bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Improved Remote Sensing Image Classification Method Based on DCNN
Authors: Xu Bin , Zong Jiafei , Ding Ling , Zhang Wei , Cao Guoguang 
Published: September 2020

Abstract
The popular deep learning method of the year established a recognition model to identify remote sensing images, which is a new method. Based on Google Inc.’s Inception-v3 convolutional neural network recognition model, this paper adopted a conception of migration learning to achieve the accurate automatic classification of remote sensing image scenes. Since the model already possesses good convolution parameters for image feature extraction, the idea of migration learning was used to retrain the model. To achieve the purpose of learning UC Merced Land Use Dataset related data types, the authors use the training set and test set to test separately. The learning model has a good learning effect and a high classification recognition rate.
Granular Computing Based on Graph Theory
Authors: Guan Sujie , Li Min , Deng Shaobo 
Published: September 2020

Abstract
The rough set theory begins with the concepts of the equivalence relations and equivalence classes. Then, we will use the equivalence relation to research granulation and granulation computing. In current paper, we present the concepts of elementary granulation and granulation, and describe how to convert a decision table into a granular network which has the characteristics of simple and intuitive for describe a decision table. We also define a granular network for a decision table. On this basis, we present a concrete method for converting a decision table into a granular network and give an example for illustrate purpose. Further, we describe a type of data reduction algorithm based on granular network. Lastly, we prove that the data reduction based on data analysis method is equivalent to the data reduction based on granular network. The data reduction based on granular network has the characteristics of simple and intuitive. Theoretical analysis and experimental results indicate that the proposed approach for the data reduction is efficient and feasible.
A CNN Based Method for Quantitatively Evaluating 3D Face Reconstruction Algorithms
Authors: Sun Miao , Singh Gurjeet , Yin Chiang Patrick 
Published: September 2020

Abstract
3D face reconstruction is an attractive topic in computer vision. We have seen dramatic rise in its development recently. Now the state-of-the-art method can reconstruct a face from a single 2D face image. Since the work conditions and cost of a 3D scanner is limited, these 3D reconstruction algorithms are tough to evaluate. Some researchers had proposed different algorithms by measuring the distances between reconstructed images and true depth face previously. Inspired by CNN’s powerful representative ability, here we investigate the issue from CNN’s perspective by comparing the performances of various 3D face reconstruction algorithms on two classification tasks. One is an identity classification task for evaluating the intra-dataset discrimination and another is a binary classification for reconstructed faces and 3D ground truth faces for evaluating the inter-dataset discrimination. To accomplish this, we adopt hourglass neural network as backbone to perform comparisons for three 3D reconstructed algorithms on two datasets. In the end, we show the pros and cons of various algorithms and explicit the mechanisms of the results.
An Accelerated U-Shapelet Time Series Clustering Method with LSH
Authors: Luo Linghao , Lv Shu 
Published: September 2020

Abstract
The clustering method of time series is based on the measurement of the whole time series, and the traditional clustering method is used for direct clustering. It was only recently that Keough came up with the concept of Shapelet. He later applied this concept to unsupervised time series clustering. This is a time series clustering method based on the feature of sub-series. This method has good clustering accuracy and stability. However, due to the fact that traversal sequences are all sub-sequences, the original violent search algorithm is very complex and difficult to be applied to a large number of time series data sets. In this paper, on the basis of the original method, you introduce the idea of data retrieval in the data preprocessing stage to quickly screen candidate subsequences. Then we introduce a position-aware hashing algorithm -LSH. The method is used to match the sub-sequences of the pre-processed time series data, and the wide representativeness of the high-quality u-Shapelet is used for selection. Therefore, LSH-us is proposed in this paper to speed up the extraction process of feature sub-sequences. Experimental results show that this algorithm can improve the speed of u-Shapelet time series clustering algorithm and ensure the accuracy of clustering.
Research on Iron Surface Crack Detection Algorithm Based on Improved YOLOv4 Network
Authors: Deng Huafu , Cheng Jianghua , Liu Tong , Cheng Bang , Sun Zhenhua 
Published: September 2020

Abstract
Metal surface defect detection has always been an important branch of target detection. In certain places, if the cracks on the metal surface can be found in time, the existing safety hazards can be eliminated. In this paper, a polarized imaging camera with strong environmental applicability is used for sampling, and the degree of polarization image is applied to the detection of iron material cracks. The iron material polarization image crack data set (PICD-iron) is established, and the Cascade-YOLOv4 (C-YOLOv4) network model is used for crack detection. Then it solves the problems of target detection in the dark environment, complex and variable crack detection, small target detection. Experimental tests verify that the detection accuracy of the C-YOLOv4 net-work has improved while comparing with the YOLOv4 network, the detection speed has also increased by 28%.
Risk Analysis of Enterprise Management Accounting Based on Big Data Association Rule Algorithm
Authors: Wang Zhenkun , Qiong Makai , Wang Hanjie 
Published: September 2020

Abstract
With the popularization of the development and applications of information technology, enterprise’s management in making decisions is paying more attention to “data driven” and depend on the objective data rather than subjective judgement, in addition to the basic financial data, enterprises in daily operation will produce a very large number of data, despite big data itself is a special kind of enterprise assets, but in order to get valuable information from these data, we must filter out the useless data based on the analysis method of big data, mine and sort out the comparable information, and provide reference for decision makers. The combination of big data and management accounting is the inevitable development trend in future, this article adopted the method Empirical Analysis to establish enterprise management accounting risk analysis model and simulated enterprise using big data analysis method, determined ten key financial indicators the enterprise needed to pay attention to in enterprise risk control, to show the application of big data analysis method in the process of enterprise management accounting, and looked forward to the future of interdisciplinary integration of big data and management accounting.
Containment Control for Lipschitz Nonlinear Multi-agent Systems with Semi-Markov Switching Topologies
Authors: Huang Hui , Zou Yuanyuan , Hu Peng , Cai Qingrong , Jiang Li , Liang Yanyang , Wang Hongmin , Tang Jianhua 
Published: September 2020

AbstractThis letter studies containment control and the communicate topologies are driven by a semi-Markov chain. The Lipschitz nonlinear stochastic systems are investigated in this paper. The error system is established by model transformation, and the containment control is transformed into error stability between followers and multiple leaders. Then, a control protocol is employed and a Lyapunov-Krasovskii method is established to analyze the protocol. Moreover, we prove the specific point that followers ultimately will converge to. Finally, we give the conditions that followers converge leader’s convex hull in mean square sense.
Dynamic Multi-view Combination for Image Classification
Authors: Li Chenghua , Wang Wanguo , Liu Fengyuan , Guo Zhenyu 
Published: September 2020

Abstract
Multi-view learning is widely used in image classification tasks to better explore the discriminative information of different views. However, existing multi-view methods commonly rely on some pre-defined assumptions or fail to fully take advantage of the combination commonality between individual images. This paper presents an efficient dynamic multi-view combination approach to dynamically combine the discriminative power of different views. Specially, we firstly utilize a group of pre-trained CNNs to extract different views of an image. Secondly, we apply a dynamic gating module to the image, which will generate a weight vector of these views to model the image-level information for the multi-view learning. Finally, the weight vector and the views are combined for the classification. Experimental results and analysis on CIFAR-10 and ImageNet show the effectiveness of the proposed dynamic multi-view combination method (DMVC) for visual classification.
Data Expansion Method Based on Wavelet Transform
Authors: Li Xiaochun , Li Wen , Liu Zhe 
Published: September 2020

Abstract
For small sample data, data expansion is an effective means to improve the accuracy of target recognition. To image magnification to enhance image resolution as a starting point, the author puts forward two kinds of image magnification method based on wavelet transform. Qualitative analysis on the experimental data and quantitative analysis using average gradient, cross entropy as performance index, show that the proposed image magnification method has obvious advantages in terms of image definition, maintaining image gray scale information. Finally, the author proposes the method of image expansion based on the image magnification data of the four methods, which greatly enriches the diversification of the training set on resolution, clarity, contrast, integrity and so on, and theoretically analyzes the feasibility of improving the recognition accuracy of the data with few samples and small targets.
Application of BP Dual Network Model Considering Internal and External Factors in Short-Term Load Forecasting
Authors: Yang Xiaoliang , Ren Hongyuan , Zhao Honggang 
Published: September 2020

Abstract
In the reform of electricity sales, the accuracy of short-term load forecasting is very important for power grid operation scheduling, user demand response and pricing mechanism. Based on the theory of internal and external factors, the paper accurately extracted temperature, humidity, entropy of peak load and load volatility as the main influencing factors. Based on the artificial intelligence learning method, it introduced experts’ experience to build a dual neural network short-term load prediction model, simulated load characteristics from multiple angles, and accurately predicted the trend. The simulation results show that the improved short-term load prediction model is more suitable to deal with dynamic problems due to its fast convergence speed and high prediction accuracy.
Exploiting Node Similarity Based on Graphical Markov Models for Link Prediction
Authors: Sun Jing , Lin Zhijie 
Published: September 2020

Abstract
Link prediction has been attached more attention in recent years. In this paper, we develop a link prediction method which has a unique perspective on using network structures. The key idea is to exploit the relationship information based on Graphical Markov models (GMM) for designing similarity indices. Specifically, networks with GMM were modeled to capture the relational influence of nodes by taking multi-hop neighbors into consideration. Then, link ties are measured for supporting relationship prediction based on the theory of weak and strong ties. Moreover, the proposed method can be used to predict the emergence of future relationships between the nodes. Finally, empirical studies on real-world dataset demonstrate that the benchmark of the proposed method improves with a significant margin compared with other methods.
Research on Observer-Based Bidirectional DC-DC Converter Current Prediction Strategy
Authors: Zhu Jiaxiong , Jing Cao , Feng Chang , Yao Jiang 
Published: September 2020

Abstract
In a DC microgrid system containing distributed power, factors such as output fluctuations and load switching of distributed power can cause DC bus voltage fluctuations. In order to increase the capacity and improve the stability of hybrid photovoltaic energy storage system, taking into account the nonlinear characteristics of bi-directional set DC-DC converter, this paper proposes an observer-based bi-directional set DC-DC converter current prediction strategy. Without additional sensors, a feedforward control channel based on a non-linear disturbance observer is added to the DC bus voltage control outer loop to compute system interference through using the disturbance observer, the disturbance feed-forward is compensated into the expected value, thereby improving the inductance The fast following performance of current expectation. MATLAB/Simulink simulation, the results show that the observer-based Bi-directional set DC-DC converter predictive current control processes and policies can restrain the voltage jump of DC bus effectively and improve the system’s rapidity and anti-interference ability under disturbances.
Research on Product Design Efficiency Improvement Based on Computer Information Technology
Authors: Yang Shuai , Yao Meijing 
Published: September 2020

Abstract
With the progress of society, computer technology has gradually penetrated into all aspects of life. The product design industry is an industry that emphasizes innovation and efficiency. In the process of new product design, the product details and existing problems need to be fully considered. This requires further optimization and innovative design. Among them, computer-aided technology can smoothly apply information technology to the forefront of product design life cycle and provide intellectual innovative solutions for the design and application of new products. The intervention of computer technology will greatly improve the efficiency of product design and help promote the development of product development companies.
Data-Driven Sensor Fault Diagnosis for Fighter under Harsh Conditions
Authors: Wang Qi , Chen Fuyang , Wang Li 
Published: September 2020

Abstract
This paper presents a multi-classification fault diagnosis scheme for the sensor of fighter. It is difficult to diagnose sensor faults for fighter under harsh conditions like hyper maneuver because the states change rapidly and the data of fault-free and faulty sensor are similar to each other. The rapidly changing states means that values of sensor faults may be small compared with states variation. Similar data may raise the difficulty to find the separating plane. Wavelet packet decomposition can transform the raw dataset to fault features by computing the energy values of each frequency band. And principal component analysis (PCA) can grasp the main features and reduce computational complexity by transform a group of dependent parameters into a group of independent parameters via orthogonal transformation. A support vector machine (SVM) trains fault features to achieve a multi-classifier via one-versus-rest method. The classifier can obtain fault diagnosis results by inputting features of new data. Numerical simulation results illustrate the effectiveness and accuracy of the proposed fault diagnosis scheme.
A Broadband Filter Transmitarray Based on Quasi-Yagi Antenna Element
Authors: Sun Fugang , Li Chuanqing , Liu Yi , Wan Lulan , Qin Fan , Liu Yi 
Published: September 2020

Abstract
A transmitarray (TA) antenna operating at X-band with band-pass filtering performance is presented. The proposed antenna is composed of a wideband TA and a filter feed antenna. Two quasi-Yagi antennas are connected as the unit cell of the proposed TA, leading to high transmission efficiency over a broadband range. By simply changing the length of the transmission lines, 360° phase shift is obtained. To verify this idea, a design with 16×26-unit cells is simulated and optimized, showing that approximately 14.5% 1-dB and 24.6% 3-dB gain bandwidths are achieved, respectively, with the peak gain of 23.1 dBi. Moreover, by employing the bandpass filter feed antenna, good bandpass filtering property is achieved, showing that the upper and lower out-of-band suppression in the 0.3GHz range can reach 27.3dB and 24.2dB, respectively, with the passband range from 9.6GHz to 11.9GHz.
Trident SSD: A Trident Single-Shot Multibox Object Detector with Deconvolution
Authors: Li Xinlong , Li Xingwei , Guan Shaojie , Jin Jiating , Ge Yizhi 
Published: September 2020

Abstract
Scale variation is one of the key challenges in the object detection area, which limits the precision of detection methods like Single shot multibox detector (SSD). This paper proposes a detection method based on SSD, which focuses on handling scale variation and better detection performance of small objects. Our method, called TridentSSD, have an architecture with three branches, which are respectively responsible for detecting different scales of objects, solving the problem of scale variation while training. Then we augment small object branch with deconvolution module and feature fusion methods to improve precision, especially for small object detection. During training, we modify the original matching rules to generate training samples. Consequently, we can use objects of different scales to train the corresponding branches respectively. Finally, Experiments have done on both PASCAL VOC2007 and VOC2012 datasets. Results show that, with an input size of 300×300, our TridentSSD achieves better performance compared to the benchmark method SSD.
An Online Intelligent Method to Calibrate Radar and Camera Sensors for Data Fusing
Authors: Liu Ming , Li Dongdong , Li Qiankun , Lu Wei , Yin Jun 
Published: September 2020

Abstract
This paper introduces an intelligent method to calibrate radar and camera sensors for data fusing. Camera can recognize pedestrians, non-motor vehicles, motor vehicles high accuracy by advanced deep learning neural networks. But it’s hard to acquire the target’s spatial position and speed information simply relying on image. For the radar sensor, it’s easy to acquire the target’s spatial position and speed information by itself, but it cannot recognize target. In other words, the advantage of radar is the disadvantage of camera, and the advantage of camera is the disadvantage of radar. So combining these two complementary sensors is valid and necessary for solving the problem: Who is doing what. Then the first and foremost thing is to calibrate the radar and camera sensors. Calibration includes time synchronization and space calibration, which will seriously affect the sensors data fusing performance. This paper emphasizes on spatial calibration. Traditional methods (based on the principle of geometric projection or four-point calibration method) cannot acquire enough high calibration accuracy in the general scenarios with low time cost or easy operation. So this paper introduces an online intelligent method to calibrate radar and camera sensor without human assistance operation in the general scenarios. Firstly, we introduce what is spatial calibration. Secondly, we introduce two conventional methods and their defects. And then we introduce the online intelligent calibration method. At last we compare three calibration methods with the actual data to further explain the advance of our new method.
Structural Fuzzy Multi-class Support Vector Machine
Authors: Li Kai , Li Jie 
Published: September 2020

Abstract
Support vector machine is a kind of generalized linear classifier that classifies data in a supervised learning manner, and its decision boundary is the maximum margin hyperplane for solving learning samples. It is used to solve the problem of binary classification. For multi-classification problems, a combination method of multiple binary classifiers is usually used to solve, but such methods are prone to generate inseparable regions of data. Therefore, on the basis of constructing a multi-class problem directly, using the pinball loss function, and introducing the structural information of different classes in the data and the role of different samples, a new support vector machine algorithm Pin-SFSimMSVM for solving multi-classification problems is proposed. It not only retains the advantages of avoiding the existence of inseparable regions and fast calculation speed of multiple types of data, but also is insensitive to noise and resampling data, and has greatly improved the accuracy. The effectiveness of the proposed algorithm is verified by experimenting on UCI standard data sets and comparing with some multi-classification algorithms.
Dynamic genesis potential index for diagnosing present-day and future global tropical cyclone genesis
Authors: Wang Bin , Murakami Hiroyuki 
Published: September 2020

Abstract
Tropical cyclone (TC) genesis potential index (GPI) has been extensively used to understand the processes governing climate variability and future change of TC genesis (TCG). However, the relative roles of the thermodynamic versus dynamic environmental factors in TC genesis remain elusive, especially under a warming world. Here we show that four leading dynamic factors, the 850 hPa absolute vorticity, 500 hPa vertical motion, tropospheric vertical wind shear, and 500 hPa shear vorticity of zonal winds, are objectively identified by the logarithmic stepwise regression analysis from 11 dynamic and thermodynamic candidate factors. We further demonstrate that the model results from a TC-permitting global model ascertain the four leading dynamical factors as the most influential in both the present-day simulation and future projection under global warming. A dynamic GPI, consisting of the four dynamic parameters, provides a diagnostic tool for understanding future change of TC genesis. Meanwhile, it improves skills in representing interannual variations of TCG frequency in the western Pacific and Southern Hemisphere oceans.
Boosting the eco-friendly sharing economy: the effect of gasoline prices on bikeshare ridership in three U.S. metropolises
Authors: He Pan , Zou Zhenpeng , Zhang Yongping , Baiocchi Giovanni 
Published: September 2020

Abstract
Transportation has become the largest CO2 emitter in the United States in recent years with low gasoline prices standing out from many contributors. As demand side changes are called for reducing car use, the fast-growing sharing economy shows great potential to shift travel demand away from single-occupancy vehicles. Although previous inter-disciplinary research on shared mobility has explored its multitudes of benefits, it is yet to be investigated how the uptake of this eco-friendly sharing scheme is affected by gasoline prices. In this study, we examine the impact of gasoline prices on the use of bikeshare programs in three U.S. metropolises: New York City, Boston, and Chicago. Using bikeshare trip data, we estimate the impact of citywide gasoline prices on both bikeshare trip duration and trip frequency in a generalized linear regression setting. The results suggest that gasoline prices significantly affect bikeshare trip frequency and duration, with a noticeable surge in short trips. Doubling gasoline prices could help save an average of 1933 gallons of gasoline per day in the three cities, approximately 0.04% of the U.S. daily per capita gasoline consumption. Our findings indicate that fuel pricing could be an effective policy tool to support technology driven eco-friendly sharing mobility and boost sustainable transportation.