arXiv subjects

Xin Shi

Publications and source records attributed to Xin Shi.

At least 55 records · Page 3Linked to original sources

arXiv2022

Time Resolution of the 4H-SiC PIN Detector

We address the determination of the time resolution for the $\rm 100~μm$ 4H-SiC PIN detectors fabricated by Nanjing University (NJU). The time response to $\rm β$ particles from a $\rm ^{90}$Sr source is investigated for the detection of the minimum ionizing particles (MIPs). We study the influence of different reverse voltages, which correspond to different carrier velocities and device sizes, and how this correlates with the detector capacitance. We determine a time resolution $\rm (94\pm1)~ps$ for $\rm 100~μm$ 4H-SiC PIN detector. A fast simulation software, termed RASER (RAdiation SEmiconductoR), is developed, and validated by comparing the waveform obtained from simulated and measured data. The simulated time resolution is $\rm (73\pm 1)~ps$ after considering the intrinsic leading contributions of the detector to time resolution.

physics.ins-det↗

arXiv2022

Timing performance simulation for 3D 4H-SiC detector

To meet high radiation challenge for detectors in future high-energy physics, a novel 3D 4H-SiC detector was investigated. SiC detectors could potentially operate in radiation harsh and room temperature environment because of its high thermal conductivity and high atomic displacement threshold energy. 3D structure, which decouples thickness and distance between electrodes, further improves timing performance and radiation hardness of the detector. We developed a simulation software - RASER (RAdiation SEmiconductoR) to simulate the time resolution of planar and 3D 4H-SiC detectors with different parameters and structures, and the reliability of the software is verified by comparing time resolution results of simulation with data. The rough time resolution of 3D 4H-SiC detector was estimated, and the simulation parameters could be used as guideline to 3D 4H-SiC detector design and optimization.

physics.ins-det↗

arXiv2022

Learning to Reduce False Positives in Analytic Bug Detectors

Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers rely on static analysis tools to regularly scan their codebases and find potential bugs. In order to maximize coverage, however, these tools generally tend to report a significant number of false positives, requiring developers to manually verify each warning. To address this problem, we propose a Transformer-based learning approach to identify false positive bug warnings. We demonstrate that our models can improve the precision of static analysis by 17.5%. In addition, we validated the generalizability of this approach across two major bug types: null dereference and resource leak.

cs.SE↗

arXiv2022

Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor

Liver cancer is one of the most common malignant diseases in the world. Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. In the past decades, many state-of-the-art medical image segmentation algorithms appeared during this period. With the development of embedded devices, embedded deployment for medical segmentation and automatic reconstruction brings prospects for future automated surgical tasks. Yet, most of the existing segmentation methods mostly care about the spatial feature context and have a perception defect in the semantic relevance of medical images, which significantly affects the segmentation accuracy of liver tumors and blood vessels. Deploying large and complex models into embedded devices requires a reasonable trade-off between model accuracy, reasoning speed and model capacity. Given these problems, we introduce a multi-scale feature fusion network called TransFusionNet based on Transformer. This network achieved very competitive performance for liver vessel and liver tumor segmentation tasks, meanwhile it can improve the recognition of morphologic margins of liver tumors by exploiting the global information of CT images. Experiments show that in vessel segmentation task TransFusionNet achieved mean Dice coefficients of 0.899 and in liver tumor segmentation task TransFusionNet achieved mean Dice coefficients of 0.961. Compared with the state-of-the-art framework, our model achieves the best segmentation result. In addition, we deployed the model into an embedded micro-structure and constructed an integrated model for liver tumor vascular segmentation and reconstruction. This proprietary structure will be the exclusive component of the future medical field.

eess.IV↗

arXiv2022

Low Gain Avalanche Detectors with Good Time Resolution Developed by IHEP and IME for ATLAS HGTD project

This paper shows the simulation and test results of 50um thick Low Gain Avalanche Detectors (LGAD) sensors designed by the Institute of High Energy Physics (IHEP) and fabricated by the Institute of Microelectronics of the Chinese Academy of Sciences (IME). Three wafers have been produced with four different gain layer implant doses each. Different production processes, including variation in the n++ layer implant energy and carbon co-implantation were used. Test results show that the IHEP-IME sensors with the higher dose of gain layer have lower breakdown voltages and higher gain layer voltages from capacitance-voltage properties, which are consistent with the TCAD simulation. Beta test results show that the time resolution of IHEP-IME sensors is better than 35ps when operated at high voltage and the collected charges of IHEP-IME sensors are larger than 15fC before irradiation, which fulfill the required specifications of sensors before irradiations for the ATLAS HGTD project.

physics.ins-det↗

arXiv2021

Effects of shallow carbon and deep N++ layer on the radiation hardness of IHEP-IME LGAD sensors

Low Gain Avalanche Diode (LGAD) is applied for the High-Granularity Timing Detector (HGTD), and it will be used to upgrade the ATLAS experiment. The first batch IHEP-IME LGAD sensors were designed by the Institute of High Energy Physics (IHEP) and fabricated by the Institute of Microelectronics (IME). Three IHEP-IME sensors (W1, W7 and W8) were irradiated by the neutrons up to the fluence of 2.5 x 10^15 n_eq/cm^2 to study the effect of the shallow carbon and deep N++ layer on the irradiation hardness. Taking W7 as a reference, W1 has an extra shallow carbon applied, and W8 has a deeper N++ layer.

physics.ins-det↗

arXiv2021

The performance of IHEP-NDL LGAD sensors after neutron irradiation

The performances of Low Gain Avalanche diode (LGAD) sensors from a neutron irradiation campaign with fluences of 0.8 x 10^15, 15 x 10^15 and 2.5 x 10^15 neq/cm2 are reported in this article. These LGAD sensors are developed by the Institute of High Energy Physics, Chinese Academy of Sciences and the Novel Device Laboratory for the High Granularity Timing Detector of the High Luminosity Large Hadron Collider. The timing resolution and collected charge of the LGAD sensors were measured with electrons from a beta source. After irradiation with a fluence of 2.5 x 10^15 neq/cm2, the collected charge decreases from 40 fC to 7 fC, the signal-to-noise ratio deteriorates from 48 to 12, and the timing resolution increases from 29 ps to 39 ps.

physics.ins-det↗

arXiv2021

Revenue Adequate Prices for Chance-Constrained Electricity Markets with Variable Renewable Energy Sources

In a commodity market, revenue adequate prices refer to compensations that ensure that a market participant has a non-negative profit. In this article, we study the problem of deriving revenue adequate prices for an electricity market-clearing model with uncertainties resulting from the use of variable renewable energy sources (VRES). To handle the uncertain nature of the problem, we use a chance-constrained optimization (CCO) approach, which has recently become very popular choice when constructing dispatch electricity models with penetration of VRES (or other sources of uncertainty). Then, we show how prices that satisfy revenue adequacy in expectation for the market administrator, and cost recovery in expectation for all conventional and VRES generators, can be obtained from the optimal dual variables associated with the deterministic equivalent of the CCO market-clearing model. These results constitute a novel contribution to the research of research on revenue adequate, equilibrium, and other types of pricing schemes that have been derived in the literature when the market uncertainties are modeled using stochastic or robust optimization approaches. Unlike in the stochastic approach, the CCO market-clearing model studied here produces uncertainty uniform real-time prices that do not depend on the real-time realization of the VRES generation outcomes. To illustrate our results, we consider a case study electricity market, and contrast the market prices obtained using a revenue adequate stochastic approach and the proposed revenue adequate CCO approach.

econ.TH↗

arXiv2021

Physics potential for the H$\rightarrow$ZZ$^{*}$ decay at the CEPC

The precision of the yield measurement of the Higgs boson decaying into a pair of $Z$ bosons process at the Circular Electron Positron Collider (CEPC) is evaluated. Including the recoil $Z$ boson associated with the Higgs production (Higgsstrahlung) total three $Z$ bosons are involved for this channel, from which final states characterized by the presence of a pair of leptons, quarks, and neutrinos are chosen for the signal. Two analysis approaches are compared and the final precision of $σ_{\mathrm{ZH}}{\cdot}$BR($H \rightarrow ZZ^{*}$) is estimated to be 7.9% using a multivariate analysis technique, based on boosted decision trees. The relative precision of the Higgs boson width, using this $H \rightarrow ZZ^{*}$ decay topology, is estimated by combining the obtained result with the precision of the inclusive \textit{ZH} cross section measurement.

hep-ex↗

arXiv2020

Radiation effects on NDL prototype LGAD sensors after proton irradiation

We study the radiation effects of the Low Gain Avalanche Detector (LGAD) sensors developed by the Institute of High Energy Physics (IHEP) and the Novel Device Laboratory (NDL) of Beijing Normal University in China. These new sensors have been irradiated at the China Institute of Atomic Energy (CIAE) using 100 MeV proton beam with five different fluences from 7$\times10^{14}$ $n_{eq}/cm^2$ up to 4.5$\times10^{15}$ $n_{eq}/cm^2$. The result shows the effective doping concentration in the gain layer decreases with the increase of irradiation fluence, as expected by the acceptor removal mechanism. By comparing data and model gives the acceptor removal coefficient $c_{A}$ = $(6.07\pm0.70)\times10^{-16}~cm^2$, which indicates the NDL sensor has fairly good radiation resistance.

physics.ins-det↗

arXiv2020

Improving Power System State Estimation Based on Matrix-Level Cleaning

Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Traditional weighted least square (WLS), as the most universal state estimation method, attempts to minimize the residual between measurements and the estimation of measured variables, but it is unable to handle the measurement error. To solve this problem, based on random matrix theory, this paper proposes a data-driven approach to clean measurement error in matrix-level. Our method significantly reduces the negative effect of measurement error, and conducts a two-stage state estimation scheme combined with WLS. In this method, a Hermitian matrix is constructed to establish an invertible relationship between the eigenvalues of measurements and their covariance matrix. Random matrix tools, combined with an optimization scheme, are used to clean measurement error by shrinking the eigenvalues of the covariance matrix. With great robustness and generality, our approach is particularly suitable for large interconnected power grids. Our method has been numerically evaluated using different testing systems, multiple models of measured noise and matrix size ratios.

eess.SP↗

arXiv2020

Unsupervised Feature Learning for Online Voltage Stability Evaluation and Monitoring Based on Variational Autoencoder

With the increase of uncertain elements in power systems and extensive deployment of online monitoring devices, it is necessary to search a more real-time and robust voltage stability assessment method. This study, using PMU monitoring data, explores a novel data-driven approach for long-term voltage stability assessment based on variational autoencoder (VAE). Our method is capable of extracting the most representative features by an unsupervised data mining method in a probabilistic learning way. Different from most of familiar feature extraction methods, it regularizes latent features in an expected stochastic distribution. Furthermore, a statistical indicator by sampling latent features after variance reduction is proposed to assess long-term voltage stability. Our approach is tested in various simulated power systems with different load increment models. Other cases show the accuracy and speed of our approach for estimating voltage collapse point. These testing cases successfully demonstrate the accuracy and effectiveness of our approach.

eess.SP↗

arXiv2020

Early Anomaly Detection and Location in Distribution Network: A Data-Driven Approach

The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network effectively. In this paper, a random matrix theory (RMT) based approach is developed for early anomaly detection and localization by using the data. For every feeder in the distribution network, a corresponding data matrix is formed. Based on the Marchenko-Pastur Law for the empirical spectral analysis of covariance `signal+noise' matrix, the linear eigenvalue statistics are introduced to indicate the anomaly, and the outliers and their corresponding eigenvectors are analyzed for locating the anomaly. As for the low observability feeders in the distribution network, an increasing data dimension algorithm is designed for the formulated low-dimensional matrices being more accurately analyzed. The developed approach can detect and localize the anomaly at an early stage, and it is robust to random disturbance and measurement error. Cases on Matpower simulation data and real SCADA data corroborate the feasibility of the approach.

stat.AP↗

arXiv2020

Search for invisible decay of a Higgs boson produced at the CEPC

The existence of dark matter has been established in astrophysics. However, there is no candidate for DM in the Stand Model (SM). In SM, the Higgs boson can only decay invisibly via $H\rightarrow ZZ^\ast \rightarrow ν\barνν\barν$ or DM, so any evidence of invisible Higgs decay that exceeds BR (H$\rightarrow$inv.) will immediately point to a phenomenon that is beyond the standard model (BSM). In this paper, we report on the upper limit of BR (H$\rightarrow$invisible) estimated for three channels, including two leptonic channels and one hadronic channel, under the assumption predicted by SM. With the SM ZH production rate, the upper limit of BR (H$\rightarrow$inv.) could reach 0.24\% at the 95\% confidence level.

hep-ex↗

arXiv2019

Charaterization of the first prototype IHEP-NDL LGAD sensor

The timing measurement of charged particles using silicon detector is widely used in synchrotron source as X-ray detectors, in time-of-flight mass spectrometer and especially in large collider experiment. To reduce the drastically event pile-up of high-luminosity large hadron collider (HL-LHC), a new concept of 4-dimension detector including timing and positon has been proposed. One of the candidates for the 4-dimension detector is a new kind of silicon detector called Low Gain Avalanche Diode (LGAD). In China, Institute of High Energy Physics (IHEP) Chinese Academic Science cooperated with Novel Device Laboratory (NDL) at Beijing Normal University have fabricated a series of LGAD sensors. The characterization of the first prototype of IHEP-NDL sensors is presented including leakage current and sensor capacitance measurement. A test system for the time resolution of jitter term using pico-second laser and fast sampling rate oscilloscope is also setup, and the time resolution of 10 ps can be achieved with these sensors.

physics.ins-det↗

arXiv2019

Estimation of high-dimensional factor models and its application in power data analysis

In dealing with high-dimensional data, factor models are often used for reducing dimensions and extracting relevant information. The spectrum of covariance matrices from power data exhibits two aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes, which represents factors caused by anomaly events. In this paper, we propose a new approach to the estimation of high-dimensional factor models, minimizing the distance between the empirical spectral density (ESD) of covariance matrices of the residuals of power data that are obtained by subtracting principal components and the limiting spectral density (LSD) from a multiplicative covariance structure model. The free probability theory (FPT) is used to derive the spectral density of the multiplicative covariance model, which efficiently solves the computational difficulties. The proposed approach connects the estimation of the number of factors to the LSD of covariance matrices of the residuals, which provides estimators of the number of factors and the correlation structure information in the residuals. Considering a lot of measurement noise is contained in the power data and the correlation structure is complex for the residuals, the approach prefers approaching the ESD of covariance matrices of the residuals through a multiplicative covariance model, which avoids making crude assumptions or simplifications on the complex structure of the data. Theoretical studies show the proposed approach is robust against noise and sensitive to the presence of weak factors. The synthetic data from IEEE 118-bus power system is used to validate the effectiveness of the approach. Furthermore, the application to the analysis of the real-world online monitoring data in a power grid shows that the estimators in the approach can be used to indicate the system behavior.

stat.AP↗

arXiv2019

Dimensionality Increment of PMU Data for Anomaly Detection in Low Observability Power Systems

Anomaly detection is an important task in power systems. To make better use of the phasor measurement unit (PMU) data collected from a low observability power system for anomaly detection, a data dimensionality increment algorithm is proposed in this paper. First, a low-dimensional spatio-temporal data matrix is formulated by using the synchrophasor measurements collected from a limited number of PMUs in a power system. Then, a data dimensionality increment algorithm based on random tensor theory (RTT) is proposed for anomaly detection. The proposed algorithm can help improve the sensitivity of random matrix theory (RMT) based and machine learning (ML) based anomaly detection approaches, and it is able to accelerate the convergence rate of model training in the ML based anomaly detection approach. Case studies on the IEEE 118-bus test system validate the effectiveness of the proposed algorithm.

eess.SY↗

arXiv2019

Adversarial Feature Learning of Online Monitoring Data for Operational Risk Assessment in Distribution Networks

With the deployment of online monitoring systems in distribution networks, massive amounts of data collected through them contains rich information on the operating states of the networks. By leveraging the data, an unsupervised approach based on bidirectional generative adversarial networks (BiGANs) is proposed for operational risk assessment in distribution networks in this paper. The approach includes two stages: (1) adversarial feature learning. The most representative features are extracted from the online monitoring data and a statistical index $\mathcal{N}_ϕ$ is calculated for the features, during which we make no assumptions or simplifications on the real data. (2) operational risk assessment. The confidence level $1-α$ for the population mean of the standardized $\mathcal{N}_ϕ$ is combined with the operational risk levels which are divided into emergency, high risk, preventive and normal, and the p value for each data point is calculated and compared with $\fracα{2}$ to determine the risk levels. The proposed approach is capable of discovering the latent structure of the real data and providing more accurate assessment result. The synthetic data is employed to illustrate the selection of parameters involved in the proposed approach. Case studies on the real-world online monitoring data validate the effectiveness and advantages of the proposed approach in risk assessment.

stat.ML↗