bluuebunny/crossref_metadata_2025_split huggingface dataset:
Q-switched fiber laser using a polysulfone membrane enhanced with biosynthesized zinc oxide and titanium dioxide nanoparticles for use as saturable absorber
Authors: Muhammad N A M , Awang N A , Zalkepali N U H H , Mahmud N N H E N , Basri H 
Published: April 2022

Abstract
We presented the polysulfone membrane enhanced with zinc oxide and titanium dioxide (Psf membrane ZnO TiO2) saturable absorber (SA) to induce passively Q-switched erbium-doped fiber laser (EDFL). The Psf membrane ZnO TiO2 was synthesized by the phase inversion technique. To obtain the Psf membrane ZnO TiO2 as a SA, the SA was deposited on the fiber ferrule through a simple exfoliation technique. The modulation depth of the Psf membrane ZnO TiO2 SA was 1.06%, with a saturation intensity of 0.0006 MW cm−2. Stable Q-switched pulses were generated at 1572 nm with a threshold pump power of 59.80 mW after inserting the prepared Psf membrane ZnO TiO2 into the EDFL ring cavity. As the pump power ranges from 59.80 mW to 165.50 mW, the repetition rate increases from 13.05 to 22.61 kHz, while the pulse duration decreases from 76.60 to 44.23 µs. When the pump power reaches a maximum power of 165.50 mW, the corresponding pulse energy and optical signal-to-noise ratio are 19.00 nJ and 61.27 dB, respectively. To our best knowledge, this is the first research utilizing Psf membrane ZnO TiO2 as SA to generate Q-switched pulses. Our research work addresses a new reference for the generation of pulsed laser using Psf membrane ZnO TiO2 and discovers that it has numerous applications in nonlinear optics and ultrafast laser technology, which significantly broadens the barrier of materials for the ultrafast laser techniques.
Toroidal modeling of energetic passing particle drift kinetic effects on tearing mode stability
Authors: Bai Xue , Liu Yueqiang , Hao Guangzhou , Zhang Neng 
Published: April 2022

Abstract
Drift kinetic effects of the neutral beam injection induced passing energetic particles (EPs) on the linear stability of the n = 1 tearing mode (TM) (with the dominant poloidal harmonic of m = 2) are numerically investigated utilizing the MARS-K code (Liu et al 2008 Phys. Plasmas
15 112503), in a tokamak plasma with finite equilibrium pressure and anisotropic thermal transport. In the low plasma pressure regime, it is found that co- (counter-) passing EPs stabilize (destabilize) the TM, agreeing with previous studies. However, as the plasma pressure increases beyond a critical value, it is found that co-passing EPs also destabilize the mode. An in-depth analysis reveals that the net effect of co-passing EPs is a result of competition between the stabilizing contribution from the non-adiabatic drift kinetic terms and the destabilizing contribution associated with adiabatic terms, with the latter becoming more dominant at higher equilibrium pressure. Non-perturbative magnetohydrodynamic-kinetic hybrid modeling also finds that co- and counter-passing EPs modify the TM eigenfunction differently, with the counter-passing EPs enhancing the sideband harmonics. Furthermore, effects of the plasma resistivity and toroidal rotation, as well as that of the equilibrium distribution of EPs in the particle pitch angle space, are also investigated, showing asymmetric results on the TM stability between the co- and counter-passing EPs. The first order finite orbit width correction is found to be stabilizing with co-passing EPs and destabilizing with counter-passing particles. Finally, drift resonances between passing EPs and the TM induce finite frequency to the mode and generate finite net torques inside the plasma, due to the neoclassical toroidal viscosity and the Reynolds stress associated with 3D perturbations.
Traffic classification algorithm using CNN and multi-head attention mechanism for representation learning
Authors: HaoLi  
Published: April 2022

Abstract
With the development of the times, the demand for privacy and security by people, enterprises and government units has become stronger and stronger, driven by this, encrypted traffic has shown a blowout growth, and the security of traffic has become an issue that cannot be ignored. To solve the traffic classification problem, this paper proposes a new traffic classification algorithm based on convolutional neural network and multi-head attention mechanism. In addition, this paper uses a feature engineering method based on representation learning and proposes a discard threshold to improve the quality of data sets obtained by feature engineering. The experimental results show that the algorithm model and the optimized feature engineering method proposed in this paper have good performance on the traffic classification task.
Design of real-time target location algorithm for industrial scene
Authors: Wang Rui , Li Di 
Published: April 2022

Abstract
In order to meet the needs of real-time visual inspection of workpiece targets in industrial environment. In this paper an algorithm for locating workpiece based on edge feature is designed by using image information. At the same time, a model description method based on generalized Hough model and an acceleration strategy based on pyramid search are proposed to solve the problem of insufficient real-time performance of the algorithm. The algorithm is mainly divided into two stages. First, in the offline stage matching model is created. In the online stage, according to the model created in advance, the pyramid based accelerated search strategy is used to match, and the parallel computing method is adopted to calculate the similarity. Experimental results show that the algorithm has strong anti-occlusion and anti-interference ability, and can achieve real-time target positioning under any Angle and coordinates. At the same time, it is proved that the proposed acceleration strategy has better acceleration performance than the existing matching speed method.
Research on hybrid collision detection algorithm based on separation distance
Authors: Hu Anlin , He Yongyi 
Published: April 2022

Abstract
In order to improve the safety of digital subtraction angiography (DSA) equipment in the treatment of cardiovascular diseases, a hybrid collision detection algorithm based on separation distance is proposed by combining oriented bounding box (OBB) algorithm with GJK algorithm. Firstly, according to the structure of DSA equipment, the real-time position of DSA equipment is simulated in robot operating system (ROS); Then, the OBB is used to envelope the moving parts, and then the generated OBB model is applied to GJK algorithm to calculate the separation distance between the moving parts and quickly eliminate the detection pairs outside the threshold; Finally, for the detection pairs within the threshold, the real model is applied with GJK algorithm to calculate the separation distance between the real models and judge whether the models collide. The experimental results show that the average detection time of the hybrid collision detection algorithm is 15ms. The application of the algorithm in practical equipment has achieved good anti-collision effect.
Optimization of flow shop scheduling based on genetic algorithm with reinforcement learning
Authors: Gao Xiaoyu , Yang Shipin , Li Lijuan 
Published: April 2022

Abstract
Genetic algorithm, as a kind of evolutionary algorithm, has the characteristics of easy operation and global search, but its stochasticity is relatively strong and highly susceptible to parameters. When facing a large-scale scheduling problem, a strategy is needed to improve the parameter adaptability to make its solution more effective. Reinforcement learning, as an optimization method, has a strong autonomous learning capability. Therefore, this paper proposes a genetic algorithm based on reinforcement learning, which uses Q-learning to self-learning the crossover probability and improve the generalization ability of genetic algorithm, so as to achieve the solution of large-scale replacement flow shop scheduling problem.
Layered Convolutive Nonnegative Matrix Factorization for Speech Separation
Authors: Yao Wang , Lv Danjv , Huang Xin , Zi Jiali , Gao Mingyuan , Xi Rui , Zhang Yan 
Published: April 2022

Abstract
Nonnegative matrix factorization (NMF) has attracted significant attention for its good performance in single-channel speech separation. The improved algorithms of NMF have become research hotspots. Layered NMF (LNMF), an improved algorithm, can express the source signal more accurately for its multilayer structure. However, LNMF sometimes performs poorly because it ignores the short-term correlation of speech signals. Based on LNMF and the advantages of Convolutive NMF (CNMF), we proposed a Layered Convolutive NMF(LCNMF) algorithm for single-channel speech separation. The LCNMF corporates the multilayer structure into the NMF and expands the convolution of the top-level NMF model. During the training, NMF is used to learn the non-top-level basis matrices, and CNMF is used to learn the top-level basis matrix, then combined with each single-layer of basis matrix. During the prediction, CNMF is used to separate mixed signals. The results on the dataset MIK-1K showed that LCNMF outperformed NMF and LNMF for separating the mixture of single-channel speech signals. LCNMF improved by 0.019, 1.049dB, 1.305dB, and 0.851dB on average compared with NMF, and improved by 0.007, 0.172dB, 0.090dB, and 0.366dB on average compared with LNMF in sort-term objective intelligibility (STOI), Source to Distortion Ratio (SDR), Source to Interference Ratio (SIR) and Source to Artifacts Ratio (SAR)
The empirical analysis of the two types of change points estimation algorithms based on MOSUM
Authors: Zhao Dan , Zi Xuemin 
Published: April 2022

Abstract
In order to make big data play a greater value in financial markets, exploring a new data processing method to improve the ability to detect structural mutations is necessary for the diagnosis and monitoring of financial data and investment direction. This article applies financial time series data, based on the moving sum (MOSUM) statistics, empirically analyzes changes of stock data in Kweichow Moutai in the past ten years. Detecting the feasibility of two multiscale MOSUM algorithms of bottom-up merging and local pruning and visualizing the results by numerical simulation, combined with the fact of the time point, we proved the consistent advantages of the two algorithms in detecting the accuracy and precision of variable points.
Distribution analysis method of key targets based on big data analysis engine for maritime space-time trajectory
Authors: Xu Yue , Yang Zhaozhe , Zhang Xinyu 
Published: April 2022

Abstract
Now a large amount of maritime space-time trajectory data has been collected, but its deep value is unable to be tapped, and the analysis of the distribution of key targets has also encountered bottlenecks. The emergence of big data technology provides us with a solution. This paper proposes a big data analysis engine for maritime space-time trajectory, which makes full use of the data characteristics and improves the overall computing performance of the engine. Based on this engine, a dispersal analysis algorithm for key military targets at sea is proposed to achieve rapid retrieval and dispersal analysis of key marine targets. It has been tested and verified that this engine has excellent performance and can become an efficient query and analysis engine for maritime space-time trajectory data. The key target distribution analysis algorithm has certain research significance and practical value.
Research on flow distribution optimization method of fluid loop system based on generalized Lagrange multiplier method
Authors: Wang Lu , Du Changshuai , He Yufeng 
Published: April 2022

Abstract
With the development of space science in China, the scale of space application payloads continues to increase, and the demand for heat dissipation is also gradually increasing. The fluid loop thermal control is a more efficient way of heat dissipation. In order to optimize the heat dissipation effect of the fluid loop system, this paper establishes a thermal model of the fluid loop system and converts it into a constrained optimization problem. The generalized Lagrange multiplier method is used to calculate the optimal flow distribution strategy, and the influence of the weight is analyzed, which provides a reference for the setting of the weight in practical applications.
Research on Vessel Behavior Modeling Based on MAXSim Simulation Platform
Authors: Guan Weidong , Wang Yumei 
Published: April 2022

Abstract
In a combat simulation system with the ocean as the main environment and vessels as the main platform, the accuracy of the behavior model of the computer-generated force will directly affect the confidence of the simulation system and the efficiency of simulation exercises. As a combat simulation application and development platform, the MAXSim simulation platform based on the Generic Black-Board structure has good secondary development. In order to research the behavior model of vessels and provide general modeling ideas and methods for the maritime combat simulation system, the architecture of the MAXSim simulation platform and the specific functions of the modules are firstly researched. Then, the common methods of behavior modeling by using MAXSim are researched from the level of weapons and equipment. Finally, starting from the system-level development of behavior models, the development methods of actions and conditions of periodic-driven and event-driven behavior models are researched, which improves the accuracy of Computer Generated Forces and supports a good basis for further improving the overall performance of the simulation system.
Comprehensive evaluation about the quality of steel strip based on combination weighting
Authors: Wang DongXu , Cui GuiMei , Pi LiXiang , Liu Wei 
Published: April 2022

Abstract
In order to solve the problem that the quality of steel strip products is rough by manual evaluation, this paper will combine expert knowledge with quality data and establish four evaluation grades by taking surface quality, mechanical properties and dimensional accuracy as evaluation indicators. Analytic hierarchy process (AHP) and entropy weight method are used to confirm the weight of each evaluation indicator. The mechanical properties and dimensional accuracy indicators are confirmed by statistical method to find out the optimal range of indicator parameters, and the indicator deterioration degree is calculated. The deterioration degree of surface quality indicator is confirmed by fuzzy language quantization. The membership degree of each indicator parameter is calculated according to the membership degree function of ridge distribution, and then the fuzzy comprehensive evaluation result matrix of strip quality is obtained by synthesizing each indicator parameter step by step. Finally, the comprehensive score of strip steel quality is obtained by quantizing and summing the evaluation result matrix. The experimental results show that the evaluation result of the model is consistent with the actual data, so it has certain practicability.
A network security situation assessment model based on BP neural network optimized by D-S evidence theory
Authors: Yin Kun , Yang Yu , Yang Jinwei , Yao Chengpeng 
Published: April 2022

Abstract
Traditional networks rely heavily on the distribution of expert experience when assessing complex network security situations, resulting in low assessment accuracy, which has been unable to adapt to the current network security needs of the big data era, and has unavoidable problems such as low efficiency and poor flexibility. In response to these problems, this paper proposes a network security situation assessment method based on D-S evidence theory to optimize neural networks. First establish the CS-BP neural network model, enhance the local search ability of the cuckoo algorithm through conjugate gradient calculation, and then introduce it into the BP neural network to improve the training convergence speed and overcome the local minimum problem; finally, in order to reduce the basic probability distribution (BPA) subjective impact, using DS evidence theory to optimize the CS-BP neural network, determine the degree of impact of each attack, and evaluate the value of the network security situation. The experimental results show that the network situation assessment model of CS-BP neural network optimized based on D-S evidence theory can effectively assess the network security situation in the environment of trusted equipment.
Maritime search and rescue probability of containment model design and simulation
Authors: Zhang Jun , Zhu Yu-zhu 
Published: April 2022

Abstract
In maritime search and rescue operations, in order to improve the success rate of search and rescue, it is necessary to quickly determine the search area after target drift according to the existing accident and Marine environment information, and establish the probability of containment model, to provide information support for subsequent search and rescue planning. AP98 wind pressure drift model is used to model the drowning person at sea, and Monte Carlo simulation is used to simulate the floating situation of the drowning person. A large number of initial hypothetical points were arranged, the factors affecting the drift trajectory were quantitatively analyzed, and the probability density of the hypothetical points in the search area after a certain period of time was counted to form the inclusive probability model. Matlab software is used to test the model in different scenarios to prove the effectiveness of the proposed design model.
Transfer learning with CNN-LSTM model for capacity prediction of lithium-ion batteries under small sample
Authors: Zou Gang , Yan Zhen , Zhang Chengying , Song Lei 
Published: April 2022

Abstract
Nowadays, the application of lithium-ion batteries in various industries including our daily life is increasing, so the evaluation of the safety and reliability of lithium-ion batteries is essential. Assessing the performance of lithium-ion batteries accurately and in real-time has become a more critical research direction. However, in most application scenarios, the problem of small sample is often hidden in performance evaluation tasks of lithium-ion batteries. A model-based transfer learning framework to solve this problem is presented in this paper. Firstly, a CNN-LSTM fusion model is proposed by combining the solid spatial feature extraction capability of convolutional neural network (CNN) and the solid temporal dimensional feature extraction capability of long short-term memory network (LSTM); further, a model-based transfer learning approach is introduced to accomplish the performance evaluation task by pre-training and re-training. The experiments are conducted on NASA’s public dataset of lithium-ion batteries, and multiple sets of comparison experiments are set up. The results show that the method can better solve the small sample problem encountered in the lithium-ion batteries performance evaluation task.
Document-level Stance Detection Method for Specific Targets Based on XLNet
Authors: Fang Yuwei , Zhang Lei , Yang Fei , Zhao Xiaoming , Song Xueqiang , Li Dong , Li Hong , Wang Haizhou 
Published: April 2022

Abstract
Social media and slef-media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. As far as we know, most of the current research focuses on short texts. However, there are relatively few studies on how to collect and extract people’s opinions and stances from long texts such as review articles. This paper proposed a target-specific hierarchical attention model based on the XLNet pre-training model, named XLNet-THAN. The attention mechanism and topic embedding are utilized to obtain the semantic representation of the document. After contrast experiments with other popular text classification models, the result shows that the model proposed in this paper can process document-level text well, and achieves great performance in the task of document-level stance detection.
Time series prediction of cabin temperature based on mechanism model
Authors: Zhang Tanke , Zhang Kanjian , Yao Xu , Wei Haikun , Fang Shixiong , Wang Tao 
Published: April 2022

Abstract
Since the Antarctic scientific research device has been operating in Antarctica for a long time, the power generation cabin needs to continuously provide heat so that its equipment can work within a suitable temperature range. In view of the complexity of the environment and the high cost of equipment, it is difficult to test and evaluate, which can be well solved by numerical simulation test. The thermodynamic mechanism model of the Antarctic redundant cabin is established to simulate the temperature distribution of the Antarctic scientific research power cabin. The temperature data of the mechanism model is combined with the time series algorithm to predict the future high temperature accumulation in the engine room, thereby providing theoretical and data support for the decision-making of the temperature control system of the scientific research device.
Research on the application of OpenPose in escalator safety systems
Authors: Zhao Xuehan 
Published: April 2022

Abstract
This paper describes the application of the OpenPose based human pose detection model in escalator safety systems. The YOLO target detection is used to obtain the position of the human body in the image, and then the human limb information is extracted by OpenPose, and the human joint connection algorithm connects the identified human limb information to obtain the human pose information in the image. The experimental results show that the algorithm can effectively identify the human body posture and can provide timely alarms for dangerous postures, thus improving the safety of escalator riders.
Non-feedback control of Lorenz chaotic system
Authors: Zhao Yang , Sun Shiyan , Wei Ping 
Published: April 2022

Abstract
Non-feedback chaos control of Lorenz chaotic system is realized with non-resonant parametric drive. The control input is a periodic signal whose frequency is far higher than the system characteristic frequency. The feasibility of this scheme is proved by averaging method and Lyapunov method, and the condition of control parameter is obtained. Numerical researches show that the controlled system can reach stable state rapidly even when the control signal is noisy.
Architecture design of battery energy storage coordinated control system based on Multi-Agent mechanism
Authors: Qiu Xuan , Tang Chun-sheng , Dong Wen-hao 
Published: April 2022

Abstract
Energy storage can realize rapid voltage, frequency support and energy adjustment, and is an important guarantee for promoting the efficient consumption of renewable energy and the safe and stable operation of the power system. The flexible adjustment of energy storage is not only affected by its own characteristics, but also closely related to the operation control mode of energy storage. Based on the PQ constant power and virtual synchronization control strategy of the battery energy storage system, this paper constructs the operation architecture of the battery energy storage system based on the Multi-Agent cooperative mechanism, and further gives the energy storage system Multi-Agent cooperative control system’s application scenarios in active frequency modulation and reactive voltage regulation. Research shows that this architecture helps to fully tap the potential of aggregation and regulation of multi-point decentralized energy storage facilities and meet the flexible regulation needs of the power system under the access of high-permeability intermittent renewable energy.