bluuebunny/crossref_metadata_2025_split huggingface dataset:
Embodiment design of Moringa Oleifera rotary dryer using VDI 2221 method
Authors: Bale J , Tarigan B V , Siagian W B O 
Published: September 2021

Abstract
In order to find the solution to the current problems of drying process of Moringa Oleifera in East Nusa Tenggara (NTT)-Indonesia such as requiring a large enough space and power, expensive, non-ergonomic and need to turn the leaves over at least once manually using hand during the drying process that can contaminate the process, a new suitable drying method is necessary. Therefore, embodiment design of drying method using VDI 2221 method was studied. The results indicated that a new suitable drying method of moringa oliefera using VDI 2221 method can be made by successfully designing and manufacturing a rotary dryer based on the selection of the best concept from a variety of existing design concepts. In summary from the perspective of engineering design, after testing, the rotary dryer was suitable and can be used to solve current problems of drying process of Moringa Oleifera in East Nusa Tenggara-Indonesia.
Analysis of slope stability based in the spencer method on the ring road section, Sikumana. Kupang City
Authors: Krisnasiwi I F , Sundari W 
Published: September 2021

Abstract
The analysis of slope stability is on of most common solution that use to answer any problems about landslide. On aplicate it, how to know the parameters that always we used on analysed slope stability is the basic way to can calculate it detail. The perameter like slope dimensions, soil sample (disturb and undisturb), and laboratory test. In this case, the slope stability is used Spencer’s Method as a of the limit equilibrium method, and in other word a a merge method of moment equilibrium (Bishop’s Method) and force equilibrium (Janbu’s Method). The soil samples are taken from the along of slope surface for 3 points, above, in the middle, and the bellow. On laboratory test we have to get the parameters like cohesi (c), friction angle (ɸ), and unit weight (γ). This analyse is to know the safety factor (Sf) value from the slope at 40 road. From the research, the slope is maked by homogeny soil, clay from the noele formation. And we can get the material properties for the samples respectly, unit weight: 16,80 kN/m3, cohesi (c): 4,88 kPa, friction angle (ɸ): 39,320 (first sample); unit weight (γ): 15,97 kN/m3, Cohesi (c): 5,08 kPa, friction angle (ɸ): 26,200 (second sample); unit weight (γ): 15,48 kN/m3, cohesi (c): 5,28 kPa, friction angle (ɸ): 26,200. And the FoS value is 0,548 base on Sencer’s Method calculate. So, is maked some slope design recommendations to get the stability condition of the slope (SF>1), are SF1=1,610; SF2=1,410.
Multi-lingual Speaker Recognition based on Asymmetric Convolution and Central Loss Function
Authors: Zhang Xiaofang , Hamdulla Askar , Ablimit Mijit 
Published: September 2021

Abstract
In order to solve the problem of poor speaker recognition performance on multi-language corpus on convolutional neural networks and large amount of calculations on multiple parameters, this paper draws on asymmetric convolution and center loss (Center Loss) functions to improve and optimize the ResNet model. And perform speaker recognition tasks in the Chinese-Uyghur corpus. The results show that, compared with the original model, the improved model has higher accuracy, fewer parameters, and reduced computational complexity.
Knowledge Graph Completion Based on the Joint Embedding of Entities
Authors: Liu Luwei , Zhu Cui , Zhu Wenjun 
Published: September 2021

AbstractAs the last step in the process of knowledge graph construction, knowledge graph completion predicts and inferences new factual triples according to the existing knowledge, which is of great significance to the downstream application of the knowledge graph. The knowledge graph completion technology learns representations of entities and relations in the knowledge graph and reconstructs triples factual relations in the representation space to measure their effectiveness, so as to predict and supplement entities and relations. At present, Graph Convolutional Network (GCN) is widely used in entity modeling, which combines the graph structure information to learn the vector representation of entities. However, most algorithms only focus on static knowledge graph completion, ignoring the important role of time in the knowledge graph, and the modeling of entities is not comprehensive enough. In this paper, a knowledge graph completion based on the joint embedding of entities is proposed, which combines structural feature embedding of the graph and dynamic embedding of entities to carry out multi-level learning of entities to improve the effect of entity prediction. Structural feature embedding of the graph takes advantage of the powerful feature aggregation capability of GCN to integrate the knowledge graph as the topological structure and node features of graph data into the representation learning of entities. Dynamic embedding of entities takes advantage of the characteristics that entities will behave in different states at different times and divides the entity features into two parts for learning. One part of the features changes dynamically over time and the other part retains its static properties. In this paper, the probability model ConvTransE is used to construct the model of relations and triples, ConvTransE uses the convolutional neural network to extract the semantic information of the triples and predict the missing elements. We have conducted experiments on public datasets and achieved better results on Hit@K and MRR.
Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing
Authors: Chen Zheng , Wang Jian , Zhang Hualiang 
Published: September 2021

Abstract
Compared with traditional 2D image processing, 3D point cloud processing has become a hot technology in the industry, but there are few researches applied to nondestructive testing of curved workpieces. This article aims at the non-destructive testing of curved workpieces, using 3D point cloud and robotic arm for water immersion ultrasonic non-destructive testing. Aiming at the 3D point cloud with many outliers, using the commonly used filter in two-dimensional images-the guide filter, the algorithm is improved and used for the filtering and downsampling of the 3D point cloud, and the adjustment of the curved surface workpiece and the robot arm's pose is introduced. Finally, the workpiece was scanned by a robotic arm with a water immersion ultrasonic nondestructive testing system to prove the feasibility of the experiment and improve the detection efficiency of traditional nondestructive testing technology.
Comparative study of different dimensionality reduction methods in hyperspectral image classification
Authors: Kang Lei , Hu Xiaoqing , Zhong Chengcheng , Zhang Kai , Jiang Yanan 
Published: September 2021

Abstract
Considering the high-dimensional characteristics of hyperspectral image (HSI) data, researchers generally adopt the dimensionality reduction (DR) methods to reduce the complexity and computing time of subsequent classification or regression tasks while preserving the intrinsic structure information of the data. At present, the research on DR of HSIs data mostly focuses on the application performance of a single method in specific tasks and few studies have been conducted on the adaptability of different DR methods to HSIs data. From the perspective of spectral domain and spatial domain, this paper makes a comparative study on the performance of various linear and nonlinear DR methods in the task of HSI classification of aerial HSI of Matiwan Village in Xiongan New Area. Specifically, it includes principal component analysis, independent component analysis, isometric mapping, Laplacian eigenmaps, autoencoder, etc. The results show that the intrinsic structure in Xiongan HSI is mainly linear structure. Compared with the nonlinear DR methods, the linear DR methods can better preserve the intrinsic structure information of data at a lower time-consuming cost. For the subsequent classification tasks, the linear DR methods have better classification performance and are more suitable for HSI data.
Biomechanical analysis of shooting performance for basketball players based on Computer Vision
Authors: Pan Hang , Li Jianwei , Wang Huidong , Zhang Kaiyu 
Published: September 2021

Abstract
Shooting technique is exceedingly crucial in basketball. In order to improve the shooting average, this study captured shooting technology action by computer vision technology, analyzed human posture in different gender and gave effective suggestions in routine training. Kinect Azure was used to collect the human skeleton information when the 10 players (5 males and 5 females) who experienced amateur basketball training shot. After smoothing and reducing noises of three-dimensional skeleton information, the kinematic parameters such as human joint angle and torso tilt angle were calculated by using Euclidean dot product formula and anti-triangular formula. The result showed that shooting average of male was higher than that of female (P<0.05). When the players squatted to the maximum extent, the angles of the knee and hip of female were significantly bigger than that of male (P<0.05). The torso was tilted to a certain extent, and the extent of female was significantly larger than that of male(P<0.05). In conclusion, by computer vision obtaining data collection, this study suggested that the players should strengthen the muscle and master the range of flexion degree which was suitable for their own strength. At the same time, so as to improve the shooting average, they should pay attention to the coordination of upper and lower limbs during shooting.
Single Image Super-Resolution Network Based on Multiple- Scale Self-Similarity Module
Authors: Xiong Jiaxin , Wang Guoyou 
Published: September 2021

Abstract
Recently, single image super-resolution (SISR) based on deep convolutional neural networks (CNNs) has been widely studied and achieved remarkable results. However, most of these methods mainly widen or deepen the network to get better results, ignoring the self-similarity in natural images. Some works have successfully leveraged this self-similarity by exploring non-local attention modules, but they only use non-local attention modules at one scale or two scales. And they need to calculate the self-similarity in each residual module, which makes the network more complex and takes a long time to train. We propose a multiple-scale self-similarity module (MSSS) that can be flexibly inserted into existing various super-resolution networks. MSSS uses convolution with different kernels and the trailing dilated convolution to obtain feature maps at different scales, and then calculates the correlation between multiple feature maps at different scales through two-head region-level recurrent criss-cross attention modules (TRRCCA), finally gets fusion output through channel attention. So MSSS can mine multiple-scale self-similarities. We only insert a MSSS module into the basic residual SISR network, and the result is significantly improved.
Image segmentation application combined with DRLSE and moving mesh method
Authors: Lee Wan-lung , Shi Hongjian 
Published: September 2021

Abstract
Aiming at the time-consuming problem of level set method in processing image segmentation, this paper presents an image segmentation algorithm combining DRLSE and moving mesh method. This method is applied to segment images into multiple blocks in different initial regions. When a large image needs to be segmented into multiple blocks, the level set method has to calculate various data at each pixel. So, there are problems such as low-efficiency and time-consuming processing. In order to solve this problem, we propose a moving mesh method to process image segmentation based on the level set method. By calculating the monitor function about the image gradient, the grid encryption is automatically realized with the change of image gradient. This makes the calculation of the image segmentation more accurate. In order to improve efficiency, our method will reduce the grid to 1/4. Then the level set method is applied to achieve the result of computational acceleration. In order to verify the performance and accuracy of the new method, our method segmented some images several times. Experiments show that our method can quickly and automatically segment some images into multiple blocks without affecting the results.
Research on Flower Image Classification Method Based on YOLOv5
Authors: Tian Ming , Liao Zhihao 
Published: September 2021

Abstract
The rapid development of deep learning has accelerated the progress of related technologies in the computer vision field and it has broad application prospects. Due to flower inter-class similarity and intra-class differences, flower image classification has essential research value. To achieve flower image classification, this paper proposes a deep learning method using the current powerful object detection algorithm YOLOv5 to achieve fine-grained image classification of flowers. Overlap and occluded objects often appear in the images of the flowers, so the DIoU_NMS algorithm is used to select the target box to enhance the detection of the blocked objects. The experimental dataset comes from the Kaggle platform, and experimental results show that the proposed model in this paper can effectively identify five types of flowers contained in the dataset, Precision reaching 0.942, Recall reaching 0.933, and mAP reaching 0.959. Compared with YOLOv3 and Faster-RCNN, this model has high recognition accuracy, real-time performance, and good robustness. The mAP of this model is 0.051 higher than the mAP of YOLOv3 and 0.102 higher than the mAP of Raster-RCNN.
Path planning of mobile robot based on improved DDQN
Authors: yunxiao Yang , Wangjun  , Hualiang Zhang , shilong Dai 
Published: September 2021

AbstractAiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm.
Committee-Voting mechanism based on Graph for Semisupervised Person Re-identification
Authors: Zhang Lin , Zhao Xiuyang 
Published: September 2021

Abstract
Recently, person re-identification technology has made great progress, mainly thanks to a large amount of annotated data. However, the current million-dollar annotations are becoming increasingly difficult to scale. Therefore, we work on the problem of semi-supervised person re-identification using only a small amount of annotated data. Inspired by QBC, we propose the committee-voting mechanism based on graph (CVG), which consists of two modules, the committee, and the chairman. The committee proposes different opinions and constructs multiple sets of information pairs for unlabelled data, and the chairman is responsible for aggregating the opinions of all committee members and making the final decision. As a sound selection mechanism, which effectively improves the accuracy of semisupervised person re-identification. Extensive experiments on three large-scale datasets demonstrate the effectiveness of the proposed method.
Research on Smoking Detection Based on Deep Learning
Authors: Zhang Zhen , Chen Huiying , Xiao Ruijie , Li Qingjie 
Published: September 2021

Abstract
Smoking detection in public places is an important means to protect people's health and safety of life and property, but small target detection will have problems such as low detection accuracy and missed detection. In response to this problem, from the perspective of single-stage detection, a smoking detection model suitable for real-time monitoring is proposed. Based on the custom attention mechanism module and the improved residual network, design a backbone network that reuses underlying features while fusing features at different stages to enhance the ability to extract small target features and improve the accuracy of target detection; the feature fusion part uses FPN The structure and PAN structure are integrated, and a lightweight Neck layer network structure is designed to retain the semantic information and location information of the target feature; the DIOU_nms algorithm is selected to improve the missed detection problem. The self-made smoking data set was detected, the average accuracy rate (mAP) reached 86.32%, and the detection speed reached 55f/s. The detection model in this paper improves the accuracy of smoking detection, improves the supervision effect of smoke-free areas to a certain extent, and provides help for eliminating fire hazards.
Space-Time Separate Modeling for Efficient Video Classification
Authors: Cao Pei , Wang Shuo , Wu Jinmeng , Hao Yanbin 
Published: September 2021

Abstract
Efficient video classification requires the deep neural network models to be much lightweight. Current deep convolutional networks generally adopt 3D convolutions or similar spatio-temporal computational counterparts to process the 3D signal of videos. However, due to the heavy computational load of those 3D units, they suffer from the problems of hard training and inefficient inferring. To address these problems, this paper proposes a novel yet efficient space-time separate (STS) modelling module. STS splits the feature channels into multiple groups and separately model various types of content information from the channel groups using different convolutional operations. Since the channel splitting mechanism can significantly reduce the model complexity, STS is much more lightweight than the existing video models. Particularly, the joint use of spatial/temporal/spatio-temporal convolutions achieves paralleled information modelling in a single block. We conduct experiments on two benchmarked video datasets to evaluate the performance of STS and demonstrate its effectiveness and efficiency on the video classification task.
Towards the Software Evolution Recovery at the Level of Software Architecture
Authors: Zhu YanXia , Zhong LinHui , Fu LiJuan , Ruan ShuHe , Xu Jing , Ye Haitao 
Published: September 2021

Abstract
In the process of software evolution, a lot of evolution history information will be stored in software repository. Because some open source repositories (such as GitHub) only record the version of every source file, it is difficult for people to comprehend and manage the evolution of software component and software architecture. In the paper, we propose the concept of software evolutionary binary tree to present the evolution history of software architecture and its components, propose a method to recover them by software architecture recovery technology as well. The experimental results of four open source software (Cassandra, Hbase, Hive and Openjpa) demonstrate that the method is effective.
Improved hybrid recommendation algorithm based on joint interpolation
Authors: Su QiChen , Su Yang , Liu TianYi 
Published: September 2021

Abstract
Aiming at the sparsity problem of the underlying score matrix of the collaborative filtering algorithm, to solve the problem of poor filling effect when the existing filling method has a large difference in the scores of items between neighbors, an improved hybrid recommendation algorithm based on joint interpolation is proposed. The algorithm first uses joint interpolation to fill in the user’s rating matrix, and then uses the similarity between the filled data and the user and item information to predict the user’s rating of the item, and then compares the item’s rating with the user’s scores of similar items, impose penalties on scores that are far apart, and finally recommend the penalized scores from high to low. Experimental results show that the algorithm has a better recommendation effect.
Effective dc-link voltage variation for renewable power application
Authors: Maheswaran G. , Ettappan M. , Kavitha T. , Senthil Kumar R. 
Published: September 2021

Abstract
The proposed method analysis the effective utilization of dc-link voltage for a Multi-Level Converter by controlling the sequential operation of switching devices as per the load requirement. Here a different switching pattern is followed, where the number of switching devices are switched ON depending on the value of input voltage needed for the inverter, thereby unnecessary switching ON of all devices are avoided. Initial DC needed voltage is generated from renewable voltage source. The proposal also quotes on various application of theses switching pattern in the field of renewable energy and voltage backup systems where voltage generated is utilized with minimal switching losses. This Varied DC link voltage method by Selective Switch Count (SSC) is used in Voltage Source Inverter. Simulation using MATLAB is used to validate the proposed SSC method feasibility.
Multi-messenger triggered searches with the ANTARES Neutrino Telescope
Authors: Colomer Molla M. , Alves S. 
Published: September 2021

Abstract
The ANTARES neutrino telescope has been operating for
thirteen years in the Mediterranean sea with the purpose of
searching for high-energy cosmic neutrinos. During the last years,
multi-messenger astronomy has become one of the most exciting topics
for Cherenkov neutrino detectors, and probably the best strategy to
identify the neutrino sources. Thus, the ANTARES Collaboration is
actively participating to the follow-up of alerts sent by different
experiments, covering the full electromagnetic spectrum and
gravitational wave interferometers.
ANTARES' real-time response to these alerts is complemented with
dedicated offline analyses, the latter being the focus of this
talk. These studies allowed to set upper limits that constrain the
neutrino emission from various sources, including Fast Radio Bursts,
Gamma Ray Bursts and compact binary mergers, as well as neutrino
alerts by others neutrino observatories (IceCube, GVD). The latest
results are presented here.
SAGE: a Monte Carlo simulation framework for experiments with germanium detectors
Authors: She Z. , Ma H. , Zeng W. , Dai W. , Geng X. , Normahmedov O. , Yang J. , Zeng Z. , Yue Q. , Cheng J. , Li J. 
Published: September 2021

Abstract
A Geant4-based simulation framework for rare event searching experiments with germanium detectors named SAGE is presented with details.
It is designed for simulating, assessing background distribution, and investigating the response of the germanium detectors.
The SAGE framework incorporates its experiment-specific geometries and custom attributes, including the event generators, physics lists and output format, to satisfy various simulation objectives.
Its docker image has been prepared for virtualizing and distributing the SAGE framework.
Deployment of a Geant4-based simulation will be convenient under this docker image.
The implemented geometries include p-type point contact and broad energy germanium detectors with environmental surroundings, and these hierarchical geometries can be easily extended.
Users select these custom attributes via the JSON configuration files.
The aforementioned attributes satisfy the simulation demands, and make SAGE a generic and powerful simulation framework for the CDEX experiment.