bluuebunny/crossref_metadata_2025 huggingface dataset:
The Atacama Cosmology Telescope: limits on dark matter-baryon interactions from DR4 power spectra
Authors: Li Zack , An Rui , Gluscevic Vera , Boddy Kimberly K. , Bond J. Richard , Calabrese Erminia , Dunkley Jo , Gallardo Patricio A. , Guan Yilun , Hincks Adam , Huffenberger Kevin M. , Kosowsky Arthur , Louis Thibaut , Madhavacheril Mathew S. , Moodley Kavilan , Page Lyman A. , Partridge Bruce , Qu Frank J. , Salatino Maria , Sherwin Blake , Sifón Cristóbal , Vargas Cristian , Wollack Edward J.
Published: February 2023

Diverse astrophysical observations suggest the existence of cold dark matter that
interacts only gravitationally with radiation and ordinary baryonic matter. Any nonzero coupling
between dark matter and baryons would provide a significant step towards understanding the
particle nature of dark matter. Measurements of the cosmic microwave background (CMB) provide
constraints on such a coupling that complement laboratory searches. In this work we place upper
limits on a variety of models for dark matter elastic scattering with protons and electrons by
combining large-scale CMB data from the Planck satellite with small-scale information from
Atacama Cosmology Telescope (ACT) DR4 data. In the case of velocity-independent scattering, we
obtain bounds on the interaction cross section for protons that are 40% tighter than previous
constraints from the CMB anisotropy. For some models with velocity-dependent scattering we find
best-fitting cross sections with a 2σ deviation from zero, but these scattering models are
not statistically preferred over ΛCDM in terms of model selection.
Functional connectivity based machine learning approach for autism detection in young children using MEG signals
Authors: Barik Kasturi , Watanabe Katsumi , Bhattacharya Joydeep , Saha Goutam
Published: February 2023

Objective. Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD. Approach. We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4–7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique. Main results. To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50–100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1–4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity. Significance. Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.
EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search
Authors: Chang Hongli , Zong Yuan , Zheng Wenming , Xiao Yushun , Wang Xuenan , Zhu Jie , Shi Mengxin , Lu Cheng , Yang Hao
Published: February 2023

Objective. Major depressive disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings’ mental activities and can be served as an objective biomarker for diagnosing MDD. Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels. Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition. Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.
IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction
Authors: Shen C. , Zheng W. , Ding Y. , Ai X. , Xue F. , Zhong Y. , Wang N. , Gao L. , Chen Z. , Yang Z. , Chen Z. , Pan Y.
Published: February 2023

Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption for disruption avoidance and gives us insight into the mechanism of disruption. This paper presents a disruption predictor called interpretable disruption predictor based on physics-guided feature extraction (IDP-PGFE) and its results on J-TEXT experiment data. The prediction performance of IDP-PGFE with physics-guided features is effectively improved (true positive rate = 97.27%, false positive rate = 5.45%, area under the ROC curve = 0.98) compared to the models with raw signal input. The validity of the interpretation results is ensured by the high performance of the model. The interpretability study using an attribution technique provides an understanding of J-TEXT disruption and conforms to our prior comprehension of disruption. Furthermore, IDP-PGFE gives a possible mean on inferring the underlying cause of the disruption and how interventions affect the disruption process in J-TEXT. The interpretation results and the experimental phenomenon have a high degree of conformity. The interpretation results also gives a possible experimental analysis direction that the resonant magnetic perturbations delays the density limit disruption by affecting both the MHD instabilities and the radiation profile. PGFE could also reduce the data requirement of IDP-PGFE to 10% of the training data required to train a model on raw signals. This made it possible to be transferred to the next-generation tokamaks, which cannot provide large amounts of data. Therefore, IDP-PGFE is an effective approach to exploring disruption mechanisms and transferring disruption prediction models to future tokamaks.
Learning-based Similarity Join for Power Data
Authors: Sun Shiming , Shan Xin , Wei Xueyun , Tai Chunliang , Liu Chao
Published: February 2023

The increasing instrumentation of physical and computing processes has given us unprecedented capabilities to collect massive volumes of time series. Power data is a typical kind of time series. Considering that the original time series data has ineluctable limitations such as uneven distribution, non-uniform length, poor sampling rate and noisy, we propose a learning=based similarity join for power data consisting of RNN encoder and matrix model. In addition, we develop the partition techniques by grouping process nodes following the matrix join model, ensuring the accuracy and efficiency of similarity join for data series. We conduct experiments on real data-set to evaluate the performance of our approach, demonstrating the effectiveness and scalability of our method.
Image dehazing algorithm based on ViTGAN and its application
Authors: Liu Jianghao
Published: February 2023

In recent years, many methods based on deep learning have emerged in the field of image dehazing. The ViTGAN generation adversarial network based on Transformer is used to design image dehazing algorithm.In ViTGAN, the generator is improved to achieve the desired effect.The first half of the collaterals can be used as a data encoder to extract the features of the original image. In this part, a lower sampling layer is placed behind each density block.Generate network can regard as the second half of a corresponding to the first encoder decoder, in this part, each layer of the sampling density behind block placed a, for the first half is passed on the image characteristics of sampling, can be reduced by the sampling image characteristics to enlarge to the image of the original size, to ensure that the network output is correct.The feature map of the decoder and the symmetric encoder can be fused to ensure that the features of the decoder can be properly represented.
Optical Characteristics Simulation of Wheat Stripe Rust Urediospores
Authors: Chen Xu , He Dongjian
Published: February 2023

Stripe rust is one of the most common diseases challenging the safe production of wheat. Rapid identification and analysis of urediospores, responsible for disease transmission, is the key to preventing and controlling stripe rust. In this study, combined with chemical analysis, spectral analysis method and finite element simulation of the intrinsic characteristics of urediospore were studied in details. Firstly, a comparative analysis of the urediospore components was carried out by HPLC-MS, and a total of 31 components were extracted. On this basis, a 3D urediospore model was established by using FEM software, the characteristic frequencies and modes were calculated. The results shown that and the resonance frequencies and modes of the elliptical structure were lower and more diverse. The method and conclusion can lay a theoretical foundation for the accurate monitoring and early control of wheat stripe rust urediospores.
Robust fractional-order fast terminal sliding mode control of aerial manipulator derived from a mutable inertia parameters model
Authors: Zheng Wenlei , Li Zhan , Xiu Bingkai , Zhao Bingliang , Guo Zhigang
Published: February 2023

The coupling disturbance between the manipulator and the unmanned aerial vehicle (UAV) deteriorates the control performance of system. To get high performance of the aerial manipulator, a robust fractional order fast terminal sliding mode control (FOFTSMC) strategy based on mutable inertia parameters is proposed in this paper. First, the dynamics of aerial manipulator with consideration of the coupling disturbance is derived by utilizing mutable inertia parameters. Then, based on the dynamic model, a robust FOFTSMC algorithm is designed to make the system fly steadily under coupling disturbance. Furthermore, stability analysis is conducted to prove the convergence of tracking errors. Finally, comparative simulation results are given to show the validity and superiority of the proposed scheme.
Design and Deployment of Django-based Housing Information Management System
Authors: Yu Xiya , Li Xianhe , Wu Changping , Xu Gongyou
Published: February 2023

With the further development of diversification and nationalization of the housing market, the demand of housing enterprises for digitalization and refinement of housing management is growing. The original web service system has problems of low development efficiency, long development cycle and difficulty in changing requirements, which can hardly meet the current growing and changing requirements. Based on the existing technology, we built a lightweight and agile development system based on Django framework. Based on the comprehensive study of MVC design pattern and MTV design pattern, we analyzed the requirements of the listing management system, wrote the data interaction logic using Django framework, used MySQL database and Redis, OSS, SMS and SMTP middleware. The core design of the housing information management system is optimized, unifying user login and registration behavior, fine-grained decentralization of system users, unifying front-end and back-end data exchange standards, and making dynamic back-end settings, recording behavior logs and developing multi-environment configuration optimization for the system, bringing positive contribution to the digitalization and fine-grained management of housing enterprises’ listings.
YOLOCa: Center aware yolo for dense object detection
Authors: Gao Zixiang
Published: February 2023

In this report, we propose a fully convolutional one-stage object detector (YOLOCa) with a novel decoupled head, an improved label assignment strategy, and a new loss computation method for dense object detection. By comprehensively considering the IoU, score and position connection between the prediction frame and the ground truth in the sample allocation, and by introducing some current mainstream loss function calculation methods such as Focal loss, DIOU nms, etc. Through the above techniques, we have effectively improved the AP. We conduct experiments on the global wheat detection dataset, and as shown in figure 1, our method achieves an AP of 49.9%, which is a 14.5% improvement over the baseline yolov3 (35.4%). This is still 0.8% AP higher than yolox which also uses the advanced label assignment strategy, and we still maintain the speed efficiency of the first-level object detector. We hope our work can provide some useful tips for developers and researchers, the source code is at https://github.com/gaozixiang13/YOLOCa.
Image detection of insulator defects in complex environments
Authors: Zhang Wancai , Zhang Nan , Yang Wenqing , Wang Tao
Published: February 2023

Aiming at the practical problems of low detection accuracy and low detection speed for the detection of power insulators and insulator defects in a complex environmental context, a method is proposed to improve the YOLOv4 algorithm for the detection of power insulator images and insulators with defects. By making a dataset of electric power insulators and insulators with defects, the K-means algorithm is used to cluster the electric power insulator image samples to obtain different sizes of prior box parameters; then a weighting factor is introduced by improving the balanced cross-entropy to increase the contribution degree of the loss function; finally, the depth of the network is deepened by adding convolution layers before and after the spatial pyramidal pooling structure. The experimental results show that the single detection time of the improved model is 3.27S, and the average detection accuracy for insulator defects is improved by 24.36% compared with the original YOLOv4 algorithm. Also the value of the mean average accuracy on the test set by the improved YOLOv4 algorithm is 84.05%, which is 17.83% better than the original YOLOv4 algorithm, fully demonstrating the ability to locate and identify the defects existing in power insulator images well.
An Energy Efficient Task Scheduling for a Cloud-edge System
Authors: Cheng Xinyun , Yu Jinghang , Zhang Mingming , Xia Fei , Zhang Zichen , Ji Mingtao
Published: February 2023

With the widespread popularity of edge computing, the energy consumption of edge devices has attracted lots of research interests. This paper studies the cloud-edge system under the solar energy supply scenario, and optimizes the system parameter configuration according to the energy supply of photovoltaic power generation, to reduce the system energy consumption and prolong the system running time. Specifically, we built an edge video detection system based on Raspberry Pi, and deeply analyzed the energy consumption model of the system. Based on this, the problem of dynamically adjusting the video detection operating parameters is modeled as a long-term optimization problem. In order to solve this problem effectively, the long-term optimization problem is transformed to an instantaneous optimization problem, so that the problem can be solved efficiently in real time, and the rationality of this transformation is proved. Finally, we propose a low-energy-consumption scheduling algorithm to optimize the task scheduling and energy consumption of the system. Through simulation experiments, the performance of the proposed algorithm is evaluated. The results show that the scheduling algorithm can reduce the energy consumption of the system by an average of 18%.
Research and Practice of Rebar Engineering Quantity Calculation for Underground Diaphragm Wall of Intercity Railway Based on BIM and Planar Representation Method of Rebar
Authors: Pan Jianying
Published: February 2023

Underground diaphragm wall is a typical form of deep foundation support for intercity railroad, which has the characteristics of high wall strength, high stiffness and large amount of steel reinforcement. In the process of quantity calculation, there are problems such as unreasonable calculation method of reinforcement quantity and inaccurate and non-uniform calculation results. This paper briefly discusses the research on quantity calculation based on BIM model by combining the classification form of underground diaphragm wall, sorting out the rules of quantity calculation and steel flat method labeling. It also discusses the functions of the rebar information extraction system based on the rebar flat method labeling and the flow structure of the rebar quantity calculation process of intercity railroad underground diaphragm wall based on BIM and rebar flat method labeling. Finally, the accuracy of the calculation results of the diameter, length and number of longitudinal bars, hoop bars and tension bars of the underground diaphragm wall of the intercity railroad is compared with the traditional calculation mode by combining the calculation model. The data showed that the accuracy rate of calculation based on BIM and steel flat marking method was higher than that of traditional calculation mode, and the accuracy rate of BIM and steel flat marking method reached 97.9% on average, while the accuracy rate of traditional calculation mode was only about 85% on average. Therefore, it is verified that the calculation of intercity railroad underground diaphragm wall reinforcement based on BIM and steel flat method has good effect.
Research on transformer internal defect detection based on large scale model
Authors: Su Lei , Zhou Haoyi , Huang Hua , Zhang Wancai , Cao Boyuan
Published: February 2023

Aiming at the problem that the traditional convolution network can not be used to detect and identify targets in the more complex target detection application scenarios in power grid business, a target detection scheme based on the ability of large model is proposed. The effects of convolution neural network layers, convolution kernel size and feature dimension on the recognition effect are systematically analyzed and compared. The differences between traditional convolution network and large model in data volume, accuracy and model construction efficiency are compared. The experimental results show that for the specific target detection business scenario downstream of power grid, the model constructed by this scheme has the advantages of fast and efficient, high accuracy and less data dependence, and the comprehensive performance is better than the traditional convolution network detection method.
External Attention Mechanism-Based Modulation Classification
Authors: Tao Xueying , Shao Huaizong , Li Qiang , Pan Ye , Fu Zhongqi
Published: February 2023

This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7% in terms of classification accuracy.
Research on parking guidance system based on computer vision
Authors: Liu Yuan , Liu Xiangju , Wang Shuaimao , Tian Rongqiang
Published: February 2023

The rapid pace of urbanization has made the urban parking problem more and more important, and various regions have introduced corresponding policies and measures to solve the parking problem, such as building large parking plants, introducing intelligent systems, etc. However, the number of parking spaces in large parking plants is large, the spatial distribution is complex, and it is difficult to find parking spaces. It is very important to improve parking efficiency through intelligent parking guidance and management. The parking space recognition system based on image recognition technology of computer vision can obtain real-time parking space utilization. The vehicle guidance system based on the generalized shortest path can guide incoming vehicles, reduce the time for vehicles to wait and find parking spaces. As the core module of the parking intelligent management system, the intelligent parking space recognition system and vehicle guidance system can significantly improve the parking efficiency.
A Case Study On The Failure Of Anti-Short-Circuit Function Of Elevator Door
Authors: Ren Zhaolin
Published: February 2023

The floor and car door circuits are very important parts of the elevator safety protection system, and once they are short-circuited, they will easily lead to shearing and falling accidents.. The newly manufactured elevator requires the addition of door loop detection function, designed to prevent normal operation of elevator from the door loop being short-circuited, however, many systems have defects in determining whether door loop is short-circuit, especially for the elevators which open the front door and the back door at the same time. By comparing the theory of single-door loop detection and double-door loop detection, this paper analyzes the existing problems of the door loop detection device and provides the corresponding rectification and suggestion.
The Failure Analysis of Unintended Elevator Car Movement Protection
Authors: Ren Zhaolin
Published: February 2023

The unintended movement protection device of elevator car can prevent the accident of passenger shearing and extrusion caused by the fracture of elevator traction wheel shaft and the failure of control system components. However, due to the relatively complex composition of this device, many car accidental movement protection devices in the actual use process do not meet the requirements of standards and specifications. In view of the problems found in the inspection, several typical failure cases are summarized, and the reasons for the failure of these devices are analyzed. At the same time, the corresponding solutions under the current technical conditions are put forward, which provides a reference for the development of field inspection and the revision of specifications and standards.
Anti-icing Performance Evaluation of OPGW Based on BOTDA and OTDR
Authors: Zhao Yongqiang , Zhang Ye , Tian Rui , Wu Jian , Hao Jiakai , Xuebin Feng
Published: February 2023

Under certain icing load, the optical fiber of OPGW is seriously affected by stress, which will not only affect the operation quality of line communication, but also threaten its own and line safety, such as cable breaking, tower falling and other accidents. At present, OTDR is mainly used to test the optical path loss of the optical fiber in the OPGW after icing, but that can’t reflect and judge the damage state of the OPGW accurately. In this paper, a method combining BOTDA and OTDR is proposed to detect the damaged state of OPGW after icing. Based on this, the anti icing performance is divided into healthy state, sub-health state and fault state.
Research and development of tunnel intelligent design system
Authors: Fang Zhongqiang , Chao Yang , Yang Jiajia
Published: February 2023

According to the characteristics of tunnel design, the tunnel BIM entity is created based on the digital ground model, forming a set of data sources, two-dimensional drawings, three-dimensional models and three expression design forms of BIM building information. The system can complete the design of tunnel building boundaries and internal contours, tunnel lining design, cave door design and auxiliary construction measures design; Among them, intelligent design is a feature of the software: the design of the cave door, lining section and auxiliary measures can be intelligently determined through the information such as the type of surrounding rock and the burial depth of the tunnel in the geological data, so as to realize the digitization and intelligence of the tunnel design. In addition, there is a template library function, a timely update function. At present, the system can meet 80% of the business needs of tunnel design, which greatly improves the design production efficiency.
Study on Temperature Measurement of Petroleum Product Density Analyzer Based on Hydrometer Method
Authors: Zhou Zhongmu , Ren Tingting , Li Ming , He Mengxi , Zhou Xiaoying
Published: February 2023

Density is a common physical index of petroleum products and is widely measured by the hydrometer method. This paper presents a novel design of temperature measurement method for a petroleum product density analyzer based on the basic functions and main metrological characteristics of the analyzer by referring to the relevant metrological technical specification JJF 1030-2010 and national standard GB/T 1884-2000. The experimental results show that the measurement method is feasible to scientifically evaluate the temperature metrological performance of the petroleum product density analyzer, and can improve the accuracy and reliability of density measurement results of petroleum products.
Research and design of testing equipment for garbage containers
Authors: Liu Fang , Xu Hua , Chen Tianyu , Feng Panpan
Published: February 2023

As to mechanical property testing of garbage containers, the testing results by traditional manual testing means are fundamentally limited by large manual error, low stability, long testing cycle, etc. Based on the common use characteristic of garbage containers in the market, we develop automatic testing equipment for performances of effective volume, opening and closing force, durability, and structural strength of garbage containers. The testing equipment is modular-designed, which can realize commands-control and data transmission of the sensor module and tooling module through the PLC module of measurement and control. Two methods are used to test the item of effective volume, which are the weighing method and the flow method, to optimize the data validity and repeatability by collecting and calibrating two kinds of testing results. The performances of opening and closing force and durability of containers with the different on-off operation modes, such as pedals mode, cover mode, and the mode of the door, are tested by the recombination tooling module with pulling force sensors and torque sensors of the equipment. The structural strength is tested by a lifting mechanism and free-fall tooling in the equipment. Compared to testing results from the traditional method, it is found that results from the testing equipment developed in this study are higher in stability and shorter in the testing cycle. Also, it can realize the automatic testing of garbage containers.
Selection and Design of Excitation Signal in Test System
Authors: Zhang Na , Wu Liu Chun
Published: February 2023

Both the test system and testing technology are inseparable from the selection and construction of the excitation signal. This paper shows excitation signals including transient excitation, harmonic excitation, random signal excitation, etc, which are commonly used in the time domain, frequency domain, and correlation analysis of the stimulus-response test. Moreover, the selection and design method of excitation signals in the test system is introduced based on the principle of signal analysis and system identification, combined with research and engineering practice experience. The application of different excitation signals in modal tests and device characterization is described at the end of this paper.
Precision Design of Probe Station Calibration Device for Wafer Test System
Authors: Bo Tao , Liu Wenjie
Published: February 2023

Aiming at the problem that the probe station of the current wafer test system cannot be measured, a calibration device for the playing table of the wafer test system is designed. The device fully considers the structure of the probe station of the wafer test system and uses a laser interferometer to realize the calibration of the probe station of the wafer test system. Various error sources of the calibration device are analyzed, and errors are allocated according to the device’s technical indicators. The calculation results show that the maximum allowable error of the designed wafer test system probe station calibration device is 0.764μm, which is in line with the technical specifications.
Study on Calibration Method of IC Aging Test Systems under Comprehensive Working Conditions
Authors: Liu Qian , Bo Tao
Published: February 2023

Domestic IC aging test systems are mostly designed with poor consideration of traceability, and self-calibration solutions, which lack systematic overall measurement. According to the comprehensive working conditions characteristics of IC aging test systems, a systematic overall calibration method of the IC aging test systems is presented in this paper.
Experimental Study on Oil Colour Grading Based on Uniform Colour Space
Authors: Gao Hongbo , Sun Duo , Zhou Yan , Zeng Limei , Shi Xiefei
Published: February 2023

Though the Lovibond colorimetric method is effective for determining oil colour, it is a visual comparison method which relies on manual visual reading. As a result, this method is vulnerable to the influence of subjective factors of testers and is of low accuracy. In this paper, the original index of oil colour grading based on Lovibond colour space is changed to the index of oil colour grading based on uniform colour space, and a new method is proposed to determine oil colour by spectrophotometry. The experimental results show that there is a strong correlation between Lovibond red value and the chroma index a*, and a strong correlation between Lovibond yellow value and the chroma index b*. So the method greatly improves the detection efficiency and avoids a series of disadvantages of the visual method.
Intelligent Monitoring System of Access Equipment in Dispatching Exchange Network Based on Multi-layer Immune Model
Authors: Zhang Yantong , Zhai Qi , Zhai Hongting , Li Liang , Quan Weihong
Published: February 2023

To visualize the soft switch traffic and traffic flow of the whole network scheduling, it is necessary to monitor the scheduling of the switching network intelligently. The original system uses fixed-point mode to monitor devices, which can not anchor multiple groups of access devices simultaneously, resulting in errors in monitoring results. In the hardware design of the system, a multilayer immune model is used to establish a three-level visualization partition, and a monitoring system framework for access devices is constructed. In the software design, the detection module, the prompt module, and the automatic decoding module are set up, respectively, and the monitoring process of the dispatching switching network is configured. The access equipment of the dispatching exchange network is intelligently monitored and controlled by an immune model with a three-layer structure. The experimental results show that the data obtained by this system is consistent with the real data and has an application effect by taking the communication data of power dispatching in a provincial power system as the test object and monitoring the communication data of multiple links in a year.
Fault Diagnosis of the Three-phase Asynchronous Motor Bond Graph Model Based on Bond Graph and Temporal Causal Graph
Authors: He Feng , Wang Junnian , Yu Wenxin , Zhong Guangan
Published: February 2023

The bond graph and temporal causal graph (TCG) are proposed to diagnose the fault of three-phase asynchronous motors. The bond graph model of the three-phase asynchronous motor is constructed using the general equations in the three-phase coordinate system. The analytical redundancy relationships (ARRs) and fault signature matrix (FSM) of the three-phase asynchronous motor are derived systematically by introducing TCG to improve the system fault isolability. Simulation experiments are conducted for the rotor-turn short-circuit fault and the rotor-break bar fault of the three-phase asynchronous motor. The experimental results show that introducing TCG for fault diagnosis in this model is feasible.
Monocular Depth Estimation Network Based on Swin Transformer
Authors: Yu Shangbin , Zhang Renyan , Ma Shuaiye , Jiang Xinfang
Published: February 2023

Estimating depth from a single image is challenging because a single 2D image may correspond to many different 3D scenes with the same depth. While most deep learning based depth prediction methods extract depth features using small convolutional kernels with small receptive fields, which results in deformed depth edges and inaccurate depth values of distant objects in the depth estimation results. Aiming at this problem, we propose a depth estimation network based on Swin Transformer and the encoder-decoder structure. We construct the encoder using the Swin Transformer network, which can encode long-range spatial dependency and extract features on various scales and across different channels. The decoder of the proposed network is in charge of fusing the features from the encoder by the operations of interpolation, concatenation, and convolution. Experiments on KITTI and NYUv2 datasets show that our proposed network can get more accurate depth edges and depth values than the state-of-the-art methods.
Independent and Related Variable Fault Detection Based on Information Concentrated Variational Auto-encoder
Authors: Yang Jingchao , Wang Li
Published: February 2023

With the rapid development of deep learning methods, the variational auto-encoder (VAE) has been utilized for nonlinear process monitoring. However, most VAE-based methods hardly consider the inner independent and related relationship of each variable. To overcome this problem, a novel VAE named independent and related variable information concentrated variational auto-encoder (IRVIC-VAE) is proposed. To concentrate the independent and related information, a loading weight matrix regularization based on the mutual information of variables with gaussian distribution is introduced so that the variables can separate into two subspaces that contain independent and related information in latent variables. The original data space decomposed via IRVIC-VAE is orthogonal and approximate to normal distribution. For process monitoring, the independent variable space and related variable space are combined to establish two statistics according to Kullback-Leibler divergence and 2-norm. Finally, the performance and effectiveness of IRVIC-VAE are testified by Tennessee Eastman (TE) process.
Thermographic Inspection Simulation of One Kind of Rail Defects
Authors: Fang Xucheng , Wang Hui
Published: February 2023

Rail is an important foundation of rail transit, and defects on the rail may pose a serious threat to rail transit. As a new non-destructive testing method, thermographic detection shows an excellent application prospect. In order to verify whether thermographic detection technology can be used to detect the defects on the rail, especially the defects at the bottom of the rail. The ANSYS software is used to model the rails under different conditions, and the heat flow simulation experiment is carried out to obtain the temperature change results when the heat flow passes through the defects and when it does not. The existence and location of defects can be determined by observing how the heat flow changes due to the existence of defects.

Although the results show that surface fracture defects and defects towards the bottom edge of the rail can be clearly detected by thermal modeling under laser heating conditions, for transverse cracks that exist near the center of the rail bottom, the simulation experiments still cannot distinguish the temperature differences at the same observation location under current parameters. So it may be necessary to further optimize the experimental process, or increase the experimental parameters, so that the thermal modeling method can be better applied for non-destructive testing of rail defects.
Study on the Relationship Between Thickness Measurement and Strength Defect of Buried Pipeline
Authors: Xu Chuandi , Li Wanze , Lv Fei
Published: February 2023

Based on the principle of electromagnetic eddy current testing, aiming at the problems of heavy workload and difficult location of defects in the periodic inspection of buried pipelines, the eddy current distribution of pipelines, probes, and defects is modeled and simulated by using ANSYS EM simulation software, the correlation model between pipeline parameters, excitation signals, and received signals is established, and the quantitative relationship between pipeline wall thickness parameters and eddy current distribution is clarified. Based on the accuracy and engineering application ability, the through pipe wall thickness detection device is optimized, and the structure of the eddy current probe is improved. The experimental results show that the improved eddy current probe can improve the detection accuracy of 6.9% in detecting the induced eddy rheology in the workpiece and effectively improve the efficiency of pipe wall thickness defect location and pipe radius detection, which can provide a new reference for the regular detection and troubleshooting of buried pipelines.
Stereo matching method of distribution box hole image based on improved AD-Census algorithm
Authors: Li Dejun , Cheng Kang , Wang Cheng , Zhou Guiyang
Published: February 2023

In mountain tunnels’ mechanical and electrical engineering, it is necessary to carry out cable distribution plates. To ensure the accuracy of the cable distribution plate, it is essential to measure the distance between the distribution box hole and tunnel mouth and the size of the distribution box hole. Stereo matching is a crucial step in using binocular vision for measurement, and the quality of the matching effect directly affects the measurement accuracy of the following steps. This study focuses on the measurement of tunnel distribution box hole size and studies the stereo-matching algorithm of distribution box hole images. AD-Census, a semi-global stereo-matching algorithm, is selected as the main stereo-matching algorithm in this paper, and the stereo-matching experiment is carried out. According to the effect of stereo matching, the AD-Census algorithm is improved, and the MSD-Census algorithm fused with edge information is proposed to obtain better disparity information, which lays a foundation for the subsequent measurement of distribution box holes.
A Similarity-based Time Series Source Dataset Selection Method for Transfer Learning
Authors: Zhang Zongxi , Zhou Kejian , Wang Xiaohu , Zhu Ming , Gao Jiahao , Mei Jie , Huang Qinqing
Published: February 2023

Source datasets are selected with high similarity to target datasets to improve the effect of transfer learning. The low similarity between the source and target datasets may lead to negative transfer. This paper proposes a similarity-based time series source dataset selection method for transfer learning. First, to reduce the complexity of the operation, we use Dynamic time-warped barycentric averaging to obtain the prototype signals of each dataset and convert the dataset similarity calculation into the similarity calculation between the prototype signals. The dynamic time warping (DTW) algorithm, commonly used to calculate time-series signal similarity, does not consider the effect of signal complexity and the amount of data the dataset contains on the signal similarity calculation. This article makes improvements to the above issues. Experiments are carried out on 20-time series datasets archived by the University of California Riverside. Experimental results show that the average transfer effect of source datasets selected by the proposed method is better than the commonly used dataset similarity measurement method based on DTW.
Performance Analysis of Machine Learning Methods
Authors: Liang Dinghai , Jin Xuan , Yuan Yuchen , Zou Ruyuan
Published: February 2023

Machine Learning has been studied worldwide for its functions in data science and artificial intelligence (AI) fields. Previous works have shown the excellent performance of machine learning methods in image classification. This paper uses various machine learning methods for fashion product classification. This paper aims to analyze the result of predictions for all classes and the first three ranked classes, and meanwhile, compare and discuss Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN), Contrastive Language-Image Pre-training (CLIP) methods’ performance. The results show that the F-Score is increased if just predicted for the first three ranked classes, and among SVM, KNN, and CNN models, CNN is the best in both conditions. From the performance of all four models, CLIP was the best model with better learning ability. Besides, the results suggest that an imbalanced dataset may harm predictions, and the CLIP method yields the best result. In the future, CLIP methods may be more likely recommended in an image classification problem with lots of classes, and an imbalanced dataset adjusted will provide new insights into unsolved and unimproved classification problems.
Application of multi-beam bathymetry system in shallow water area
Authors: Guo Qiang , Fu Chuanyu , Chen Yikang , Zhang Yu
Published: February 2023

Multi-beam bathymetry technology plays an important role in ocean, river and lake surveying and mapping. According to the measurement principle and technical requirements of MS8200 multi-beam bathymetry system, this paper introduces the composition and working principle of the system, the installation and calibration of the system, and the data in the post-processing process, the sea area and terrain characteristics near Wenchang Qinglan Port are taken as the research object to discuss the dual multi-beam bathymetric technology and data processing process, and compare the data of single multi-beam in the same sea area. The research results show that the dual probe of multibeam bathymetry system can better reflect the topographic features of the seafloor compared with the single probe, and shorten the time of mapping, reduce the labor intensity and improve the measurement efficiency, and provide data reference for the stability and reliability evaluation study of the dual probe of multibeam bathymetry system.
Design of a radiation shield applied to surface air temperature monitoring
Authors: Dai Wei , Tan Mengqing , Zhu Huanan
Published: February 2023

Due to the solar radiation effect, the current thermometric
instruments may produce a radiation error of about 1°C. In
this paper, a new radiation shield was designed. Initially, a
Computational Fluid Dynamics (CFD) method was employed to create the
radiation shield structure. Then, the CFD method was used to
quantify the radiation errors of the radiation shield under various
environmental conditions. Next, a Multi-layer Perceptron (MLP)
network was employed to form a radiation error correction approach
for this radiation shield. Finally, the air temperature observation
experiments were performed to verify the accuracy of the new
radiation shield. A 076B fan aspirated radiation shield was served
as an air temperature reference during experiments. The mean
absolute error, root mean square error, and correlation coefficient
between the radiation error results provided by the correction
approach and the measured radiation error results given by the
experiments are 0.012°C, 0.015°C, and 0.796°C,
respectively.
A 128-channel time-to-digital converter based on the 24-phase shift-clock sampling method for RPC
Authors: Xue Zhengwei , Liang Hao , Li Jiaxuan , Ge Jiajin , Li Quanyin
Published: February 2023

The Resistive Plate Chamber (RPC) are widely used in high
energy physics (HEP) experiments. In recent years, new designs and
application scenarios of RPCs abound, and an advanced
time-to-digital converter (TDC) system is in demand. The TDC
implemented in the field-programmable gate array (FPGA) is widely
used in HEP experiments with changeable conditions as a flexible
time measurement method. This article presents a multi-phase
shift-clock sampling FPGA-based TDC with 128 channels. The TDC
module is implemented in a Xilinx Kintex-7 FPGA. The sampling clock
period (2.22 ns) is subdivided into 24 phases to achieve finer time
resolution. The TDC performance measurements show that the
root-mean-square (RMS) time resolution of the TDC module in the time
interval measurement is 45.3 ∼ 57.7 ps, and the integral
nonlinearity (INL) is smaller than 0.2368 least significant bit
(LSB). In addition, some target designs are made to adapt to the
application scenarios. This article also shows measurement results
acquired from an RPC by this TDC. This TDC was used to measure the
RPC efficiency plateau and the TOT of the RPC readout signal.
The role of neurofilament transport in the radial growth of myelinated axons
Authors: Nowier Rawan M. , Friedman Anika , Brown Anthony , Jung Peter
Published: February 2023

Constrained by published data, a computational model is presented that can explain the radial growth of myelinated axons in vivo. It is found that the radial expansion of these axons is driven primarily by an increase in the influx of neurofilaments at early times and by a slowing of neurofilament transport at later times.