bluuebunny/crossref_metadata_2025_split huggingface dataset:
A self learning algorithm based on data value lifecycle model for the accurate chemical dosing of wastewater treatment
Authors: Zhai Zhengang , Gao Bingtao , Liu Dan , Yao Tengjun , Zhang Li , Pan Zhiyuan , Cao Jing 
Published: March 2020

Abstract
In the process of wastewater treatment (WWT), the instability and hysteresis of the chemical dosing cannot make sure the stability of the water quality. At the same time, chemicals were wasted or overdosed in the process. In this study, we proposed a self learning algorithm, which based on the regression method modified using the data value lifecycle model to computer the accurate quantity of chemical dosing. The mechanisms of discard and cultivation data were established to make it alive in the algorithm. The algorithm is self learning according to the wastewater characteristics as the time goes on using the mechanism. We can make sure the quality of the wastewater is stability and economical by using the artificial intelligence. The experiment approves that the artificial intelligence algorithm was useful and economical to chemical dosing of wastewater treatment.
Research on Charity System Based on Blockchain
Authors: Hu Baokun , Li He 
Published: March 2020

Abstract
The charity organizations in China lack transparency and the supervision to them is difficult to achieve, which has a negative impact on the willingness of the people to donate. Blockchain as a underlying technology of Bitcoin system provides a new solution for the charity system in terms of technology. This paper proposed a charity system based on blockchain technology and expounds the design pattern, architecture and operational process of the platform. Some core functions of the charity platform have been realized and verified on Ethereum in this article. We hope to increase the transparency of charities to enhance the public’s trust in charities and promote the development of philanthropy by blockchain-based charity system.
Research of Human Appearance Level Quantification Algorithm Based on BP Neural Network
Authors: Xue Shanshan 
Published: March 2020

Abstract
BP neural network has strong mistake tolerance and unsupervised speaker adaptation, so it has a wide range of applications in the field of pattern recognition. In this paper, an appearance level quantification algorithm based on BP neural network to extract face geometric features is proposed. We use 30 feature distances corresponding to facial features as the quantitative factor to calculate the appearance level and predict the appearance scores of the given face images.
Comprehensive Evaluation on Economic Performance of Flexible Substation Power Distribution Technology based on BP Neural Network
Authors: Lv Ke , Lu Yan , Yang Meng , Yu Min , Cheng Chen 
Published: March 2020

Abstract
With improvement of energy utilization level, the effective way of power distribution becomes a key research focus. The traditional rigid power transformation is featured in low transformation efficiency and serious power loss, while the power distribution technology based on flexible substation could exactly solve this problem. This paper, aiming at technical performance, economic benefits, environment impact, policy factors, selects system suitability, transmission efficiency, operational reliability, system transient characteristics, costs, investment efficiency, natural environment impact, social environment impact, and policy influence as evaluation indexes, designs five technical and economic evaluation grade and establishes fuzzy comprehensive evaluation system in combination with analytic hierarchy process (AHP), fuzzy mathematics and BP neural network to analyze the technical economic performance of AC/DC power transformation technology based on flexible substation, and provide references to subsequent project with example verification.
Research on SSD base network
Authors: Kang Yixuan 
Published: March 2020

Abstract
In this paper, eight neural networks are selected instead of VGG16 in the SSD model to construct the corresponding SSD model and were tested on ImageNet and VOC datasets. The accuracy, predict time and model size of the eight neural networks in object classification are analyzed. The mAP, FPS and model size of the eight neural networks as the SSD base network in the object detection are analyzed. The result can provide reference for object detection tasks under specific requirements.
Using different combination strategies to combine features for audio scene classification based on Convolutional Neural Networks
Authors: Lin Chan , Leng Yan , Wang Rongyan , Lu Zhaoli 
Published: March 2020

Abstract
Audio scene classification (ASC) which can make good use of audio information for classification is currently a hot topic. In this paper, taking Convolutional Neural Networks (CNN) as the classification model, we have designed three combination strategies to combine different kinds of features for classification. In the first combination strategy, the multi-channel CNN is used to combine different features; in the second combination strategy, different kinds of features are concatenated, and the concatenated features are used as the input of the CNN; in the third combination strategy, new features are first extracted from each kind of feature through CNN, and then these new features are concatenated for classification. Experiments are done on the DCASE2017 datasets to test the effectiveness of the combination strategies, and the experimental results show that the second combination strategy performs best, also the experimental results show that on the whole, combining more features would get better classification results.
Real-time Traffic Sign Text Detection Based on Deep Learning
Authors: Peng Ximing , Chen Xianqiao , Liu Chang 
Published: March 2020

Abstract
Aiming at the task of traffic sign text detection in natural scenes, a two-stage cascade detection model based on deep learning is proposed. The proposed model first locates the regions of interest (RoI) of text-based traffic sign by applying improved SSD (Single Shot MultiBox Detector) network. Then a rotation-based text detection network is used to detect text strings in the located RoI. Moreover, the lightweight convolutional neural network MobileNetV2 is combined with the deep learning component in two stages, which reduces network parameters and improves detection speed of the model. On the one hand, the proposed approach takes full advantage of the information of traffic signs. By this way the search area of text detection can be reduced, which makes it possible to simplify the text detector. On the other hand, it can obtain more complete text detection results by using rotation-based text detector. The experimental results show that the proposed method can perform well on different data sets, it can not only keep a high accuracy of detection, but also meets the realtime requirements.
Comparative Study on III-V MOSFET and Si-MOSFET Model Parameters based on BP Neural Networks Algorithm
Authors: Dai Jingjing , Li Chong , Lan Tian , Wang Zhiyong 
Published: March 2020

Abstract
MOSFET is the basic unit of modern integrated circuit, and main basic characteristics are: current-voltage (IV) characteristics, capacitor-voltage (CV) characteristics and source-drain contact characteristics. The introduction of group III-V semi-conductor, high k- metal date and SBSD allows MOSFET device characteristic device may keep downsizing based on Moore’s Law. However, as II-V MOSFET devices keep downsizing, the short-channel effect and quantum effect are more obvious so that it is more complicate to calculate and extract characteristic parameters of III-V MOSFET. This paper proposes a kind of III-V MOSFET characteristic parameter modeling method based on BP neutral networks algorithm. Compared to other semi-empirical models, this method needs not to calculate characteristic parameters of devices. In stead, it calculates current and voltage output characteristics and transfer characteristics of devices through BP neutral network models according to test data. Through verification, the trained and predicted output relative error is within 5%. The model has short operation time, high calculation precision and good stability. The models established may be applied extensively to other types of transistor, and feasible for practical engineering application.
The spectral conjugate gradient method in variational adjoint assimilation for model terrain correction I: Theoretical frame
Authors: Tao Sulin , Li Yuhong , Mugume Isaac , Shen Shuanghe 
Published: March 2020

Abstract
A spectral conjugate gradient (SCG) method is proposed within the mathematical framework of a variational adjoint assimilation system to correct the bottom terrain of a shallow-water equations model. The formulation of this method is described from the mathematical point of view with determination of the descent direction by using Andrei’s limited-memory form and the step length by solving the tangent linear model. It is proved to benefit from (i) the iterative regularization strategy, (ii) the inverse Hessian approximation involving the second-order information from Broyden-Fletcher-Goldfarb-Shanno class (BFGS), and (iii) the optimal step length. The regularization term introduced to the cost function will allow the incorporation of the known information about the desired bottom terrain and guarantee the uniqueness of the optimal solution.
Sub-pixel Center Extraction Method for Line structured Light Stripe
Authors: Liu Yuanjiong , Song Jian , Yuan Wenxin , Xue Hailang , Li Sanfeng 
Published: March 2020

Abstract
The extraction of line structured light center is a key technology in line structured light measurement system. The precision of light stripe extraction affects the accuracy of the measurement system. Due to the differences of the materials and quality of the laser device, the light stripes is variable in different position that the energy distribution and morphological characteristics are easily changed. It is difficult to extract the center of the stripes by using traditional light stripe extraction methods accurately. Our study proposes an extraction algorithm for the light stripe center. First of all, separating a single stripe from the image according to the morphological characteristics of the light stripe, and the effective region of the light stripe was initially determined. Then, using the grayscale attribute method as well as the grayscale threshold method to determine the region of interest in the center of the light stripe. Finally, the sub-pixel center of the light stripe in the regions of interest was calculated by the grayscale barycenter method. The experimental results showed that the algorithm in the study can be used to extract light stripes with changeable characteristics. It is more accurate and stable comparing with the traditional extraction algorithm of light stripe center.
Research on System Engineering Method of Equipment Software Quality Management
Authors: Wang Qiang , Li Qiang , Shang Jingwei 
Published: March 2020

Abstract
Taking the limitations and challenges of current equipment software quality management into account, this paper proposes a system engineering method for equipment software quality management based on the characteristics and development trends of equipment software. Based on the modified Hall model, this paper defines the system engineering of equipment software quality, analyzes and studies the elements, relationships, boundaries and environment of the system from the perspective of software reliability engineering. Finally, the system engineering level of equipment software quality management is quantitatively evaluated by system stability and system effectiveness.
Target detection and tracking based on improved HOG-color feature fusion
Authors: Dai Tianle , Hao Ziqiang , Wang Zhongyuan , Zhan Weida , Tang Yanfeng 
Published: March 2020

Abstract
In the process of constructing the target appearance model, the traditional HOG feature does not take into account the relationship between adjacent regions when the image is segmented in the feature extraction process, resulting in the regional aliasing effect, and the single feature is inefficient in target detection and tracking. An improved HOG-color feature fusion method for target detection and tracking is proposed. Firstly, HOG feature and color feature were extracted from the target samples respectively. During the HOG feature extraction process, trilinear interpolation was used to eliminate regional aliasing effect. Secondly, Bhattacharyya distance is used to select the HOG feature after interpolation, and the appropriate feature is selected as the HOG feature. Then combine the HOG feature after selection with the color feature; Finally, the filter is obtained by learning the kernel correlation filter, and the image is correlated detected with the filter, and the response output is obtained. The experimental results show that this method is superior to the traditional HOG-color feature fusion target detection and tracking method in both tracking speed and tracking efficiency.
Application of Railway Passenger Flow Statistics Based on Mask R-CNN
Authors: Meng Di , Xu Shangzhi 
Published: March 2020

Abstract
This paper discusses and implements the application of Mask R-CNN in railway passenger flow statistics. Mask R-CNN is a target detector based on candidate region, combining deep learning, neural network and image recognition technology, and has a wide range of application value in the field of intelligent monitoring.The paper focuses on the algorithm named Mask-Flow that Mask R-CNN uses the same color for tracking on the same target, and counts the number of pedestrians. And the passenger flow statistics are compared with Retina-Net in the railway scene of the dense crowd, which proves the accuracy of the method in the paper.
Research on Two-dimension pattern based on shear cutting Problems
Authors: Ji Jun , Zhang Saikun , Li Yuhang , Shi Ning 
Published: March 2020

Abstract
The cutting problem is a practical engineering problem. It is classified according to the two-dimensional pattern problem. According to the requirement of blanking technology, a blanking method based on homogeneous block is proposed, and the blanking characteristics of this kind of blanking method are pointed out.
Development of Parametric Drawing System of Corrugated Boxes Based on SolidWorks
Authors: Mao Wenwu , Zhang Xinggao , Tantai Sunxiao , Shen Shujing , Zhang Zewen , Wang Jiahui 
Published: March 2020

Abstract
Corrugated box is a widely used means of transportation packaging, there are various box types such as slotted type, nested type, folding type, slide type, fixing type, automatic type and other types of boxes, special standards on the type and size of corrugated box is formulated by Chinese national standards. However, when drawing corrugated boxes with various CAD software now, we need to model and edit according to the box shape, specific size of corrugated boxes and the drawing efficiency is low. In this paper, on the basis of the established corrugated box access database, we use the Visual Basic to develop corrugated box parametric drawing system on SolidWorks platform. The users only need to select the desired corrugated box carton and the key parameters, then can get the 2D expansion diagrams and 3D stereoscopic diagrams in accordance with the national standard, which significantly improve the efficiency of the corrugated box drawing.
Two implementation methods of handwritten numbers recognition
Authors: Zhang Hui , Fan Yani 
Published: March 2020

Abstract
In order to realize the recognition of handwritten numbers in daily life by artificial intelligence, to save labor cost and time cost, a machine learning KNN (K-nearest neighbor) algorithm and CNN algorithm (convolution neural network) in deep learning are proposed. According to the calculation, the recognition accuracy of more than 95% can be achieved. In the actual verification, the recognition accuracy of 98.77% and 98.69% are obtained respectively. Practice has proved that these are effective artificial intelligence recognition methods.
An Image Classification Approach based on Deep Learning and Transfer Learning
Authors: Han Kaixu , He Jinxin , Wang Yongzhi , Xiong Yue , Zhang Chi 
Published: March 2020

Abstract
Target recognition of high-resolution images is an important direction of today’s classification technology, and some classification models have emerged, but there are still many technical problems to be solved. The main source of this paper is the Google Photo Collection, which includes five types of city daisy, rose, tulip, dandelion and sunflower. Using CNN (Convolutional Neural Networks) deep learning model and Google’s Inception transfer learning model to train and classify the sample images, the final accuracy of the overall test set can reach 88.3%; However, the accuracy of training without transfer learning is only 60.2%. Thus, it is more efficient to combine deep learning with transfer learning to image classifications.
Development and design of on intelligent medicine box
Authors: Chen RenYi , Wang Peng , Su Peng , Chen QiaoRui , Hu JunFeng , Li WenSong , Han Lei 
Published: March 2020

Abstract
This paper proposes an intelligent medicine box which can be used to provide safe medication to the elderly who are living alone. The design is based on STM32F103 micro-controller and other intelligent control components. The basic working principle is analyzed as well as the hardware structure. The proposed medicine box is full-automation, high-intelligent and multi-functional which can effectively avoid the health and safety risks caused by the memory declination of the old people.
Object Detection Based on Minimum Convex Hull and Generalized Hough Transform
Authors: Zhang Tong , Song Chenfei 
Published: March 2020

Abstract
In view of the low efficiency and fallibility of the feature-based matching algorithm in object detection, a new algorithm based on convex hull region and generalized hough transform is proposed.By using the region of interest to narrow the detection region and applying the key feature points to the voting elements of the generalized hough transform, the computation is greatly reduced.Firstly, the fused key feature points are extracted and filtered to generate the convex hull region which is combined with the GBR clustering algorithm to determine the region of interest. Secondly, the key feature points are taken as the voting elements in the generalized hough transform algorithm to obtain the final result. In this paper, dozens of various transformation experiments(scaling, rotation, noise, etc.) based on the proposed algorithm are compared with other similar methods, and it is verified that the method has higher efficiency and accuracy according to the theoretical basis and concrete implementation, which can meet the real-time and high-precision requirements in industrial detection.
Photovoltaic panel anomaly detection system based on Unmanned Aerial Vehicle platform
Authors: Xie Xiaoping , Wei Xiangui , Wang Xingyu , Guo Xincheng , Li Ju , Cheng Zhifeng 
Published: March 2020

Abstract
The emergence and rapid development of the Unmanned Aerial Vehicle (UAV) Photovoltaic inspection system have become an effective means of solving the operation and maintenance of photovoltaic power plants. In order to cooperate with the current UAV platform for photovoltaic panel anomaly detection, this paper proposes a photovoltaic infrared target anomaly detection system. In this paper, the Sobel operator is used to extract the photovoltaic slab area of the image, and the canny operator is used to obtain the photovoltaic small plate area to realize the complementary advantages. At the same time, the deep learning is applied to the algorithm to learn the discriminative characteristics of the image, and the brightness statistics are used. The method is supplemented by the method to achieve anomaly detection of the photovoltaic panel area. The effectiveness of the proposed algorithm is proved by a large number of experiments.
Research on prediction classification and compensation for silicon contents in blast furnace based on ridge regression
Authors: Sun Ling , Yin Linzi , Jiang Zhaohui , Guan Yuyin , Xu Xuemei 
Published: March 2020

Abstract
In blast furnace, the accurate prediction of silicon contents in hot metal plays an important role in comprehensively judging the furnace states and making correct decisions. However, traditional prediction models are easily affected by multi-collinearity among the independent variables and imbalance sample distribution in hot metal datasets. The prediction values of silicon contents would be difficult to track the real values accurately when the furnace conditions fluctuate dramatically. In this paper, a classification and compensation model for silicon contents is proposed by combining the ridge regression and random forest classification algorithm. Firstly, the ridge regression algorithm is applied to fit and optimize the prediction values outputted by a front-end prediction model. Then, the random forest technology is expected to classify and identify the furnace states. Finally, the predicted values are compensated according to classification results. In this paper, the random forest regression and BP neural network algorithm were used to predict silicon content as front-end models. The related applications show that the proposed classification and compensation model improves the accuracy of predicted values effectively.
Optimization of Random Forests Algorithm Based on ReliefF-SA
Authors: Zhou Zibo , Wang Yunfan , He Xinrui , Zhang Xuecong 
Published: March 2020

Abstract
As a widely used classification integration algorithm, the random forests based on decision tree combination is mostly used for classification and regression problems, but its deficiencies still need to be improved. In this paper, we propose simulated annealing algorithm, and its parameters is combined with the global search optimal solution and local optimal solution to optimize search. Then, we propose a random forests hybrid algorithm by combining the Relief algorithm and annealing algorithm, briefly introduce its thoughts and algorithm flow, and also carry out simulation experiments. According to the experimental results, the classification effect of the hybrid algorithm is generally the best, which greatly improves the overall generalization performance of the random forests.
Typical status recognition of gearbox based on big data
Authors: Zhang Lixia , Yang Zhaohong , Wang Hongyu 
Published: March 2020

Abstract
In this paper, the experimental data of five different working states of the gearbox during the normal operation and the failure of the gearbox are taken as the research object, the python-based data analysis tools Numpy and Pandas are used to establish a fault detection model, by collecting time-domain and frequency-domain data of several faults such as fracture, crack and wear of transmission gear, and analyzing the characteristic parameters of the data, the classification of transmission faults is realized, thus laying a foundation for the application of big data tools in fault diagnosis and analysis.
Abnormal target tracking and localization algorithm for UAV PV inspection scenarios
Authors: Xie XiaoPing , Wei Xiangui , Wang Xingyu , Guo Xincheng , Li Ju , Cheng Zhifeng 
Published: March 2020

Abstract
In order to adapt to the development of UAV inspection technology and achieve accurate positioning of abnormal targets of photovoltaic panels, this paper applies deep learning to abnormal target location, and proposes abnormal target tracking and localization algorithm for UAV PV inspection scenarios. The advantage of deep learning lies in the ability to learn the general expression of the target. In order to obtain an efficient deep network, this paper uses a large amount of data to train the deep network, so that the features extracted by the network can achieve accurate representation of the tracking target. By testing in the abnormal target location test database, it is proved that the proposed algorithm can accurately locate the abnormal target.