bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-Markovian entanglement dynamics of three independent qubits within atomic-cavity system
Authors: Kosasih J S , Wasesatama V A 
Published: June 2022

Abstract
All realistic quantum systems will interact with their environments. This fact presents challenges to the implementation of quantum systems for practical systems. One of the most important quantum resources is the implementation of entanglement. In this paper, we investigate the interplay between the effects of noisy environments and the dynamics of entanglement. The open quantum system model that will be used is cavity quantum electrodynamics (CQED) with three-qubit interacting independently with a variation of environmental model. We use master equation to describe the dynamics of the open quantum system and a lower bound concurrence (CLBC
) to measure the entanglement of tripartite qubit. We derive the exact dynamics of each model and use CLBC
method proposed by Li et.al to visualize entanglement dynamics. By adjusting the environmental parameters, we also found revivals and dark-periods of entanglement which are unique to non-Markovian processes.
Photoluminescence Properties of CsPbCl3 and CsPbBr3 Nanocrystals synthesized by LARP Method with Various Ligands and Anti-solvents
Authors: Yandri Valdi Rizki , Wulandari Priastuti , Hidayat Rachmat 
Published: June 2022

Abstract
Cesium lead halide (CsPbX3; X= Cl, Br, I) is inorganic materials that can be used for the absorber of Perovskite solar cell and active layer of sensor. In this research, synthesis of cesium lead chloride (CsPbCl3) utilizes cesium chloride (CsCl) and lead (II) chloride (PbCl2) as raw materials then oleic acid and linoleic acid are used as ligands. CsCl and PbCl2 can be utilized to synthesize CsPbCl3 by using ligand-assisted reprecipitation (LARP) method at room temperature. Dimethyl Sulfoxide (DMSO) is used as the solvent of CsCl and PbCl2 to form 0.1 M solution. Similar to CsPbCl3, synthesis of CsPbBr3utilizes CsBr and PbBr2 at room temperature. Antisolvent used in this experiment are chloroform, toluene and tetrahydrofuran (THF) for colloidal solution CsPbCl3 and CsPbBr3. This research will analyze the impact of different ligands and different antisolvent, so it will be obtained the most suitable ligand and antisolvent for CsPbCl3 and CsPbBr3. The wavelength of photoluminescence spectra for samples of CsPbCl3 and CsPbBr3 colloidal solution are 434.00 nm and 512.82 nm. Based on transmission electron microscope (TEM) results, nanocrystals have been formed in samples of CsPbCl3 and CsPbBr3 colloidal solution. Powder samples of CsPbCl3 and CsPbBr3 can be obtained by heating at 190°C (the boiling point of DMSO) then CsPbCl3 and CsPbBr3 are identified in each powder sample by x-ray diffraction (XRD).
Research and Design of An Early Warning System for Emerging Infectious Diseases Based on UWB
Authors: Wang Xiaohong , Han Hongbin , Lin Yuting , Ma Qingbian , Zhu Xianghao 
Published: June 2022

Abstract
Aiming at the problem of cross-infection in hospitals and other areas of infectious diseases, we designed a system based on ultra-wideband and using TDOA positioning method combined with smart wearable devices to realize health warning and contact trajectory tracking of medical staff. In the hospital area with stricter management, we obtain the user’s consent and wear smart bracelets to the users. These bracelets are used for positioning and temperature detection.. First, we model the indoor area based on the measurement data of the indoor area and install the base station, use the ultra-wideband technology combined with the wireless time synchronization TDOA method to achieve high-precision positioning in the indoor area, and use Kalman filter to filter the positioning noise data. Then, it combines the body temperature detection data of the wearable bracelet and the system positioning data to establish a user track tracking service to further record the user’s activity range. In addition, the area is strictly controlled, the trajectory of the infected person is restored and the contact person is quickly located, and timely warning can be provided through information such as electronic fence and body temperature. The system can trace the source of regional outbreaks of infectious diseases, provide health warnings for users, and quickly locate the relevant contacts of virus carriers.
Exploration on Homogenization of Threshing and Redrying Products in Qujing Cigarette Factory
Authors: Sheng Xiaohe , Wang Rui , Zhu Yi , He Xiaoqiang , Guo Ge 
Published: June 2022

Abstract
Take homogenization as the main line of threshing and redrying, promote the extension of homogenization production from quality concept to management mode, firmly establish the concept of "process to ensure product quality", and ensure the homogenization of finished product quality with process homogenization. The method of normal distribution and central value collocation was used to solve the requirements of homogenization collocation of unknown material components. The frequency normal distribution map of tobacco nicotine value and the central value were used for warehousing, and the warehousing of raw materials was realized by the combination of nicotine value interval chemistry or central value. After the detection of chemical composition of raw materials, the warehousing task was issued on the vehicle terminal based on the digital warehousing logistics system to remind and monitor the forklift operation task. Through the exploration of homogenization of threshing and redrying products, the homogenization standard module ratio is calculated, and the ratio is based on the nicotine value of tobacco leaves in the chemical composition of the original tobacco, which effectively solves the imbalance of human factors in the current ratio. At the same time, combined with information technology, the information transmission function of forklift vehicle terminal is realized.
SuperDetector: A Framework for Performance Detection on Vulnerabilities of Smart Contracts
Authors: Dai Meiyi , Yang Zhe , Guo Jian 
Published: June 2022

Abstract
The technology of Ethereum blockchain enables the implementation of smart contracts. Nowadays smart contracts are one of the most successful applications of blockchain technology, which are widely used in many fields, such as finance, energy and services. The decentralization and immutability properties of Ethereum blockchain provide security for transactions from smart contracts. However, these properties are possible to lead to unfixable vulnerabilities of smart contracts. In recent years, vulnerability detection on smart contracts has attracted more attention from researchers and many related tools have emerged. Nevertheless, the existing vulnerability detection tools have not yet been put into formal use. Due to the lack of suitable smart contract sets and fair metrics with other factors, it remains complex work to conduct authoritative performance tests on these tools. In this paper, we first summarize eight common vulnerabilities of smart contracts, and then divide them into call-related and call-irrelated vulnerabilities according to whether they involve caller functions or not. In addition, we propose a detection framework called SuperDetector, which combines a variety of vulnerability detection tools based on static analysis technology. And the framework mainly measures the detection performance, which contains vulnerability coverage, detection accuracy, usability and execution time on the two types of vulnerabilities. The results indicate that the framework can analyze the performance from different perspectives, and the performance of vulnerability coverage and detection accuracy on call-related vulnerabilities is much better than that on call-irrelated vulnerabilities. Finally, we design a selector based on the detection results in the framework for improving the vulnerability coverage and detection accuracy.
Prediction and Application of Network Business Traffic based on LSTM
Authors: Lv Tiantian , Wang Ying , Yuan Jingjing , Wu Yanqin , Zhang Le 
Published: June 2022

Abstract
Traffic prediction is the basis for guiding bandwidth adjustment in the process of network operation and maintenance. Most of the existing traffic prediction models focus on network traffic prediction based on a single model or single characteristic, and cannot accurately predict whether the current bandwidth can meet user requirements. This paper proposes a prediction method of network traffic based on the long short-term memory (LSTM) model. Based on LSTM model, the traffic interval deviation and the network traffic are predicted, and the two prediction results are combined, then the network traffic interval is predicted. First, this study uses the historical data from 2019 to 2021 to predict the historical deviation value in February based on the LSTM model, and then predicts the traffic interval on October 2021 based on the historical data in September 2021, and compares it with the bandwidth whether the forecast is out of bounds. Finally, compare with other models, including Auto Regression Model (AR), Moving Average Model (MA), Autoregressive Integrated Moving Average Model (ARIMA) and linear regression model, the precision rate(p), recall rate(r) and comprehensive value(f) are all higher than those of the other two models, which proves the reliability of the LSTM model.
Experimental Measurement and Markov Chain Modelling of Stroke Order Intuition
Authors: Xin Yong , LYU Ruimin 
Published: June 2022

Abstract
As a feature of Chinese characters, stroke order plays an important role in Chinese character education, Chinese character recognition and handwriting identification. However, in recent years, the research on stroke order is limited to elaboration and supplementation of existing stroke order and stroke order rules. There is no special research that focuses on the principle behind stroke order. To quantify the internal regularity of stroke order, we start with the stroke order intuition in the actual writing. Because stroke order has individual differences, we select a group of students with small differences as testers. In this paper, we design an algorithm to generate random strokes and collect the timing data during testers copying strokes. After pre-processing the collected data, a Markov chain is introduced to model stroke order intuition, which can divide the stroke sequences into three states. And then we classify them into three situations according to the stroke distribution. Next, we compare them with some well-known stroke order rules. The results show that the probability distribution of strokes in different situations is not always consistent with the empirical rules, as well as the relations between them.
Short-run Wind Power Combination Projection Model based on CEEMDAN-TPA-TCN-MRFO
Authors: Yuxin YE , Xiaodong SHEN 
Published: June 2022

Abstract
Wind power is playing an increasingly critical role in new energy sources. In order to modify the precision of short-term wind power prediction, this thesis proposes a combined model for short-term wind power prognosticate based on Complete Ensemble Empirical Mode Decomposition of Adaptive Noise (CEEMDAN), Time Pattern Attention mechanism (TPA), Time Convolution Network (TCN) and Multi-strategy Manta Ray Foraging Optimization (MRFO). Firstly, this thesis uses CEEMDAN algorithm decompose the wind power sequence, and obtain the sub-sequence components. Combined with key meteorological variable data, this thesis constructs a training set. Then, it optimizes the convolutional network hyperparameters with MRFO. Finally, the final forecasting value is obtained after reconstructing the forecasting results by predicting sub-sequence components respectively with TPA and TCN. The practical cases reveal that the propound combined forecasting model can better predict the wind power change trend. Compared with other methods, this model can significantly improve the short-term presaging accuracy of wind power.
AIVMAF: Automatic Image Quality Estimation Based on Improved VMAF and YOLOv4
Authors: Du Juan 
Published: June 2022

Abstract
The current most widely used way of image quality estimation relies heavily on the subjective assessment, while majority of past objective estimation methods are not satisfactory on accuracy. To solve them and realize unsupervised image quality estimation with high precision, this paper creates a linear way with “Proportional Partition” controlled by horizontal and vertical rates of extracted pixel to get best representations of the image with patching, balance the uneven distribution of image quality in each source image, and offer dynamic compatibility to images with high resolution. Besides, it estimates the image quality automatically with a model trained by current best artificial intelligence (AI) algorithm for target detection YOLOv4 with 1000 images random selected from ImageNet2013 database. The proposal also uses the spirit of joint indices from the current widely used method named Video Multimethod Assessment Fusion (VMAF). But we replace its Visual Information Fidelity (VIF) with Visual Saliency-induced Index (VSI) and add VSI to our target function because of VIF’s dependence on subjective assessment, and also for VSI’s better performance surpassing most recent IQA estimators as TOP3 best model in recent world. Besides, contrast masking is also included by objective function for the KL-divergence to simulate the human visual perception better. A creative “Batch Learning” way is found to address patches for less calculation and faster speed. All source images are pretreated with colour space transformation and normalization to improve descriptiveness of images and reduce the redundant points, and a threshold is devised to formulate suppression mechanisms. The proposed solution is tested to be a good image quality assessor in many aspects such as correctness, consistency, linearity, monotonicity and speed, and performs well on even HD images.
Athlete Exercise Intensity Recognition Method based on ECG and Convolutional Neural Network
Authors: Zhu Yingbo , Wang Baiyang , Zhang Fuchun , Zhu Haiyan 
Published: June 2022

Abstract
Unreasonable exercise will cause damage to the body. In physical education, coaches only use physiological indicators such as heart rate and breathing to judge the physiological state of athletes, which is highly subjective and is not conducive to accurately judging the physiological state of athletes. In order to effectively monitor athletes in exercises, a method for identifying athletes' exercise intensity based on ECG and convolutional neural network was proposed. In this method, the more informative ECG signal is used as the physiological indicator of the athlete's exercise intensity, combined with the convolutional neural network for feature extraction, and finally the training model is used to monitor and evaluate the athlete's exercise intensity. The method implements automatic feature extraction and recognition of athletes' ECG signals. The simulation results of the dataset show that the method can effectively judge the exercise intensity, and the accuracy can reach 98.6%. At the same time, the algorithm has a small amount of calculation and a fast convergence speed, in the daily training of athletes has a good auxiliary role.
Internet of Things Detection and Location Based on Remote Communication Fault of Power Consumption Acquisition Module
Authors: Zhu Yiqun , Zheng Jijun , Li Zhen , Liu Xiaolei 
Published: June 2022

Abstract
The access of distributed generation makes the single-source radial distribution network become a complex multi-source network. The power flow, network loss, voltage, and so on are also changing. At the same time, it aggravates the complexity of remote communication fault location of remote communication fault power acquisition module based on the Internet of Things. In this paper, a new switching function and evaluation function are used to adapt to the situation of distributed generation access. Considering that the fault section location is a discontinuous problem, the binary particle swarm algorithm is used to make full use of its fast convergence and combine with the operation of diversity preservation to realize the fault section location. The experimental results show that the hybrid algorithm in this paper has a good effect on fault recovery, and has certain advantages and theoretical research value in optimization ability.
An Adaptive Threshold Detection Circuit for Action Potential
Authors: Zhuang Zhilin , Li Xiaolong , Wang Jie , Dai Minghui , Ma Shumiao 
Published: June 2022

Abstract
In order to find the action potential hidden in the neural signal, this study proposes a method of detecting the action potential through adaptive threshold, and designs an adaptive threshold detection circuit which can be used for the action potential. Different from the past, the circuit controls the gain by the current of the input signal itself, so as to generate an appropriate adaptive comparison threshold. This circuit not only retains the advantages of the traditional threshold detection method, but also can quickly respond to the input signal, generate an appropriate comparison threshold and detect the action potential without external supervision. The spectre simulation shows that when the power supply voltage is 1.8V, the adaptive threshold generation circuit can accurately detect the action potential without missing or wrong detection.
Application of BP neural network in on-board charger fault diagnosis
Authors: Duan Xiuzhen , Li Xiaopeng , Liu Yaning 
Published: June 2022

Abstract
With the accelerated growth of the number of electric vehicles, the frequent occurrence of electric vehicle charging safety accidents has brought some safety hazards to people. The fault diagnosis of on-board charger circuit has gradually become the focus of attention of scholars. This paper uses simulation software to conduct modeling simulation analysis, failure mode analysis and data acquisition for the on-board charger of electric vehicles, and establishes a fault diagnosis model based on BP neural network, which is applied to the fault diagnosis of on-board charger circuits. The optimal fault diagnosis model is obtained by continuously adjusting the number of neurons in the hidden layer of the BP neural network, and the open-circuit faults of diodes and MOSFETs are mainly analyzed experimentally. The simulation results show that the expected effect has been achieved in the fault diagnosis of the on-board charger circuit, and the established simple BP neural network model can be applied in practice. Some feasible solutions are provided for improving the charging safety of electric vehicles.
Analysis of reactive power compensation of railway 20kV primary through line
Authors: Guo Wenlong , Tian Mingxing 
Published: June 2022

Abstract
With the increase of power load along the railway and stations and the popularization and use of 20kV distribution network, it has a great impact on the selection of voltage level of railway power supply. To solve this problem, the capacity rise effect of power through line, reactive power compensation capacity configuration of 10kV and 20kV Voltage levels are calculated and analyzed, and the MATLAB simulation model is established for simulation verification.
A Design of Tunable Filter Integrated SPDT Switch Based on PIN Diode
Authors: Jing Yuxiang , Cui Hengrong 
Published: June 2022

Abstract
In this paper, a tunable filter integrated SPDT switch based on PIN diode is proposed. The band-pass filter response is realized by using microstrip lines with different characteristic impedances, and the filter-integrated SPDT switch with tunable center frequency is designed by loading a varactor diode and PIN diodes. The measured results show that its filter response can be tuned in the range of 1.5 to 2.065 GHz, the insertion loss is 1.53-2.69 dB, and with well return loss and port isolation.
Power Quality Indices Online Prediction Based on VMD-LSTM Residual Analysis
Authors: Xu Da , Hu Xiaoyuan , Hong Wenhui , Li Mengshi , Chen Ziming 
Published: June 2022

Abstract
In order to ensure the provision of high-quality power quality and the smooth operation of the power system, aiming at the problem of low prediction accuracy of power quality indices, this paper proposes an online prediction method of power quality indices based on VMD-LSTM residual analysis. Firstly, the VMD-LSTM is used to simulate and predict the normal power quality signal; then the predicted signal data is different from the actual power quality signal data to obtain the residual signal, and the residual signal is input into the one-dimensional convolutional neural network for training, and finally the power quality indices prediction model is obtained. Finally, taking voltage flicker as an example, simulation experiments compared with other prediction models verify the better effectiveness and accuracy of the proposed method.
Research on thermal resistance Measurement method of high power LED
Authors: Fu Qiang , Zhang Junchao , Zhang Jie 
Published: June 2022

Abstract
Due to the special packaging and working conditions of high-power LEDs, it is a huge challenge to accurately measure the junction-to-case thermal resistance. The traditional transient dual interface and structure function methods have been successfully applied to measure the thermal resistance of high-power LEDs. In this paper, a different interface condition is designed to change the contact thermal resistance between the DUT and the heat sink, making the Z
th curve separation point clearer. More importantly, the structural thermal resistance between multiple layers on the heat flow path of the device is obtained by converting the Z
th curve. Finally, the effectiveness of the method is verified by comparative analysis of experimental data.
Research on a Microwave Filter with Pentagram Grooves
Authors: Hou Y F , Qian J 
Published: June 2022

Abstract
This paper introduces a microwave filter with pentagram grooves, which belongs to an artificial surface plasmon (SSPPs) type microwave bandpass filter.The filter adopts a two-stage structure. The first section is a slot-line waveguide section, and one side of this section adopts an exponential curve structure to prevent sudden changes in electromagnetic impedance and achieve a good connection with the second section.The second segment is the SSPPs segment, which adopts a novel mirror-symmetric pentagram-shaped groove air gap structure. By adjusting the geometric size of the pentagram-shaped groove, the microwave subwavelength confinement effect can be further improved,making the SSPPs filter more excellent in pass-band characteristics and anti-space electromagnetic interference.The filter can be applied to the civil microwave communication system of L∼S band.
Research on Optimization Simulation of Three-level Inverter in UPS Based on PSO Algorithm
Authors: Li Xiaoqian , Zhang Guichen , Zhang Qi 
Published: June 2022

Abstract
As a kind of precision power equipment, the uninterruptible power supply system (UPS) can supply power to the load through a DC-AC converter to ensure the uninterrupted power supply in case of grid failure. The voltage waveform output by the inverter has a decisive effect on the power quality, performance of the UPS system is determined by the power quality. Therefore, this paper aims to optimize the output power of the inverter from two aspects: on the one hand, the three-level space vector pulse-width modulation (SVPWM) technology is combined with the double closed-loop control system to improve the voltage waveform of the inverter; On the other hand, the PI parameters in the double closed loop are optimized by the particle swarm optimization (PSO) algorithm, the dynamic performance of the system is optimized, the adjustment time and overshoot are reduced, thus the control performance of the UPS system is improved.