bluuebunny/crossref_metadata_2025_split huggingface dataset:
NL2SQL Generation with Noise Labels based on Multi-task Learning
Authors: Long Lingli , Zhu Yongjin , Shao Jun , Kong Zheng , Li Jian , Xiang Yanzheng , Zhang Xu 
Published: June 2022

Abstract
With the rapid development of artificial intelligence technology, semantic recognition technology is becoming more and more mature, providing the preconditions for the development of natural language to SQL (NL2SQL) technology. In the latest research on NL2SQL, the use of pre-trained models as feature extractors for natural language and table schema has led to a very significant improvement in the effectiveness of the models. However, the current models do not take into account the degradation of the noisy labels on the overall SQL statement generation. It is crucial to reduce the impact of noisy labels on the overall SQL generation task and to maximize the return of accurate answers. To address this issue, we propose a restrictive constraint-based approach to mitigate the impact of noise-labeled labels on other tasks. In addition, parameter sharing approach is used in noiseless-labeled labels to capture each part’s correlations and improve the robustness of the model. In addition, we propose to use Kullback-Leibler divergence to constrain the discrepancy between hard and soft constrained coding of noisy labels. Our model is compared with some recent state-of-the-art methods, and experimental results show a significant improvement over the approach in this paper.
A Compressed Sensing Reconstruction Algorithm Based on Hyperbolic Function
Authors: Wang Hui 
Published: June 2022

Abstract
The accuracy of the compressed sensing theory reconstruction algorithm is important for signal recovery. Sparsity Adaptive Matching Pursuit (SAMP) has a fixed step size in each iterative process of reconstruction, which has a significant impact on accuracy in actual use, often leading to overestimation and underestimation. In order to solve this problem, combined with the advantages of regular backtracking and variable step size, the article introduces the idea of retrospective in the atom selection stage. Then, the atoms are inspected using backtracking and re-screened. The algorithm uses regularization at the beginning to select atoms with higher energy and reduce the number of atoms in the candidate set at the threshold stage. During reconstruction, because the energy difference of the reconstructed signal decreases rapidly at the initial stage, and then the energy difference decreases slowly. This algorithm takes advantage of the large change rate of the hyperbolic function at the beginning and the slower change rate at the later stage. At first, a large step size is used. When the energy difference reaches a certain threshold, a small step size is used instead. After the actual simulation, the improved algorithm improves the reconstruction accuracy and the effect is better.
Self-Knowledge Distillation For the Object Segmentation Based on Atrous Spatial Pyramid
Authors: Wang Xiaobing , Zheng Yunfei , Zhang Xiongwei , Cao Tieyong , Li Chenming , Wang Xiulai , Wang Yong , Fang Zheng , Wang Yang 
Published: June 2022

Abstract
This paper presents an effective Self-Knowledge Distillation (SKD) framework via Atrous Spatial Pyramid Structure (ASPS), which is able to enhance the performance of the object segmentation network without increasing network parameters. In the framework, a lightweight object segmentation network is constructed to achieve the pixel-level object segmentation efficiently. A SKD learning model, including the SKD representation structure based on ASPS and loss function, is proposed to transfer the knowledge in the ASPS into our object segmentation network and improve its generalization ability. The experimental results confirm that compared with recent typical object segmentation networks, our object segmentation network contains the fewest parameters but achieves better performance. Moreover, the proposed SKD method achieves the best performance-boosting compared with recent SKD methods
Modeling and Research on Route Planning
Authors: Liu Ying 
Published: June 2022

Abstract
In view of the problem of fixed and mobile threat areas in the navigation of reconnaissance aircraft, this paper proposes to connect the position information with the time information, and build a distance matrix based on the ant colony algorithm to model and solve the optimal navigation route.This article is based on the problem of the threat area in the area where the reconnaissance aircraft is sailing, since the distance between two target points is no longer a straight line distance but a curve distance, so consider building a distance matrix.The simulation experiment results show that the algorithm proposed in this paper can select the optimal path faster and has good accuracy when traversing each target point.
A Hierarchical Intrusion Detection System Based on Machine Learning
Authors: Kong Dehua , Peng Sicheng , Zhai Yihong , Liu Zhangyuan , Zhang Luming , Wan Zixuan 
Published: June 2022

Abstract
The Intrusion detection system (IDS) is one of the most important tools for defending against abnormal flow and attack messages. Most of the existing IDSs use detection technology based on security policies, and there is a risk that it cannot be accurately analyzed and evaluated. Therefore, machine learning techniques provide a new direction for solving this problem. This paper uses and analyzes the CIC-IDS series datasets, but there is a data imbalance in this dataset. In order to solve the problem of data imbalance and reduce the accuracy of the model, this paper proposes a hierarchical detection model. Experiments have shown that the stratified detection module has good classification accuracy for attack types with a small sample size.
A simple implement of Q-learning in robot path planning
Authors: Gao Haoran , Liu Yujie , Su Shiqi , Yao Wetao 
Published: June 2022

Abstract
This paper firstly gives an introduction of robot path planning problem, which includes the brief definition of path planning, some representative methods and previous applications of Q-learning. Secondly, the paper compares some typical methods, like Breadth First Search and Depth-First-Search, A* and deep learning, with corresponding pseudo codes in detail. Their advantages and disadvantages are also listed in this part. Thirdly, we carry out a simple simulation experiment by applying Q-learning method. The experiment is clearly presented in several parts which includes environment establishment, realizing the Q-learning, simulation experiment and interpretation of the Q-table. In the end, a short conclusion summarizes the achievement of our results.
Research on mathematical modeling of order and transshipment optimal distribution
Authors: Cui Lin , Liu Yin , Liu Zhi 
Published: June 2022

Abstract
Aiming at the problems of production arrangement and ordering, this paper conducts quantitative analysis, overall analysis and individual analysis on the supply characteristics of 402 suppliers, and constructs a ratio ranking model of 402 suppliers in the past five years, which reflects the important mathematical model to ensure the production of enterprises. And multi-index evaluation model, comprehensive use of Matlab, Excel and other software programming to solve, to determine the 50 most important suppliers. On the basis of mathematical modeling, a control scheme is given, and further analysis is completed, so as to optimize the ordering and transportation of raw materials in the production enterprise.
Research on multi-motor coordinated control under load disturbance
Authors: Hu J S , Zhou Z Y 
Published: June 2022

Abstract
With the iterative development of industrialization and automation, more and more mechanical and electrical equipment rely on the joint operation of multiple motors, and the efficient operation of mechanical and electrical equipment benefits from the reliable cooperative control of multiple motors. Therefore, to ensure the stability of multi-motor coordination control and improve the precision of multi-motor coordination control has important practical significance. In this paper, the multi-motor master-slave controls structure and ring-coupling control structure with random disturbance are simulated and analyzed, and it is verified that the annular coupling control structure has stronger anti-interference ability and higher control accuracy under random load disturbance in the case of four-motor coordinated control.
Hybrid institution vacuum circuit breaker action time prediction
Authors: Dong Enyuan , Hou Peng , Wang Yongxing , Zhang Liyan , Tian Yu , Liu Geyang , Zhuang Ruida 
Published: June 2022

Abstract
Phase-control technology requires a sufficiently small dispersion of switching operation times, however, even though the switch itself is sufficiently stable, action time dispersion is still a key issue in phase-control technology due to various factors. In this paper, by building a simulation and test model of the hybrid mechanism, the action time variation caused by influencing factors such as fast switching temperature, capacitance voltage, capacitance degradation are investigated, and the test and simulation data are collated and analysed form a database. The Generalized Regression neural network has excellent prediction, self-learning, non-linear mapping and tolerance capabilities, and the prediction error of the GRNN algorithm is verified to be less than 0.2ms for the closing time.
Research on rolling bearing fault diagnosis based onTVFEMD-SVD and random forest algorithm
Authors: YANG Cheng 
Published: June 2022

Abstract
In order to solve the problem that it is difficult to extract the early fault of rolling bearing under the background noise, a rolling bearing fault diagnosis method based on TVFEMD-SVD and random forest algorithm is proposed. In this method, the original vibration signal of the bearing is decomposed into a series of eigenmode components with physical significance by using time-varying filtering empirical mode decomposition. The components with higher fault information are selected according to the kurtosis, and SVD decomposition is carried out. The singular values obtained by decomposition are input into the random forest as eigenvalues for bearing fault diagnosis. Experiments show that this method can effectively diagnose the fault of rolling bearing.
Transformer vibration fault detection based on spectrum analysis
Authors: Hu Yuan , Wu Peng , Li Yan , Zhao Penghui , Wang Ning , Zhang Shuai , Zheng Junguan , Hao Jianliang 
Published: June 2022

Abstract
Measuring the vibration signal of transformer is one of the ways to verify whether the transformer is faulty. In this paper, taking the vibration detection of transformer as the technical starting point, the signals before and after the vibration fault of transformer are compared by frequency spectrum analysis, and the vibration fault detection of transformer is realized. The vibration test system is constructed and tested in the field. The results show that the non-contact vibration sensor can be used to detect the uninterruptible power failure of the transformer through spectrum analysis and processing, which is simple, safe and reliable, and has good application value.
An optimization method for the inverted pendulum problem based on deep reinforcement learning
Authors: Zhu Shenghan , Liu Song , Feng Siling , Huang Mengxing , Sun Bo 
Published: June 2022

Abstract
The inverted pendulum problem is a classical problem. The inverted pendulum starting at a random position keeps moving upwards and aims to reach an upright position. The problem has been solved through some methods based on deep reinforcement learning (DRL) such as Deep Deterministic Policy Gradient (DDPG). However, DDPG also has disadvantages. Deterministic policy is not conducive to action exploration. Moreover, the Q value needs to be estimated reasonably accurately for the policy to be accurate. Nevertheless, at the beginning of the learning, there is a certain difference in the Q value estimation, and the parameters learned at this time are easy to deviate. Therefore, this paper combining AdaBound with DDPG algorithm proposes an optimization method for the inverted pendulum problem, and compares the performance with that of four published baselines. The experimental results show that for the inverted pendulum problem, the proposed method outperforms the above four baselines to a certain extent.
LVF-based FBG interrogation simulation calculation
Authors: Liu Zhaoyi 
Published: June 2022

Abstract
We propose a miniature FBG(Fiber Bragg Grating) interrogation based on the LVF(Linear Variable Fliter ), which is very suitable for measurements in aircraft because of its small size, light weight and vibration insensitivity compared to conventional interrogation. We analyzed the relationship between the performance of the interrogation and the optical properties of the LVF by simulating the light passing through the LVF demodulator by means of theoretical simulations before the actual construction of the instrument, and we also find that the inhomogeneous illumination of the incident light can impair the performance of the interrogation.
Optimization and Design of Key Expansion of SM4 Algorithm Based on ZYNQ
Authors: Feng YeJi , Liu XiaoDong , Jing Shuai , Yang XiaoTing , Hao FuQing , Mao YueJian , Yang ZiHeng 
Published: June 2022

Abstract
As the application of "China Intelligent Manufacturing" in the field of production becomes more and more extensive, it becomes crucial to ensure the security of information interaction between production equipment. China's domestic encryption algorithm - SM4 block encryption algorithm came into being. The algorithm consists of a key expansion function and an encryption and decryption function. The key expansion function collects 128-bit keys at a time, and generates thirty-two 32-bit encryption keys after XOR operation, nonlinear permutation operation (S-box) and linear shift operation.The XOR operation parameter CK of the original key expansion function is obtained by the modulo method, which is similar to the system parameter FK and is a fixed value. In order to reduce the risk of deciphering caused by fixed parameters, the lightweight two-dimensional chaos algorithm Hemon is introduced for optimization design. The system parameters used in the SM4 algorithm are randomly generated with the change of the key, which improves the security of the algorithm. And use the hardware encryption platform ZYNQ-7000 series model xc7z100ffv900-1 FPGA side design and implementation, relying on the hard to decipher of hardware encryption to complete the optimization of SM4 algorithm key exchange.
A Fault Tolerant Method Based on LQR for the Electric Motor Servo System
Authors: LIU Zhiyong , Jing Lei , Wang Xiaohong 
Published: June 2022

Abstract
In the paper, a PI fault tolerant method based on LQR (Linear Quadratic Regulator, LQR) is proposed for the coal mine motor servo system. In the proposed strategy, the proportional part is arranged in the feedback loop of the control system firstly, so that the PI control law can be described in the form of system state feedback. And then the PI fault-tolerant control law is obtained based on the PI control law, the parameters are adjusted adaptively with the LQR control method Finally, the proposed method was applied to an electric motor, the simulation illustrates the effectiveness of the proposed method.
Study of Full Composite Pole-Aluminum Conductor Composite Core System Ice-Shedding Response
Authors: An Liqiang , Zhang Luojia , Wang Zhixu 
Published: June 2022

Abstract
Full composite pole-aluminum conductor composite core system is gradually used in the 10kV distribution network. In order to study the different ice-shedding forms of the dynamic response of this distribution system, the author established a conductor-insulator and pole-conductor system with two finite element models to study the linear section and corner section of the line in different forms of ice-shedding and ice-shedding rate under the influence of the line ice-shedding response degree. The results show that the pole-conductor model is a good model for the design of distribution lines with carbon fiber cores. The results also show that the ice jump height of the pole-conductor model is smaller than that of the conductor-insulator model, which is related to the coupling relationship between the composite pole and aluminum conductor composite core. The stay wire of the corner pole will absorb most of the energy generated by ice-shedding. In addition, when non-uniform ice-shedding, the line vibration is more violent, the jump height is higher, and the maximum axial force generated after ice-shedding is greater, which has certain safety risks.
Defect Analysis and Process Improvement of Metal-Oxide Arrester
Authors: Wu Liwei , Guo Caihong , Wu Jinping , Xie Zhenzhen 
Published: June 2022

Abstract
Metal-Oxide Arresters are important equipment to ensure the reliable operation of power systems. An experimental protocol for judging the nature of MOA defects accurately is introduced in this paper and applied to engineering practice. Through diagnostic tests and dismantling inspections, it is determined that a certain batch of 110kV line MOA has a familial defect of poor sealing. Then, the reasons for dampness are analyzed, and other defects except the dampness of the varistor are discovered. Besides, the improvement of manufacturing process is proposed from aspects of terminal structure and the coating material on the side of varistor;the actual data proves that adding fillers such as quartz sand or silica gel inside the MOA can reduce the failure rate effectively. This article provides a certain technical reference for the manufacture and defect disposal of MOAs.
Design of Control System for a new Intelligent Tree-climbing and Pruning Robot
Authors: Yang Xi , Gan Tuanjie , Zheng Hai , Cai Quishen , Chen Yan 
Published: June 2022

Abstract
This paper designed the control system of a new intelligent tree-climbing and pruning robot which included the control of remote control mobile terminal and intelligent control terminal. Remote control module, power circuit, motor drive, automatic holding detection, height detection, side branch detection and other modules were designed, Then the main program based on STM32 embedded MCU, interrupt program and communication program were designed. The control system could enable the tree climbing robot to climb automatically, detect the side branches in the climbing process, trim the side branches automatically, and saw the top branch after climbing to a certain height. The robot also had the function of image monitoring. Through image monitoring, the working process of the robot could be adjusted remotely. Finally, the prototype was tested. The test results showed that the selection of controller and sensors were appropriate, the design of remote control circuit, motor driving circuit and detection circuit was reasonable, and the structure and algorithm of the program were reasonable. The development of the prototype laid a foundation for the popularization of fast-growing forest pruning robot.
A novel contour error evaluation index of the five-axis machine tools
Authors: Fu Meng , Wang Liping , Guan Liwen , Chen Yanyu 
Published: June 2022

Abstract
The accuracy of high speed five-axis machine tool is mainly determined by the contour accuracy of machine tools. The contour accuracy evaluation and improvement of 5-axis machine tools have always been concerned. In order to simplify the calculation of five-axis contour error considering the synchronization between tool tip position and tool orientation, a novel five-axis contour evaluation index TCCE(Two-point Cutter location Contour Error) is proposed in this paper, which is based on the tool axis trajectory surface. The calculating method of the proposed index by NC servo encoder input from 5-axis machine tools is introduced. Finally, the experiment is performed to evaluate the trajectory accuracy and servo matching performance of the machine tool with the proposed contour error evaluation index and the calculating method.
Biomimicked large-area anisotropic grooves from Dracaena sanderiana leaf enhances cellular alignment and subsequent differentiation
Authors: Yadav Shital , Majumder Abhijit 
Published: June 2022

Abstract
Cellular alignment is important for the proper functioning of different tissues such as muscles or blood vessel walls. Hence, in tissue engineering, sufficient effort has been made to control cellular orientation and alignment. It has been shown that micro-and nanoscale anisotropic topological features on cell culture substrates can control cellular orientation. Such substrates are fabricated using various lithography techniques such as photolithography and soft lithography. Although such techniques are suitable for creating patterns in small areas to establish a proof-of-concept, patterning large areas with intricate features is an unsolved problem. In this work, we report that a replica of the groove-like anisotropic patterns of the abaxial side of a Dracaena sanderiana (bamboo) leaf can be used for large-area patterning of cells. We imprinted the leaf on polydimethylsiloxane (PDMS) and characterised its surface topography using scanning electron microscopy. We further cultured bone marrow human mesenchymal cells (BM-hMSCs), skeletal muscle cells (C2C12), and neuroblastoma cells (SHSY5Y) on the patterned PDMS on which the cells orient along the direction of the grooved pattern. Further, we observed enhanced neuronal differentiation of SHSY5Y cells on biomimicked pattern compared to flat PDMS as measured by percentage of cells with neurites, neurite length and the expression of neuronal differentiation marker beta-III tubulin (TUJ1). This process is simple, frugal, and can be adopted by laboratories with resource constraints. This one-step technique to fabricate large-area anisotropic surface patterns from bamboo leaves can be used as a platform to study cellular alignment and its effect on various cellular functions, including differentiation.