Panax notoginseng continuous cropping on the seed germination and seedling growth of the plant. Under continuous cropping, the maximum seed germination speed of P. notoginseng increased, germination vigor changed little, while the germination rate, germination index, and fast germination stage decreased, compared with those under 1-year cropping. The seed germination vigor, germination rate, and germination index in root region soil and under-root region soil decreased significantly, and the maximum germination speed also decreased, compared with those in outer-root region soil. The 1-year P. notoginseng-cropped soil had no obvious obstacle effect on the seedling growth of followed cropped P. notoginseng, but the 2-year P. notoginseng-cropped soil had stronger inhibitory effect than the 3-year P. notoginseng-cropped soil. Our results suggested that P. notoginseng continuous cropping could produce obstacle effect on the seed germination and seedling growth of the plant, and autotoxicosis could be one of the main reasons that led to the P. notoginseng continuous cropping obstacle.
Causal Dynamic Bayesian Networks for Simulation Metamodeling

A traditional metamodel for a discrete-event simulation approximates a real-valued performance measure as a function of the input-parameter values. We introduce a novel class of metamodels based on modular dynamic Bayesian networks (MDBNs), a subclass of probabilistic graphical models which can be used to efficiently answer a rich class of probabilistic and causal queries (PCQs). Such queries represent the joint probability distribution of the system state at multiple time points, given observations of, and interventions on, other state variables and input parameters. This paper is a first demonstration of how the extensive theory and technology of causal graphical models can be used to enhance simulation metamodeling. We demonstrate this potential by showing how a single MDBN for an M/M/1 queue can be learned from simulation data and then be used to quickly and accurately answer a variety of PCQs, most of which are out-of-scope for existing metamodels.
Linking Brain Signals to Visual Concepts: CLIP based knowledge transfer for EEG Decoding and visual stimuli reconstruction

Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. This study introduces a novel approach using a convolutional neural network (CNN) to classify images from the ImageNet dataset, leveraging electroencephalography (EEG) recordings. We collected EEG data from 6 subjects, each viewing 50 images across 40 distinct semantic categories. These EEG signals were transformed into spectrograms, serving as the input for training our CNN. A unique aspect of our model is the incorporation of knowledge distillation from a pre-trained image classification teacher network. This approach enabled our model to achieve a top-5 accuracy of 80%, notably surpassing a plain CNN baseline. Furthermore, we integrated an image reconstruction pipeline founded on pre-trained latent diffusion models. This innovative concatenation not only decodes images from brain activity but also provides a plausible reconstruction, facilitating rapid and subject-specific feedback experiments. Our work thus represents a significant advancement in the field, bridging the gap between neural signals and visual perception.
Optimal Sizing of Li-Ion Capacitor for Off-Road Electric Vehicles

In hybrid energy storage systems (HESS) powered by battery (BAT) and lithium-capacitor (Li-CAP), sizing problems are crucial in optimizing system operation and reducing device costs. This study provides a novel hierarchical sizing technique based on mixed integer nonlinear programming and Pontryagin's minimum principle. With a multi-objective optimal problem, the algorithm allows for determining a series of feasible solutions satisfying given constraints. These constraints include nominal voltage of a source package, nominal current of a cell, and the maximum volume. The proposed algorithm's effectiveness is numerically validated.
The Common-Mode Voltage Reduction Method for Two-Stage Power Conversion System

This paper proposes a method to reduce a common-mode(CM) voltage that causes an electro-magnetic interference(EMI) noise in a two-stage power conversion system consisting of a 3-phase 3-level neutral-point clamped(NPC) converter and a 3-parallel 3-level DC-DC converter. the CM voltage of the system is defined as the difference between the CM voltage generated by each converter. The proposed switching sequence controls the DC-DC converter to output the CM voltage that matches the CM voltage of the NPC converter controlled by a large-medium-zero(LMZ) PWM. The proposed method is verified by experiments through the two-stage power conversion system.
Enhancement Method for Tool Wear State Textual Dataset Based on Improved GAAE Model

During the cutting process of end milling cutters, the cutting surface will wear out, and the degree of wear is usually divided into three stages: initial wear stage, normal wear stage, and severe wear stage. Obviously, the sampling data in the normal wear stage is much larger than that in the initial wear stage and severe wear stage, resulting in an imbalance in the data set under the tool wear label, which will reduce the accuracy of the deep learning network model in predicting the tool wear state. To address this issue, this article proposes an enhanced method for tool wear condition monitoring dataset based on improved GAAE, which leverages the reconstruction accuracy of GAAE and the sample control ability of cGAN to fully exploit the advantages of both models. The sensor collect the vibration signal during the milling process, convert the vibration signal into spectral data and input them into GAAE. GAAE learns the data distribution characteristics through the autoencoder to generate initial sample data of the tool wear state. The generated samples are input together with the condition vector into the discriminator of cGAN. The discriminator of cGAN further distinguishes between generated samples and real samples, and introduces the condition vector to identify the specific characteristics or attributes of the samples. Afterwards, the enhanced dataset is input into a deep learning network model for classification, testing the usability of the generated data. The experimental results show that training a deep learning network model with enhanced tool wear state data sets can effectively improve the accuracy of the model for tool wear state monitoring, with a prediction accuracy of 96.7%.