bluuebunny/crossref_metadata_2025_split huggingface dataset:
No-reference Image Quality Assessment Based on Regional Information
Authors: Peng Tong , Dong Jing , Li Qinhang , He Kun 
Published: April 2020

Abstract
Human vision can quickly assess the quality of images because of the identity in a region and the divisibility between different regions. According to human visual characteristics, we propose a method of image quality assessment without reference information. Firstly, the image was segmented into a series of homogenous regions. Secondly, we analyzed statistical characteristics for each region. Lastly, the assessment model was constructed according to the statistical differences among regions. The results experimented on the LIVE database II show that this method is competitive to SSIM and PSNR.
An Improved GANs Model for Steel Plate Defect Detection
Authors: Tang Renjie , Mao Keming 
Published: April 2020

Abstract
Automatic steel plate defect detection is very important for it can monitor the product quality. This paper makes a study on steel plate defect detection based on machine learning. The main difficult is that there is not enough data to make powerful detection models. We propose a Generative Adversarial Networks based method to generate synthetic training image. A novel structure is designed with type related variable incorporated in Generator and a classification branch added to Discriminator. With expanded dataset, two detection algorithm, Faster R-CNN and YOLO are adopted. Various model structures, optimization methods, batch sizes and model execution time are evaluated and the influence of parameters are also analyzed. The experimental results show that the proposed novel data generation method can effectively improve the model performance.
A New Lightweight DenseNet Based on Mix-Structure Convolution
Authors: He Yi 
Published: April 2020

Abstract
In this paper, we propose a new and efficient lightweight DenseNet which optimizes the parameter redundancy and high FLOPs in the DenseNet model. According to the distribution of the weight value, the element of the Lightweight Mix-Structure Convolution (LMSC) is realized in the model, which reduces the calculation and parameter required for model construction, and ensures that the accuracy does not decline significantly. The experimental results show that compared with the DenseNet-40-P model the model only uses 45.5% of the parameters and 54.3% of the FLOPs, and the accuracy is only reduced by less than 0.4%.
Research on the Automatic Detection Method of Pulmonary Nodules Based on Deep Learning
Authors: Yu Jingwen , Zhu Dongbi , Xiao Xinyi 
Published: April 2020

Abstract
Lung cancer has become the highest incidence and mortality of malignant tumors in China and even the world. It is truly the “first killer of cancer”. Pulmonary nodules are the early manifestation of lung cancer. Early detection, early treatment and early diagnosis of pulmonary nodules can greatly reduce the mortality of lung cancer. Due to their different sizes and complex structures, it is difficult for doctors to recognize the specific characteristics of pulmonary nodules. In order to help doctors detect and analyse pulmonary nodules faster and more accurately, based on deep learning, this paper proposed a new method to detect pulmonary nodules by cascades of improved VGG network model and improved Resnet network model. The improved network was evaluated on the LIDC-IDRI database, and the experimental results demonstrate the effectiveness and robustness of the model, which can help doctors accurately and effectively detect pulmonary nodules.
The Simulation Analysis of Factors Affecting Lightning Withstand Level of the Distribution Lines in Oilfield
Authors: Qi Jingjing , Li Jianing , Hou Zengpeng 
Published: April 2020

Abstract
Simulation analysis is an important method to study the lightning withstand level of distribution lines and conduct the design of line lightning protection. In the process of simulation, the components of the overhead line and the selection of the specific model of lightning current should be taken into account. All the work can be used to get the simulation results which are more consistent with the actual results. Based on the investigating the parameters of oil field lines, insulators and earth resistances, a lightning protection simulation model for typical distribution lines in oil field is built by using ATP-EMTP electromagnetic transient analysis program. By simulation analysis, the influence of line insulation level, earth resistance, layout parameters of arrests and lightning conductor on the lightning withstand level are studied. The results provide a scientific basis for the proposing of the comprehensive lightning protection measures of the distribution lines in oil field.
Study on Design Issues of Special Clothing against High Temperature Operation Based on Four Parameter Index Model
Authors: Qi Limei , Zhou Baoping 
Published: April 2020

Abstract
The design issues of special clothing against high temperature operation was studied. In accordance with the relationship of temperature difference between layers, using the relevant knowledge of heat transfer, heat conduction and the law of energy conservation, the heat relation model of each layer per unit area was established, and the temperature distribution of each layer of fabric materials was obtained, and the temperature distribution results of three layers of fabric materials were analyzed and verified. By using MATLAB software, the four parameter exponential model of skin temperature was obtained. The model can also be extend to the study of the optimal selection of insulation materials.
Optimization Models for Interpolating Overlapping Interval Data
Authors: Yang Jing , Han Xuli 
Published: April 2020

Abstract
We consider a general robust spline interpolating computation problem where the intervals representing the ranges in positions of knots after clustering can be overlapped. We discuss and analyze the similarities and differences of different overlapping situations, propose two definitions of overlapping types for the simple overlapping situations: PIO (partial interval overlapping) and WIO (whole interval overlapping). An optimization model called PHOM-S (position-height optimization model with a single overlapping interval) is presented and formulated as a convex semi-infinite optimization problem which is capable of solving the two overlapping types mentioned. Then a brief discussion is given on how to build PHOM-M (position-height optimization model with multiple overlapping intervals) based on PHOM-S for solving general simple overlapping problems with multiple overlapping intervals. Also, simplification of a finite optimization model called PHOM-S-3 using the cubic polynomial spline is given for the applicability of computation. A numerical example is presented to show the feasibility and effectiveness of the proposed robust model PHOM-S-3.
Will Enterprises Choose Low-carbon Production Technology? Evolutionary Game Model Considering Consumers’ Environmental Protection Moral and Market Clearing
Authors: Zheng Jialin , Zhou Ya 
Published: April 2020

Abstract
Considering that manufacturing enterprises have two production strategies, one is traditional production strategy, and the other is low-carbon production strategy. Consumers have environmental protection moral, they are composed of green consumers and non-green consumers. In addition, enterprises are bounded rational. Based on this, we constructed an evolutionary game model of low-carbon production strategy selection, and analyzed the conditions for achieving the evolutionary stable equilibrium (ESS). Furthermore, the co-evolution of the proportion of companies adopting low-carbon strategy and the proportion of green consumers is also analyzed.
Asymptotic Behavior of Discounted Aggregate Claims in a Nonstandard Two-dimensional Risk Model with Dependent Claim Sizes
Authors: Dong Yinghua 
Published: April 2020

AbstractIn this article, we study a nonstandard bivariate renewal risk model with dependent claim sizes, where two kinds of claim sizes constitute a sequence of independent and identically distributed random vectors satisfying a certain dependence structure, two claim-number processes are not necessarily independent. When the marginal distributions of the random vectors belong to the intersection of the class of long-tailed distributions and the class of the dominated variation distributions, we obtain joint tail asymptotic formulas of discounted total claims. The result shows that the dependence between two kinds of claims has an effect on the joint asymptotic tail probability of discounted total claims.
Simulation Decision of Bridge Demolition Schemes Based on Multi-Factor Decision-Making Method
Authors: Zhong Zheng , Zhang Yue , Chen Weidong , Yang Jing , Wang Qiuping , Dai Zhicheng 
Published: April 2020

Abstract
Bridge demolition project involves a variety of factors, belonging to the category of multi-factor decision-making. How to choose a reasonable demolition scheme from a variety of schemes is a problem to be considered. This paper is to use multi-factor decision-making method to optimize and select bridge demolition scheme. Taking the formulation of a certain bridge demolition scheme in China as an example, this paper demonstrates in detail the process of optimization selection and design as well as the result of the bridge demolition scheme. AHP method is used to decompose each factor of bridge demolition to form a hierarchical structure. On this basis, TOPSIS method is used to calculate the proximity between each demolition scheme and the positive ideal solution. Finally, the bridge demolition scheme is selected according to the calculation value.
Design of Microring Resonant Sensor Based on Mach Zehnder Interferometer
Authors: Shi Mingdeng , Han Qinghua 
Published: April 2020

Abstract
Ultra-high sensitivity (high quality factor) and low detection threshold are necessary for measuring a wide range of biosensors. To improve the sensitivity and measurement range of the sensor, This paper proposes a micro-ring model based on Mach-Zehnder. The finite element method is used to simulate the resonant wavelength corresponding to the commonly used refractive index. The device is available in sizes 6*32*85.948 um3. The simulation results show that the transmission is stable at -2dB above the width of the waveguide at 4.8-5.1um. The radius of the microring and the transmittance change into a damped oscillation curve with a period close to 1.6um. The microring resonant sensor can detect the detected object by detecting the drift of the resonant wavelength. The microring resonators is easy to operate, simple in equipment, fast and accurate in measurement, and is suitable for medical, biological, and environmental monitoring.
Research on Product Appearance Detection System Based on Image Sparse Representation
Authors: Chen Xiaoning , Zhao Jian 
Published: April 2020

Abstract
The USB camera is used as the front device of the image acquisition. The host computer is a PC for the detection system. Aiming at low resolution of USB camera, the super-resolution image reconstruction method based on sparse representation is used to overcome the low resolution for the low-cost imaging sensor and improve the image resolution. In order to reduce the influence due to shooting light, angle, etc., visual keywords are used for image matching to improve the robustness of the system. The experiment proves that the system is simple in structure and easy to operate, and the detection accuracy of the system can reach 98.67%.
Clothing Image Detection and Recognition Based on Faster R-CNN
Authors: Ji Shuaifei , Han Runping , Wei Jianfeng , Wang Rui 
Published: April 2020

Abstract
Clothing image detection and recognition algorithm is proposed in this paper, which involves the design of two neural network models based on improved Faster R-CNN [1]. In our work, in order to train neural networks, ModaNet [2] dataset is selected and improvements on it are made, that is, the improper bounding boxes of footwear and boots in ModaNet dataset are modified based on their polygon annotations. Moreover, by making the following improvements on Faster R-CNN, our two neural network models are established, which use VGG16[3] and the first 40 layers of ResNet50 [4] as feature extration network, respectively. When using ResNet50 for feature extraction, the last 10 layers of ResNet50 are selected as part of the final classification and regression network. In addition, the softmax activation function in RPN is replaced with sigmoid. Through experimental comparison, it is found that both of these two models can well accomplish the task.
Multiple Microphone Speaker Recognition System for Second Language Based on Biomimetic Pattern Recognition with Big Data Fusion
Authors: Xian Guangming 
Published: April 2020

Abstract
The speaker recognition problem for second language based on biomimetic pattern recognition with big data fusion by multiple microphone has been addressed in this paper. Biomimetic pattern recognition is a new machine learning algorithm which can be used to study the geometric characteristics of a large number of sample points in high-dimensional space. Machine learning is an important way for computer to realize intelligence. The development of artificial intelligence can not be separated from the support of machine learning. Big data Unifield architecture of Hadoop system integrates machine learning and big data processing. Several speaker feature extraction methods of big data are described. Cepstrum and Δcepstrum can offer a significant computational advantage in the eigenvalue problem in result of enhancing correct rate. The big data fusion framework of neural network multiple microphone verification system is presented; in particular, a neural network constructed by multiple value neural network algorithm of big data fusion is also proposed for adjusting the parameters. The experimental results of big data fusion using multiple microphone speaker recognition system for second language illustrate the effectiveness of the proposed biomimetic pattern recognition method.
An Improved Deep Neural Network Model Based on Searching Space Limitation for Object Localization
Authors: Wu Renchao 
Published: April 2020

Abstract
Image object localization is the primary research direction in computer vision. A lots of algorithms had been proposed based on the ResNet structure and good results have been obtained on mainstream datasets. However, these methods still have the problem of too large ROI search space on weakly supervised data. This paper proposes a method that further limits the search space by directly extracting the high-level parameters of ResNet. The better search results and search efficiency are achieved and the candidate ROI search space is better reduced. The space information of the original images can be retained and training/testing time can be saved. The experimental results on the benchmark datasets show that this network structure has good object localization accuracy under weakly supervised data and the search efficiency is improved compared to other methods.
Optimized Faster-RCNN in Real-time Facial Expression Classification
Authors: Bao Jiadi , Wei Shusong , Lv Jingfan , Zhang Wenli 
Published: April 2020

Abstract
In order to make convolutional neural network adapt to the mobile terminals which lack of hardware resources in facial expressions recognition. We modified the recent algorithm of real-time CNNs on facial expression. Firstly, we used Gaussian distribution to reduce the irrelevant data. Secondly, we used random forest to reduce the time complexity. We implemented the model and algorithm on raspberry pi. As a result, we reduce the amout of data by about 40% and the time complexity to logarithmic level. Thus, our system can run smoothly on mobile terminals with lack of hardware resources. We validate the accuracy of our system on raspberry pi which has the ability to detect faces and classify the emotion. The accuracy in facial expressions recognition remain stable as the original algorithm. In all, compared with the traditional algorithm, our optimize algorithm improve the number of frames remarkably without reducing the accuracy.
Route Planning of Intelligent Aircraft under Multiple Constraints
Authors: Liu Jinling , Wang Yue , Hong Lijun , Hong Xiaochun 
Published: April 2020

Abstract
In the study of multi-constraint route planning, the commonly used methods include genetic algorithm (GA), particle swarm optimization (PSO) etc. However, these algorithms are often too complex in practical applications and often get local optimal solutions when the number of calculations is too small. In this paper, we proposed the tangential progressive correction (TPC) model to solve the problem of route planning, it’s a kind of guided model with the characteristics of simple realization, good results and high calculation speed for planning aircraft route problem. At the end of the paper, we provided the simulation for relevant data.
Type-II Fuzzy Neural Networks for Image Stabilization of the Airborne Camera
Authors: Zhang Yingbo , Sheng Ziheng , Li Di 
Published: April 2020

Abstract
The vibration rule of the airborne camera was studied to solve the image vibration in aerial photography of the Micro Aircraft Vehicle. A method based on the ability of function approximation of type 2 fuzzy neural networks with self-organizing recurrent intervals (SRIT2FNN) to simulate the vibration rule of airborne camera in the MAV and predict the vibration displacement vectors during image stabilization was proposed. The SRIT2FNN has no initial rules, which are generated from the simultaneous on-line parameter and structure learning. The results show that SRIT2FNN control system is more stable and the higher precision, and good real-time performance than combined BP neural networks.
Multi - mechanism Coalescence Design and Matrix Expression of Logic Action Sequences of the Over-turn Nursing Robot Part I: Functions and Coalescence Design
Authors: Gu Lizhi , Gao Shanping , Gu Chunyang , Huang Yuantang , Xu Feiyue , Zhong Xiang , Chen Kai , Song Jinling 
Published: April 2020

Abstract
In order to effectively solve the problem in over-turn of a bedridden person with the assistance of external force, a double bed face- three embedded leave over-turn nursing robot with the flexible compensation was put forward, with the abstraction of the bedridden person as an organism. This robot, on the basis of concept gesture of the person in bed and the state of the robot supporting and proving the gesture with the actions and combination of the two bed faces, held the complete function of over-turn nursing with 7 states corresponding to 5 gestures of the bedridden person obeying the fundamental requirements of safety, rapidity, and comport. The design method of “PS-MM-KD” was proposed for multi-mechanism coalescent system with related specific tasks induced from the original problems with Systems Engineering. Mechanics and Mechanisms, then applied in the concrete sub-system design followed by analysis and verification of both the scheme and the sub-systems in the design, using the Kinematics and Dynamics, implementing the gears, chain wheel, slewing mechanism, screw nut and mortise and tenon joint type clutch mechanism design successfully. Based on those above, a “two-bed face/three-leaf embedded flexible compensation nursing robot” was designed adopting to all ages, people of various kinds of body geometry. PLC, sensor and logic algorithm were used to carry out the control and operation of 7 state-5 posture sequences for realization of the automation and intelligent over-turning in safety, comfort, and convenience.
Dynamic Modelling and Motion Controller Design for Industrial Palletizing Robot and Collision Detection
Authors: Gu Kai , Cao Qixin , Wang Pengfei 
Published: April 2020

Abstract
The 4-DOF parallelogram industrial palletizing robot is taken as the object of study. This paper designs and develops the real-time industrial controller based on the IPC and high-way EtherCAT fieldbus. What’s more, the control algorithm and structure based on the dynamic feedforward contributes to the compensation of robot dynamics in theory. Experiments on the task switching and master delay show that this control system has a good real time response quality. Based on the kinematics analysis of robot parallelogram structure, this paper builds the robot dynamic model. To increase the computing speed and ensure the real-time performance, the model has been simplied and proved to be effective by simulation contrast verification.
Robotic Polishing of the Meat Grinder Blade under Path Planning and Controlled Force
Authors: Liang Xinrui , Mohsin Imran , Xu Yaohui , Yan Chaoyu , He Kai 
Published: April 2020

Abstract
In recent years, industrial robots have been widely used in many applications such as assembling, stacking, welding, spraying and others. Whereas, the surface grinding and polishing are the applications which are still required a lot of improvement especially for the curved and free form surfaces. Most commonly used articulated robots are suitable candidates for such surfaces because its end effector can provide orientations required for polishing of the free from surfaces. Besides the reachability, tool path planning and the contact force between polishing tool and surface are other two important factors which required proper consideration. This paper presents the robotic polishing of the meat grinder blade under suitable path planning and controlled force. Computer-Aided Manufacturing (CAM) software Mastercam is used to achieve the trajectory path of the robot during the polishing process. Experiments are conducted to prove the adopted methodology.
Switched Motion Cueing Algorithm for Flight Simulator Upset Prevention and Recovery Training
Authors: Wu Dongsu , Li Peng , Zhang Zesheng 
Published: April 2020

Abstract
Upset prevention and recovery training (UPRT) has been mandatorily required in the Level-D flight simulator by some civil aviation authority currently. Due to the limit motion capability of six-DOF motion platform, motion perception of pilot can’t be effectively satisfied with traditional classical motion cueing algorithm (MCA) during this kind of upset scenario training. In this paper, a switched MCA which optimized by GA was proposed. The switching mechanism ensured that MCA output optimal motion simulation signal to motion platform in different upset training phases. The motion command outputs, which may have sudden change during the transit of adjacent flight phases, were smoothed by a sigmoid function window. Simulation results showed that the proposed switched motion cueing algorithm had better motion replication performance and also made better usage for the motion platform’s workspace than the classical MCA.
Research on Hunting Instability Monitoring System of High Speed Freight Bogie
Authors: An Wei , Wu Rongkun , Shi Junjie , Gan Feng , Qi Yayun 
Published: April 2020

Abstract
High speed freight trains have become a new trend in international train operation, and many countries have studied the high speed freight trains. Therefore, it is necessary to carry out relevant safety monitoring research on high speed freight trains. According to the existing acceleration alarm devices for high speed trains and normal speed trains, and combined with the reality condition of high speed freight train, an empty train hunting alarm device is proposed. Through the simulation analysis of the hunting wavelength and acceleration of freight car, it is proposed that the wave filtering frequency should be controlled at 0-15Hz, and the recommended value of lateral alarm limit of frame lateral acceleration is 0.6g. Finally, the suggestion of axle box acceleration installation is put forward, which is installed in the middle position above the axle box to evaluate the wheel rail state. It can mainly evaluate the common wheel rail damage such as wheel polygon, wheel flat and corrugation of rail.