bluuebunny/crossref_metadata_2025_split huggingface dataset:
PV generator nominal power estimation using a ground sensor and the PVLIB online irradiance database
Authors: Angulo J R , Conde L A , Muñoz E. , de la Casa J , Töfflinger J A 
Published: February 2022

Abstract
The nominal power of a photovoltaic generator is a crucial parameter. For instance, it is a required input parameter for modelling the energy produced by a PV system. Determining the nominal power in-situ requires measuring the irradiance and module temperature. Usually, the irradiance is measured with a sensor placed on the ground and at the plane of the array. The present work first compares the irradiance measurements of a calibrated PV module with the openly available irradiance database PVLIB. Second, it contrasts the daily estimated nominal power values considering both irradiance sources. The results indicate that the irradiance database is suitable to calculate the nominal power.
Agricultural residues-based activated carbons impregnated with chitosan nanoparticles by hydrothermal treatment
Authors: Cruz G J F , Aguirre D , Canepa C , Gómez M M , Solís J L , Santiago J , Solís-Castro R L , Cruz J F , Keiski R L 
Published: February 2022

Abstract
Much research related to the production of materials with high-quality adsorption capacity from agricultural residues has been done. However, the adsorbents themselves do not have the antibacterial capacity, an essential property in different water treatment applications. Hydrothermal treatment is an environmentally friendly approach to impregnated nanoparticles over a substrate. This paper aims to obtain and characterize activated carbon made of corn cob and red mombin seed impregnated with chitosan via the hydrothermal treatment to be tested as an antibacterial material for aqueous phase applications. The FESEM micrographics, RAMAN spectroscopy, and FTIR-ATR analyses confirmed the presence of chitosan nanoparticles (NPs) over the surface of the activated carbons. The impregnation of Chitosan NPs affected the pore size distribution in the activated carbon, but not total pore surface area or pore volume. The impregnation of chitosan increases the antibacterial capacity against Escherichia coli and Shigella flexneri.
Design of an autonomous photovoltaic system for desalination of groundwater by electrodialysis
Authors: Oré D G , Pretell V H , Pilco A W 
Published: February 2022

Abstract
Ultrafiltration-electrodialysis (UF-ED) systems for water desalination at different scales require electrical energy. The objective of this work is to size and select the components of an autonomous photovoltaic system (APVS) to supply energy to the UF-ED system at laboratory scale in a continuous process and to determine the percentage of salinity removal (PSR) and the specific energy consumption (SEC) at different conditions of the ED system. Two PV modules and a battery were selected. A maximum power of 150 Wp was chosen for each PV module and a capacity of 200 Ah and 12 V for the battery. A charge controller was selected with a power greater than 300 Wp and a nominal charge current greater than 18.52 A and an input voltage greater than 21.6 V. An inverter with an output power greater than 112 W was selected. The PSR and SEC were determined for different operating conditions (6 V and 25 mL/min, 12 V and 25 mL/min, 6 V and 50 mL/min, 12 V and 50 mL/min). The UF-ED system reduced the salinity of the groundwater, obtaining 64% removal at 12V and 25 mL/min in a continuous process.
Multi-level interactions for RGB-D object detection
Authors: Li Qi , Dai Meng , Zhang Qing 
Published: February 2022

AbstractIn order to efficiently utilize high-level information and depth information in RGB-D saliency object detection, multi-level fusion is studied. Different from existing methods which ignore feature dilution in the process of feature downward transmission, a multi-level interactive fusion method is designed and compared with five advanced models through four evaluation indexes. The experimental results show that the model in this paper is advanced.
Insulator defect detection algorithm based on a lightweight network
Authors: Lan Ying , Xu Wenxiang 
Published: February 2022

Abstract
Insulators are the key components of transmission lines. The identification and detection of insulator defects are directly related to the stable operation of transmission lines. In order to improve the efficiency of the insulator and its defect location, a faster defect detection algorithm based on YOLOv5 is proposed. Firstly, a lightweight Ghost module was introduced in the YOLOv5 backbone network, which significantly improved the detection speed with ensuring accuracy. Secondly, Secondly, CBAM is introduced into YOLOv5 Neck network to further improve the detection accuracy. The experimental results show that the model of the improved post-network is smaller compared to the YOLOv5 original network, and the detection speed improves greatly while ensuring the detection accuracy. It is of great significance to power grid operation and maintenance.
A New Method for Spinning Projectile Aerodynamic Estimation: Extreme Learning Machine Optimized by Cuckoo search algorithm
Authors: Xia Youran , Yi Wenjun 
Published: February 2022

Abstract
Aiming at the problem of aerodynamic parameter identification of spinning projectile, a Cuckoo search algorithm for extreme learning machine algorithm is proposed in this paper. The algorithm uses Cuckoo search algorithm to optimize the hidden layer weight and threshold of the extreme learning machine, so as to avoid the problem of unstable identification results caused by the random weight and threshold of the traditional extreme learning machine. The simulation results show that the algorithm can effectively identify the aerodynamic parameters of projectile, with high identification accuracy and fast convergence speed. The proposed algorithm is practical in engineer applications.
An Adaptive Low-illumination Image Enhancement Algorithm based on Weighted Least Squares Optimization
Authors: Zhao Erxun , Gao Jingmin 
Published: February 2022

Abstract
An adaptive low-illumination image enhancement algorithm based on the weighted least squares optimization is proposed to solve the difficulty of detailed feature recognition in low-illumination images that collected by visible light imaging equipment. First, the image is converted from RGB channel to LAB channel. Second, we use an edge-preserving smoothing operator based on the weighted least squares optimization to coarsen smooth base layer and extract multi-scale details in brightness channel. Then, an adaptive weight is proposed and applied to the weighted fusion of smooth base and detail features. Finally, the Retinex enhancement is performed to obtain a ultimate enhanced image. Experiments result show that the image enhanced by this method has suitable visual brightness and clear details. In terms of objective indicators, it has good and stable performance in NIQE, TMQI, and information entropy.
Marine ship detection and classification based on YOLOv5 model
Authors: Zhang Xuan , Yan Mingzhong , Zhu Daqi , Guan Yang 
Published: February 2022

Abstract
An improved deep learning neural model YOLOv5-DN based on YOLOv5 is proposed for marine ship detection and classification in the area of harbours and heavy traffic waterways. The CSP-DarkNet module in YOLOv5 is replaced by CSP-DenseNet to promote the accuracy of target detection and classification in the proposed model. Sample marine ships in the data set are divided into six classes: ore carriers, general cargo ships, bulk cargo ships, container ships, passenger ships, and fishing ships to meet the detection needs in the areas of ports and waterways. The data set are grouped into a training set, testing set, and validating set by the proportion of 6:2:2. Experiments show that the improved model has better average accuracy, from 62.2% to 71.6%.
An Optimization of Image Super-Resolution Method Based on Residual Network
Authors: Pan Yue 
Published: February 2022

Abstract
In this paper, an optimization with deep residual networks for the super-resolution method will be proposed. Meanwhile, the optimized method will be used into MNIST database to improve the image resolution, and the accuracy of the image identification of the improved database will be test by the residual network. In the optimization design, there are 4 convolutional layers, and 5 residual blocks will be used to replace the 3 convolutional layers of initial design, and there are 3 convolutional layers in each residual block. After the residual network testing, the experiment is positive and effective, and total number of errors is 34, and the accuracy is 99.43%.
Research on Grey Neural Network Optimal Combination in Wind Power Generation Forecasting Technique
Authors: Liu Yue , Liu Ming , Sun Jia 
Published: February 2022

Abstract
In this paper, combining BP neural network prediction model with grey prediction model effectively as an optimal combination forecasting technique is proposed. This model not only considers the influence factors such as wind power, wind direction and temperature, but also comprehensively considers the historical wind power generation data, through the effective combination of BP neural network prediction model and grey prediction model, the prediction accuracy is improved. The results show that the error of the new grey neural network optimal combination prediction is lower than that of the single grey prediction and BP neural network prediction, which has a certain research value.
Denoising Adversarial Examples Using CNN Models
Authors: Jing Juntong 
Published: February 2022

Abstract
It has always been a complicated problem to resolve adversarial attacks because figures with adversarial attacks look similar to the original figures so that models can be fooled. With deceptive data, adversarial attacks can be a threat to neural networks. There are various ways to generate adversarial attacks. For instance, they are using one-step perturbation and using multi-step perturbation. In both methods, noise is added to the images. Therefore, a question pops up: are adversarial attacks similar to normal random noise? This paper aims to find if there is anything in common between random noise and adversarial attacks. A normal denoising CNN model is trained with random noise. Then groups of adversarial examples are collected by training on LeNet. Next, the denoising CNN model has been used to denoise those adversarial examples. Finally, after denoising the adversarial examples with the CNN model trained on normal random noise, the classification accuracy increases. Thus, it is reasonable to conclude that normal random noise and adversarial tracks have some common patterns.
Morphologic recovery method of product label noises based on image features and application in defect detection
Authors: Chen Yuanhao , Yi Jianjun , Zhang Yajun , Tao Sheng 
Published: February 2022

Abstract
To ensure the reliability of carbon fiber material products and eliminate all kinds of possible defects, it is necessary to take effective means to check their quality. X-ray based nondestructive testing technology combined with image recognition algorithm is considered to be a fast and efficient solution. However, the surface of the processed material is often accompanied by labels which contains various information. These labels are confused in the image and make most of the defect detection methods easy to misidentify. This paper mainly studies the recovery method of product label noises based on image features and application in defect detection. What’s more, the proposed method can effectively and efficiently eliminate noises without affecting the rest of the image information which ensures the correct identification of defects in materials.
Performance Analysis of Three kinds of Neural Networks in the Classification of Mask Images
Authors: Chi Jinkui , Liu Yuxi , Wang Victor , Yan Jiaxuan 
Published: February 2022

Abstract
The global spread of COVID-19 has led to a massive increase in public demand for effective measures to slow the spread of the virus. In response, the World Health Organization has advised people to wear face masks every day to stop the spread of the virus. In facial mask recognition, traditional manual feature extraction methods are cumbersome and inaccurate, considering the various types of masks and other practical problems. This paper considers various neural network models comprehensively and selects three classical CNN networks, including VGG, ResNet, and DenseNet. After using the data expansion and optimization methods, good data results are obtained for the three network models. Remarkably, the improved DenseNet accuracy can reach 99.55%.
Cluster analysis of MNIST data set
Authors: Pei Yuanzhao , Ye Linlin 
Published: February 2022

Abstract
The full name of MNIST is Mixed National Institute of Standards and Technology database. This is a very large database of handwritten digits. The data set is divided into two parts: the first part is 60,000 training set images, the second part is 10,000 test set images, each image is composed of 28×28 pixels, and the value of each pixel between 0 and 1, based on this data set, not only can a judgment method for handwritten numbers be designed, but also the accuracy of the recognition algorithm can be compared through the test set. At present, machine learning experts around the world use different learning methods to test the data set. At present, the mainstream methods with higher accuracy are: support vector machines, convolutional neural networks, and naive Bayes classification. This article tries to use the clustering method to train the MNIST data set. First, the image data of the training set is converted into 60000×785 rows of two-dimensional matrix data, one of which is the real value of the image, and then through K-means the algorithm divides the data into 10 categories, uses the T-SNE algorithm to reduce the dimensionality, and visualizes by randomly selecting 1000 training data. It is found that the clustering effect of the numbers 0, 6, and 8 is the best, while the number 1 is completely invisible, the accuracy of K-means algorithm clustering is 83.33%. In order to further explore the test effects of different clustering methods on the data set, other clustering methods are introduced: MiniBatchKMeans algorithm. When the cluster is set to 10, the clustering of data 6 and 8 in MiniBatchKMeans the effect is good. At this time, the accuracy of the MiniBatchKMeans algorithm clustering is 87.67%. By comparing the test results of other algorithms, it is found that the MiniBatchKMeans algorithm is better than the K-means algorithms and the clustering result of is better than some supervised learning methods.
Intelligent fault diagnosis system design and implementation of diaphragm pump based on artificial intelligence
Authors: Xu Xiaoqing , Guo Zhihao , Liang Xiaowei 
Published: February 2022

Abstract
This article designs and implements a fault diagnosis system for diaphragm pump. The key components of the diaphragm pump are equipped with three-axis vibration intelligent sensor to collect vibration data, the edge side completes the data pre-processing, and then transmits it to the cloud platform through NBIoT. For the non-linear, non-smooth complex system, the time frequency representation is obtained by continuous wavelet transformation, after compressing the time frequency representation as the input of the two-dimensional neural network model, using the two-dimensional convolutional neural network model for training and monitoring, to complete the identification and positioning of diaphragm pump fault.
Research and application of calibrating device for pipeline detector
Authors: Hu C A , Li G , Ouyang H M , Du W B 
Published: February 2022

Abstract
Underground pipeline is an important part of urban infrastructure, known as the “lifeline” of the city. At present, there are many incomplete data of urban ground line pipelines, only part of the data is not high precision or inconsistent with the status quo, which is easy to cause serious accidents in the construction of underground pipelines often cut or damaged, so it is particularly important to establish a reliable underground pipeline database as soon as possible. Pipeline detectors currently occupy tens of thousands of units in the market, and the annual growth rate of 20%∼30%. At present, the positioning error (sounding error) of pipeline detector is faced with the problem of undetermined and untraceable. The relevant calibration device established in this paper can provide reliable data and provide strong guarantee for the data of pipeline detector.
Analysis of fatigue characteristics of dual-drive axial piston motor cylinder block
Authors: Shi W W , Deng H S , Hong D B , Shi X Y 
Published: February 2022

Abstract
To improve the service life of axial piston motor with dual-driving, the cylinder model of axial piston motor with dual-driving is established, the cylinder force is derived and used as the boundary condition, based on the results of finite element analysis, the S-N curve of the cylinder material is modified and fatigue solution is carried out. The results show that: the main stress and strain of the cylinder block are mainly located at the cylinder block hole and the flow distribution window, the stress concentration is generated at the structural mutation, and the stress and strain are the largest; the cylinder block will be repeatedly subjected to high-pressure oil action will produce fatigue damage, fatigue damage mainly occurs at the location of higher stress.
Design of Pipeline Flowmeter Calibration System Based on PLC Control
Authors: Liu Cong , Zhang Xu , Gai Xuhui , Xu Lenian 
Published: February 2022

Abstract
In order to solve the problems of error and data storage and calculation in the calibration process of traditional pipeline flowmeter due to human factors, a calibration system based on PLC control was designed on the basis of a standard method. PLC controls the frequency converter through CAN bus, and then controls the motor to supply water to the calibration system. At the same time, the PLC extended IO modules are used to collect the real-time flow data of the standard meter and the tested meter. PLC and upper computer through Ethernet information exchange, and the use of the Kingview software to achieve the basic control of the PLC and the data display, data storage, data calculation. The test data show that when the errors of the standard table meets ±0.5%, the errors of the tested meter can meet ±1%, and the calibration efficiency is high and the data are accurate. Therefore, compared with the manual calibration method, the calibration system based on PLC control can better control the errors, and is more convenient for data storage and calculation, and has higher practical value.
Research of Intelligent Video Surveillance System based on Artificial Neural Network
Authors: She Chuyun , Zheng Runlan , Yang Qiaoyin , Liang Shaowei 
Published: February 2022

Abstract
In order to improve the video monitoring capabilities, this paper designs an intelligent video surveillance system. Firstly, it analyzes the current problems of video surveillance, and then proposes intelligent requirement in three aspects. In the training process, the model is obtained by searching, gray conversion, and training. Finally, the correctness of this system is verified by the actual application in real station.
Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine
Authors: Zou Xin , Lv Rongxin , Li Xinyan , Chen Huan , Wang Ziqi , Yang Hanning 
Published: February 2022

Abstract
With the increase of electrical equipment, the hidden danger of its failure also rises. Aiming at the problem that the classification accuracy of the basic traditional classification algorithm is not high when judging whether the electrical failure occurs, this paper proposes a Gray Wolf Optimization Support Vector Machine model to improve the recognition rate of electrical diagnosis, referred to as GWO-SVM. We first collected the most common linear (incandescent lamps) and non-linear (microwave ovens) household electrical appliances in normal working and arc fault waveform signals in real life, and then carried out frequency domain feature extraction. Finally, compared with no-optimized SVM and BP neural network, we employed the gray wolf optimization algorithm to optimize support vector machine, and the accuracy of GWO-SVM reaches 90%.
A Layered Settlement Monitoring Device for Foundation and Application of its Assembly Method
Authors: Qian Bin , Guo Changqi , Li Bingyi , Zhan Xinjie , Fan Mingqiao 
Published: February 2022

Abstract
For the vacuum preloading foundation treatment project, water covering construction is often carried out during vacuum pumping. It is very inconvenient for the monitoring personnel to measure the readings in the water with layered settlement instruments and equipment. This paper introduces an assembly method of foundation layered settlement monitoring device, including the assembly process of the monitoring device itself and the assembly process of embedding the monitoring device in the foundation. It has the advantages of simple structure, simple installation and stable data. It can realize the in-situ monitoring of different foundations, save manpower and material resources, and greatly improve the monitoring efficiency and accuracy.
Design and Cutting Test Analysis of Chinese Cabbage Harvester
Authors: Cao Liwen , Miao Shuaitong , Gu Lizhi 
Published: February 2022

Abstract
Chinese cabbage is a major vegetable widely planted in most areas of my country. With the increase in market demand and the growth of Chinese cabbage planting area, traditional manual harvesting can no longer meet the development needs of the times, and agricultural mechanization has become a trend. In view of the actual production situation of Chinese cabbage in China, the important parts of the Chinese cabbage harvester are designed based on the TRIZ theory, and the working principle of the Chinese cabbage harvester is clarified. The designed Chinese cabbage harvester includes the functions of pulling, transporting and cutting roots, and completes most of the work required for manual harvesting. Carrying out cutting experiments to explore the best cutting parameters in the root cutting process is conducive to the subsequent design and research of the Chinese cabbage harvester.
Experimental Study on Performance and Base Compaction of Cement Stabilized Macadam with Vibration Mixing
Authors: Wang Bingchen 
Published: February 2022

Abstract
In order to improve the engineering application prospect of vibration mixing cement stabilized macadam, the performance of vibration mixing cement stabilized macadam and the compaction performance of base course are studied in this paper. The proportioning scheme of cement crushed stone stabilized soil is designed, and the proportioning of cement stabilized crushed stone is established by compaction method and testing compressive strength. Through the indoor test, the 7-day and 28-day unconfined compressive strength of ordinary mixing and vibration mixing are compared. The results show that the 7-day strength is increased by 38.6% and the 28-day strength is increased by 32.5%. The compaction test of ordinary mixing and vibration mixing cement stabilized macadam base is carried out. The research results show that the compactness of vibration mixing cement stabilized macadam after rolling for one time and final rolling is increased by 1.5% and 3.1% respectively. The research shows that the improvement of compaction technology of cement stabilized soil pavement under vibration mixing can improve the performance index of pavement and can be popularized.