bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computer Aided Evaluation of Fir Tree Blade Root Profile Based on Particle Swarm Algorithm
Authors: Li Jianhua , Chen Fumin , He Shuai 
Published: July 2019

Abstract
Aiming at the problems of inefficiency and inaccuracy in the evaluation of fir tree blade root profile, a method of computer aided profile evaluation based on particle swarm algorithm is proposed. The evaluation process is automated by this method, the theoretical profile and the coordinates of the measured points are read automatically, the measured points and the theoretical profile are aligned automatically, the profile deviation is calculated, and whether the blade root is qualified is determined. The mathematical model and calculation method of profile evaluation are given, the measured points and theoretical profile are aligned precisely according to the principle of least square by making use of the particle swarm optimization algorithm with adaptive inertia weight(APSO) after coarse alignment, and the influence of position error between design reference and measurement reference on the results of profile evaluation is eliminated. Simulation experiment and practical application manifest better accuracy and stability can be obtained by using APSO algorithm compared with other algorithms when evaluating the fir tree blade root profile. The computer aided profile evaluation method proposed in this paper could totally replace the traditional manual method.
Research on the incremental learning SVM algorithm based on the improved generalized KKT condition
Authors: Xie Wenhao , Liang Gongqian , Yuan Pengcheng 
Published: July 2019

Abstract
In order to adapt to the classification of the large-scale data and the dynamic data, this paper proposes an incremental learning strategy of SVM called GGKKT–ISVM algorithm based on the generalized KKT condition. The algorithm sets the generalized extension factors by the samples distribution density in order to make the useful samples become new support vectors, and it trains a new classifier. Then this algorithm modifies the classifier secondly, and it can not only keep the historical classification information, also can make full use of the new samples’ information, and structure the classifier that has stronger generalization ability. The experimental results show that the algorithm has a good classification effect.
A Second-order 16.5 Bit Analog-to-Digital Converter with a 145 dB Operational Amplifier Gain
Authors: Wei Rongshan , Lin Jiacheng 
Published: July 2019

Abstract
In this study, an IDC applicable for use with temperature and humidity sensors was designed. To this end, we take into account the factors of area and accuracy, a simple second-order chain of integrators with forward (CIFF) architecture was used to achieve an effective number of bits (ENOB) of 16.5. A gain-boosted operational amplifier (op amp) with a gain of up to 145 dB was adopted for the first-order integrator, and the digital filter adopted a sincL structure. In Section 2.1, the working principle of the proposed second-order. IDC is introduced. In Section 2.2, the design specifications are presented, the system timing and circuit structure are introduced, the circuit structure of the first-order integrator is described in detail, and the overall simulation results are provided. In Section 3, the test results obtained for a physical chip implemented using the SMIC 0.18 μm process are presented and compared with those obtained by similar previously proposed designs. Measurements show that the incremental converter achieves 99.1 dB SNR higher than the other second-orders or even some three-order IDC. And the chip occupied an effective area of 0.211 mm2 smaller than the other IDC. Finally, the conclusions of this study are given in Section 5.
Research on Information Retrieval Behavior Based on College Readers’ Cognition
Authors: Hu Jianfei , Meng Luyang , Zhang Bin 
Published: July 2019

Abstract
This paper takes the information retrieval behaviors of college students and researchers as the research object, and discusses the reading cognition and reading behavior habits of college readers through empirical analysis. The study found that there are large differences in reading cognition and reading needs among people of different backgrounds and levels. In the reading and retrieval behavior pattern, they are different in their grasp and use of information retrieval. The main innovation of this paper is to take the central colleges and universities as an example, and analyze the information retrieval behaviors of different levels of students in colleges and universities, and summarize the regular characteristics, which has certain demonstration significance.
Pulsed laser diode dual-end pumped double-end bonded Tm:YAG transient thermal effect analysis
Authors: Wen Ya , Liu Ning , Fan Zhen , Wu Chunting , Jin Guang-yong 
Published: July 2019

Abstract
Based on heat transfer differential equations, by using integral transform we got the internal temperature distribution of pulse LD dual-end pumped Tm: YAG crystal and the thermal focal length of crystal was obtained by the temperature field. At pumped power of 11W and repetition rate of 100Hz, the maximum temperature inside crystal is 27.68°C. As repetition frequency varying from 100Hz to 200Hz, the maximum temperature inside crystal is increasing to 28.24°C. In the case of duty cycle unchanged, as the repetition rate increases, the thermal focal length is distributed in a saw-tooth pattern and gradually becomes shorter, eventually it tends to be periodical distribution. The theoretical analysis of the thermal focal of pulse LD dual-end pumped Tm: YAG crystal is demonstrated by the comparison between the theoretical results and the experimental results.
Social Sentiment Analysis Using Classifiers and Ensemble Learning
Authors: Zhang Linxiang 
Published: July 2019

Abstract
Instant message applications has become the trend of present social network for connecting people nowadays. With such massive information transmitted between end users, it may provide valuable hidden knowledge for data analysing and help us to obtain some insights of how the messages can influence current social trends. During the analyses, the author uses several different classifiers to conduct sentiment collection and use Ensemble Learning to aggregate the results. As a result, the author maps data about users’ sentiment graph in database of given category.
Case Reasoning Based Design System for Product Packaging
Authors: Wang Zheng 
Published: July 2019

Abstract
The overall design procedure of product packaging is divided into 2 phases: generation of packaging design scheme and detailed design scheme, based on which, overall structure of design system for product packaging PACKAGING is designed. By using CBR technology, generation of product design scheme in PACKAGING is achieved, Meanwhile, each key technological step in generating procedure of design scheme for product packaging is discussed in detail.
Performance Analysis of UAV Uplink Communication Technology
Authors: Feng Yongxin , Song Bixue , Liu Fang , Jiang Qiang 
Published: July 2019

Abstract
In order to meet the reliability and anti-jamming requirements of UAV communication system, the anti-jamming performance of the up-link communication technology is analyzed by comparing the influence of different jamming on the signal and the influence of Rice channel on the signal on the basis of in-depth study of the communication mechanism of the low-altitude UAV communication link. The simulation results show that the anti-jamming performance of the signal is better when the interference is added in the upstream communication technology, and the anti-broadband interference of the signal is better than other interference types when the channel is added.
Manual and Semi-manual Comparison in the Annotation of CAPT Speech Corpus
Authors: Xie Yanlu , Wei Xing , Wang Wei 
Published: July 2019

Abstract
Annotation plays an important role in a robust computer aided pronunciation training (CAPT) system. Manual annotation is time and annotator consuming. Automatic annotation is more efficient, but the accuracy is lower. This paper proposes to provide phoneme level labelling candidates with ASR models, and compare this method with manual annotation. In the previous work, phoneme level mispronunciation patterns were modelled and detected to provide readable pronunciation erroneous tendencies (PETs) feedback. In this paper, it is proposed to use this model to provide phoneme level labelling candidates, then annotators could choose the appropriate labels and make final decision. Experimental results show that compared with manual annotation, the consistency rate of semi-manual annotation increased from 88.52% to 92.88%. In addition, the false positive rate (FPR) reduced by 3%, the posterior F1 score declined more than 3%. The reported results demonstrated the efficiency of the proposed method.
HRRP Target Recognition Based on Sparse Representation
Authors: Peipei Duan , Yan Zhang 
Published: July 2019

Abstract
When high resolution range profiles(HRRP) are used to recognize radar target, a few traditional recognition methods analyze the sparseness of HRRP samples. In order to overcome the large sample size problem and simplify the recognition procedure, sparse representation is an effective way to compress HRRP samples and extract the features. Thus, a structure redundant dictionary and a fast sparse representation algorithm are introduced to implement radar target recognition here. The simulation results show that this algorithm has higher recognition rate and better denoising performance. It is easy and practical for radar target recognition.
Spatially Constrained Fuzzy c-Means Clustering Algorithm for Image Segmentation
Authors: Li Xiaohe , Qu Zhan , Yang Xiaojing 
Published: July 2019

Abstract
The fuzzy c-means (FCM) clustering is an unsupervised clustering method, which has been widely used in image segmentation. In this paper, a spatially constrained fuzzy c-means clustering algorithm for image segmentation is proposed to overcome the sensitivity of the FCM clustering algorithm to noises and other imaging artifacts. Firstly, the local prior probabilities of pixel classification are defined according to the fuzzy membership function values of neighbouring pixels, and then those local prior probabilities are incorporated into the objective function of the standard FCM. Thus, the local spatial information embedded in the image is incorporated into the FCM algorithm. Experimental results on the synthetic and real images are given to demonstrate the robustness and validity of the proposed algorithm.
An Improved Mapping and Optimization Method for Implication-based Memristive Circuits Using And-Inverter Graph
Authors: Wang Xiaoxiao , Han Jiaxin , Yang Yifang , Li Yu 
Published: July 2019

Abstract
Any logic operation can be realized in implication-based memristive circuits characterized by low energy consumption and nanometre level size. The synthesis method transforms And-Inverter Graphs (AIGs) representation for logic functions into implication-based networks. It includes a mapping process which converts a cascade of nodes in AIGs to a series of implication gates and detects fan-out nodes simultaneously. An optimized copy process is employed to reduce the delay and area of memristive circuits in occurrence of a fanout node. Experiments are carried out over a benchmark set including 33 functions with input variables from 3 to 41. Experimental results are compared with that from the original algorithm and another Majority-Inverter Graphs (MIG) based mapping method. It shows that the improved algorithm can obtain better performance in latency and area on most of the functions in the test set.
Image Super Resolution Based on Gradient Constrained POCS Method
Authors: Jin Jie , Dong Lili , Jiang Yuhang , Zhang Meng , Xu Wenhai 
Published: July 2019

Abstract
A gradient constraint set based on edge detection is proposed to solve the problem of edge blurring in traditional projections onto convex sets (POCS) images reconstruction. Using the complete edge information of the original low-resolution(LR) image, the edge gradient constraint is applied to the reconstructed image, and the high-frequency information of the reconstructed image is retained to solve the problem of blurring the reconstructed image edge. And this paper uses the resolution test card to carry out simulation experiments, quantitative evaluation of the reconstructed image with respect to the LR images to improve the actual resolution of multiple. Simulation results show that the proposed method can reduce the edge blur of the reconstructed image, and the actual resolution is greatly improved compared with the traditional convex set projection method.
Parallax-Tolerant Image Stitching with Optimal Homography
Authors: Xu Peng , Chen Kuo , Xie Airong , Xiong Shiyong 
Published: July 2019

Abstract
Artificial ghosting is a serious problem in image stitching with wide-baseline, which makes it difficult to achieve seamless stitching. A robust parallax-tolerant image stitching method combined with optimal homography matrix selection and seam-cutting processing is proposed. Optimal homography matrix is obtained by analysing feature point distribution and minimizing the registration error. And then, the aligned area is enhanced by the optimal homography matrix, which can facilitate the seam searching. To realize seamless stitching, the luminance difference and the geometrical structure are used in seam-cutting processing. At last, the experimental results show that the artificial ghosting is solved quickly and effectively in the images stitching with large parallax.
Image Stitching Based on Binocular Vision
Authors: Zhang Bo , Ma Yue , Xu Min 
Published: July 2019

Abstract
In the field of photography, image stitching is used to generate panorama. In most cases, the position and intrinsic parameters of the camera can not be obtained, so the transform matrix can only be calculated using the information of the image. In industrial machine vision detection system, the information of the camera can be obtained by calibration. It is feasible to improve the speed of image stitching algorithm by using the information of camera calibration. An image stitching algorithm based on binocular vision was proposed. Compared with the traditional stitching algorithm based on image information only, it greatly improved the running speed of the algorithm. The effectiveness of the algorithm was verified by experiments.
A Method of Network Coding to Ensure Public Network Security
Authors: Liang Zihe 
Published: July 2019

Abstract
When connecting to the wireless network in public places, users’ personal information will be exposed in the public network, which is easy to be stolen and tampered with by third parties. In this paper, the author proposes a method to protect user information when connecting to public wireless networks in order to protect users’ privacy. Random linear network coding is used to encrypt the accessed user serial number, which is then forwarded to the upper layer. When transmitted to a specific node, the encrypted information is transmitted to the server for information decryption. After decoding the user serial number, the node is accessed. In this way, we can encrypt user information, reduce the delay time of forwarding, and improve the transmission efficiency. The time complexity is O(n2).
Detection and Recognition of Flower Image Based on SSD network in Video Stream
Authors: Tian Mengxiao , Chen Hong , Wang Qing 
Published: July 2019

Abstract
At present, most flower images could only be recognized but not detected. They can only be used in the scenes with a single target instead of the scenes with two or more targets. Some application scenarios require the human-computer interaction mode with the current location information of flowers; moreover, due to the complexity of the environment and the similarity and difference between flowers, the traditional computer visual methods are inefficient and inaccurate. Therefore, this study introduced SSD deep learning technology into the field of flower detection and identification. The flower data set published by Oxford University was used as the research object, and it was used as the input of the neural network model for training and testing. The experimental results show that the average accuracy is 83.64% based on the evaluation standard of Pascal VOC2007, and 87.4% based on the evaluation standard of Pascal VOC2012. The processing time of an image on PC is 0.13s, which indicates that high-quality automatic detection and recognition can be performed, which can facilitate the retrieval of agricultural plant information database and help people to popularize related information of flowers.
Research and Improvement of Single Image Super-Resolution Based on Generative Adversarial Network
Authors: Zhang Tianji 
Published: July 2019

Abstract
This paper proposes a new image super-resolution method based on Generative Adversarial Network (GAN). Firstly, the algorithm model includes generating model and discriminant model, generating model to generate high-resolution image, discriminating model to distinguish the image true or false, the original image is true, and the generated image is false. Using alternate training method, the generated model and discriminant model achieve Nash equilibrium, and finally generate high-quality image. Compared with previous super-resolution method based on generative adversarial network (SRGAN), the following changes have been made: modifying the network structure, removing the unnecessary batch normalization layer in the standard residual block, deepening the network layer number and improving the loss function. The experimental results show that compared with the traditional bicubic interpolation method and compared with SRGAN, the proposed algorithm improves the actual image effect, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in varying degrees.
Facial Landmark Detection based on Cascade Neural Network
Authors: Chen Xuzhan , Huang Luxi , Chen Yingming 
Published: July 2019

Abstract
A three-stage cascade convolutional neural network is proposed in this paper to learn the facial features. In order to improve the robustness and accuracy of facial landmark detection, several parallel sub-networks are involved in each stage of network. The convolutional neural network performs global advanced feature extraction for the entire facial region in the preceding stage, so as to obtain more accurate positioning. Since the first-stage input covers the whole image, the global facial information can be effectively used to weaken or even avoid the detection errors caused by occlusion, large posture changes, extreme illumination and etc. During facial detection, the first-stage network will make initial prediction of facial landmarks, and then get the facial area narrowed down and accurate according to the prediction results; the second-stage network will take the narrowed-down facial area above as the input, the three parallel convolution networks are used to classify the facial landmarks for quadratic estimates, so as to improve the accuracy of prediction results based on the results of first-stage network. The input of the third-stage network is limited to the small neighborhood of the second-stage prediction results, which can make the prediction results more accurate. The experimental data shows that our method has certain advantages in detection accuracy and reliability.
A Wake-up Radio protocol based on adaptive backoff and dynamic CCA in Wireless Sensor Networks
Authors: Xie Haofei , Ke Junyi , Wang Ping , Yuan Xingwei 
Published: July 2019

Abstract
Asynchronous wake-up communication based on wake-up radio (WuR) is an important energy-saving method in wireless sensor networks (WSNs). The Clear Channel Assessment (CCA) is a major section of energy consumption in asynchronous WuR-based nodes. The traditional static backoff will lead to longer waiting delay which increase the energy consumption of successful sending packets when the network traffic becomes heavier. So a protocol DNAP-WuR is proposed to solve these problems in the terminal initiated (TI) communication mode. It first implements the dynamic CCA optimization (DCO) mechanism which allows nodes to interrupt the CCA process as soon as the channel is busy. Nodes can go to sleep faster during busy periods, instead of performing CCA detection until the counter is reduced to zero. And then, the WuR based adaptive logarithm backoff (WALB) algorithm is used to predict the backoff time sequences which can reduce the WuC’s collision probability and decrease the energy consumption according to the nodes number and the packet arrival rate. In order to avoid the phenomenon that the backoff window size is reduced too fast or increased slowly after packets transmitted. Compared with CSMA-WuR protocol, the DNAP-WuR has 4.6% reduction in energy consumption and 12.1% promotion in throughput.
An Animal Spinal Cord Neural Signal Regeneration Circuit
Authors: Li Wenyuan , Yang Zhaoyuan 
Published: July 2019

Abstract
An animal spinal cord neural signal regeneration circuit is presented in this paper. The circuit consists of an on-chip amplifier which was designed in TSMC 0.18μm 1P6M standard CMOS process and an output circuit using an off-chip high voltage transistor. A low temperature drift bias circuit and a constant-gm rail-to-rail input circuit employing a novel structure called Max Current Selection are designed in the amplifier. The amplifier occupies a die area of 300μm×300μm. The simulation results show that the consumption of the amplifier is about 1.45mW and gain of the whole circuit is 40dB. The output voltage reaches a wide swing of -1V∼4V under +5V and -1.5V off-chip power supplies. The circuit is expected to be used in vitro test for regenerating neural signals of animal’s damaged spinal cord nerve, as a new method for trying to cure nervous injury.
Discussion on coding rules and labelling specifications of defect image samples of substation equipment
Authors: Yang Sha , Xiong Wei , Zhang Zhao , Chen Liang , Huang Shuxin 
Published: July 2019

Abstract
In the image recognition of substation equipment defects based on deep learning, image sample coding rules and labeling specifications are one of the key factors to ensure the quality of sample data. At present, the image of the defect of the substation equipment is scarce, and the sample management specification is not uniform. There is a low level of image recognition for equipment defects in the substation intelligent inspection. Therefore, the paper carries out analysis and thinking on the coding rules and labeling specifications of the substation key equipment defect image samples. The sample making process is standardized through the standardization of the coding of typical substation equipment type, component type, defect type and annotation format, to some extent, which improves the accuracy of sample data quality and substation equipment defect recognition, and improves the level of defect management in intelligent inspections of substation. Finally, the results show that the standardized labeling samples help to improve the recognition accuracy and have wide application value.