bluuebunny/crossref_metadata_2025 huggingface dataset:
CZTSe solar cells prepared by co-evaporation of multilayer Cu–Sn/Cu,Zn,Sn,Se/ZnSe/Cu,Zn,Sn,Se stacks
Authors: Mwakyusa Lwitiko P , Neuwirth Markus , Kogler Willi , Schnabel Thomas , Ahlswede Erik , Paetzold Ulrich W , Richards Bryce S , Hetterich Michael
Published: June 2019

In this work, thin-film kesterite Cu2ZnSnSe4 (CZTSe) solar cells were prepared using a novel precursor configuration employing co-evaporated layer stacks of Mo/Cu–Sn/Cu,Zn,Sn,Se/ZnSe/Cu,Zn,Sn,Se. It is found that this sequential deposition of the constituants leads to the formation of large CZTSe grains on the surface and fine grains at the Mo interface of the absorber, respectively. Prototype CZTSe solar cells using this stacked approach achieve power conversion efficiencies of up to 7.9% at an open-circuit voltage of 430 mV and a fill-factor of 62%. The analysis of temperature-dependent current density–voltage characteristics indicates that bulk Schottky–Read–Hall recombination is the dominant recombination mechanism for the devices fabricated from the proposed stack. In addition, the influence of pre-annealing of each stacked layer on the absorber growth and device performance is examined and discussed.
A microfabricated, 3D-sharpened silicon shuttle for insertion of flexible electrode arrays through dura mater into brain
Authors: Joo Hannah R , Fan Jiang Lan , Chen Supin , Pebbles Jeanine A , Liang Hexin , Chung Jason E , Yorita Allison M , Tooker Angela C , Tolosa Vanessa M , Geaghan-Breiner Charlotte , Roumis Demetris K , Liu Daniel F , Haque Razi , Frank Loren M
Published: June 2019

Objective. Electrode arrays for chronic implantation in the brain are a critical technology in both neuroscience and medicine. Recently, flexible, thin-film polymer electrode arrays have shown promise in facilitating stable, single-unit recordings spanning months in rats. While array flexibility enhances integration with neural tissue, it also requires removal of the dura mater, the tough membrane surrounding the brain, and temporary bracing to penetrate the brain parenchyma. Durotomy increases brain swelling, vascular damage, and surgical time. Insertion using a bracing shuttle results in additional vascular damage and brain compression, which increase with device diameter; while a higher-diameter shuttle will have a higher critical load and more likely penetrate dura, it will damage more brain parenchyma and vasculature. One way to penetrate the intact dura and limit tissue compression without increasing shuttle diameter is to reduce the force required for insertion by sharpening the shuttle tip. Approach. We describe a novel design and fabrication process to create silicon insertion shuttles that are sharp in three dimensions and can penetrate rat dura, for faster, easier, and less damaging implantation of polymer arrays. Sharpened profiles are obtained by reflowing patterned photoresist, then transferring its sloped profile to silicon with dry etches. Main results. We demonstrate that sharpened shuttles can reliably implant polymer probes through dura to yield high quality single unit and local field potential recordings for at least 95 days. On insertion directly through dura, tissue compression is minimal. Significance. This is the first demonstration of a rat dural-penetrating array for chronic recording. This device obviates the need for a durotomy, reducing surgical time and risk of damage to the blood-brain barrier. This is an improvement to state-of-the-art flexible polymer electrode arrays that facilitates their implantation, particularly in multi-site recording experiments. This sharpening process can also be integrated into silicon electrode array fabrication.
Exploring An Easy Way for Imbalanced Data Sets in Semantic Image Segmentation
Authors: Xia Xiaoling , Lu Qinyang , Gu Xin
Published: June 2019

In recent years, deep convolutional neural networks have gradually become the preferred method for image processing. After the development of Classification, Detection and Segmentation, a large variety of state-of-the-art models and algorithms have emerged in the field. However, for some specific data sets or tasks, not all methods are applicable, which is inconvenient to researchers. This paper took the data set provided in the airbus ship detection challenge in Kaggle as an example to explore an easy and effective method for segmentation tasks of data sets with class imbalance. This paper used U-Net with a pre-trained ResNets model, and tried different methods to explore the feature of the set. In the process of training ResNets, this paper proposed a new convolutional block structure which is inspired by Fibonacci sequence, but the effect is not good. In the end, the mF2 values of the models this paper trained achieved good results, which is better than the model of the combined training of ResNets and ordinary U-Net34. Moreover, the training parameters are less than that. This paper believe that this simple and effective training method will bring convenience to researchers in related fields.
Statistical Analysis of Tibetan Orientation Words from the Perspective of Cognition
Authors: Ding Hailan , Yu hongzhi , Qi kunyu
Published: June 2019

This paper explores the direction of Tibetan orientation words in real texts from the perspectives of grammar, semantics, statistics and cognitive linguistics. Studying on the expression of the research direction in the text, it further explores the grammaticalization and lexicalization of Tibetan positional words, especially the sentence components of the positional words in the text. Finally, the direction category of position words is analyzed from the perspective of cognitive grammar, which also paves the way for the deep research of cognitive mechanism.
Research on Tibetan Question Answering Technology Based on Deep Learning
Authors: Zhu Shengyin , He Xiangzhen , La Bingjun , Yu Hongzhi
Published: June 2019

By applying the theory, technology and system of the Chinese Question Answering system, combining with the background of large data and cloud processing, integrating the theoretical knowledge of Tibetan linguistics, and covering computer high-tech of Tibetan machine translation, neural network learning, problem analysis, classification and answer generation, etc, this paper explores the optimization model of the new Tibetan-Chinese bilingual Question Answering system. Finally, the Tibetan/Chinese bilingual Question Answering system based on deep learning is completed to provide technical support for the economic development of ethnic minority areas.
An Architecture with Mixed Complex Event Processing Technology For CPS
Authors: Zhou Cheng , Feng Youqian , Jiang Yaru
Published: June 2019

Cyber-Physical System (CPS) is an emerging intelligent systems, which is considered as a combination of computers and physical world. Events are usually as a natural way for interactions and communications, which occur in the physical world and can be responded in the cyber world. Additionally, using complex event processing (CEP) technology, some complex events can be processed that are better suited for complicated and intelligent CPS. At first, a component-model framework is proposed for modeling CPS architecture based on event-driven pattern operational semantics, which provides a tool to understand the transition based on event-driven pattern in CPS. In order to improve the immense workload on centralized CEP, a mixed master-slaves CEP is proposed. Meanwhile, an architecture of CPS with master-slaves CEP is constructed and its execution mechanism is introduced in detail.
Study on NN-based Intelligent Exploring Module of the Firesprinkling System
Authors: Feng Xianzhou , Zhang Pengchong
Published: June 2019

The intelligent exploring module is precondition of the Fire-sprinkling System effective-working.The NN-based intelligent exploring module explores the condition of the cabin and fuses all the explored information, including temperature, flame, smog, pressure.The method is effective to improve the performance of the system.
Overview of Side Channel Cipher Analysis Based on Deep Learning
Authors: Song Shijie , Chen Kaiyan , Zhang Yang
Published: June 2019

According to the widespread use of embedded cryptographic devices, one of the major security threats, side channel attack and protection research, has grown rapidly in the last decade. Along with the popularity of machine learning and deep learning, artificial intelligence technology combined with side channel technology has gradually emerged, which has achieved quite good processing results. Due to the popularity of deep learning in recent years, it is indeed to complete the work with making an overview of side channel analysis based on deep learning. So in order to perfect the theory, this paper begins with the basic theory of the side channel attack. Combined with common models, the methodological analysis of deep learning side channel analysis is summarized and the existing research results are introduced next. Finally, the paper looks forward to the future deep learning side channel technology.
Design and Implementation of Multi-parameter Healthcare System with Positioning Function
Authors: Chen Hanlin , Yin Jingyi
Published: June 2019

Based on multi-parameter physiological module CSN808 and GSM/GPRS engine SIM508 supporting GPS function, this paper presents a multi-parameter healthcare system with positioning function. The system can acquire ECG, respiration, blood pressure, body temperature signals and GPS position information of patients, analyse signals quickly and feedback diagnoses to patients. Under the circumstance of a sudden heart attack, the system can alarm automatically, and doctors in the healthcare center can guide patients’ self-treatment via GSM audio communication and take quick rescuing measures on the basis of patients’ GPS position information. To ensure the correctness of data transmission, Grøstl hash algorithm is realized to verify the integrity of data. This system can provide a round-the-clock, rapid-response, stable and reliable monitoring.
A Machine Learning-based Software Code Weakness Detect Approach
Authors: Lu AiGuo , Luo KangYi
Published: June 2019

In recent years, machine learning and deep learning have yielded immense success on computer vision, speech recognition and natural language processing. However, exploration of machine learning on software code weakness detect haven’t been fully investigated. Software source code could be thought of a kind of natural language but has the intrinsic difference from common natural language. In this work we propose a novel machine learning based approach to tackle the problem of software code weakness detection. Differing from traditional algorithm based tools, flawfinder, for example, we do not design the code weakness detection processing logics directly, instead we propose to use a variant version of Attribute-Relation File Format (ARFF) to preprocess the source code and utilize a state of the art classification algorithm in machine learning to build a unite code weakness detection model to learn the processing logic indirectly as a general framework. In order to exam the detection performance of our model, we collected a couple of datasets for each common weakness of Common Weakness Enumeration (CWE) List. Extensive results on these collected datasets show that our model achieves comparative results from traditional algorithm based tools in both false positive rate and false negative rate.
Research on Influencing Factors of College Students’ Intention of Online Health Information Behavior Based on Social Cognitive Theory
Authors: Kong Jia , Deng Sanhong , Zhang Yue
Published: June 2019

Based on the Social Cognition Theory, we constructed the influencing factors model of college students’ intention of online health information behavior from three levels of individual, society and information systems, which was analyzed by using the PLS-SEM. Research shows that self-efficacy, information service quality, social support, information literacy and information quality significantly influence college students’ intention of online health information behavior. However, the influence of perceived risk and information system quality is not significant.
The Development of High-Precision & High-Speed Synchronous Data Acquisition System in Vacuum-Thermal Test
Authors: Shao Jingyi , Ning Juan
Published: June 2019

The Vacuum-Thermal test is an important test to verify the correctness of the thermal design and ensure its long-term reliable operation during the development of the spacecraft. At present, the temperature measurement in the Vacuum-Thermal Test mostly adopts the serial mode to complete the data acquisition of the large data volume temperature sensor. This method balances performance and costs well. However, in some special circumstances, it cannot accurately reflect the instantaneous temperature of the test piece. Aiming at this problem, a high-precision and high-speed fully parallel data acquisition system is designed. Based on the accuracy of the original temperature data acquisition, high-speed synchronization technology is used to accurately acquire the transient temperature data of the spacecraft to verify the transient heat of the spacecraft. Features provide a data foundation.
An Intelligent Per-decision Method of State Grid Accidents in Big Data Environments
Authors: Li Dianyang , Tan Chen , Wang Hongzhe , Han Da , Qin Ling , Zhang GuangCheng , Lou Tianyue , Xiu Tian
Published: June 2019

Aiming at the problem of hard to per-alarm the state grid accidents in big data environment, this paper proposes an intelligent per-decision method of state grid accidents. This method divides the management of state grid data into two parts, i.e. analysis of state grid data and intelligent expert database. The part of analysis of state grid data consists of input layer, facility layer and accident layer. Establish the self-learning intelligent expert database by means of connecting weight values of input and output nodes and threshold values of output nodes. Compare with the per-data that comes from the part of analysis of state grid data and the data from intelligent expert database, then achieve the goal of per-decision of state grid accidents and dispatch the accidents. A case and simulation results show that this method is implementable. In addition, this method provides beneficial thinking to solve the state grid accidents per-alarm in big data environment.
An Intelligent Method for Expert Finding Based on Knowledge Organization Systems: Taking the Example of Oncology
Authors: Pei-yan Song , Dong-fang Wang , Yan Cao
Published: June 2019

In this paper, the experts are mapped and found through the concept and semantic relations of knowledge organization systems. First, a hierarchical model of expert description is designed based on the literature data and knowledge organization systems, as foundation of expert rapid finding, intelligent reasoning and dynamic updating. Then, 5 key steps are introduced based on knowledge organization systems, by which the research directions of experts can be identified accurately and visually in TF-IDF and LDA model. Finally, taking the example of Oncology, the experiment indicates that experts with the same or near research direction can be aggregated fast and effectively by semantic linkage compared with VSM agritourism. This method is expected to find out the most suitable experts by calculation and reasoning automatically.
Research on data cleaning technology based on instance level
Authors: Li Chuan , Hou Yunqi , Yu Zhang
Published: June 2019

In the era of current data explosion, data cleaning becomes an important part of data analysis, and it is also one of the important means to improve data quality. In this paper, the concept, principle, process, detection method and related cleaning algorithm of structural data cleaning are introduced in detail through the data cleaning technology based on instance level. In view of the outstanding data quality problems based on instance level, relevant experiment is designed, the operation and verification process of structural data cleaning will explain concretely through visual programming technology and machine learning algorithm. Finally, the research of data cleaning technology in the future is prospected.
Analysis of the normalization theory of intracavity anti-Stokes laser
Authors: Jintian Wang
Published: June 2019

Anti-Stokes laser generation is a frequency up conversion. It is a four-wave mixing process of two fundamental photons, one Stokes photon, and one anti-Stokes photon. So, the conversion efficiency of the anti-Stokes laser is low. Therefore, it is particularly important to guide experiments through theoretical optimizations. In this paper, the rate equations under plane wave approximation of intracavity anti-Stokes lasers are normalized. By numerically solving the normalized equations, a group of adaptable curves are obtained to describe the operation of the intracavity anti-Stokes lasers. The influences of the composite variables on the performance of the lasers are analysed.
An Endoscope Manipulating Robot Design
Authors: Xiaohui Xie , Xiuan Zhang , Lining Sun , Gongxian Wang , Yue Yu , Cheng Zhang , Xiaochen Zhou
Published: June 2019

This paper mainly studies a new type of endoscope manipulating robot to replace the nurse or assistant to manipulate and hold the endoscope in surgery. By analyzing the operating environment of minimally invasive surgery, the paper presented the basic requirements of the endoscope manipulating mechanism design, and developed a endoscope manipulating system with seven degree of freedom. In order to meet the intraoperative spatially invariant point required by the minimally invasive surgery, a new romote-center-of- motion(RCM) mechanism, parallelogram RCM mechanism, was designed by improving composite parallelogram mechanism. In addition, a wire-driven modular joint with single-degree-of- freedom was also developed, which has been used for the endoscope operating in-out the human body. The motion test of RCM mechanism shows that the robot has good flexibility and can fully meet the design requirements of the endoscope manipulating mechanism.
Research on Intelligent Decision-Making Method of Enterprise Financial Crisis Early Warning
Authors: Yuan Ziyan , Hou Yan
Published: June 2019

Many situations in the crisis of enterprise development are caused by financial crisis. This situation not only makes the enterprise suffer great economic losses, but also affects the normal production and operation of the enterprise. Therefore, in the face of this situation, how can we timely and accurately warn the financial crisis of the enterprise? This is the problem we need to solve. This paper puts forward the enterprise finance. Intelligent decision-making method of crisis early warning is expected to provide theoretical support for dealing with financial crisis of enterprises.
The Analysis of Heart Rate Fragmentation for Congestive Heart Failure
Authors: Yiqi Wang
Published: June 2019

Background: Congestive heart failure (CHF), also known as chronic heart failure, is the end stage of all kinds of cardiovascular diseases with high mortality. Heart rate fragmentation (HRF) indices may be a potentially valid approach to diagnose CHF.

Objective: The aims of this study were to: (1) find out the differences of HRF indices between healthy subjects and patients with CHF; (2) explore whether HRF indices can be applied to diagnose CHF.

Methods: Open-access cardiac interbeat (RR) interval databases (Physiobank) were introduced here. Twenty subjects were selected from CHF RR interval database and normal sinus rhythm RR interval database, and included into CHF group and healthy group respectively. Then the values of PIP, IALS, PSS, and PAS were calculated and compared according to these 40 subjects. Statistic difference between two groups on same indice was computed.

Results: PIP (Healthy vs CHF, 0.72 VS 0.77, P=0.006), IALS (0.72 VS 0.78, P=0.0036), PSS (0.86 VS 0.95, P=0.0008), and PAS (0.11 VS 0.12, P=0.8392) of CHF group are significantly higher than that in healthy group.

Conclusion: HRF indices could be used as an efficient and easy tool to diagnose CHF. What’s more, HRF indices are a valuable approach to analyse heart rate.
Real-Time Data Analysis and Processing Based on UDP Communication Platform Based on LabVIEW
Authors: Liu Wenjing , Li Zhi , Wang Jianguo
Published: June 2019

In order to solve the problem that the portable data acquisition device cannot analyze the on-site data.Design a processing, analysis, and display scheme for transmitting critical data blocks to a computer with processing capabilities through network transmission. The UDP communication method based on LabVIEW is proposed to realize the data analysis and processing method in the professional computer calling MATLAB through network transmission. The method not only realizes the data analysis and processing function without replacing the hardware device, but also has a simple implementation method and a short development cycle. Therefore, the method provides a solution for real-time data processing of the portable data acquisition device system.
A Method of Characterizing the Filling Power of Cut Tobacco Through the Cigarette Maker Parameters
Authors: Yuliang Li , Shuai Yang , Lihong Zhang , Qiang Zhu , Yucan Qiu , Ming Li
Published: June 2019

Filling power is one of the important parameters to characterize the physical structure of cut tobacco. At present, the detection method of cut tobacco filling power cannot reflect the true filling power value of tobacco in real time and accurately, so an intelligent online filling power detection method of the cigarette machine is designed. According to the mechanical position of the suction forming part and the position parameters of the suction belt of the cigarette maker, the linear regression relationship between simulated filling power value and standard filling power value was obtained, the regression relation is applied to the cigarette manufacturing process, then calculate filling power value of cut tobacco in real time. This method has the following advantages:1. in real time;2. reflecting the abnormality of tobacco structure in the cigarette manufacturing process when the filling power exceeds a certain range;3. realizing the linkage between the change of cigarette cut tobacco structure and the physical indicators of cigarettes, then guaranteeing the stability of mainstream smoke chemical indicators and sensory intrinsic quality.
An Overview of the Application on Big Data Technology in Intelligent IOV
Authors: Wan Xing
Published: June 2019

The traditional automotive industry is currently moving towards intelligent and informational technology. IOV (Internet of Vehicles) technology is the latest technology which can adapt to the future. At present, IOV technology faces many unresolved technical problems, and one of the core technologies is big data technology. This paper introduces the application of big data technology in intelligent IOV, and introduces various aspects of big data technology in IOV.
Data And Content Analysis For Social Network Using LDA Text Model
Authors: Zhang Linxiang
Published: June 2019

WhatsApp is a popular cross-platform application for communicating and connecting people. In this paper, the author analyzes massive data from WhatsApp public groups and obtains so-called tacit knowledge and social complex knowledge. During the processing, the author uses LDA(Latent Dirichlet Allocation) model to analyze the content which helps us to be aware of many aspects from public groups such as social trend, sentiment analysis and other hidden information. In the end, author get the results about topic for each group and the occurrence of words for each topic.
The PEk Algorithm of Solving the Quasi-Block Tridiagonal Systems
Authors: Xuefei Lu , Fengjuan Dong
Published: June 2019

In this paper, the
PEk

method of solving special linear algebraic systems-block quasi-tridiagonal systems is presented. On the condition that the coefficient matrix is Hermitian positive definite, the solvability and convergence of the new method are proved, and the selection range for the parameters k is also given.
Whale Optimization Algorithm with Chaos Strategy and Weight Factor
Authors: Li Yintong , Han Tong , Han Bangjie , Zhao Hui , Wei Zhenglei
Published: June 2019

Whale optimization algorithm (WOA) is a novel optimization algorithm inspired by humpback whale hunting behavior. Due to the defect of unbalanced exploration and exploitation by using control parameter with linear change, WOA has slow convergence and is easy to fall into local optimum. Thus, a novel whale optimization algorithm with chaos strategy and weight factor (WOACW) is proposed to improve the convergence speed and accuracy. In this work, the chaos strategy is executed to initialize the population to enhance the diversity of the initial population. The weight factor is introduced to adjust the influence degree of the current optimal solution on the generation of new individuals in order to improve the convergence speed and accuracy. At the same time, the convergence factor is adjusted by cosine function to better balance the relationship between exploration and exploitation. In addition, using greedy strategy to fully retain the dominant individuals from the parents and the generated candidates to generate offspring, improves the convergence speed of the algorithm. To verify the performance of our approach, WOACW is benchmarked on 13 classical benchmark functions, and the statistical results are compared with the original WOA and three other WOA variants, IWOA, WOAWC, and two state-of-the-art algorithms, SSA, GWO. The experimental results and Wilcoxon signed ranks test show that WOACW has a higher convergence speed and precision than compared algorithms, which verifies the effectiveness of WOACW in this work.
Research of Ant Colony Algorithm Based on Multi-objective Optimization in Cloud Platform Virtual Machine Initialization
Authors: Wang Zhen , Du Jing , Miao Quanqiang , Cheng Ruosi
Published: June 2019

In order to solve the problem of resources waste in the process of virtual machine deployment in the cloud platform, we propose an ant colony algorithm based on multi-objective optimization. Firstly, the multi-objective optimization combination model is established. Then, we apply the model to ant colony algorithm. Finally, we modify the ant colony algorithm to adapt to cloud platform virtual machine initialization. In order to verify the effectiveness of the algorithm, cloudsim cloud simulation platform was used to carry out simulation experiments. The experimental results showed that the algorithm proposed in this paper could reach a relatively good level in SLA violation rate, power consumption, and resource loss of cloud platform compared with other commonly used algorithms.
An Approach Inspired from Nuclear Reaction Processes for Numerical Optimization
Authors: Wei Zheng-lei , Zhang Zhuo-ran , Huang Chang-qiang , Han Bo , Tang Shang-qin , Wang Le
Published: June 2019

As optimization problems become more complex, the need of new algorithms becomes greater. In this paper, a new powerful algorithm based on nuclear reaction process, namely, nuclear reaction optimization, is proposed for numerical optimization. The NRO algorithm which includes nuclear fission and fusion phases mimics the fission and fusion processes. In the first phase, the Gaussian walk and differential evolution operators are used for modeling fission process. In the second phase, the variants of differential evolution operators are employed for modeling the ionization stage and fusion stage. Additionally, six benchmark functions and unconstrained engineering design problem are evaluated by NRO and other compared algorithms. The optimization results indicate that the NRO has presented feasibility and superiority performance.
Personalized Diet Recommendation Based on K-means and Collaborative Filtering Algorithm
Authors: Yuan Zhicai , Luo Fang
Published: June 2019

With the improvement of people’s living standards, people pay more and more attention to the health of diet, and traditional dietary recommendations are difficult to meet the user’s dietary preferences and nutritional balance. This paper first uses the k-means clustering algorithm to divide the food set into multiple disjoint subsets, then uses the user-based collaborative filtering algorithm to recommend the food that the user may like. The recommended food and food in standard recipes set by user’s own situation are in the same cluster, which meets the user’s nutritional balance. The experimental results show that the recommended effect of this method is effective, and the recommended accuracy rate is over 70%.
An algorithm based on ABNL-MRF model for SAR image segmentation
Authors: Qingping Wang , Tao Xie , Boyuan Liu , Chang Wang , Naichang Yuan
Published: June 2019

In this letter, a segmentation method is proposed based on an adaptive Bayesian non-local MRF (ABNL-MRF) model. Aimed at the multiplicative speckle existing in SAR images, a patch-similarity measure based on a ratio of probability is introduced firstly. Then non-local similar pixel-blocks are adopted to guide the segmentation of the noisy image. However, non-local method over-smoothes edge regions and makes them inaccurate though it is robust for speckle. A rectification index is designed according to the homogeneity degree of local window. Based on the value of rectification index, a non-local constraint term is adaptively integrated into the potential function. Thus a suitable prior is defined that guarantees both the smoothness of the denoised image and the preservation of its structure. Experimental results demonstrate the very good performance of the proposed method.
ISAR Imaging Based on Block Sparse Smoothed L0 Norm Recovery Algorithm
Authors: Feng Junjie , Zhang Xuwen , Wang Yonghui
Published: June 2019

In order to obtain high resolution inverse synthetic aperture radar (ISAR) sparse images, a block sparse signal recovery ISAR imaging algorithm is proposed by considering the cluster characteristics of target in this paper. Firstly, the ISAR sparse imaging model is established, the imaging is converted to L0 norm optimization problem. Secondly, one negative exponential function sequence is used as smoothed function to approach the block L0 norm. Finally, the revised step is added to ensure solving the optimization problem along the steepest descent gradient direction and the cost function is updated for the next loop. Simulation results verify the proposed algorithm is effective.
Influence of feature scaling on convergence of gradient iterative algorithm
Authors: Wan Xing
Published: June 2019

Feature scaling is a method to unify self-variables or feature ranges in data. In data processing, it is usually used in data pre-processing. Because in the original data, the range of variables is very different. Feature scaling is a necessary step in the calculation of stochastic gradient descent. This paper takes the computer hardware data set maintained by UCI as an example, and compares the influence of normalization method and interval scaling method on the convergence of stochastic gradient descent by algorithm simulation. The result of study has a certain value on feature scaling.
A New Power Flow Algorithm for Hybrid AC/DC Power Grid
Authors: Li Jiawei , Han Xueshan , Li Wenbo , Jiang Zhe
Published: June 2019

Aiming at the problem of grid power flow calculation with VSC (Voltage Source Converter) unit in the context of the energy internet, this paper presents a new converter equivalent model. This study introduces a novel unified power flow algorithm for hybrid AC/DC power grids. The method simplifies the converter model by means of equivalent substitution and power conversion. The model better inherits the advantages of traditional AC power flow calculation. Finally, a unified iterative method is used to calculate an IEEE-57 node.
Research on Chaotic Digital Image Encryption Algorithm
Authors: Jia Ye , Yu-fu Zheng , Zi-wei Yang
Published: June 2019

In view of the problems of pseudo-randomness, complexity, short period and limited precision of traditional digital image encryption technology, this paper improves the traditional chaotic digital image encryption technology. Firstly, a random sequence is generated from the chaotic map according to the definition, and then the design process is improved according to the encryption algorithm, and finally the simulation experiment is performed. The improved algorithm uses the Henon-mapped chaotic sequence to generate the shuffle control parameters, and replaces the gray value with the improved two-dimensional Logistic map sequence, shuffles the array using the Henon map, and changes the image pixels using the improved two-dimensional Logistic map. Grayscale values, these algorithms are highly secure against exhaustive attacks, statistical attacks, and plain-text attacks. Even if the attacker knows the algorithm, it does not know the parameters of the algorithm and cannot obtain image information. The improved encryption algorithm can increase the key space and improve the security performance of the encryption algorithm. The image viewing effect is also very good, and the time is greatly shortened, which can fully meet the real-time requirements.
Research on Alignment in the Construction of Parallel Corpus
Authors: Zong Zhaorong , Hong Changchun
Published: June 2019

Parallel corpora are of great value in the field of machine translation and cross-language information retrieval. Benefiting from the development of machine learning and deep learning, the technology of the construction of corpus evolves from vocabulary alignment, phrase alignment to chunk alignment. The high quality of automatic bilingual chunks alignment in corpus plays an important role in the performance improvement of machine translation systems, especially in computer-aided translation systems. In the study, the degree of adhesion and relaxation is used to measure the tightness and looseness of the inter-word connection when a chunk is identified, which can be expressed by a mathematical mode. The task of chunk alignment in the construction of a parallel corpus can be described as the three steps: input bilingual sentences, segment chunks, and semantic alignment. At present, most algorithms are based on statistical methods, and the output alignment results are machine-oriented.
Identification of Interference Sources in Spiral Galaxy Images Based on Convolutional Neural Network
Authors: Wu Shiqing , Hu Tao
Published: June 2019

In the spiral galaxy image, the luminosity of the precursor star due to its close proximity to the spiral galaxy reaches an approximate order of magnitude, which makes the precursor stars become an important interference source in the galaxy image and makes the computer difficult to distinguish them. Therefore, it is of great significance to remove the precursor stars from the galaxy image. In this paper, a modified convolution neural network (CNN) is used to classify spiral galaxies and stars in order to find pre-star interference. Convolutional neural network (CNN) is a popular image classification technology, which is included in depth learning. It solves the problem of a large amount of data and serious non-linearity on the basis of neural network. Based on CNN, this paper studies and improves the selection of network level, the optimization algorithm, batch normalization and so on. The two types of astronomical images of the target are trained and predicted, and the classification results are very good. At present, the error rate is around 3%.
Constructions of New Entanglement-Assisted Quantum Codes
Authors: Qian Jianfa , Zhang CLina
Published: June 2019

Entanglement-assisted quantum error-correcting codes are considered as a new research direction of quantum coding theory. In this work, based on cyclic codes, we present the constructions of new entanglement-assisted quantum error-correcting codes. Several new families of nonbinary entanglement-assisted quantum error-correcting codes with length n=2(q-1) can be efficiently constructed.
Theoretical and Empirical Study on Risk Measurement Method Statistics and Portfolio Model
Authors: Zhu Jiale
Published: June 2019

The fundamental purpose of securities investment is to obtain benefits. In order to diversify risks, many investors invest many kinds of securities simultaneously to achieve the maximum returns. Risk measurement methods and portfolio model have become the major issues faced with the financial sector. This paper analyses the calculating method and applicable condition of Markowitz risk remuneration model, Arrow-Pratt risk remuneration model and Jia & Dyer standard risk model. This paper gives the investment model optimized by mean entropy and portfolio model including transaction cost. Empirical analysis shows that the portfolio model is suitable for calculating the return and risk of portfolio. According to the model, investor’s different degree of risk avoidance will form different investment strategies.
Convolutional neural network for Iceberg Classifier
Authors: Shi Zhouan , Lu Xuheng , Hou Lunqing
Published: June 2019

Image classification has been a popular research field in computer version, and we employ convolutional neural network to solve a practical problem that is iceberg classifier. Iceberg classifier is critical import to keep the ship safe in the ocean, because the ships can program another routine after they detect the iceberg. When radar detects an object, it cannot separate it from ship or any other solid object. The radar images contain the shape, size and brightness features. There are several ways to solve the iceberg classification problem: (1) Hough transform [1] (2) convolution neural network [2]. Convolutional neural network can obtain image features that are invisible for human beings. Convolution neural network has achieved expected performance in many image classification problems. Our experiment shows that convolution neural network can classify the iceberg from any other object and we get accuracy 96% and F1 score 96%. Support vector machine [3] achieves accuracy 79% and F1 score 78%.
Research on Microservice Anomaly Detection Technology Based on Conditional Random Field
Authors: Cao Wei , Cao Zhiying , Zhang Xiuguo
Published: June 2019

Recently, the existing microservice anomaly detection methods which mainly monitor the system metrics and analyze the system logs have low efficiency and consume much time. This paper proposes a microservice anomaly detection method based on Conditional Random Field(CRF). The method takes the collected system parameters such as CPU utilization, memory utilization, bandwidth occupancy as the characteristic values of the observation sequence, and labels the abnormal types of the sequences corresponding to the feature values, and then creates the microservice fault matrix. Operators can quickly retrieve the specific anomaly information of the microservice error according to the microservice fault matrix. Compared with the traditional Hidden Markov Model-based detection method, this method can find the abnormal sequence corresponding to the observation sequence in the global scope. The experimental results show that the method proposed by this paper can accurately find the faults in the microservice system, and the accuracy and recall rate are relatively high.
Research on Service Dependency Mining Technology Based on Tag Extension and Bipartite Graph
Authors: Zhang Liangjun , Zhang Xiuguo , Cao Zhiying
Published: June 2019

Due to the rapid development of service portfolio model on network, service computing has become a hot topic in recent years. It is important for both service management and service recommendation to clarify the dependencies between services. Tag, as a widely used network information resource in the current service field, can intuitively reflect the function, purpose and other attributes of the service, and it has become a common resource in the field of service discovery and combination. Tag sparsity, however, will reduce the accuracy of service relationship analysis. Therefore, this paper proposes a service dependency mining method based on tag extension and bipartite graph. By extending the existing tags and abstracting the combination of service and the relationship between the extension tags into a bipartite graph, dependency propagation mechanism in bipartite graph is applied to improve the comprehensiveness and accuracy of the service dependency mining. The programmable Web APIs and Mashups are used to show the validity of the service dependency mining method.
Research on Combination Forecast Model of Aerospace Relay’s Storage Life
Authors: Wang Ying , Liang Kai , Liu Qi , Zhang Ting , Li Shou , Gou Yao , Li Bao Hua , Liu Jin Ran
Published: June 2019

With the rapid development of China space industry, relay is widely used in aerospace control system, thus it has a higher request for the relay reliability. The space relay is stored for a long time and may have a storage life of 15-20 years or much longer. In order to ensure that the relay always keeps in the activated state, it has very practical significance to predict the storage life of relay. Firstly, a single prediction model of space relay storage life is established, such as gray system theory prediction model and neural network prediction model. Then the prediction results are analyzed and the model error evaluation is carried out. According to the results of comparison, the advantages and disadvantages are obtained and improved. Finally, the storage life combination model of space relay is established, and Matlab software is used for the simulation and prediction of gray neural network to improve the accuracy of the prediction model
High Resolution Measurement with Asynchronous Optical Sampling
Authors: Jiang Jiaxin , Abuduweili Abulikemu
Published: June 2019

Asynchronous optical sampling is an emerging new measuring technique that exploits the frequency resolution, frequency accuracy of frequency combs for ultrahighresolution, high-sensitivity broadband spectroscopy and time-offset measurement. This review describes principles and applications of Asynchronous optical sampling and summarizes the current state of the art. As laser technology progresses, Asynchronous optical sampling will continue to mature and could surpass conventional frequency and time measurement technology for a wide range of laboratory and field applications.
Design of Portable Reflectivity Test Probe
Authors: Ping Wei , Lewu Deng , Lei Zhang , Peng Cui
Published: June 2019

This paper introduced the design of a portable probe which can measure the reflectivity of the surface radar stealth coating. By using swallowing pulse in the design of variable frequency division phase-locked frequency synthesis technology to microwave circuit, automatic control circuit and the H horn probe integration, solve the rigid connection deformations of RF cable and adapter vulnerability caused by the phase and impact the measuring accuracy of standing wave change. With the special design of the new probe, it has the characteristics of high stability, good repeatability, small effect of environmental temperature, excellent reliability and automation.
Research and Design of Block Cipher Special Instruction Set
Authors: Chang Zhongxiang , Dai Zheng , Song Wei , Zhu Hang , Chen Mengfei
Published: June 2019

The existing instructions are used to implement the block cipher algorithm with low efficient. So this paper classifies the operations in the cryptographic algorithm, and analyses the characteristics of bit width, combination and parallel mode in the cipher algorithm of different operations. Further develop the parallelism of S-box and shift instructions, improve the hybrid computing ability of arithmetic operation instructions, research and design new types of instructions for data position transformation, such as extract shift, shift insertion, data grouping and data mixing. At the last, this paper implements the cipher algorithm with the new instruction, such as DES, IDEA, BEST and other block cipher algorithms. The number of instructions is 14% ∼ 35% of Haswell processor, and 40% to 68% of the COBRA cipher processor.
Design of Software and Hardware of Analog Display Board Based on RS485 Communication
Authors: Pengchong Zhang , Xianzhou Feng
Published: June 2019

As an important human-machine interaction component in the ship damage control system, the analog display board has a large number of indicator lights arranged on the board. But meanwhile, the indicator lights are in different working states and need to be controlled by multiple driving circuit boards. Thus, the application of RS485 bus communication can effectively solve the problem of communication between the controller side and multiple driving circuit boards. And the multiplexing of output ports on each driving circuit board will be regarded as a pragmatic approach to solve the numerous indicator lights that are in different working states on the board.
Research on Technical Architecture and Application of Big Data Cloud Platform for Electric Power Measurement
Authors: Huaiying Shang , Yan Liu , Angang Zheng , Songfeng Qin , Yong Wang , Wulei Zhang
Published: June 2019

This paper studies the technical architecture and composition of the cloud computing platform. The data types and data source related to the big data of the power energy measurement are discussed. The design of the large data application service scene of the power energy measurement is discussed in combination with the technical targets and business objectives. Finally, the development of cloud platform and its application in business field are prospected.
TeaBOT Order Packaging Machine Design
Authors: Zhang Qiao Ling , Ai Ting
Published: June 2019

A Circuit design for meeting the rules of Tea Dispensing Machine, To achieve create data objects are what actually push the data objects to the database. Weight Sensor Load Cell and Motor Controller for The Raspberry Pi communicates both with the local hardware and with the database in order to dispense and complete orders as they come in.
Spectrum Allocation Based on Simultaneous Wireless Information and Power Transfer in Cognitive Radio Networks
Authors: Shi Qiuju , Li Jianbin
Published: June 2019

This paper proposes an incentive simultaneous wireless information and power transfer (SWIPT) scheme for cognitive radio networks, wherein the secondary user (SU) provides wireless power transfer for the primary user (PU) in the exchange for partial bandwidth of the latter. To enable the proposed scheme, both the secondary transmitter (ST) and the primary receiver (PR) are equipped with multiple antennas. Specifically, SU helps to charge PU via adjusting its beamforming vector and PR harvests energy with a subset of its antennas; as a reward, PU allocates part of its bandwidth to SU. Our goal is to jointly optimize the beamforming vector of SU, the bandwidth allocation and covariance matrix of PU, such that the transmission rate of SU is maximized and meanwhile PU’s requirements in transmission rate and energy are satisfied. This optimization problem is non-convex. To handle this non-convex problem, we decompose it into two independent subproblems. One is to optimize the covariance matrix, the other is to optimize the beamforming vector of SU as well as the bandwidth allocation of PU, which is divided into a two-layer optimization problem. In the inner subproblem, we obtain the optimal beamforming vector for any given fixed bandwidth allocation, while in the outer subproblem we propose a gradient based algorithm and obtain the optimum bandwidth allocation. Numerical results are given to show the achievable system performance with varying parameters and also the convergence of the proposed algorithm.