Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotation Scheme Based Power Consumption Analysis for Wireless Sensor Network

In wireless sensor network (WSN) application, one of the most important problems to be considered is power consumption. As a result, a number of methodologies or methods for analyzing the power consumption of the application have been presented. These methodologies may aid in the prediction of the lifespan of WSNs, the providing of suggestions to application developers, and the optimization of the amount of energy utilized by WSN applications. All of the sensors in wireless sensor nodes are rechargeable batteries devices with a limited amount of battery power available. It will be difficult to replace every power supply battery available in the network on an individual basis after deploying of sensor nodes. To sort out these issues, an efficient analyzing technique for analyzing the power consumption of WSN is proposed. In this research work, a prototype model with a simulation model is presented. The proposed research work may lower the amount of power used by each sensor node, while also increasing the life of sensors by shutting down certain of their components in order to achieve higher energy savings and longer life. It is necessary to do an analysis of the power usage of each sensor node before using this power management strategy. As a result of this, the proposed method analyzes the WSN power consumption in real time manner.
WLAN Intrusion Detection System Based on SVM

Detection of outages is a key issue in safeguarding the security of the data framework, particularly given the general prevalence of digital attacks. This document focuses on the use of vector support machines to further develop the Remote Neighborhood Interrupt Discovery (SVM) framework. SVM distinguishes outages in light of recently recognized assault designs. The results of the recreation show that the proposed recognition framework can distinguish anomalies and produce a warning. Furthermore, the assessment produced a superior result in terms of location knowledge and false problem rate, which could provide better inclusion and make recognition more effective. This paper proposed an Aid Vector Machine SVM calculation for the WLAN break location which initially determines the data gain of the organization break information and then chooses the properties with the best effect on the raw information layout to simplify the SVM limitations. Finally, to achieve the network identification behavior, an advanced SVM computation is used to identify the remote organization information. Based on the re-enactment results, the SVM-based WLAN outage recognition model has a high correct identification rate, low unlucky rate, and low out-of-base alert rate.
Lesion2void: Unsupervised Anomaly Detection in Fundus Images

Anomalous data are usually rare in the field of medical imaging, in contrast to normal (healthy) data that account for the vast majority of the real-world medical image data, leading to challenges of developing image-based disease detection algorithms. In this work, we propose an unsupervised anomaly detection framework for diabetic retinopathy (DR) identification from fundus images, named Lesion2Void. Lesion2Void is capable of identifying anomalies in fundus images by only leveraging normal data without any additional annotation during training. We first randomly mask out multiple patches in normal fundus images. Then, a convolutional neural network is trained to reconstruct the corresponding complete images. We make a simple assumption that in a fundus image, lesion patches, if present, are independent of each other and are also independent of their neighboring pixels, whereas normal patches can be predicted based on the information from the neighborhood. Therefore, in the testing phase, an image can be identified as normal or abnormal by measuring the reconstruction errors of the erased patches. Extensive experiments are conducted on the publicly accessible dataset EyeQ, demonstrating the superiority of our proposed framework for DR-related anomaly detection in fundus images.
Chaotic system identification using Wiener‐LSSVM model

In this paper, a new method is proposed for identifying chaotic system based on a Wiener‐least squares support vector machine (Wiener‐LSSVM) model. The model consists of a linear dynamic subsystem followed by a static nonlinear function, which is represented by LSSVM in this paper. The parameters of the linear dynamic part and those of LSSVM are estimated simultaneously by solving a set of linear equations using the least squares (LS) method. The proposed method incorporates partial structure information into the identification process and does not assume that the parameters of linear dynamic part are known. On the other hand, the LS algorithm is more efficient than gradient‐descendent‐based algorithms for estimating the parameters of Wiener‐LSSVM. Three identification examples are given to validate the effectiveness of the proposed method. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Multi-View Fusion Convolutional Neural Network for Automatic Landmark Location on Spinal X-Rays

In clinical practice, landmark location plays an important role in spine deformity assessment, which is the foundation for measurement of several spinal morphological parameters. The clinicians usually use both anterior-posterior(AP) view X-rays and lateral(LAT) view X-rays of the same patients for diagnosis. However, for automatic landmark location, the information between multi-view X-rays is seldom considered. Addressing this problem, in this paper, we propose a multi-view fusion convolutional neural network for automatic landmark location on AP X-rays and LAT X-rays simultaneously. Based on an object detection framework, for two channels representing multi-view X-rays, we first share their network parameters in convolutional backbone, and then we design an image-level fusion module and an object-level fusion module respectively, which can combine the information of both channels. Finally we insert a landmark prediction branch to the end of each channel for landmark location. The experiment results show that our proposed method achieves more accurate vertebra detection and more precise landmark location than predicting them separately, which can provide reliable assistance for clinicians.
Feature Gradient Flow for Interpreting Deep Neural Networks in Head and Neck Cancer Prediction

This paper introduces feature gradient flow, a new technique for interpreting deep learning models in terms of features that are understandable to humans. The gradient flow of a model locally defines nonlinear coordinates in the input data space representing the information the model is using to make its decisions. Our idea is to measure the agreement of interpretable features with the gradient flow of a model. To then evaluate the importance of a particular feature to the model, we compare that feature’s gradient flow measure versus that of a baseline noise feature. We then develop a technique for training neural networks to be more interpretable by adding a regularization term to the loss function that encourages the model gradients to align with those of chosen interpretable features. We test our method in a convolutional neural network prediction of distant metastasis of head and neck cancer from a computed tomography dataset from the Cancer Imaging Archive.
Delay minimization for intelligent reflecting surface assisted federated learning

Federated learning (FL), which allows multiple mobile devices to cooperatively train a machine learning model without sharing their data with the central server, has received widespread attention. However, the process of FL involves frequent communications between the server and mobile devices, which incurs a long latency. Intelligent reflecting surface (IRS) provides a promising technology to address this issue, thanks to its capacity to reconfigure the wireless propagation environment. In this paper, we exploit the advantage of IRS to reduce the latency of FL. Specifically, we formulate a latency minimization problem for the IRS assisted FL system, by optimizing the communication resource allocations including the devices' transmit-powers, the uploading time, the downloading time, the multi-user decomposition matrix and the phase shift matrix of IRS. To solve this non-convex problem, we propose an efficient algorithm which is based on the Block Coordinate Descent (BCD) and the penalty difference of convex (DC) algorithm to compute the solution. Numerical results are provided to validate the efficiency of our proposed algorithm and demonstrate the benefit of deploying IRS for reducing the latency of FL. In particular, the results show that our algorithm can outperform the baseline of Majorization-Minimization (MM) algorithm with the fixed transmit-power by up to 30%.
New Energy Converter Control Method Based on the Droop of Hyperbolic Tangent Function

Aiming at the problems of slow response speed of the output active power of the new energy converter based on droop control, and large active power oscillation and overshoot in the dynamic process, a new energy converter control method based on the drop of the hyperbolic tangent function is proposed. This method first calculates the average value of the output active and reactive power of the converter; then performs the hyperbolic tangent function droop control on the active power to obtain the angular frequency command of the output voltage; performs linear droop control on the reactive power to obtain the output voltage Amplitude command; finally, double closed-loop control of output voltage and inductor current in dq coordinate system. Compared with the traditional droop control, the simulation results show that the output active power response speed becomes faster, and it has a shorter dynamic adjustment process.
Construction and Analysis of Cross-layer Aggregation Neural Network for AMI Intrusion Detection

Due to two-way communication with the internet, advanced measurement infrastructure (AMI) is vulnerable to network attacks. In order to improve the reliability of AMI intrusion detection, this paper proposes the cross-layer aggregation neural network. Firstly, the random forest algorithm is used to reduce the interference of redundant features. Then, the cross-layer model based on convolutional neural network and gated recurrent unit is used to extract local and temporal features, and concat() and multi-layer perceptron network are used for features aggregation to obtain comprehensive features with multi-domain features. According to the comprehensive features and softmax classifier, the communication state of AMI is detected. Finally, the four types of intrusion detection experiments are carried out on KDDCup 99 and NSL-KDD data sets. Experimental results show that the cross-layer model has better detection performance than multi layer perceptron, convolutional neural network, gated recurrent unit, serial neural network and others.
Active Disturbance Rejection Control for Uncertain Nonlinear Systems With Sporadic Measurements

This paper deals with the problem of active disturbance rejection control (ADRC) design for a class of uncertain nonlinear systems with sporadic measurements. A novel extended state observer (ESO) is designed in a cascade form consisting of a continuous time estimator, a continuous observation error predictor, and a reset compensator. The proposed ESO estimates not only the system state but also the total uncertainty, which may include the effects of the external perturbation, the parametric uncertainty, and the unknown nonlinear dynamics. Such a reset compensator, whose state is reset to zero whenever a new measurement arrives, is used to calibrate the predictor. Due to the cascade structure, the resulting error dynamics system is presented in a non-hybrid form, and accordingly, analyzed in a general sampled-data system framework. Based on the output of the ESO, a continuous ADRC law is then developed. The convergence of the resulting closed-loop system is proved under given conditions. Two numerical simulations demonstrate the effectiveness of the proposed control method.
Deep Reinforcement Learning Based Approach for Active Power Security Correction Control of Power System

Active power security correction of power system is of great significance for the safe operation of power grid under bad conditions. In this paper, an active power security correction control method based on deep reinforcement learning (DRL) is proposed. Firstly, by modeling the Markov process of active power security correction, the state space, action space and reward value of reinforcement learning method for active power security correction are designed. Secondly, using twin delayed deep deterministic policy gradient algorithm (TD3), TD3 agent automatically learns the global optimal decision from the interaction with the environment. Then, a control framework combining “off-line training” and “on-line control” of power system security correction based on TD3 is built. Finally, the effectiveness of the method is verified by IEEE 39 -bus system.
Semi-Supervised Domain Generalization for Medical Image Analysis

Despite of their recent progress in medical image analysis, the deep learning methods typically fail to generalize well in the case of domain shift (e.g. introduced by different imaging devices). To relieve the above problem, this paper introduces a general regularization-based semi-supervised domain generalization method. Specifically, the stability and orthogonality of the learned features are introduced as two regularization factors of the learning objective. The former encourages the model to learn to represent as many domain-invariant features as possible, while the latter constrains the features to be orthogonal to each other so that a more complete set of features is represented. Implemented as a feature correlation matrix, both regularization factors can be applied to both labelled and unlabelled data. Experimental results on two benchmark datasets, Fundus Image Segmentation and Chest X-ray Diagnosis, have shown the promise of the proposed method.
The Impact of Accidental Traffic Incidents on Network Traffic Status from the Perspective of Smart Cities

This article gives some indicators to characterize the impact of emergencies on the transportation network. These indicators are suitable for user optimization, system optimization, and efficiency loss. The nature of these indicators and the relationship between the indicators in the case of user optimization, system optimization, and efficiency loss have been discussed. Three types of dynamic flow loading models are summarized-speed density flow loading model, cell transmission model and road section transmission model. The velocity density model is based on the functional relationship between velocity and density, and the latter two models are based on the functional relationship between flow and density. The dynamic traffic loading model under the influence of the accident is improved, the path selection probability at the departure time is described based on the selection principle, and the pseudo-dynamic model under the influence of the accident is established.
Day-Ahead Scheduling With Renewable Generation Considering Shared Energy Storage

The use of renewable energy is causing changes in the energy field, among which wind and water energy power generation technologies have developed rapidly. In the new power system with new energy as the main character, energy storage will be needed to assume the role of peak shaving and valley filling, voltage regulation and frequency regulation in the power system. At present, most energy storage power stations operate in an exclusive manner, resulting in low energy storage utilization. Based on the concept of resource sharing, considering the economics and feasibility of shared energy storage system with new energy characteristics, first analyze the complementary and controllable capabilities of energy storage that promote new energy consumption, and then study the multiple energy storage sharing mechanism that promotes new energy consumption Finally, a coordinated regulation strategy for shared energy storage system that takes into account reducing wind curtailment and smoothing output is proposed to provide customers.
A MmWave communication testbed based on IEEE 802.11ad with scalable PtMP configuration

The sub-6G band is too crowded to accommodate higher data rate, while the millimeter wave (mmWave) bands have abundant spectrum resources and massive MIMO can provide high spectral and energy efficiency. Therefore, the combination of the two, namely mmWave-MIMO system, has attracted intensive research interests. In this paper, we develop a high-speed mmWave-MIMO communication system and conduct exhaustive field tests. The detail of the system design is provided and the key modules of the testbed are analyzed. The testbed exploits high gain of mmWave RF and flexible configuration of embedded system. The validation and field tests show that the developed testbed can provide up to 2.3 Gbps network layer data rate in single channel with low latency and support point-to-multi-point (PtMP) transmission aided by relay. The testbed can be used in future B5G and 6G systems to provide high reliability and low latency wireless coverage.
Reconstruction of Standard-Dose Pet From Low-Dose Pet Via Dual-Frequency Supervision and Global Aggregation Module

Positron emission tomography (PET) imaging is a widely used technology in clinics. To meet the clinical diagnostic requirement, standard-dose tracers with radioactivity need to be injected into the body when acquiring PET images. To reduce imaging radiation hazard while maintain PET image quality, in this paper, we propose a novel and effective approach to reconstruct standard-dose PET (SPET) images from low-dose PET (LPET) images. Specifically, we first design a two-branch network to preserve richer high-frequency details by reconstructing low-frequency component and high-frequency component separately. Then, we design a global integration module to integrate global information for achieving better quality in those difficult-to-reconstruct regions. Validation on a real clinical dataset suggests that our approach outperforms previous methods both qualitatively and quantitatively.
Mask-Free Radiotherapy Dose Prediction via Multi-Task Learning

In radiotherapy, an ideal treatment plan always takes the treatment planner several days to tweak repeatedly, owing to the complex dose distribution. To facilitate this process, many deep-learning-based studies have been devoted to automatic prediction of dose distribution. However, besides the computed tomography (CT) image, these methods usually require extra segmentation masks of target tumor and organs at risk as input, and such annotations are quite time-consuming to acquire. In this paper, we propose a mask-free dose prediction model based on the multi-task learning, which only needs CT images as input and outputs dose distribution maps efficiently. Specifically, considering the high correlation between the tumor anatomical structure and the dose distribution, we develop a multi-task architecture consisting of 1) a primary dose prediction task aiming to generate accurate dose distribution map, and 2) an auxiliary segmentation task applied to provide anatomical information for the primary task. Experiments on an in-house dataset demonstrate the effectiveness of our method.
Economic Analysis of the Potential Mechanisms to Couple the European Carbon Market with Emerging Countries

In the international negotiations on climate change, new market mechanisms are considered to include emerging countries in the carbon market developed by more industrialized countries beyond the Clean Development Mechanism (CDM). The dissertation is composed of complementary analyses that aim at quantifying the impacts to expect from these sectoral mechanisms. Chapter 1 simulates sectoral trading between Chinese electricity sector and a hypothetical cap and trade in the United States, with the MIT Emissions Prediction and Policy Analysis model. This computable general equilibrium approach is extended to the case of coupling between the European carbon market and the electricity sector of several emerging countries. Without any limit on the amount of permits that can be traded, the welfare in the emerging countries involved decreases despite the financial transfers associated with the trade in permits. Chapter 2 introduces a limit on the amount of permits that can be traded, in the case of coupling between the European carbon market and Chinese electricity sector. The limit induces a price difference between the two regions. When the associated rent is allocated to Chinese households, sectoral trading can be pareto improving. Chapter 3 is a time series analysis of the coexistence between CDM credits and European carbon permits. It examines the impact of the financial nature of carbon permits on the interactions between carbon markets. Chapter 4 quantifies the conditions of deployment of wind power in Denmark and compares them with the carbon price level observed in the first two chapters, and in Phase II of the European trading scheme.
Influence Based Re-Weighing for Labeling Noise in Medical Imaging

Labeling for pathology detection and grading in medical imaging is laborious and error-prone. High inter-rater variability is common even among board-certified sub-specialty radiologists. A typical approach to identify and correct labeling errors is to have experts with the same qualification review all the rated cases again, which is inevitably very costly. To reduce the expert annotation effort, we propose a re-weighing method based on TracIn [1], a labeling noise identification method, to assign low weights to potentially noisy samples during training. The weights are calculated based on self-influence scores from TracIn. The re-weighing TracIn leads to significantly improved performance for lumbar spine stenosis detection in MRI, with the macro accuracy of 77.2%, compared against the baseline accuracy of 75.4%, and the TracIn without re-weighing accuracy of 76.6%. The proposed re-weighting TracIn is also agnostic to the choice of training models, and thus can generalize to to other deep learning approaches. Also, to increase data efficiency, we investigated unsupervised data augmentation (UDA) and contrastive self-supervised learning (SimCLR) to utilize unlabeled data.
Research and application of solutions of forensics of anti-electric larceny

Electricity stolen behavior of human is the main reason of the long-term high line loss,while it is difficult to find the evidence which can confirm it.Also it is difficult to obtain a valid proof by our existing technical means and methods that can punish them according to law.This article presents a frontier technology of network remote video forensics system.It adopt new methods,new technology on electric field network centralized monitoring and it can realize the real-time alarm,recording,storage,transmission of video data.Through this practical application,we can provide a conclusive evidence of judicial recognition which makes our work on anti stealing electricity work easier.