bluuebunny/crossref_metadata_2025_split huggingface dataset:
Offline Signature Verification System Using SVM Classifier with Image Pre-processing Steps and SURF Algorithm
Authors: Goon Li Wen , Kheng Eng Swee 
Published: December 2021

Abstract
A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.
Effect of Temperature on Phase Transformation of NiTi-based Shape Memory Alloy
Authors: Ma Xingxing , Zhou Yun , Chen Xia , Lu Gailing , Ji Jingjing , Zhao Shilei 
Published: December 2021

Abstract
For NiTi-based alloys, the phase transition temperature directly affects and limits their application fields. In order to apply the NiTi-based shape memory alloy in the wider field, it is necessary to control the phase transformation temperature. Studies have shown that the content of Ni element in the NiTi-based alloy and the precipitates of the alloy, such as NiTi2, Ni3Ti2 and Ni4Ti3, will affect the phase transition temperature of the alloy. At the same time, adding a third or even a fourth element to the NiTi binary alloy can also effectively regulate the phase transition temperature of NiTi-based alloy. We then pay attention to the problems confronting the current state of the NiTi-based shape memory alloy. We have confidence that the NiTi-based shape memory alloy have a bright future in the development and innovation of excellent properties.
Sodium-ion battery technology: Advanced anodes, cathodes and electrolytes
Authors: Lu Wanyu , Wang Zijie , Zhong Shuhang 
Published: December 2021

Abstract
The development of electric vehicles has made massive progress in recent years, and the battery part has been receiving constant attention. Although lithium-ion battery is a powerful energy storage technology contemporarily with great convenience in the field of electric vehicles and portable/stationary storage, the scantiness and increasing price of lithium have raised significant concerns about the battery’s developments; an alternative technology is needed to replace the expensive lithium-ion batteries at use. Therefore, the sodium-ion batteries (SIBs) were brought back to life. Sharing a similar mechanism as the lithium-ion batteries makes SIBs easier to understand and more effective in the research. In recent years, the developed materials for anode and cathode in the SIB have extensively promoted its advancements in increasing the energy density, power rate, and cyclability; multiple types of electrolytes, either in the form of aqueous, solid, or ions, offers safety and stability. Still, to rival the lithium-ion batteries, the SIB needs much more work to improve its performance, further expanding its application. Overall, the SIB has tremendous potential to be the future leading battery technology because of its abundance.
Slow Light Tuning in Annular Slotted Photonic Crystal Waveguide with Incoming Polymer
Authors: Zhu Kongtao , Yang Hongxue , Du Hui 
Published: December 2021

Abstract
An advanced post-processing scheme of reconfigurable dielectric infiltration into an annular slotted photonic crystal waveguide (ASPhCW) is proposed in this paper. Ionic liquids have had prominent effects in enhancing the optical properties of photonic crystals, especially in the aspect of tuning the transmission rate and velocity through optical materials. Using the two-dimensional plane wave expansion method, the flat band dispersion of the slow light is obtained and the tuning of the operating wavelength of the crystal could be realized by incoming polymer technology. The operating wavelength tuning range could be as large as 459.27nm and the group index could be tuned as high as 44.8 with a near zero group velocity dispersion. Using this method, a high group index equaling 45 with the bandwidth equaling 11.3nm and the normalized delay bandwidth product (NDBP) equaling 0.25 is realized. This incoming polymer technology provides an effective method of getting flat band of slow light flexibly and makes it possible to offer longer delay and low group velocity after fabrication.
Analysis of Infrared Radiation Intensity by Water-Mist Spraying in Ship Exhaust System
Authors: Sun Zhongke , Wang Zhen , Chen Zhongwei , Liu Zhihua , Liu Mo , Zhang Shuai 
Published: December 2021

Abstract
The infrared radiation intensity in 3~5μm of conventional ship exhaust system are so severe [1] that can be easily captured by detector. Therefore, it’s necessary to take measure like spraying water mist to decrease temperature of exhaust system in order to decrease infrared radiation intensity. In this paper, the calculation of infrared radiation intensity of conventional ship exhaust system with water-mist spraying will be given. The results show that the average and maximum infrared radiation intensity of the exhaust system can be reduced by 90.3% and 95.7% after water mist cooling.
1550nm Infrared Laser Heating NaYF4:Er3+ Nanoparticle In Isolated Pig Liver and Up-conversion Fluorescence Intensity Ratio Temperature Measurement
Authors: He Xinyu , Liao Songsong , Feng Zhuohong , Huang Wenqi , Huang Yantang 
Published: December 2021

Abstract
Hyperthermia is an important means of cancer treatment which is called “tumor green therapy”. The clinical heat therapy heating sources include ultrasound, microwave, alternating magnetic field, infrared laser. However, it is still difficult to accurately measure the temperature of tumor hyperthermia and the dose of hyperthermia. Here, NaYF4:Er3+ nanoparticles were injected into a small part of isolated pig liver, and heated it with 1550nm infrared laser. At the same time, the laser excited the NaYF4:Er3+ nanoparticles to generate up-conversion green fluorescence. The fluorescence intensity ratio was used to measure the temperature of the heating part in real time.
Human-posture-tracking sensor selection for motion recognition
Authors: Tan Chongwei 
Published: December 2021

Abstract
Very recently special attention has been paid to soft sensors for motion tracking. It is known from the work by Chen et al [2021, Comp Anim Virtual Worlds. 2021; e1993.] that a wearable motion tracking system was developed, in which five sensors were placed around the region of arm and shoulder. In this study, we explore the effect of different sensors on motion recognition and select the sensors with excellent differentiation for different movements.
Decentralized Path Planning for Multi-Objective Robot Swarm System
Authors: Wu Zhuokai 
Published: December 2021

AbstractThe multi-robot path planning aims to explore a set of non-colliding paths with the shortest sum of lengths for multiple robots. The most popular approach is to artificially decompose the map into discrete small grids before applying heuristic algorithms. To solve the path planning in continuous environments, we propose a decentralized two-stage algorithm to solve the path-planning problem, where the obstacle and inter-robot collisions are both considered. In the first stage, an obstacle- avoidance path-planning problem is mathematically developed by minimizing the travel length of each robot. Specifically, the obstacle-avoidance trajectories are generated by approximating the obstacles as convex-concave constraints. In the second stage, with the given trajectories, we formulate a quadratic programming (QP) problem for velocity control using the control barrier and Lyapunov function (CBF-CLF). In this way, the multi-robot collision avoidance as well as time efficiency are satisfied by adapting the velocities of robots. In sharp contrast to the conventional heuristic methods, path length, smoothness and safety are fully considered by mathematically formulating the optimization problems in continuous environments. Extensive experiments as well as computer simulations are conducted to validate the effectiveness of the proposed path-planning algorithm.
Research on Optical Fiber Sensor Signal Processing Technology Based on Variational Mode Decomposition and Singular Spectrum Analysis
Authors: Dong ZheHao , Ding YanHong , Zhang ZhiLi , Zhu HaiQiang 
Published: December 2021

Abstract
To better apply the phase-sensitive optical time-domain reflectometer (Φ-OTDR) in projects with complex environments, given the problem of the low signal-to-noise ratio of the Φ-OTDR signal, a variational modal decomposition (VMD) is proposed. Signal processing algorithm combined with singular spectrum analysis. First, the equalization optimizer (EO) optimizes the VMD. The VMD decomposes the preprocessed signal into a multi-layer eigenmode function (IMF), selects the IMF component according to the correlation coefficient, and after the SSA noise reduction process, the signal Reconstruction to realize the noise reduction processing of the Φ-OTDR signal. Through experiments, it is proved that the relative mean square error (RMSE) of the method in this paper is lower than that of EO-VMD, and the noise reduction performance is better.
Waterway control assembly for rope core drilling rig and its application method
Authors: Fan Dong , Lu Feifei 
Published: December 2021

Abstract
In the process of conventional rope core drilling, the connection and disassembly of waterway occupy a lot of auxiliary time, which seriously affects the drilling efficiency, and even causes drilling or safety accidents. The waterway control assembly and application method for the rope coring rig developed by the author’s team can control the waterway flow direction. It does not need to disassemble the water pipe frequently to control the raising speed of the inner pipe and avoid damaging the drilling tool too fast.Introduction.
Design and Experimental Study of Array Eddy Current Sensor for Internal Inspection of Natural Gas Pipeline
Authors: Zhou Qingxiang , Li Gangqing , Hou Kai , Cao Feng , Song Kai 
Published: December 2021

Abstract
Array eddy current detection (ECAT) technology has the advantages of fast scanning speed and high detection efficiency, and has a wide range of application prospects. However, the traditional array eddy current sensor has a poor coupling effect with the inner wall of the pipeline and is not suitable for in-pipe inspection. Based on the basic principles of ECAT, a flexible array eddy current sensor made by flexible printed circuit board (FPCB) technology is designed and developed, which can realize 360° detection of defects on the inner wall of natural gas pipelines. The paper uses ANSYS finite element software to establish a simulation model of multi-parameter effects, study the influence of sensor size parameters and detection parameters on eddy current signals, carry out detection experiments on blind hole defects in the inner wall of steel pipes, analyze the response characteristics of defect signals, and verify the feasibility of using the FPCB array eddy current sensor for internal inspection of natural gas pipeline.
Different improvement designs of conventional comparator
Authors: Fu Xinmiao , He Miao , Zhang Yuan 
Published: December 2021

Abstract
Comparators play a significant role in the semiconductor industry and have become indispensable in the design of ADC. The delay and energy consumption are two important indicators of the comparator. Many designs have been made to reduce the delay and energy consumption, such as separated gata-biasing cross-coupled transistors for a new latching stage, and pMOS is used to replace nMOS in comparators. This paper analyzes the working principle of the proposed comparators designed for different needs reported on different papers. It compares their simulation results about key data such as delay and energy consumption.
SELNN: Effective Crossover Features by Attention Mechanism and Logarithmic Conversion for CTR
Authors: Meng Lu , Wang Li 
Published: December 2021

Abstract
With the continuous increase of web resources, information overload is becoming more and more serious. Getting the information that meets the needs of a large amount of data has become an urgent problem. One of the popular studies in click-through rate prediction is to construct feature interactions to improve prediction accuracy. Traditional models with low-order interactions cannot adequately represent the intersection information, and deep models with undifferentiated feature intersections lack relevance. In this paper, we propose a model called SELNN, which improves the accuracy of click-through rate prediction through attention mechanism and logarithmic conversion. On the one hand, improving attention mechanisms learns the importance of each feature itself. On the other hand, combining logarithmic conversion structure with feedforward neural networks to learn different orders of feature interactions. The experimental results indicated that the AUC values of SELNN on the Criteo and Movielens-1M datasets were 81.46% and 87.36%, respectively. SELNN effectively improves the prediction accuracy and reduces the number of parameters and computational effort.
Feature extraction and recognition of rotating machinery fault noise based on convolutional neural networks
Authors: Xu Yongshao , Liu Bingzheng , Shang Haotian , Ge Yueqi 
Published: December 2021

Abstract
Rotating machines are common equipment in industrial production, which may cause failure for a long time. Because of its convenient use and non-destructive to itself, acoustic detection method is suitable for fault diagnosis of rotating machinery. The convolution neural network model is used to identify several typical rotating machine faults. The repeatability experiments and different training sets show that the method has good universality. A visual fault identification system is built, and the effect of the system is verified by experiments.
Data detection technology of wireless sensor network based on non-stationary filtering
Authors: Lu Xinghua , Huang Jiahao , Luo Guohua 
Published: December 2021

Abstract
By optimizing the data detection performance of distributed wireless sensor networks, the data sensing and collecting ability of wireless sensor networks can be improved. Traditional methods adopt statistical characteristic parameter detection algorithm for distributed wireless sensor network data detection. Distributed wireless sensor network data has strong time-frequency coupling, so it is difficult to realize frequency domain spatial parameter clustering in frequency domain, and the detection performance is not good. A distributed wireless sensor network data detection algorithm based on non-stationary filtering and high-order statistical feature peak retrieval is proposed. The data model of distributed wireless sensor network is constructed under the interference of color noise. The weak vibration signal is subjected to time-frequency analysis and noise separation by non-stationary filtering, and the spectral peak of distributed wireless sensor network data is searched by the fourth-order cumulant slice post-operator to realize the optimal detection of signals. The simulation results show that the algorithm has a high probability of accurate detection, and has a good ability of suppressing noise and noise sidelobe information interference, which improves the probability of accurate detection of distributed wireless sensor network data under low signal-to-noise ratio.
A High Speed Redundant IO Bus for Energy Power Controller System
Authors: Lou Qinghui , Sun Liguo , Lu Haisong , Xu Weifeng , Wang Zhebei , Cai Dan , Shi Xiangjian 
Published: December 2021

Abstract
This paper designs and implements a High Speed Redundant IO Bus for Energy Power Controller System. The physical layer adopts multi-point low-voltage differential signal standard. This bus has the characteristics of high real-time, high throughput and easy expansion. The controller communicates with IO module by A/B bus alternately, monitors link status in real time and collects IO module data. Non real time slots can be used to control non real time messages for IO modules such as time synchronizing and memory monitoring. The controller ARM core runs QNX real-time operating system, and transmits the message needed to communicate with IO modules to the FPGA through DMA. After receiving the message, the FPGA parses the message and automatically fills in the CRC check code and frame end flag at the end of the message. When the FPGA receives the data feedback from the IO module, it performs CRC verification. If the verification passes, it fills the corresponding module receiving buffer. Otherwise, it fills the CRC verification error flag in the register of the corresponding IO module to reduce the load of the arm core.
Application in photovoltaic MPPT based on improved hysteresis loop comparison method
Authors: Shang Liqun , Li Fan 
Published: December 2021

Abstract
In order to overcome the misjudgement and oscillation of the traditional perturbation observation method when tracking the maximum power point of the PV system, this paper proposes a fixed-zone variable-step hysteresis comparison algorithm. The bi-directional perturbation of the hysteresis loop can effectively avoid the occurrence of misjudgement; after setting the partition voltage threshold, different step lengths can be used to track specific regions, which can ensure the tracking speed and maintain the system stability. Finally, the proposed algorithm is validated in Matlab/simulink and the results show that the method is effective in tracking the maximum power point of the PV system in all aspects compared to conventional algorithms.
The application of Deep Reinforcement Learning in Coordinated Control of Nuclear Reactors
Authors: Li Jing , Liu Yanyang , Qing Xianguo , Xiao Kai , Zhang Ying , Yang Pengcheng , Yang Yue (Marco) 
Published: December 2021

Abstract
The nuclear reactor control system plays a crucial role in the operation of nuclear power plants. The coordinated control of power control and steam generator level control has become one of the most important control problems in these systems. In this paper, we propose a mathematical model of the coordinated control system, and then transform it into a reinforcement learning model and develop a deep reinforcement learning control algorithm so-called DDPG algorithm to solve the problem. Through simulation experiments, our proposed algorithm has shown an extremely remarkable control performance.
A spatial-temporal correlation based method for advanced persistent threat detection
Authors: Wang Luoli 
Published: December 2021

Abstract
Advanced Persistent Threats (APT) have caused severe damage to the core information infrastructure of many governments and organizations. APT attacks usually remain low and slow which makes them difficult to be detected. In this case, the way of correlatively analyzing massive logs generated by various security devices for effectively detecting the new type of cyber threat turns out to be more and more significant. In this paper, on the basis of analyzing the principles and characteristics of APT, we propose an intelligent threat detection method based on the expanded Cyber Attack Chain (CAC) model and the long short-term memory network (LSTM) autoencoder to extensively correlate malicious behaviors from spatial and temporal dimensions, which provides a brain new idea for the application and practice of complex network attack detection.
An Absolute-value Detector with Threshold Comparing for Spike Detection in Brain-machine Interface
Authors: Yuan Mingzheng 
Published: December 2021

Abstract
This research designs an absolute-value detector with the function of threshold comparing. Specifically, it is an essential device in the spike detection of the brain-machine interface. The optimized design in the research can accomplish the main functions in spike detection and has good performance in both delay and energy consumption. It comes up with two types of design at the beginning. To make the design reliable and comprehensive, it decides to discuss both methods in this paper. The first design is using a full adder, multiplexer and comparator. The concept of its logic circuit is adding the logic one to the input when the given input data is negative, keeping the original information as the given input data is positive. To achieve the function of adding, this study chooses the full adders. The primary purpose of using multiplexers is to select from the processed input and original input, and the choice depends on the most significant bit (MSB) of the input data. To compare the absolute value of the input data with a given threshold, this research used a multi-bit comparator. The second design is based on the fundamental algorithms of calculating total numbers. It indicates that this study can operate it with the threshold value through a subtractor when the input is negative. On the contrary, an adder can be used when the information is positive. Based on the concept of logic optimization, this study chooses to use the only subtractors, and it just needs to focus on the borrow bit, which can indicate the more significant number. By connecting the MSB of the input with the subtractors through XOR gates, the selection can be achieved without using any multiplexer. In the process of removing and replacing the devices, it reached the optimization of the design. Then, this paper compared the minimum delay by calculating each stage’s size and finding that the second design is better. Finally, based on the dual design, this essay computed the energy consumption in the circuit and implement VDD
optimization to obtain the minimum energy.
Improved Design of a 4-bit Absolute-Value Detector Using Simplified Chain Carry Adder
Authors: Dong Xinhang , Jing Boyuan , Yang Xiang 
Published: December 2021

Abstract
4-bit absolute-value detector (AVD), as one of the basic implementations of bit arithmetic with logic circuits, can help grab a better understanding about digital integrated circuits. Conventional 4-bit AVDs scheme in a multi-comparator and multiplexers, or need to consider multiple situations of overflow and carry-in, both of which could make the final circuit to be complex, labyrinthine and inefficient in the meantime. In this paper, a new design of 4-bit AVD is proposed, the topology of which includes a 2’s complement calculator and a specially designed logic circuit known as chain carry adder (CCA). The whole circuit is concise and the critical path is rigorously considered to make it as short as possible. The delay is set to 1.5 times its minimum, which is positively corresponding to the length of the critical path, the energy accordingly reaches its lower limit. Gate sizing and Device Voltage (VDD) optimization are proceeded for the exact purpose of proving that the circuit energy is minimized.