bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vibration damping of a vertical axis wind turbine in operating conditions
Authors: Castellani F , Natili F , Astolfi D , Hirschl A , Peppoloni M 
Published: June 2022

Abstract
Distributed power generation technologies have been, over the years, gaining more and more attention from the industry as well as from the academia, due to the global interest in the exploitation of renewable energy sources even in urban areas. From this perspective, small size wind turbines, often with vertical axis layout and a nominal power between 1-3 kW, are being installed in urban areas on rooftops of residential, commercial, industrial buildings and even on metallic carpentry towers. As wind turbines tend to produce noise and vibrations, especially in presence of vortexes, wakes and unsteadiness, efficient devices for mitigating the vibrations transmitted to the supporting structure have to be added in order to limit the generation of stresses. Actually, the social acceptance of the installation of small wind turbines in built environment is tightly connected to the acoustic comfort for the inhabitants. The present study has been developed thanks to a joint collaboration between the Department of Engineering of the University of Perugia (Italy) and FH Technikum Wien (Austria) within the participation to the IEA Wind-Task 41: “Distributed Wind”. The work aims at mechanically characterizing the vibration damping provided by a mechanical device installed on the top of the turbine tower support. The decoupler, specifically designed for this purpose, has been tested in different conditions (in field and laboratory) and with different approaches, in order to have an overall evaluation of its performance. At first, vibrations are measured in open field in a real test case scenario with two installation arrangements: on the rooftop of a small building and on a tubular tower. Subsequently, the decoupler has been removed from its original location and tested in laboratory with external excitation from shaker.
Deep Reinforcement Learning for Perception and Control of Autonomous Vehicles
Authors: Zhao Dongbin 
Published: June 2022

Abstract
Deep reinforcement learning is an effective combination of the decision-making ability of reinforcement learning and the perceptual ability of deep learning, which has achieved a series of milestones in the field of artificial intelligence in recent years. This method has also obtained many beneficial attempts in the field of intelligent driving, which has strongly promoted the development and progress of key technologies of autonomous vehicles. This report briefly reviews the development of deep reinforcement learning methods and specifically introduces the research progress of my team in perception and decision making of autonomous vehicles based on deep reinforcement learning methods.
Surface Defect Segmentation and Detection of Printing Roller Based on Improved FT Algorithm
Authors: Shuang Huang , Shaozhong Cao , Weijun Zhu , Chenyang Bao 
Published: June 2022

Abstract
The method based on machine vision is one of the important ways of printing roller defect detection. It has the advantage of intuitively reflecting the surface state of printing roller, but the effect of detecting the position where the surface defect of printing roller is not obvious is not ideal. Aiming at the problems of different printing roller surface defects and large interference of background texture, a printing roller surface defect detection algorithm based on improved frequency tuning significance and threshold segmentation is proposed. Firstly, the nonlocal mean filter method is used to preprocess the image to solve the influence of noise on the image; Then, the saliency map is extracted by converting the lab color space of FT algorithm to HSV color space, normalizing and weighting each eigenvalue respectively; Finally, the salient image is binarized by iterative threshold segmentation to obtain the final defect image. The experimental results show that the accuracy of the algorithm is 97.1%, which is better than other similar algorithms. It can accurately detect the roller image with different size of surface defects and texture background interference, and the comprehensive performance is outstanding.
Design and Implement of University Teachers’ Workload Management System
Authors: Liu Qian , Zhang Hui , Fan Lin , Li Tong-han , Jin Xue 
Published: June 2022

Abstract
In order to achieve the information management of the workload of university teachers and effectively raise the work efficiency of the staff, in this paper, we used platforms like Microsoft Visual Studio 2015 and Microsoft SQL Sever 2012, developed a professional management system based on C/S architecture, which aims to efficiently and informationally manage the scientific research and teaching work information of the university teachers. The system is easily to operate and it enables the functional improvement. Facilitate the statistics and rewards of the teachers’ workload, and also helps teachers to fully understand their own work and pave the way for the next work. It is of great significance to stimulate teachers’ work enthusiasm and improve work efficiency.
A Study on the Connotation Remodeling of Science and Technology Vocabulary Based on the Application of Knowledge Graphs
Authors: Liu Weihang , Tuo Jiawei , Xue Jianwu 
Published: June 2022

AbstractDisruptive Technology is also often translated as disruptive technology or breakthrough technology, which was first proposed by Professor Christensen of Harvard Business School in 1990s. Nowadays, the competition among countries is increasingly fierce, especially in the field of science and technology, which often leads to the backwardness and impoverishment of the whole industry due to the technological gap. Today, with the rapid consumption of social resources, social and economic development urgently needs disruptive technologies to promote industrial transformation, optimize economic structure and improve people’s quality of life. Countries that master disruptive technologies can open up new industrial development paths and lay a foundation for unique technological advantages, thus bringing huge technological dividends to national economic development. Visuwords is a knowledge graph application platform, through which the meaning and semantic association of words can be visualized. This study takes the word “famished” as an example to analyze the relationship between its new connotation and original meaning to verify its feasibility at the communication level. Today’s social science knowledge in the general public needs to be disseminated from the original relatively obscure and academic to an easily acceptable form. This study applies the idea of scientific popularization to a knowledge graph visualization tool, while using the connotation of linguistic ideas to analyze and deconstruct specific wordsThe study combines the connotation concept of linguistics with the disciplinary tools in the field of knowledge graph, and specifically analyzes the role of scientific terms, especially some abbreviations, in the mass science communication.
Recurrent Adversarial Video Prediction Network
Authors: Wang Meiju , Zhong Guoqiang , Deng Zhaoyang , Zhang Kang , Jiang Peng 
Published: June 2022

Abstract
Mining the intrinsic information of sequential data to predict the future data has a promising research prospect. Considering the temporal features of sequential data, existing approaches generally adopt recurrent neural network and its variants for the prediction. However, for sequences with complex structure, such as video frame sequence, these approaches cannot guarantee to obtain promising prediction results. In this paper, to address the above issue, we propose a novel architecture, called recurrent adversarial video prediction network (RAVPN), which can not only extract the temporal and spatial features of video sequences, but also optimize the generator and discriminator based on the adversarial strategy. Specifically, we use sliding windows with length t +1 and set the (t + 1)-th frame as the label of its previous t frames. The generator takes the first t frames as input and tries to generate the (t + 1)-th frame, while the discriminator distinguishes whether a sample is real or fake to boost the performance of the generator. Experimental results show that our novel RAVPN can obtain a promising performance on video prediction tasks compared with other deep sequence prediction models.
Long Memory Gated Recurrent Unit for Time Series Classification
Authors: Hong Binjie , Yan Zhijie , Chen Yingxi , Xiaobo-Jin  
Published: June 2022

Abstract
Time series analysis is an important and challenging problem in data mining, where time series is a class of temporal data objects. In the classification task, the label is dependent on the features from the last moments. Due to the time dependency, the recurrent neural networks, as one of the prevalent learning-based architectures, take advantage of the relation among history data. The Long Short-Term Memory Network (LSTM) and Gated Recurrent Unit (GRU) are two popular artificial recurrent neural networks used in the field of deep learning. LSTM designed a gate-like method to control the short and long historical information, and GRU simplified those gates to obtain more efficient training. In our work, we propose a new model called as Long Memory Gated Recurrent Unit (LMGRU) based on such two remarkable models, where the reset gate is introduced to reset the stored value of the cell in Long Short-Term Memory (LSTM) model but the forget gate and the input gate are omitted. The experimental results on several time series benchmarks show that LMGRU achieves better effectiveness and efficiency than LSTM and GRU.
Adversarial Attack and Defence on Handwritten Chinese Character Recognition
Authors: Jiang Guoteng , Qian Zhuang , Wang Qiu-Feng , Wei Yan , Huang Kaizhu 
Published: June 2022

Abstract
Deep Neural Networks (DNNs) have shown their powerful performance in classification; however, the robustness issue of DNNs has arisen as one primary concern, e.g., adversarial attack. So far as we know, there is not any reported work about the adversarial attack on handwritten Chinese character recognition (HCCR). To this end, the classical adversarial attack method (i.e., Projection Gradient Descent: PGD) is adopted to generate adversarial examples to evaluate the robustness of the HCCR model. Furthermore, in the training process, we use adversarial examples to improve the robustness of the HCCR model. In the experiments, we utilize a frequently-used DNN model on HCCR and evaluate its robustness on the benchmark dataset CASIA-HWDB. The experimental results show that its recognition accuracy is decreased severely on the adversarial examples, demonstrating the vulnerability of the current HCCR model. In addition, we can improve the recognition accuracy significantly after the adversarial training, demonstrating its effectiveness.
Improved Alexnet-Based Character Detection Method
Authors: Liu Fuxiang , He Song , Chen Zhisheng 
Published: June 2022

Abstract
Extracting text information in complex images is a hot spot in pattern recognition research with broad application prospects. Natural scene door numbers produce serious distortion due to blurred images, uneven illumination and low light illumination, which makes it difficult to achieve ideal results in character recognition, and recognizing characters of arbitrary length is even more of a challenge. In this paper, we adopt the improved Niblack’s local threshold segmentation method to segment the images, and mark the connected areas of the segmented images to highlight the important features, and finally input the above pre-processed images to the improved AlexNet network for target detection on SVHN (Street View House Number) to realize the detection of characters in real scenes. The experimental results show that the improved Alexet-based target detection method is able to complete the detection task of streetscape door number characters well with 92.89% correct recognition rate.
Outpainting Natural Scenery Images by Fusing Forecasting Information
Authors: Geng Yujie , Gao Penglei , Yang Xi , Yan Yuyao , Huang Kaizhu 
Published: June 2022

Abstract
Image outpainting receives less attention due to the challenges of predicting the spatial content consistency and maintaining the high quality in generated images. All-sides image prediction is a generalized task in outpainting, aiming to extrapolate the outside content for all sides of an image. It is a challenging task to maintain spatial and semantic consistency between the original input and the generated multi-step regions. In this paper, we embed a novel Multi-view Recurrent Content Transfer module into an Encoder-Decoder architecture for long-range all-side image outpainting. A multi-head attention mechanism is leveraged to fuse information from different representation sub-spaces at different positions to enhance the consistency of generated images and the original input. Our model could obtain sufficient temporal information in predicting the extended feature maps, which improves the quality of long-range images extrapolation. We experimentally demonstrate that our proposed method could produce visually appealing results for outside image outpainting against the state-of-the-art image inpainting and outpainting approaches. Modelling the temporal relationship could help generate the outside regions and reconstruct the input regions smoothly and realistically. In addition, an attempt to possibly allow for arbitrary output resolutions is supported as well.
Tight Source Domain Match for Partial Domain Adaptation Based on Maximum Density
Authors: Ni Zi-Ao , Su Zixian , Yang Xi , Wang Qiufeng , Huang Kaizhu 
Published: June 2022

Abstract
Unsupervised domain adaptation (UDA) aims at transferring knowledge between a well-labelled ’source domain’ and an unlabelled ’target domain’ by decreasing distribution discrepancy. In real scenario, partial domain adaptation (PDA), where target domain only includes part of the classes of source domain, is adopted as fully-shared label space is often unavailable. Non-identical label spaces across domains lead to performance degradation due to source-unique classes being mis-matched to the target domain, i.e. negative transfer of the target domain. Although existing PDA approaches have produced promising results, they still confront with negative transfer problem without rigorous generalization bounds. In our work, a novel PDA model has been proposed based on margin disparity and maximum source intra-class density divergence (MDSD). It matches the feature distributions with shared labels and congregates source samples in the source with affirmative labels. Removing maximum target density with pseudo labels, it effectively avoids over-fitting and accelerates learning speed. In addition, we construct a new garbage classification dataset, which is comprised of source domain - Product (Pr) and target domain - Garbage (Ga) for PDA validation. Experiments show that our proposed unsupervised partial domain adaptation method has a good performance on Office-31, Office-Home and Pr-Ga datasets.
Sketch to Building: Architecture Image Translation Based on GAN
Authors: Jiang Sidong , Yan Yuyao , Lin Yiming , Yang Xi , Huang Kaizhu 
Published: June 2022

Abstract
Image-to-image translation with Generative Adversarial Networks has been extensively studied recently. However, it remains still very challenging for sketch-to-image translation owing to large domain gap and information asymmetry problems. In this paper, we introduce a novel two-step method as well as designing an interactive system which can generate different styles of architecture images from a hand-drawn sketch. In our framework, either an architecture sketch can be drawn and then translated into a color image, or an architecture photo can be loaded, followed by sketch generation, modification, and translation. Style images can also be manually selected for different output styles. Experimental results show that our method has better performance on both image quality and diversity than the other competitive methods.
Research on Interactive Picking Method of 3D Model Graphics Based on BVH and Qt
Authors: Wang Xiaohui 
Published: June 2022

Abstract
In the front and rear graphics operations of finite element analysis software, traditional methods use ray picking and callback functions to complete the graphics picking interaction, which is inefficient and inflexible, which affects the software user experience. The method in this paper adopts the ray picking algorithm based on the BVH structure and the signal-slot data communication mechanism of Qt to realize multi-threaded concurrent communication, supports efficient and free interactions such as single-point picking of 3D models and rectangular frame selection, and improves the interaction friendliness and friendliness of the finite element analysis system. User operation efficiency.
Thermoelectric properties of monolayer and bilayer buckled XTe (X = Ge, Sn, and Pb)
Authors: Lubis P , Amalia N , Wella S A , Sholihun S 
Published: June 2022

Abstract
Lowering the dimension of 3D materials, so that the confinement length L of the low-dimensional material is less than the thermal wavelength Λ of its bulk phase, is expected to be a sufficient way to enhance their thermoelectric performances. Using density functional theory incorporating the linearised Boltzmann transport equation with a constant relaxation time approximation, we calculate the electronic and thermoelectric properties of monolayer and bilayer XTe (X = Ge, Sn, and Pb). It is shown that the ideal figure of merit of monolayer XTe is larger than that of bilayer XTe, suggesting the importance of downsizing the bulk XTe up to single-layer thickness to have a better thermoelectric performance. The n-type monolayer buckled SnTe is predicted to exhibit remarkable thermoelectric performance with ZT > 1.6 at T = 900 K compared to other monolayer and bilayer XTe.
Invited Commentary: Mechanistic and Biologically Based Models in Epidemiology—A Powerful Underutilized Tool
Authors: Meza Rafael , Jeon Jihyoun 
Published: June 2022

Abstract
Mechanistic and biologically based mathematical models of chronic and behavioral disease processes aim to capture the main mechanistic or biological features of the disease development and to connect these with epidemiologic outcomes. These approaches have a long history in epidemiologic research and are complementary to traditional epidemiologic or statistical approaches to investigate the role of risk factor exposures on disease risk. Simonetto et al. (Am J Epidemiol. 2022;191(10):1766–1775) present a mechanistic, process-oriented model to investigate the role of smoking, hypertension, and dyslipidemia in the development of atherosclerotic lesions and their progression to myocardial infarction. Their approach builds on and brings to cardiovascular disease the ideas and perspectives of earlier mechanistic and biologically based models for the epidemiology of cancer and other chronic diseases, providing important insights into the mechanisms and epidemiology of smoking related myocardial infarction. We argue that although mechanistic modeling approaches have demonstrated their value and place in epidemiology, they are highly underutilized. We call for efforts to grow mechanistic and biologically based modeling research, expertise, and awareness in epidemiology, including the development of training and collaboration opportunities to attract more students and researchers from science, technology, engineering, and medical field into the epidemiology field.
PHACTR1modulates vascular compliance but not endothelial function: a translational study
Authors: Wood Alice , Antonopoulos Alexios , Chuaiphichai Surawee , Kyriakou Theodosios , Diaz Rebeca , Al Hussaini Abtehale , Marsh Anna-Marie , Sian Manjit , Meisuria Mitul , McCann Gerry , Rashbrook Victoria S , Drydale Edward , Draycott Sally , Polkinghorne Murray David , Akoumianakis Ioannis , Antoniades Charalambos , Watkins Hugh , Channon Keith M , Adlam David , Douglas Gillian 
Published: June 2022

AbstractAimsThe non-coding locus at 6p24 located in Intron 3 of PHACTR1 has consistently been implicated as a risk allele in myocardial infarction and multiple other vascular diseases. Recent murine studies have identified a role for Phactr1 in the development of atherosclerosis. However, the role of PHACTR1 in vascular tone and in vivo vascular remodelling has yet to be established. The aim of this study was to investigate the role of PHACTR1 in vascular function.Methods and resultsProspectively recruited coronary artery disease (CAD) patients undergoing bypass surgery and retrospectively recruited spontaneous coronary artery dissection (SCAD) patients and matched healthy volunteers were genotyped at the PHACTR1 rs9349379 locus. We observed a significant association between the PHACTR1 loci and changes in distensibility in both the ascending aorta (AA = 0.0053 ± 0.0004, AG = 0.0041 ± 0.003, GG = 0.0034 ± 0.0009, P &lt; 0.05, n = 58, 54, and 7, respectively) and carotid artery (AA = 12.83 ± 0.51, AG = 11.14 ± 0.38, GG = 11.69 ± 0.66, P &lt; 0.05, n = 70, 65, and 18, respectively). This association was not observed in the descending aorta or in SCAD patients. In contrast, the PHACTR1 locus was not associated with changes in endothelial cell function with no association between the rs9349379 locus and in vivo or ex vivo vascular function observed in CAD patients. This finding was confirmed in our murine model where the loss of Phactr1 on the pro-atherosclerosis ApoE−/− background did not alter ex vivo vascular function.ConclusionIn conclusion, we have shown a role for PHACTR1 in arterial compliance across multiple vascular beds. Our study suggests that PHACTR1 has a key structural role within the vasculature.
Environmental DNA analysis for macro-organisms: species distribution and more
Authors: Minamoto Toshifumi 
Published: June 2022

AbstractIn an era of severe biodiversity loss, biological monitoring is becoming increasingly essential. The analysis of environmental DNA (eDNA) has emerged as a new approach that could revolutionize the biological monitoring of aquatic ecosystems. Over the past decade, macro-organismal eDNA analysis has undergone significant developments and is rapidly becoming established as the golden standard for non-destructive and non-invasive biological monitoring. In this review, I summarize the development of macro-organismal eDNA analysis to date and the techniques used in this field. I also discuss the future perspective of these analytical methods in combination with sophisticated analytical techniques for DNA research developed in the fields of molecular biology and molecular genetics, including genomics, epigenomics, and single-cell technologies. eDNA analysis, which to date has been used primarily for determining the distribution of organisms, is expected to develop into a tool for elucidating the physiological state and behaviour of organisms. The fusion of microbiology and macrobiology through an amalgamation of these technologies is anticipated to lead to the future development of an integrated biology.