bluuebunny/crossref_metadata_2025 huggingface dataset:
Big Data for Sound Policies: Toward Evidence-Informed Hearing Health Policies
Authors: Gutenberg Johanna , Katrakazas Panagiotis , Trenkova Lyubov , Murdin Louisa , Brdarić Dario , Koloutsou Nina , Ploumidou Katherine , Pontoppidan Niels Henrik , Laplante-Lévesque Ariane
Published: November 2018

Purpose

The scarcity of health care resources calls for their rational allocation, including within hearing health care. Policies define the course of action to reach specific goals such as optimal hearing health. The process of policy making can be divided into 4 steps: (a) problem identification and issue recognition, (b) policy formulation, (c) policy implementation, and (d) policy evaluation. Data and evidence, especially Big Data, can inform each of the steps of this process. Big Data can inform the macrolevel (policies that determine the general goals and actions), mesolevel (specific services and guidelines in organizations), and microlevel (clinical care) of hearing health care services. The research project EVOTION applies Big Data collection and analysis to form an evidence base for future hearing health care policies.

Method

The EVOTION research project collects heterogeneous data both from retrospective and prospective cohorts (clinical validation) of people with hearing impairment. Retrospective data from clinical repositories in the United Kingdom and Denmark will be combined. As part of a clinical validation, over 1,000 people with hearing impairment will receive smart EVOTION hearing aids and a mobile phone application from clinics located in the United Kingdom and Greece. These clients will also complete a battery of assessments, and a subsample will also receive a smartwatch including biosensors. Big Data analytics will identify associations between client characteristics, context, and hearing aid outcomes.

Results

The evidence EVOTION will generate is relevant especially for the first 2 steps of the policy-making process, namely, problem identification and issue recognition, as well as policy formulation. EVOTION will inform microlevel, mesolevel, and macrolevel of hearing health care services through evidence-informed policies, clinical guidelines, and clinical care.

Conclusion

In the future, Big Data can inform all steps of the hearing health policy-making process and all levels of hearing health care services.
An Application of the Medical Research Council's Guidelines for Evaluating Complex Interventions: A Usability Study Assessing Smartphone-Connected Listening Devices in Adults With Hearing Loss
Authors: Maidment David W. , Ferguson Melanie
Published: November 2018

Purpose

The purpose of this study is to provide an example of the Medical Research Council's guidelines for evaluating complex health care interventions in the context of smartphone-connected listening devices in adults with hearing loss.

Method

Twenty existing hearing aid users trialed 1 of the following smartphone-connected listening devices: made-for-smartphone hearing aids, a personal sound amplification product, and a smartphone “hearing aid” application used with either wireless or wired earphones. Following 2 weeks of use in their everyday lives, participants completed self-report outcome measures.

Results

Relative to conventional hearing aids, self-reported use, benefit, and satisfaction were higher, and residual disability was lower for made-for-smartphone hearing aids. The converse was found for the other smartphone-connected listening devices trialed. Similarly, overall usability was judged to be “above average” for the made-for-smartphone hearing aids, but “below average” for the remaining devices.

Conclusions

This developmental work, guided by the Medical Research Council's framework, lays the foundation for feasibility and pilot studies, leading to high-quality research assessing the effectiveness of smartphone-connected listening devices. This future evidence is necessary to guide health care commissioners and policymakers when considering new service delivery models for adults living with hearing loss.
Toward Better Outcomes in Audiology Distance Education: An Educational Data Mining Approach
Authors: Penteado Bruno Elias , Paiva Paula Maria Pereira , Morettin-Zupelari Marina , Isotani Seiji , Ferrari Deborah Viviane
Published: November 2018

Purpose

This article introduces concepts and a general taxonomy used by the educational data mining (EDM) community, as well as examples of their applications, with the aims of providing audiology educators with a referential basis for developing this area.

Method

A narrative review was carried out to present an overview of EDM and its main methods. Some of these methods were exemplified with analysis of real data from an Internet-based specialization course on pediatric auditory rehabilitation.

Results

The review introduced EDM main concepts and applications and described methods from its area. Real data examples illustrated EDM use to predict interpersonal help-seeking, model interpersonal interaction, analyze students' trajectories within a course's module, and understand how students approached group assignments. Some of the insights provided by EDM to support teaching and learning processes were also described.

Conclusions

EDM methods offer new tools to discover knowledge from digital traces (i.e., logs) and support key stakeholders (students, instructors, or course administrators) to raise awareness about course dynamics. This approach has the potential to foster a better understanding and management of educational processes in audiology distance education.
The Intensive Cognitive-Communication Rehabilitation Program for Young Adults With Acquired Brain Injury
Authors: Gilmore Natalie , Ross Katrina , Kiran Swathi
Published: November 2018

Purpose

This study investigated the effects of an intensive cognitive-communication rehabilitation (ICCR) program for young individuals with chronic acquired brain injury.

Method

ICCR included classroom lectures; metacognitive instruction, modeling, and application; technology skills training; and individual cognitive–linguistic therapy. Four individuals participated in the intensive program (6 hr with 1-hr lunch break × 4 days × 12 weeks of treatment): 3 participants completed 3 consecutive semesters, and 1 participant completed 1 semester. Two controls did not receive treatment and completed assessments before and after the 12-week treatment interval only.

Results

All 4 experimental participants demonstrated significant improvements on at least 1 standardized cognitive–linguistic measure, whereas controls did not. Furthermore, time point significantly predicted participants' scores on 2 of the 4 standardized outcome measures, indicating that as duration in ICCR increased, scores also increased. Participants who completed multiple semesters of ICCR also improved in their therapy and personal goals, classroom behavior, life participation, and quality of life.

Conclusion

After ICCR, participants showed gains in their cognitive–linguistic functioning, classroom participation, and individual therapy. They also demonstrated improvements outside the classroom and in their overall well-being. There is a gap between the large population of young adults with acquired brain injury who wish to return to higher education and a lack of rehabilitation programs supporting reentry into academic environments; ICCR is a first step in reducing that gap.
Concerning the occurrence of bow shocks around high-mass X-ray binaries
Authors: Prišegen M.
Published: November 2018

Context.We investigate the occurrence of stellar bow shocks around high-mass X-ray binaries (HMXBs) in the Galaxy.

Aims.We seek to conduct a survey of HMXBs in the mid-infrared to search for the presence of bow shocks around these objects.

Methods.Telescopes operating in the mid-infrared, such as theSpitzerSpace Telescope or Wide-field Infrared Survey Explorer (WISE), are potent tools for searching for the stellar bow shocks. We used the available archival data from these telescopes to search for bow shock candidates around the confirmed and candidate HMXBs in the Galaxy.

Results.We detected extended mid-infrared structures around several surveyed confirmed and candidate HMXBs. Two of these structures, associated with Vela X-1 and 4U 1907+09, are genuine bow shocks that have been studied previously. However, there are no new unambiguous bow shocks among the rest of the objects. The paucity of bow shocks around HMXBs suggests that the majority of these systems still reside within hot, low-density bubbles around their parent star clusters or associations. This also implies that the dynamical ejection of massive binaries is apparently less efficient than the ejections caused by the supernova explosions inside a binary.
Mass and shape of the Milky Way’s dark matter halo with globular clusters from Gaia and Hubble
Authors: Posti Lorenzo , Helmi Amina
Published: November 2018

Aims. We estimate the mass of the inner (< 20 kpc) Milky Way and the axis ratio of its inner dark matter halo using globular clusters as tracers. At the same time, we constrain the distribution in phase-space of the globular cluster system around the Galaxy.

Methods. We use the Gaia Data Release 2 catalogue of 75 globular clusters’ proper motions and recent measurements of the proper motions of another 20 distant clusters obtained with the Hubble Space Telescope. We describe the globular cluster system with a distribution function (DF) with two components: a flat, rotating disc-like one and a rounder, more extended halo-like one. While fixing the Milky Way’s disc and bulge, we let the mass and shape of the dark matter halo and we fit these two parameters, together with six others describing the DF, with a Bayesian method.

Results. We find the mass of the Galaxy within 20 kpc to be M(<20 kpc) = 1.91−0.17+0.18×1011 M⊙, of which MDM(<20 kpc) = 1.37−0.17+0.18×1011 M⊙ is in dark matter, and the density axis ratio of the dark matter halo to be q = 1.30 ± 0.25. Assuming a concentration-mass relation, this implies a virial mass Mvir = 1.3±0.3×1012 M⊙. Our analysis rules out oblate (q <  0.8) and strongly prolate halos (q >  1.9) with 99% probability. Our preferred model reproduces well the observed phase-space distribution of globular clusters and has a disc component that closely resembles that of the Galactic thick disc. The halo component follows a power-law density profile ρ ∝ r−3.3, has a mean rotational velocity of Vrot ≃ −14km s−1 at 20 kpc, and has a mildly radially biased velocity distribution (β ≃ 0.2 ± 0.07, which varies significantly with radius only within the inner 15 kpc). We also find that our distinction between disc and halo clusters resembles, although not fully, the observed distinction in metal-rich ([Fe/H] > −0.8) and metal-poor ([Fe/H] ≤ −0.8) cluster populations.
Weak-lensing shear measurement with machine learning
Authors: Tewes M. , Kuntzer T. , Nakajima R. , Courbin F. , Hildebrandt H. , Schrabback T.
Published: November 2018

Cosmic shear, that is weak gravitational lensing by the large-scale matter structure of the Universe, is a primary cosmological probe for several present and upcoming surveys investigating dark matter and dark energy, such as Euclid or WFIRST. The probe requires an extremely accurate measurement of the shapes of millions of galaxies based on imaging data. Crucially, the shear measurement must address and compensate for a range of interwoven nuisance effects related to the instrument optics and detector, noise in the images, unknown galaxy morphologies, colors, blending of sources, and selection effects. This paper explores the use of supervised machine learning as a tool to solve this inverse problem. We present a simple architecture that learns to regress shear point estimates and weights via shallow artificial neural networks. The networks are trained on simulations of the forward observing process, and take combinations of moments of the galaxy images as inputs. A challenging peculiarity of the shear measurement task, in terms of machine learning applications, is the combination of the noisiness of the input features and the requirements on the statistical accuracy of the inverse regression. To address this issue, the proposed training algorithm minimizes bias over multiple realizations of individual source galaxies, reducing the sensitivity to properties of the overall sample of source galaxies. Importantly, an observational selection function of these source galaxies can be straightforwardly taken into account via the weights. We first introduce key aspects of our approach using toy-model simulations, and then demonstrate its potential on images mimicking Euclid data. Finally, we analyze images from the GREAT3 challenge, obtaining competitively low multiplicative and additive shear biases despite the use of a simple training set. We conclude that the further development of suited machine learning approaches is of high interest to meet the stringent requirements on the shear measurement in current and future surveys. We make a demonstration implementation of our technique publicly available.
Application of Bayes Classification method in mobile phone spam short message filtering system
Authors: Lu Li , Xue Xiangui , Li Tao
Published: November 2018

The paper discussed the use of Bayes classification method in filtration system of short message spam (SMS). The method can classify the content of SMS, thus realizing effective filtering. Finally the paper carried out the result analysis and the appraisal of the Bayes classification model, which testified the model has some actually feasibility and extensibility.
Business-oriented customized big data query system and its SQL parser design and implementation
Authors: Ge Wenshuai , He Gang , Liu Xinwen
Published: November 2018

This paper proposes a big data query system for customized queries based on specific business needs. This paper introduces the components and structure of the query system. ANTLR tools are used as language recognizer to design and implement a customized SQL dialect. The system builds a simpler and easier query interface on Spark SQL, which satisfies the query requirements of the Internet user behavior analysis platform.
An improved long short-term memory neural network for stock forecast
Authors: Lv Liujia , Kong Weijian , Qi Jie , Zhang Jue
Published: November 2018

This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in the last two years. Compared with typical algorithms by simulation, we find the LSTM has better performance in reliability and adaptability, and the improved PSO-LSTM algorithm has better accuracy.
A bike sharing system based on Blockchain platform
Authors: Guo Hanyue , Zhou Jiting , Wang Jiaqi , Wang Xiaodong
Published: November 2018

Leakage of user privacy and vandalism of the sharing bike have been the most serious problem since sharing bike came on the scene. Accordingly, it is very urgent to rebuild the underlying trust mechanism. Most bike sharing systems are centralized, leading to overpressure on the central server. This paper proposes a bike sharing system based on blockchain service platform and a shared operation mode of C2C. The system uses the blockchain system as the trust guarantee. The extra chain payment - lightning network is used to improve the efficiency of the blockchain system and the smart contract is used to provide the rights and interests of the two parties.
Research on the multi-terminal adapting Web system construction
Authors: Ma Liang , Zhao Xiaole
Published: November 2018

Among the various applying patterns of Internet programs, the Web system based on B/S architecture has the congenital advantage of cross platform. The same application could be accessed via the same domain name with any operating system, as long as the client-side software (browser) is installed. Thus, in the development of Internet application, if the function could meet the requirement, the Web system construction should be given the top priority. With the wide development of information technology, such as intelligent terminal, mobile interconnection and cloud computing, how to construct the Web system which could adapt to multi terminals is deserving of research. Based on a systematical and comprehensive investigation, this article aims to explore feasible ways to construct the multi-terminal adapting Web system catering to the current social concern, through feasibility analysis of prototype design.
Anomaly Analysis Technology Based on Deterministic Characteristics of Intranet
Authors: Chen Zhiwen , Wang Guihua , Zhang Weiyan , Zhou Dali
Published: November 2018

An enterprise intranet has the characteristics of service determination, limited network components, descriptive and observable characteristics, and the state of network components and network interaction behaviors need to strictly comply with security policies. Therefore, a variety of descriptive certainty can be used to describe the subject, object, and action of the network access. According to this important feature, the anomaly analysis method is simplified, and the abnormal discovery of the intranet is transformed into the problem of network dynamic feature collection and deterministic feature characterization. Based on the network state and behavior collection and analysis network dynamic characteristics, combined with the deterministic feature priori knowledge of the network, an anomaly analysis model which is especially suitable for deterministic intranet is proposed. Based on the model design, a traffic-based anomaly analysis system is implemented. The system can effectively find a variety of high-risk anomalies in the intranet.
Analysis of Three Channel SW Interference Adaptive Cancellation System
Authors: Li Mingyang , Jiang Yunhao , Yang Jian , Liu Shiqi , Li Xing
Published: November 2018

Aiming at the adjacent channel interference problem of short-wave, a model of three-way short-wave interference signals has been established. The interference cancellation of the short-wave interference signal of three adjacent channels is analyzed, and the time-domain characteristics of the adaptive interference cancellation system are simulated. The theoretical and simulation analysis of the adaptive system show that the offset of the bypass signal will be affected when the intermediate signal frequency is biased to one side because of the existence of the intermediate channel bandwidth.
Construction of Mobile Learning Model Based on Network Collaborative Inquiry from Bigdata
Authors: Zhang Cizhen , Zhong Jiaming
Published: November 2018

The rapid development of information technology represented by media and network is changing our work, life, and learning style. In recent years, with the development of information age and bigdata, mobile learning is being widely used in numerous industries and areas. It is also gradually penetrated into information education. Through the creation of network collaborative inquiry learning model and its application in teaching, this paper tries to study a mobile learning model of network collaborative inquiry based on bigdata.
Research of Tree-based Multicast Routing Protocols for Ad Hoc Networks
Authors: Jiao Yang
Published: November 2018

Multicast is an important function of Ad Hoc network. It is a point to multipoint or multipoint to multipoint packet transmitting mode. Group management and maintenance, multicast packet routing are two factors of multicast protocol. This paper analyzes the classification of multicast protocols and focuses on tree based multicast protocols, compares several protocols and analyzes the advantages and disadvantages of tree based multicast protocols.
Research on General Programming Environment Technology Based on Web
Authors: Yu Liu
Published: November 2018

As the MOOC develops, many users have begun to gradually like and get used to programming learning on the MOOC platform, so virtual online experiments have become a hot spot in the online programming education industry, which is also known as online IDE or online programming environment. Compared with the traditional programming environment, the online programming environment does not require a complicated establishment of local environment, making it easy to be used. At the same time, the online programming environment can also be integrated into the MOOC for learning, and can also be combined with OJ for evaluation. However, the existing online programming environment usually only supports few programming languages, such as C language and Java language. Even some online platforms only have the online compiling function with one language. Based on the above phenomena, this paper will propose a general online programming solution. And based on the idea, a platform that supports multi-language online compiling function is built through simple code writing and the construction of server environment.
FlexRay Vehicle Network Predictive Control Based on Neural Network
Authors: Huan Li , Chao Li
Published: November 2018

We propose a design method of FlexRay vehicle network forecasting control based on the neural network to solve the security and reliability of FlexRay network control system, where the control performance and stability of the system are reduced when transmiting data under heavy load, by sampling the working state of the vehicle network at the present time to predict the next-time network state, and adapting to the dynamic load in the vehicular network system by on-line adaptive workload adjustment. The method used the nonlinear neural network model to predict the performance of the future model. The controller calculated the control input and optimized the performance of the next-time network model. The simulation results from the Matlab/Simulink showed that the neural network predictive control had good learning ability and adaptability. It could improve the performance of FlexRay vehicle network control system.
Design of an on-line check device for safety valve
Authors: Yan Zhipeng , Li Qing , Jin Muda , Zhong Haijian
Published: November 2018

As the main pressure relief component of the tanker, the spring type safety valve is indispensable for its periodic verification. An on-line check device is designed for the check problem of the safety valve. The check device is divided into mechanical structure, program control, motor drive, tension detection, displacement measurement, upper computer, etc., and the safety valve is verified according to the safety valve check principle and the determination method. Compared with the check device that uses the hydraulic system to provide the pulling force, the device has the advantages of simple operation, high speed, convenient disassembly, strong applicability, and the like, and has certain market application prospects.
A scalable ASIP for BP Polar decoding with multiple code lengths
Authors: Qiao Wan , Liu Dake
Published: November 2018

In this paper, we propose a flexible scalable BP Polar decoding application-specific instruction set processor (PASIP) that supports multiple code lengths (64 to 4096) and any code rates. High throughputs and sufficient programmability are achieved by the single-instruction-multiple-data (SIMD) based architecture and specially designed Polar decoding acceleration instructions. The synthesis result using 65 nm CMOS technology shows that the total area of PASIP is 2.71 mm2. PASIP provides the maximum throughput of 1563 Mbps (for N = 1024) at the work frequency of 400MHz. The comparison with state-of-art Polar decoders reveals PASIP’s high area efficiency.
Virtual Reality Games for Health Care
Authors: Lin Alice J , Chen Charles B , Cheng Fuhua (Frank)
Published: November 2018

Virtual reality (VR) and interactive technologies have become increasingly common in today’s world, and there are many applications for this technology in numerous fields. In this paper, we present a review on recent and ongoing research related to applications of VR and VR games in the healthcare field. VR technology is allowing medical practitioners to help their patients in a number of innovative ways in addition to helping health care providers improve their skills. Lastly, we propose a VR game design for rehabilitation and palliative care.
Processing the Non-parallel Bistatic SAR Data by Using Chirp-z Transform
Authors: Ran Jinhe , Shen Yang , Li Xiuhe
Published: November 2018

A new analytical imaging algorithm based on Chirp-z transform (CZT) for focusing non-parallel bistatic SAR data is proposed. The reference function multiplication (RFM) is firstly performed to finish the bulk focusing. Then the phase terms of spectrum are decomposed into range phase term and azimuth phase term, and their space-variances are eliminated by CZT independently to finish the differential focusing. The algorithm can be applied to wide scene imaging, and its effectiveness is verified by simulations.
A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images
Authors: Zheng Li , Lei Yiran
Published: November 2018

The detection and segmentation of lung nodules based on computer tomography images (CT) is a basic and significant step to achieve the robotic needle biopsy. In this paper, we reviewed some typical segmentation algorithms, including thresholding, active contour, differential operator, region growing and watershed. To analyse their performance on lung nodule detection, we applied them to four CT images of different kinds of lung nodules. The results show that thresholding, active contour and differential operator do well in the segmentation of solitary nodules, while region growing has an advantage over the others on segmenting nodules adhere to vessels. For segmentation of semi-transparent nodules, differential operator is an especially suitable choice. Watershed can segment nodules adhere to vessels and semi-transparent nodules well, but it has low sensitivity in solitary nodules.
The Magnetic Leakage Inversion Method Based on Singular Value Decomposition of Magnetic Dipole Forward Model
Authors: Du Jieru , Li Yansong , Liu Jun
Published: November 2018

Ferromagnetic materials are widely used in many fields of national economy. In actual engineering, under the influence of stress or environment, ferromagnetic materials can be defective and have serious consequences. Therefore, magnetic flux leakage inversion, which is speculating defects information according to the detected magnetic leakage signals, is of great practical significance. In allusion to the identification of irregular defects, this paper presented an inversion method based on singular value decomposition of magnetic dipole forward model, which is very effective in identifying irregular defects. This paper contrasted and analyzed the distribution characteristics of magnetic intensity horizontal component Mx when there was no defect and irregular defect, and the comparison verified that the magnetic intensity horizontal component Mx could be used as an inversion gist. Then this paper presented the magnetic dipole forward model B=LM. On account of the magnetic intensity component M containing defects information, this paper adopted the arithmetic of singular value decomposition of coefficient matrix L to solve the inversion equation LM=B and then acquired the distribution of magnetic intensity component M. In the end, this paper verified the validity of this method.
An Image Segmentation Method for Global Vision Robot Fish Competition
Authors: Jia Yifan , Li Juanjuan , Hu Liang
Published: November 2018

Image segmentation is a key link of vision system of the global vision bionic robot fish, and a precondition of target localization and tracking. In this paper, we propose a visual threshold method for color image segmentation. Firstly, a visualization research on the R, G and B components in different regions of the image is carried out to find out the main factors, which influence the image segmentation effect, and then an image segmentation method is proposed based on R and B components. It is proved by experiments that the image segmentation method is simple and practical, which is more suitable for the image segmentation and target tracking in our test-bed than the Gaussian mixture model. The image segmentation method also provides reference for other fields.
RST-based Discourse Coherence Quality Analysis Model for Students’ English Essays
Authors: Huang Guimin , Tan Min , Sun Zhenglin , Zhou Ya
Published: November 2018

Against the problems which can’t be solved by the word-level based local coherence analysis model, we propose a new discourse coherence quality analysis model (abbreviated RST-DCQA) by analyzing the full hierarchical discourse structure of English essays. Under the framework of rhetorical structure theory (RST), firstly, we design an RST-style discourse relations parser to capture the deep hierarchical discourse structure of essays; secondly, we transform the discourse relation information into a discourse relation matrix; finally, we design an algorithm to analyze the discourse coherence quality of student’s English essays. The experimental results show that the average error of our model’s score and teacher’s score is only 2.63, and the Pearson correlation coefficient is 0.71. Compared with the other models, our RST-DCQA model has a higher accuracy and better practicality in the field of students’ essays assessment.
Video Target Detection In Underground Mine Based On Background Difference And Edge Detection
Authors: Gong Shangfu , Xu Fengzhi , Jia Pengtao
Published: November 2018

In view of the complex environment in the underground mine, the detection of moving targets in surveillance video often had the problems of low detection efficiency and the detection result was greatly affected by noise and shadows. A target extraction method based on fusion background subtraction, inter-frame difference and edge detection was proposed. Firstly, the method used the hybrid gaussian background modeling (GMM) to obtain the accurate background image of the dynamic environment, and the extracted moving targets by using background subtractiont. Then based on the three-frame differential and Canny edge detection, the foreground image and the moving object blob was obtained, which was combined with the background subtraction to eliminate noise and voids, and to avoid missed detection of the moving target. Finally, the shadows in the detection process were removed through pixel ratio and threshold screening, and morphological and connected domain processing were performed. Comparing the improved algorithm with the traditional algorithm, the test results show that the improved algorithm can effectively remove the noise and voids, suppress the shadow, avoid the missed detection target, and have a good detection effect.
Design of Real-Time Database for the Vacuum Cold-Black Environment Simulator
Authors: Shao Jingyi , Ning Juan , Liu Yangyang
Published: November 2018

There is an urgent need to develop a real-time database (RTDB) to handle large amounts of real-time data, to realize process monitoring of vacuum cold-black environment simulation, cold-black environment data acquisition, fault processing, data storage, etc. This paper conducts real-time database design through network architecture, functional modules, communication interfaces, data management, and transaction scheduling. Simulation test and associated debugging test show that the real-time database can efficiently process large data volume, and the data interaction efficiency is greatly improved to meet the needs of space environment simulation equipment, laying a solid data foundation for subsequent applications.
Design and implementation of fuzzy image enhancement system in police application
Authors: Liu Cheng , Bu Fan-liang
Published: November 2018

A set of image processing system based on fuzzy image enhancement algorithm is designed and implemented in the light of the actual needs of image material evidence extraction in the criminal investigation process of public security department, which is combined with the computer technology such as C++ language and graphic development tools. The system can enhance the processing efficiency by continuously selecting different algorithms for input images. The same type of fuzzy image can be processed by various algorithms and the ideal image can be obtained by comparing the image evaluation indexes, and the optimal selection of image quality and algorithm is realized.
Design of Oscillator Based on A Deformed Structure Parallel Coupled Line Filter
Authors: Wang Enhao , Pan Jin , Yang Deqiang , Liu Wenzhe , Tang Tao
Published: November 2018

This paper proposes an oscillator based on a deformed structure parallel coupled line filter design. The requirements of this design are the output power meet 10dBm, and phase noise is below -90dBc/Hz at 10kHz off the centre point. This work adopts parallel positive feedback form, and it use Parallel Coupled Line Filter, making this design simple in structure and low in cost. This design has low phase noise and high output power features, working at 2.4GHz, intended to use in a through-the-wall radar system.
Machine Reading Comprehension Based On Multi-headed attention Model
Authors: Xu Hui , Zhang Shichang , Jiang Jie
Published: November 2018

Machine Reading Comprehension (MRC) refers to the task that aims to read the context through the machine and answer the question about the original text, which needs to be modeled in the interaction between the context and the question. Recently, attention mechanisms in deep learning have been successfully extended to MRC tasks. In general, the attention-based approach is to focus attention on a small part of the context and to generalize it using a fixed-size vector. This paper introduces a network of attention from coarse to fine, which is a multi-stage hierarchical process. Firstly, the context and questions are encoded by bi-directional LSTM RNN; Then, more accurate interaction information is obtained after multiple iterations of the attention mechanism; Finally, a cursor-based approach is used to predicts the answer at the beginning and end of the original text. Experimental evaluation of shows that the BiDMF (Bi-Directional Multi-Attention Flow) model designed in this paper achieved 34.1% BLUE4 value and 39.5% Rouge-L value on the test set.
Study on the Prediction Model of Short-term Bus Passenger Flow Based on Big Data
Authors: Wang Cheng , Cao Zhiying , Zhang Xiuguo , Zhang Weishi , Zhai Huawei
Published: November 2018

Prediction of short-term bus passenger flow can help bus managers timely and accurately get the changes of the passenger flow and make scientific and reasonable vehicle scheduling to meet passengers' needs. In this paper, a SLMBP model is constructed to predict the bus passenger flow. The SRCC(Spearman rank correlation coefficient) method is used to determine the factors that have significant influence on passenger flow changes. The Levenberg-Marquardt algorithm is used to optimize the BP neural network to avoid getting stuck in local optimal solutions and prompt the convergence speed. A SLMBP neural network parallel algorithm is constructed to perform multiple stations prediction. The experimental results show that the SLMBP neural network parallel algorithm can not only guarantee the accuracy of short-term passenger flow prediction, but reduce the time spent on model learning and prompt the prediction speed.
Application of Deep Learning in Liver Pathological Image Diagnosis
Authors: Wang Yan , Liu Jin , Yu Bing
Published: November 2018

Hepatopathy is a kind of disease with high incidence, so the progress in the field of liver disease research is highly valued. Medical image, as an important part of medical diagnosis, provides an important basis for doctors to make correct diagnosis. Pathological images play a significant role in clinical application because they can facilitate doctors to clearly observe the degree of lesions and make accurate judgments. As an important component of computer vision, deep learning has been paid more and more attention by researchers. The application of computer aided technology in medical image detection has become an important application of computer vision. In view of this situation, an automatic diagnosis method of liver pathological images based on deep learning method is proposed. We analyze the image features, then design and train the classification model. The final results confirmed that this method can effectively classify the pathological images of liver and has a high accuracy rate.
Transmit and Receive Array Structure Design of Two-Dimensional hybrid Phased-MIMO Radar based on Nested Array
Authors: Cheng Tianhao , Wang Buhong , Liu Qiaoge , Tian Jiwei
Published: November 2018

In order to reduce the loss of Degree of Freedom (DOF) brought by the transmit subarray splitting of two-dimensional hybrid phased-MIMO radar, this paper presents a design method of transmitting and receiving array based on nested array structure. Firstly, a two-dimensional hybrid phased-MIMO radar transmitting array based on one-dimensional nested array is presented. On this basis, the receiving end is set as a nested array, and finally a virtual array and difference coarray are formed to expand the number of virtual array elements. The expansion increases the DOF of arrays while preserving the advantages of hybrid phased-MIMO radars. Simulation experiments show that compared with the traditional and coprime hybrid phased-MIMO radar, the proposed method can effectively improve the array DOF and Direction-of-Arrival (DOA) estimation accuracy.
Illegal parking road recognition based on video detection equipment
Authors: Fan Xinyue , Shen Qi , He Qinglong
Published: November 2018

Based on the big data collected by the video detection equipment, the network topology table of the city level video detection equipment is constructed by using the time relation and the spatial position relation of the data. By using the steepest descent method and adaptive method, the travel confidence time randomness model is constructed, which can describe whether a traveler can finish his travel time on time. It overcomes the shortcomings of the existing travel time reliability calculation model, which is difficult to combine with the actual use of video detection equipment data, then examples analysis are followed. The results show that, for the data collected by the video detection device, the travel confidence time randomness model is more accurate than the existing models. It can describe the probability of the traveler arriving at the destination in a given time more accurately, which can be used to identify illegal parking road and provide a reliable basis for traffic management departments in traffic planning, dividing road network status and traffic situation prediction.
An Edge Detection Method for Suspicious Local Regions in CT images with Jaxtapleural Nodules
Authors: Feng Changli , Wei Haiyan , Li Min , Li Xin , Ding Min
Published: November 2018

Juxtapleural lung nodules are often excluded from the lung region in many CT image processing algorithms which are based on intensity information. For solving this problem, a suspicious edge line detection algorithm is proposed to obtain the edge line of the suspicious local lung region in this manuscript. Firstly, the lung region in the CT image is extracted by a fixed threshold. Then a SIFT algorithm is used to detect the feature point in the lung region. To filter out the useless feature points, a closest point matching method is used. Then a K-mean method is introduced to divide those feature points into several parts in which the edges of juxtapleural Lung nodules are contained. Experiments over CT slices show that the proposed method has a great performance in detecting the edge line of suspicious regions.
The Recognition of Rice Area Images by UAV Based on Deep Learning
Authors: Wei Hongyuan , Mao Jian
Published: November 2018

Aiming at the target detection of remote sensing rice field of uav, the image of large-size uav is firstly segmented, and the type of each image is manually identified, and the image training set and verification set are made. Then, the training model of convolutional neural network is realized by python programming. The advantage and disadvantage of the two-layer convolutional neural network and ResNet50 are compared, and it is found that the training set is less and the picture feature complexity is not high in practical application. In the end, the feature recognition of rice field is realized, which has certain application value.
Research on Key Technology of Diamond Particle Detection Based on Machine Vision
Authors: Liu Xiaomin , Mao Jian
Published: November 2018

Most traditional methods for detecting the quality of tiny particles are manually detected by measurement tools. Manual detection has the limitations of low efficiency, high false detection rate and low detection accuracy, and is gradually replaced by non-contact measurement. In this paper, a new denoising algorithm based on median and mean filtering is proposed in diamond particle image analysis. Dynamic threshold segmentation is combined with the Canny algorithm for edge extraction. In the edge extraction process of the particle edge, it will lead to non-closed problems. A contour edge location method based on Hough transform is proposed. Thereby, parameters such as particle size, roundness and ellipticity of the diamond particles are measured.
Fatigue lifetime prediction of a 3D-printing dental implant by Finite Element Analysis
Authors: Wang Zeyuan , Zhou Yufeng
Published: November 2018

This study clarifies the differences between additive manufacturing and traditional manufacturing and the differences in product properties. The fatigue life of 3D printed dental implants was studied by finite element analysis. Pure titanium dental implant made by Concept laser3D printer in Germany is modelled by Solidworks 2016. Finite element analysis was performed in Ansys17.0 and the fatigue lifetime test is predicted in its post-processing system. These components are assembled by software Solidworks according to ISO14801. The maximum and minimum values of each index are obtained in the fatigue post process.
Application of Artificial Bee Colony Algorithm for timing of Road Entrance Signal
Authors: Chen Ming-xia , Zhang Han , Li Xu-yong
Published: November 2018

Aiming at the problem of signal timing at road intersections, this paper uses the artificial bee colony algorithm to optimize the road model of single point multi phase intersection. In this paper, the cost function is the weighted sum of the average delay time, the average number and the capacity. Using the artificial bee colony algorithm to optimize the signal timing of a typical crossroad, and using MATLAB experimental platform to simulate, it shows that the artificial bee algorithm can enhance the road traffic efficiency.
A K-means Algorithm Based On Feature Weighting
Authors: Xu Yan , Fu Xueliang , Li Honghui , Dong Gaifang , Wang Qing
Published: November 2018

Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects. K-means clustering algorithm has been receiving much attention from scholars because of its excellent speed and good scalability. However, the traditional K-means algorithm does not consider the influence of each attribute on the final clustering result, which makes the accuracy of clustering have a certain impact. In response to the above problems, this paper proposes an improved feature weighting algorithm. The improved algorithm uses the information gain and ReliefF feature selection algorithm to weight the features and correct the distance function between clustering objects, so that the algorithm can achieve more accurate and efficient clustering effect. The simulation results show that compared with the traditional K-means algorithm, the improved algorithm clustering results are stable, and the accuracy of clustering is significantly improved.
An Optimization Strategy Based on Hybrid Algorithm of Adam and SGD
Authors: Wang Yijun , Zhou Pengyu , Zhong Wenya
Published: November 2018

Despite superior training outcomes, adaptive optimization methods such as Adam, Adagrad or RMSprop have been found to generalize poorly compared to stochastic gradient descent (SGD). So scholars (Nitish Shirish Keskar et al., 2017) proposed a hybrid strategy to start training with Adam and switch to SGD at the right time. In the learning task with a large output space, it was observed that Adam could not converge to an optimal solution (or could not converge to an extreme point in a non-convex scene) [1]. Therefore, this paper proposes a new variant of the ADAM algorithm (AMSGRAD), which not only solves the convergence problem, but also improves the empirical performance.
Research and Appalication of Software Defect Predictionn based on BP-Migration learning
Authors: Zhang Jie , Wang Gang , Jiang Haobo , Zhao Fangzheng , Tian Guilin
Published: November 2018

Software Defect Prediction has been an important part of Software engineering research since the 1970s. This technique is used to calculate and analyze the measurement and defect information of the historical software module to complete the defect prediction of the new software module. Currently, most software defect prediction model is established on the basis of the same software project data set. The training date sets used to construct the model and the test data sets used to validate the model are from the same software projects. But in practice, for those has less historical data of a software project or new projects, the defect of traditional prediction method shows lower forecast performance. For the traditional method, when the historical data is insufficient, the software defect prediction model cannot be fully studied. It is difficult to achieve high prediction accuracy. In the process of cross-project prediction, the problem that we will faced is data distribution differences. For the above problems, this paper presents a software defect prediction model based on migration learning and traditional software defect prediction model. This model uses the existing project data sets to predict software defects across projects. The main work of this article includes: 1) Data preprocessing. This section includes data feature correlation analysis, noise reduction and so on, which effectively avoids the interference of over-fitting problem and noise data on prediction results. 2) Migrate learning. This section analyzes two different but related project data sets and reduces the impact of data distribution differences. 3) Artificial neural networks. According to class imbalance problems of the data set, using artificial neural network and dynamic selection training samples reduce the influence of prediction results because of the positive and negative samples data. The data set of the Relink project and AEEEM is studied to evaluate the performance of the f-measure and the ROC curve and AUC calculation. Experiments show that the model has high predictive performance.