bluuebunny/crossref_metadata_2025_split huggingface dataset:
Initial results from the field testing of the “rotor as a sensor” concept
Authors: Bertelè M. , Bottasso C.L. 
Published: March 2020

Abstract
This work reports preliminary findings on the field validation of the “rotor as a sensor” concept. In a nutshell, the idea is to use the rotor response in order to measure the wind inflow, effectively turning the whole rotor into a sort of generalized anemometer capable of estimating wind speed, shears and directions. In turn, these quantities can be used for optimizing turbine and farm-level performance. Here a purely data-driven method is used, where a wind observer is first identified using flow field measurements (obtained by a met-mast) together with the corresponding blade root load harmonics. Once the observer has been identified, the wind inflow is estimated online during turbine operation by feeding it with measured blade root harmonics. Preliminary results reported herein seem to be very encouraging.
Mesoscale to Microscale Coupling for Wind Energy Applications: Addressing the Challenges
Authors: Haupt S E , Berg L , Churchfield M , Kosovic B , Mirocha J , Shaw W 
Published: March 2020

Abstract
The purpose of the US DOE’s Mesoscale to Microscale Coupling (MMC) Project is to develop, verify, and validate physical models and modeling techniques that bridge the most important atmospheric scales that determine wind plant performance and reliability. The project seeks to create a new predictive numerical simulation capability that represents a range of dynamic atmospheric flow conditions impacting wind plant performance.
Wind LiDAR Measurements of Wind Turbine Wakes Evolving over Flat and Complex Terrains: Ensemble Statistics of the Velocity Field
Authors: Zhan Lu , Letizia Stefano , Iungo Giacomo Valerio 
Published: March 2020

Abstract
Wind velocity measurements of wakes generated by utility-scale wind turbines were performed with the University of Texas at Dallas (UTD) mobile LiDAR station for wind farms in flat and complex terrains. Single-wake LiDAR measurements are clustered according to incoming wind speed at hub height and atmospheric stability regime through the wind shear exponent. Ensemble statistics of the LiDAR data shows that the velocity field in the near-wake is mainly affected by the rotor thrust coefficient, while atmospheric stability is the prevailing factor governing wake recovery. For the wind farm in complex terrain, the wind field is significantly affected by the local orography, showing either local speed-up or low-velocity regions. The analysis of the SCADA data corroborates the occurrence of these wind features and enables quantifying their effects on the wind plant performance.
Three-Dimensional Planetary Boundary Layer Parameterization for High-Resolution Mesoscale Simulations
Authors: Kosović B , Jimenez Munoz P , Juliano T W , Martilli A , Eghdami M , Barros A P , Haupt S E 
Published: March 2020

Abstract
Wind energy applications including wind resource assessment, wind power forecasting, and wind plant optimization require high-resolution mesoscale simulations. High resolution mesoscale simulations are essential for accurate characterization of atmospheric flows over heterogeneous land use and complex terrain. Under such conditions, the assumption of grid-cell homogeneity, used in one-dimensional planetary boundary layer (1D PBL) parameterizations, breaks down. However, in most numerical weather prediction (NWP) models, boundary layer turbulence is parameterized using 1D PBL parameterizations. We have therefore developed a three-dimensional (3D) PBL parameterization to better account for horizontal flow heterogeneities. We have implemented and tested the 3D PBL parameterization in the Weather Research and Forecasting (WRF) numerical weather prediction model. The new parameterization is validated using observations from the Wind Forecast Improvement 2 (WFIP 2) project and compared to 1D PBL results.
Assessing the stability of wind resource and operating conditions
Authors: Pryor S C , Shepherd T J , Bukovsky M , Barthelmie R J 
Published: March 2020

Abstract
Wind energy is both a key potential mechanism to reduce climate forcing and a ‘weather-dependent’ energy source. Thus, while wind energy is making an increasing contribution to mitigation of human-induced climate change, climate variability and change have the potential to induce changes in both the average (expected) wind resource, the inter-annual variability in power production and the conditions in which wind turbines will operate. We present simulations with the Weather Research and Forecasting (WRF) model conducted at 12 km grid-spacing (resolution) over the eastern USA and use them to quantify the spatiotemporal variability in one aspect of wind turbine operating conditions (extreme wind speeds) and possible changes in the magnitude and interannual variability of expected wind power generation. We also discuss possible approaches that can be applied to assess the differential credibility of model-derived assessment of these properties at different locations using examples drawn from the eastern US.
Selective and Coverage Multi-head Attention for Abstractive Summarization
Authors: Zhang Xuwen , Liu Gongshen 
Published: March 2020

Abstract
Although the Transformer model has outperformed traditional sequence-to-sequence model in a variety of natural language processing (NLP) tasks, it still suffers from semantic irrelevance and repetition for abstractive text summarization. The main reason is that the long text to be summarized is usually composed of multi-sentences and has much redundant information. To tackle this problem, we propose a selective and coverage multi-head attention framework based on the original Transformer. It contains a Convolutional Neural Network (CNN) selective gate, which combines n-gram features with whole semantic representation to obtain core information from the long input sentence. Besides, we use a coverage mechanism in the multi-head attention to keep track of the words which have been summarized. The evaluations on Chinese and English text summarization datasets both demonstrate that the proposed selective and coverage multi-head attention model outperforms the baseline models by 4.6 and 0.3 ROUGE-2 points respectively. And the analysis shows that the proposed model generates the summary with higher quality and less repetition.
Research on Image Segmentation Algorithm Based on Information Theory
Authors: Ding Shuhan , Yang Yuan 
Published: March 2020

Abstract
With the development of science and technology, the requirements of images processing have been greatly improved. When processing complicated images, segmentation can be firstly applied, which makes the characteristics of the image more distinct, and divides the image into multiple regions with homogeneous properties for subsequent processing. And the image segmentation algorithm based on information theory is one significant technology, which can identify, compress, and encode images, with relatively high accuracy. This paper introduces the principles of information theory and image segmentation, and research on image segmentation algorithm based on information theory, hoping to promote the better development of image processing.
Enhancing Dark Images Captured On Camera
Authors: Liu Jiamin , He Ning , Yin Xiaojie 
Published: March 2020

Abstract
In recent years, dark image enhancement technology has emerged as a popular subject of research, and has played an important role in security monitoring, traffic management, military reconnaissance, and film production. To solve the problems posed by images captured in poor lighting such that the target is not visible or is difficult to read, we propose a method to process such images to identify the targets in them. An algorithm is developed that uses the dynamic threshold method to determine the white reference point in the image as well as the brightest ten percent of pixels according to size. It then adjusts the white point and balances the area to obtain the final result. Using this, the gamma transform is used to adjust the brightness of the image. The results of experiments show that the proposed method can enhance image brightness without loss of color in the image to preserve its naturalness and improve object recognition in natural scenes.
Face and occlusion Recognition Algorithm based on Global and Local
Authors: Yu Guanwen , Zhang Zhaogong 
Published: March 2020

Abstract
Nowadays, the face recognition direction of unobstructed has been greatly developed, but there is still a huge deficiency in face recognition with occlusion. In order to improve the recognition rate under occlusion, this paper proposes a global and local occlusion recognition algorithm using deep learning for feature learning. Due to occlusion, local significant features will be missing, the impact will cause the accuracy of global recognition to decrease. Therefore, SIFT and SVM algorithms are used to discriminate partial face occlusion, and cut off the occluded local face to form a global face. Global facial features and unmasked local feature are extracted together. In the feature extraction, the LeNet-5 lightweight convolutional neural network and the improved LeNet-5 lightweight convolutional neural network are combined with SoftMax multi-classification. The final classification results use the improved hierarchical voting method to make final decisions on the face, which speeds up the training and prediction speed of the multi-classifier. This experiment will perform occlusion face verification on the AR dataset. The final experimental results show that the method greatly improves the accuracy of occlusion face recognition and has strong robustness to occlusion.
Grey Wolf algorithm based on S-function and particle swarm optimization
Authors: Liu ChenYang , Wang Yongli 
Published: March 2020

Abstract
Based on the analysis of the shortcomings of the grey wolf optimization algorithm, an improved grey wolf optimization algorithm (SGWO) is proposed. The algorithm uses the convergence factor based on S-function change to balance the global search and local search ability of the algorithm. At the same time, the proportion weight based on Euclidean distance of step size and the individual optimal position of the particle swarm optimization algorithm are introduced to update the grey wolf position, thus speeding up the convergence speed of the algorithm to 8. The simulation results of three classical test functions show that the SGWO algorithm has higher accuracy and better stability.Introduction
Research on college graduates employment prediction model based on C4.5 algorithm
Authors: Zhou Fujie , Xue Lingxiao , Yan Zhiqiang , Wen Yongxian 
Published: March 2020

Abstract
Introducing C4.5 algorithm into graduates’ employment field, organizing and analyzing the collected tremendous amount of historical data, building classify Decision-Tree, generating rule set, constructing graduates’ employment prediction model. Using Cross Validation algorithm to verify the accuracy of the model, and gradually accomplish a high-accuracy and high-practically prediction model, applying this prediction model to the employment related decision making and college major structure optimizing.
Mechanism Research and Equipment Development of Laser Cleaning Rust
Authors: JiNan Liu , YanHe Sun , Yuan Fang , Jun Tie , ChunYu Fan , Hua Zheng , ChengBing Zhang 
Published: March 2020

Abstract
With the continuous development of laser technology, more and more attention has been paid to the research of laser cleaning mechanism and the development of finished equipment. This paper introduces the application of laser cleaning technology in rust removal of iron and steel, summarizes the physical mechanism of laser cleaning, and puts forward the condition of synchronous cleaning and passivation of floating rust layer and the method of double beam wet cleaning and passivation. In addition, a cleaning method and device for wet cleaning and de-rusting with two beams is proposed in this paper.
Multi-sensor fusion localization algorithm for outdoor mobile robot
Authors: Li Chang , Sun Hanxu , Ye Ping 
Published: March 2020

Abstract
In this paper, the multi-sensor fusion positioning technology of outdoor mobile robot is studied, and a real-time outdoor positioning algorithm combining GPS and lidar odometry is proposed. Lidar odometry based on 3D lidar for point cloud feature matching is realized. At the same time, a covariance matrix reflecting the local positioning information of the lidar odometry is constructed in real time according to the matching distance and the absolute median difference, and the accuracy factor, the circular probability error, etc. are used as the standard GPS. The positioning effect is evaluated to measure the respective positioning effects of the lidar odometry and GPS. On this basis, the GPS is used as the global factor in the pose structure, and the lidar odometry is used as the local factor to construct the pose observation constraint, and the global nonlinear trajectory optimization is carried out to realize the outdoor real-time positioning of the mobile robot. Experimental results and data analysis verify the effectiveness and practicability of the proposed method.
AI based systems for diabetes treatment: a brief overview of the past and plans for the future
Authors: Zhou Huaping , Myrzashova Raushan 
Published: March 2020

Abstract
Humanity continues to suffer from deadly diseases. Successes of science are great, but diseases that cannot be treated still exist. The only solution is to continue research in the search for drugs, as well as in methods of treating and preventing the onset of these diseases. This article is an overview of the development of AI on the issue of diabetes in a larger population of the planet over the past couple of years. The article contains information about the latest existing innovations of medical AI that helps in the fight against type 1 and type 2 of diabetes in 2019. A general assessment of existing AI systems and research has been conducted, as well as statistics on the distribution and usage of these technologies in the world today. The main problems that have not yet found a solution in the field of diagnosing and treating diabetes of both types are presented in conclusion.
RETRACTED: Research on Smart Grid Data Layout Strategy Based on Cloud Computing
Authors: Bin Chen , Xiaoyi Yu 
Published: March 2020

Abstract
This paper first analyzes the experimental conditions of smart grid data layout based on cloud computing. Then the specific experimental content is analyzed, mainly including global layout and dynamic layout. Finally, taking performance and load balance degree as breakthrough points, the advantages and disadvantages of different layout schemes are analyzed. The purpose is to gives a reference to the related personnel so that they can master reasonable data layout strategy and realize reasonable application of cloud computing technology.
Multimodal Fusion of Speech and Gesture Recognition based on Deep Learning
Authors: Qiu Xiaoyu , Feng Zhiquan , Yang Xiaohui , Tian Jinglan 
Published: March 2020

Abstract
This paper proposes a multimodal fusion architecture based on deep learning. The architecture consists of two forms: speech command and hand gesture. First, the speech and gesture commands input by users are recognized by CNN for speech command recognition and LSTM for hand gesture recognition respectively. Secondly, the obtained results are searched by keywords and compared by similarity degree to obtain recognition results. Finally, the two results are fused to output the final instructions. Experiments show that the proposed multi-mode fusion model is superior to the single-mode fusion model.
Real-time multimodal emotion recognition system based on elderly accompanying robot
Authors: Dou Shaosong , Feng Zhiquan , Yang Xiaohui , Tian Jinglan 
Published: March 2020

Abstract
In recent years, people have been paying more attention to the impact of technological development on human emotion recognition. At the same time, China has become the country with the most elderly population in the world. However, due to the lack of real-time multimodal emotion recognition technology for the elderly accompany robots, this paper proposes a deep learning decision-level fusion of real-time emotion analysis model which is based on the background of the elderly care. The results of image and audio recognition are used for intersection and union operation to get emotional classification result, and the obtained emotional result is corresponding to the feedback behavior of the accompany robot.And after the experiment, the recognition algorithm proposed in this paper accuracy can reach about 90%, which is nearly 10% higher than the single mode, and the feedback of the robot has achieved the expected effect.
Research on Application of Key IoT Technology and Computer IoT Technology
Authors: Maliping  
Published: March 2020

Abstract
China has enjoyed breakneck development of technology in these years, Internet technology is widely used in diverse industries, and by virtue of its advantages, draws wide attention and becomes the modern society’s key technologies. Adoption of key IoT technology and computer IoT has boosted advancement of medical service, traffic and transportation, power grid management, people’s daily lives and agriculture, so they are indispensable support for smart cities. In this context, this paper first introduces the definition, features, and structure of Internet of things, and from the perspective of reality, makes in-depth analysis of key technology of Iot and its application in hopes of giving the value of IoT full play in people’s daily lives.
Research on Linux Real-time and performance Evaluation for Loongson 3A3000 processor
Authors: Chen Xuebing 
Published: March 2020

Abstract
In order to solve the real-time problem of Linux system for loongson3A, the real-time enhancement design of Linux kernel is carried out by using the idea of PREEMPT_RT patch, the performance analysis support based on ftrace tool is improved, and the real-time optimization method based on reserved core scheduling is proposed. The real-time performance of the system is tested from four aspects: context switching, interrupt response, scheduling latency and network latency. Five context switching methods are proposed, including active switching, priority preemptive switching, semaphores, message queuing and signal distribution latency. A test method based on the internal timer of the PCI board is designed. The test results show that the maximum latency of real-time Linux system based on Loongson3A3000 is less than 30us, the reserved core scheduling latency is the minimum, and the maximum jitter of network communication latency is less than 120us. In conclusion, the system based on loongson3A3000 can meet the demand for hard real-time applications at millisecond level.
Security Threats Caused by Public Event Callback in Android Application
Authors: Guo Chenkai , Zhu Jingwen , Yan Xiaoyu , Li Yan 
Published: March 2020

Abstract
The feature of event-driven acts as a key role that makes Android application differentiate from traditional PC software. Since many of those events are hardly predicted and could not be observed by other applications, attackers are similarly impossible to engage corresponding attacks by finding the vulnerabilities of such an event-driven mechanism. However, of various kinds of events offered by either user or system, there are still events that can be received by more than one application and further, which could offer important basic resources to predict specific behaviours of targeted application. In this paper, we aim to analyse potential security threats inside them and demonstrate typical kinds of proof-of-concept attack examples. Apart from that, the critical mechanism-public event callback (PEC) that may cause the threat is firstly modelled and studied, where its four main parts are introduced in detail.
A review of methods for text detection in imagery of natural scenes
Authors: Zhang Boyu 
Published: March 2020

Abstract
Text detection in natural scenes plays a key role in recognition and understanding of text in computer vision, while providing the basic possibilities for combining with Natural Language Processing (NLP). The ability of detection for small text and adjacent text is weak although the text detection in natural scenes has evolved from the horizontal text detection initially to the arbitrary shape text detection. Meanwhile, the environment of natural scenes is complex and changeable, which is a huge challenge for text detection. In this paper, we discuss and compare the horizontal and multi-directional detection methods based on target detection, as well as the curved and arbitrary shape detection methods based on the instance segmentation and semantic segmentation. The algorithm ideas are described and their advantages and disadvantages are analysed. Moreover, we found two research routes of curved text and arbitrary shape text detection known as Top-Down and Bottom-Up. According to the evaluations, PMTD [20] performs best in multi-directional text detection, while SPCNet [16] wins in the curve and arbitrary shape text detection.
Research on tidal harmonic analysis based on correlation observation error
Authors: Zhang Jin , Li ShuJun , Jiang HaiGang 
Published: March 2020

Abstract
The analysis and calculation of tides play a very important role in the correction of water level. The analysis and calculation accuracy are directly related to the correction of water level and the calculation of depth reference surface. For the problem that the observation error is not correlated in the tidal harmonic analysis, and the actual situation of the correlation of the observation error is taken into account. On the basis of the traditional harmonic analysis model, the cofactor matrix is derived by calculating the correlation coefficient sequence of different time intervals of residual water level, and the new observation weight matrix is obtained by inversion. The tidal harmonic analysis model based on the correlation of observation errors is derived and improved. The experimental analysis is carried out by tidal data of monthly adjustment and analysis, and the experimental results show that most of the main tidal harmonic constants are closer to the results of 2019, and the forecast tidal level is closer to the real tidal level. The improved method has achieved certain improvement effects.
Study on Test method of Kiwifruit Hardness Based on Hyperspectral Technique
Authors: Chen Xinyuan , Zheng Lina , Kang Zhiliang 
Published: March 2020

Abstract
Flesh hardness is an important index to measure the quality and storage character of fresh fruit. At present, destructive test method is usually used to test kiwifruit hardness by sampling, while a new method based on hyperspectral technique is proposed in this study. Firstly, hyperspectral images of kiwifruit samples are collected in the spectral range of [400nm, 1000nm], then the hyperspectral images are pre-processed by standard normal transformation, and three sub-ranges of the best combination are selected through synergy interval Partial Least Square (siPLS). Secondly, kernel principal component analysis is adopted for dimension reduction of spectral bands of the best combination sub-intervals, and the first two principal components are input into the trained partial least square (PLS) as characteristic spectral bands. Finally, the test results are compared with physicochemical measurement values. The experimental results show that the Cross-validation Root Mean Square Errors (RMSEC) and the correlation coefficients of the training set and the testing set obtained by the proposed method are 0.2698/0.9315, and 0.3573/0.8738 respectively, indicating that it can effectively test kiwifruit hardness.