bluuebunny/crossref_metadata_2025_split huggingface dataset:
The preparation of an insulator material using renewable sources and application of palm oil tree – A Review
Authors: Elias Nur Amirah , Othman Siti Amira 
Published: January 2022

Abstract
Excessive heat will cause discomfort while doing daily life even the use of electrical items such as fans and air conditioner are not entirely helpful. This is because the building does not have a layer to absorb the heat. Then, the best initiative to lessen the heat in building is by adding thermal insulation material into the building material. Past researcher shown most thermal insulators used are fiberglass, mineral wool, polystyrene and polyurethane foam. However, there are some flaws with the thermal insulator that cause new ideas to emerge namely cellulose. Cellulose is an insoluble substance that is a major component of plant cell walls and vegetable fibres such as cotton. Normally cellulose can be found in the plants. By cause to the increasing surplus of oil palm trees then the use of cellulose in oil palm is the best. This paper review discusses about preparation of an insulator material using renewable sources and application of palm oil tree.
Microstructural Evolution of Cu/Sn-58Bi/Sn-3.0Ag-0.5Cu Composite Solder Joint during Isothermal Aging
Authors: Somidin Flora , Abu Bakar Muhammad Fakarudin , Abdul Razak Nurul Razliana 
Published: January 2022

Abstract
A composite solder joints made of Cu/Sn-58Bi/Sn-3.0Ag-0.5Cu (in weight percent) was successfully fabricated. The composite solder was made from combining a Sn-3.0Ag-0.5Cu solder ball and Sn-58Bi solder paste. The composite solder was reflowed on Cu-OSP (copper-organic solderability preservatives) substrate to form a composite solder joint. The microstructure evolution of the composite solder joints under different reflow temperature and thermal aging conditions were investigated using an optical microscope. This study shows that a composite solder joint of SAC305/Sn-58Bi can be assembled at lower temperature (160 °C) and gradually mixed through isothermal aging.
Mechanical properties and deformation modes of grade 1 commercially pure titanium at cryogenic temperature
Authors: Lee Min-Su , Jun Tea-Sung 
Published: January 2022

Abstract
In this study, we have investigated the deformation behaviour of Grade 1 commercially pure titanium at 20°C, -100°C and -170°C. Optical microscope and electron backscatter diffraction were applied to characterise the twins activated during tensile testing and the crystal orientations of the sample, and further to analyse the orientation dependent slip activity. The higher density of twins was exhibited at lower deformation temperature. The twinning activity was also higher in the parallel loading (RD) than transverse loading (TD) to rolling direction. According to the Schmid factor analysis, the grains were most favourable for <a>-prism slip in the RD sample, whilst the large fraction of grains was favourable for <a>-basal slip in the TD samples. The activation of slip systems is anticipated to be changed with decreasing temperature due to the different effect of temperature on critical resolved shear stress between slip systems. The work-hardening was affected by twinning- and dislocation-induced hardening depending on the temperature and crystal texture. Consequently, the high work-hardening capacity increased or maintained the total elongation (EL) with decreasing temperature (EL: 66% vs. 70% (RD) and 55% vs. 51% (TD) between 20°C and -170°C).
Effect of the nickel coated precipitated calcium carbonate addition on microstructure, phase and wettability of Sn-9Zn solder
Authors: Leong Wai Keong , Mohamad Ahmad Azmin , Mohd Nazeri Muhammad Firdaus 
Published: January 2022

Abstract
This paper presents the effect of nickel-coated precipitated calcium carbonate (Ni-coated PCC) on the wettability, microstructure and phase change of Sn-9Zn solder. The microstructure and phase analysis of the material plays a very important role as it determines the physical and mechanical behavior of materials. Both Sn-9Zn and Ni-coated PCC/Sn-9Zn produced almost similar microstructure, which contain needle-like, dark phase Zn-rich phase and distributed evenly in beta-Sn rich matrix. However, compared to pure Sn-9Zn, the addition of Ni-coated PCC produce finer size of Zn rich phase needle-like structure. The presence of new phases of nickel tin and calcium carbonate compounds was also found with the addition of Ni-coated PCC. This believed to help in refining the size of Zn-rich needle structure, while retaining the original phases of Sn and Zn of the Sn-9Zn solder, Furthermore, Ni-coated PCC/Sn-9Zn have a smaller contact angle compared to pure Sn-9Zn. This can be explained that, Ni-coated PCC reinforced particle was the interfacial active element and decrease the interfacial energy between the solid and liquid. The microstructure and phase analysis of the material plays a very important role as it determines the wettability behavior of the material
Sample Preparation of the Natural Source Hydroxyapatite (HAp) derived from Black Tilapia Fish Scales
Authors: Jaffri Muhamad Zaki , Mohd Ismail Zamratul Maisarah , Dermawan Siti Khadijah , Abdullah Hasan Zuhudi 
Published: January 2022

Abstract
Hydroxyapatite (HAp) is a biomaterial with the chemical formula Ca10(PO4)6(OH)2. Normally, it was used and applied in biomedical applications, cooking recipes and healthy food products. Due to this, it has attracted the researcher to work on HAp synthesis and extraction. In this study, the hydroxyapatite was extracted from fresh water Black Tilapia Fish Scales (BTFS) was analysed. The crystallinity of the HAp was characterized by using an X-ray diffractometer (XRD) whereas the Fourier transform infrared (FTIR) analysis was used to characterize the presence of HAp from the samples. Scanning electron microscopy (SEM) provided with an Energy Dispersive Spectroscopy, (EDS) was used to investigate the morphology and element of the hydroxyapatite powders. The BTFS samples were heated at 100 °C to composed and eliminated the remaining meat and other impurities. A mechanical crusher was applied to make a miniscule and fine hydroxyapatite powder using a thermal calcination technique using a furnace. The BTFS samples powder were calcined at a temperature of 900 °C for 3 hours correspondingly. The XRD spectra findings, uncovered the existence of the obtained BTFS HAp are articulate with Joint Committee on Powder Diffraction Powder Standard data (JCPDS) from the library. From the SEM results, HAp powder presence in hexagonal shape. The findings show the potential of producing high-value products from fishing waste, such as HAp with Ca/P ratio 1.74, for biomedical applications.
Effect of stencil wall aperture on solder paste release via stencil printing
Authors: Mohamed Sunar M. S. , Abu Bakar M. , Jalar A. , Che Ani F. , Ramli M. R. 
Published: January 2022

Abstract
Solder paste printing is a process by which the correct amount of solder paste is applied to the printed circuit board via a stencil. The solder release from the stencil printing process very much depends on the type of solder paste and stencil conditions such as the shape of the aperture, size, and thickness of the stencil. This paper investigates the stencil condition in particular the stencil wall aperture and its relationship to the solder release ability. In this work, two types of stencil wall openings A and B were used, which differ in a different ways of cutting to achieve the wall aperture. The cutting process produced different surface roughness of the wall aperture of the stencil. Stencil printing was performed to print the solder paste onto the PCB pad. The release of solder paste was observed by solder paste inspection (SPI) and analyzed qualitatively and quantitatively. The results show that stencil B gives a better solder compared to stencil A. This is due to the smoother wall aperture compared to stencil A which has a roughened wall aperture. This shows that the performance of stencil printing in terms of solder printing quality is highly dependent on the surface roughness of the stencil aperture. Stencil quality is important as it affects the performance of solder paste printing, and this process is mainly carried out in the electronics industry. Therefore, understanding stencil conditions is important for electronic technology that uses solder printing.
End-to-end Saliency Face Detection and Recognition
Authors: Gao Jingqian , Xu Minqiang , Wang Huan , Zhou Ji 
Published: January 2022

Abstract
Face recognition is a long-lasting hot topic in compute vision. The face recognition system mainly includes face detection, alignment and feature extraction. In the forward task, the extracted features are used to measure the similarity between faces, and outputs whether those are same person or not or which person it is in the registered set. Typically, the three stages of recognition system training independently of each other have the following shortcomings: 1) redundant calculation of feature maps; 2) unable to end-to-end optimization; 3) detecting an extracting so much useless face. A lightweight model for saliency face detection and recognition that can be optimized end-to-end is proposed. While maintaining accuracy, it meets the real-time and memory limitation requirements in embedded devices or terminals.
Building and Applying Knowledge Graph in Edge Analytics Environment
Authors: Zhang Hui , Niu Yingtao , Ding Kun , Kou Shaoqin , Liu Liu 
Published: January 2022

Abstract
For the scenario of limited hardware resources and restricted software environment in edge computing architecture, the method of building and applying knowledge graph in edge analytics environment is proposed. The main process includes: building knowledge graph in the cloud, storing knowledge base with RDF format at the edge through customization and transformation, and performing query and analytics at the edge with SPARQL graph search language. The method is simulated in a communication anti-jamming test environment, and the results show that the relevant technical solutions can better meet the requirements of construction of knowledge graph and data analysis in a restricted edge analytics environment.
Research on the Application and Analysis of Mechanical Equipment Intelligence Based on Knowledge Graph
Authors: Qi Rui , Chen Jing , Yu ZengWen , Ke WenJun , Wang Rui 
Published: January 2022

AbstractWith the rapid evolution of manufacturing technology, mechanical equipment products are being replaced more frequently, and competition in the industry is becoming increasingly fierce. The collection and analysis of mechanical equipment technical intelligence is a key need for many enterprises. Using artificial intelligence technology to build an intelligence analysis system for all types of data is the inevitable trend of intelligence analysis. This paper proposes an intelligent processing and analysis framework of mechanical equipment intelligence. Based on the construction of knowledge graph, the knowledge embedding model is used to complete knowledge reasoning, predict development trends for mechanical equipment, and allow for more intelligent decision-making. A case study involving 3D printing shows the functions of the intelligence process and analysis framework.
Spatio-Temporal Transformer for Online Video Understanding
Authors: Du Zexu , Zhang Guoliang , Lu Weijiang , Zhao Ting , Wu Peng 
Published: January 2022

Abstract
Leading methods in the field of online video understanding try to extract useful information from the spatial and temporal dimensions of an input video. But they are suffering from two problems: (1) These methods can only extract local video information, and cannot relate to the important features of the temporal context in the video. (2) Although some methods can quickly process the information of each frame in the video, the processing efficiency of the whole video is not good, so this type of method cannot be applied to online video understanding tasks. This article introduces a Transformer-based network, which considers spatial and temporal content, and can quickly process each video at the same time. Our approach can efficiently handle up to 170 videos with hundreds of frames per second for action classification. Our method achieve 10 to 90 times faster than existing methods on the action classification datasets.
Multi-contrast MRI Reconstruction via Multi-scale Patched-based Cross-contrast Channel Selection
Authors: Yang Zehua , Liu Chengliang , Xu Yong 
Published: January 2022

Abstract
In this paper, we tackle the under-sampled MRI reconstruction problem with auxiliary contrasts. Instead of adopting a naive fusion scheme before feeding the multi-contrast features into the networks, we propose a more interpretable early fusion approach. By utilizing the inherent multi-contrast nature of MR image feature maps, we propose a Cross-contrast CHannel Selection (CCHS) module to explicitly explore dependency between channels for better cross-contrast fusion in reconstruction of the target one. Furthermore, the CCHS module is extended into a more adaptive version by introducing a multi-scale patch-based learning strategy. Quantitative and qualitative experimental results on the fastMRI dataset demonstrate the effectiveness of our approach.
Load Clustering Method Based on Improved Density Peak and Gaussian Mixture Model
Authors: Zhe Dong , Ya Wang , Chuanxiao Ma , Weihan Ai 
Published: January 2022

Abstract
A load clustering method based on improved density peak and Gaussian mixture was proposed to solve the problems that standard Gaussian mixture clustering algorithm (GMM) with random initial clustering center was easy to fall into local optimum in the clustering analysis of residential electricity behavior, and the density peak algorithm (DPC) needed to manually select the clustering center.The method combines the Cosine distance and Euclidean distance, which reflect the similarity of curve shape, and selects the clustering center automatically through the sine value relation of relative Angle between two points in γ descending graph. The clustering center selected by the improved DPC algorithm was used to initialize the parameters of the GMM model. Finally, load clustering is carried out by GMM model. The results of calculation examples show that the proposed method can effectively improve the efficiency of daily load curve clustering.
Polyphyletic Loss: Litchi Flower Detection with Occlusion
Authors: Ye Jin , Wu Menglan , Qiu Wenjie , Yang Juan , Lan Wei 
Published: January 2022

Abstract
Controlling shoot and promoting flower of litchi is one of the key technologies for the high yield of fruit trees, but the occlusion caused by dense growth has a great impact on the detection of flowers. To solve the problem, this paper designs a polyphyletic loss function to detect the occlusive litchi flowers. A new aggregation loss term is proposed in the loss function to force the proposal box to approach and compactly locate the corresponding target. At the same time, the segmentation loss of the bounding box specially designed for the dense crop scene is added to keep the prediction box away from the surrounding objects and improve the robustness of detecting a large number of flowers. We conducted experiments on the self-built litchi flower data set and the multi species fruit flower data set at the same time. Compared with other methods, the proposed method has higher robustness and detection accuracy, which provides an important idea and method for flower number statistics and fruit yield prediction in dense scenes.
Performance Analysis of SWIPT System with Imperfect Channel State Information
Authors: Zhang Chenxi , Meng Chao , Yang Yu , Gao Yan 
Published: January 2022

Abstract
This paper studies the system performance of the multiple-input-single-output (MISO) system using simultaneous wireless information and power transfer (SWIPT) technology under imperfect channel state information. A power split strategy is used in the receiver, which divides the received radio frequency (RF) signal into two parts, one part is directly used for signal decoding, and the other part is used as energy harvesting. This paper studies the optimization of system channel capacity under the constraints of harvested energy and the transmission rate. The resulting optimization problem is a two-layer non-convex optimization one, which is simplified by Cauchy-Schwarz inequality. Through the linear search of the energy division ratio in its value range, it becomes a one-dimensional optimization problem only related to the transmission power, which can be solved by CVX. The simulation results verify the effectiveness of the algorithm.
Research on Automatic Driving Target Detection Based on YOLOv5s
Authors: Li Chao , Cao Yining , Peng Yakun 
Published: January 2022

Abstract
Aiming at the problems of low detection accuracy, small targets and inaccurate recognition of automatic driving in natural scenes such as different lighting, different weather and different perspectives. The yolov5s network, migration learning method and varifocal loss function are used, and the hyper-parameter adjustment and network fine-tuning are completed. Experimental results show that the proposed method achieves mean Average Precision of 46.68%, and the detection speed can reach 7ms/frame. Automatic driving target detection based on YOLOv5s model network has fast training results, easy deployment, strong robustness, and high recognition accuracy in different weather conditions. It can not only meet the accuracy of automatic driving target detection, but also meet the real-time detection.
Implementation and Error Analysis of MNIST Handwritten Dataset Classification Based on Bayesian Decision Classifier
Authors: Ma Rui , Han Long , Geng Hujun 
Published: January 2022

Abstract
In recent years, with the continuous development of computer technology, pattern recognition technology has gradually entered people’s life and learning, and people’s demand for pattern recognition technology is also growing.In order to adapt to people’s life and study, the application of pattern recognition theory is more and more, such as speech recognition, character recognition, face recognition and so on.The main methods of pattern recognition are statistics, clustering,neural network and artificial intelligence.Statistical method is one of the most classic methods, and Bayesian classification is widely used in statistical method because of its convenience and good classification effect.
Intrusion Detection Model Based on Autoencoder and XGBoost
Authors: Kang Yunxiang , Tan Minsheng , Lin Ding , Zhao Zhiguo 
Published: January 2022

Abstract
In recent years, machine learning algorithms have been extensive used for intrusion detection field. At the same time, these algorithms still suffered from low accuracy due to data imbalance. To improve accuracy of detection, an intrusion detection model based on Autoencoder (AE) and XGBoost (IDAE-XG) is proposed. The training algorithm and detection algorithm related to IDAE-XG are given. IDAE-XG constructs the training set with preprocessed normal data. Data preprocessing includes feature selection and feature grouping. Through detection, XGBoost is used to predict results, which effectively improves prediction accuracy. The superiority of the proposed IDAE-XG is empirically demonstrated with extensive experiments conducted upon CSE-CIC-IDS2018. The experimental comparison show that IDAE-XG performs better than the KitNet model in the test, and has achieved a great improvement in accuracy and recall rate.
Hybrid Pruning Method Based on Convolutional Neural Network Sensitivity and Statistical Threshold
Authors: Guo Changyi , Li Ping 
Published: January 2022

Abstract
The hybrid pruning algorithm can not only ensure the precision of the network, but also achieve a good balance between pruning ratio and computation. However, traditional pruning algorithms use coarse-grained or fine-grained pruning networks, which have the tradeoff problem between pruning rate and computation amount. To this end, this paper presents. A hybrid pruning method of sensitivity and statistical threshold. Firstly, coarse-grained pruning is carried out on the network, and a fast sensitivity test is conducted on the convolutional layer of the network to determine the channels that need pruning within the tolerance range of network precision decline. Then, fine-grained pruning is performed on the network. Count the weights of the pruned network, calculate the thresholds of the weights of each layer, and delete the weights less than the thresholds so as to further reduce the size of the network and the amount of calculation. Hybrid pruning performs very well in AlexNet and Resnet networks. In particular, the method proposed in this paper is used in CIFAR-10 dataset, and the compression of FLOPs is 60%, while the compression of parameter number is nearly 80%. Compared with single pruning method, Hybrid pruning is better.
Multi-factor Investment Model Based on TabNet
Authors: Sun Jixiang , Yang Fuping 
Published: January 2022

Abstract
The prediction of the stock market is a complex and nonlinear prediction, which is affected by multiple information sources. However, the traditional investment method can only rely on people’s subjective experience to invest and select stocks. In this way, quantitative investment shows itself in various fields with the huge quantitative operation ability of computers. In the past research on quantitative investment, the methods of decision trees or neural networks have their own shortcomings. In this paper, TabNet is used to simulate the process of decision tree in the way of multi-factor investment. We select the data of China Securities Index (CSI) 300 as the data set. Through this method, we improve the problems of low precision of decision tree and easy overfitting of neural network. The empirical results show that the model has good prediction performance, which is better than the currently used LSTM and Gan and Gated Recurrent Units(GRU) models, and the model also has good interpretability.
Overview of Long-form Document Matching: Survey of Existing Models and Their Challenges
Authors: Cheng Yaokai , Chen Ruoyu , Yuan Xiaoguang , Yang Yuting , Jiang Shan , Yang Bo 
Published: January 2022

AbstractLong-form document matching is an important direction in the field of natural language processing and can be applied to tasks such as news recommendation and text clustering. However, long-form document matching suffers from noisiness and sparsity of semantic information in long text. Using short-form document matching methods on a long-form matching problem is not satisfactory. Long-form document matching has attracted the attention of researchers, who have proposed many effective methods. Methods for matching long texts can be divided into three categories: traditional bag-of-words-based models, traditional deep learning-based models, and pre-training-based models. This study reviews typical methods of long-form document matching, analyzes their advantages and disadvantages, and discusses possible future developments.
Document-level Core Events Extraction based on QA
Authors: Wang Jimin , Han Bin , Wang Fei , Zhang Lei 
Published: January 2022

AbstractDocument-level core events extraction model based on QA is provided in this paper. Question with prior information is constructed input into the BTBiLSTM model. Firstly, extract events from documents. Secondly finds event arguments from the text to be extracted. Lastly fuse events to select core events. The experimental results on ACE2005 Chinese corpus show that the event argument role question with prior information has a good effect on event argument extraction. Compared with existing method, our method in this paper realizes cross sentences events arguments extraction, it also applicable to documents without headline.