Research Hot Issues Research Articles

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

High Sensitivity Multiple Microcavity Enhanced 3-D Printed Micro-Stud Array Ultrafast Response Detector at 6G Frequency

The next generation of communication 6G technology in the terahertz band is crucial for the development of the future high-speed information society. How to achieve a highly sensitive response to low power density terahertz signals at room temperature has been one of the frontier research hot issues within this field. In addition, the reproducible line height of the device and its availability for mass production are also urgent issues to be addressed. Here, We have proposed a 6G detector based on combining the advantages of sub-wavelength micro-structures, 3D micro-cavity, and Weyl-semimetallic films. The localized surface plasmon (LSP) effect by the micro-stud array is used for the enhancement of the light-matter interaction. Also, the 3D structure leads to a micro-cavity allowing for a near enhancement of the interaction on a small scale. Furthermore, the 0-band gap of the Weyl-semimetal and the enhanced response to low-energy photon displacement currents near makes the Weyl-semimetal more suitable for detecting terahertz waves. In addition, the use of projection micro stereolithography 3D print technology combined with magnetron sputtering of thin films makes this terahertz detection technology solution reproducible, low cost, simple processing, and promising for industrial production, contributing to the development and diffusion of 6G technology.

A new symbolic representation method for time series

Time series symbolic representation methods have been a research hot issue. Among them, the most representative symbolic methods such as Symbol Aggregation Approximation(SAX) and Symbol Fourier Approximation(SFA) have been widely used in various scenarios. However, they all have some flaws in some way. For SAX, the converted data in the approximation phase displays a shrinkage distribution(standard deviation σ shrinkage) that does not meet the assumptions in the definition. For SFA, it only obtains global frequency domain information, which leads to poor recognition ability for reciprocating frequency conversion sequence. Simultaneously, the symbol distance defined has poor interpretability due to spanning two spaces.In this paper, we propose a novel symbolic method called Symbol Fractional Fourier Approximation(SFFA), which shows multivariate approximation capabilities by Fractional Fourier Transform (FrFT) and adds a new supervised strategy for symbol mapping based on the chi-square distribution. It not only effectively avoids the influence of shrinkage distribution, but also has a strong ability to distinguish special sequences. Moreover, the SFFA symbol distance is proved to satisfy the low boundary lemma. Furthermore, it can achieve the same effect as SFA when the appropriate parameters and strategies are selected. Finally, when combined with the Vector Space Model (VSM) to classify time series, a large number of experiments show that SFFA-VSM outperforms SFA on all open-source data sets.

Highly efficient photocatalytic organic dyes degradation based on 1D magnetic Bi2Fe4O9/C@AgBr composite

AbstractWastewater treatment has become a research hot issue in recent decade. Organic dyes as one of the main pollutants can change the water behavior. To deracinate the organic pollutants from wastewater, there are numerous methods have been taken into consideration. Photocatalysis is the promising strategy for the dye degradation with low cost, high efficiency, and environmental friendliness. Hence, a novel one‐dimensional (1D) magnetic Bi2Fe4O9/C@AgBr composite photocatalyst was prepared employing electrospinning, calcination, and deposition–precipitation methods. The morphology and composition of photocatalyst were characterized, as well as the photocatalytic activities of degrading methylene blue (MB) under visible light were investigated. And the results exhibited that the efficiency of Bi2Fe4O9/C@AgBr under visible light for 60 min is 97.4%. After three cycles, the degradation efficiency of MB could still reach 88.0%. Moreover, radical quenching experiment implicated superoxide radical anions as the primary active species responsible for photodegradation of MB. This work provides a new insight into the design of effective photocatalysts for practical application.

A Literature Review of Foreign Studies on the Impact of CALL on Second Language Acquisition from 2015

With the rapid development of economy and technology, more and more fields are combined with electronic intelligence and artificial intelligence, and language teaching is no exception. As a research hot issue, computer-assisted language learning (CALL) has attracted more and more people's attention. Based on computer-aided technology, teachers can not only provide students with rich language input, but also improve classroom efficiency. However, there are still some problems in this technology. This paper reviews some empirical studies on the impact of computer-assisted language teaching from 2015 and reveals the relevant progress and shortcomings of foreign researches in terms of six aspects from the perspective of language teaching, in order to provide some enlightenment for future research.

Detection and recognition method of monocular vision traffic safety information for intelligent vehicles

Intelligent vehicle technology has become a research hot issue in recent ten years, the reason is that intelligent vehicles can not only be used as a flexible weapon platform in the military. And in life, it is also a system that provides convenience and security for people. For example, driverless cars and advanced driver assistance systems (ADAS). Information processing is the key to the degree of intelligence, and the detection and recognition of traffic safety information based on monocular vision is the core of information processing, it’s also the bottleneck problem. Because of the complexity and diversity of the environment have brought great challenges to this problem. In this paper, the existing lane detection methods in structured and semi-structured roads do not specifically consider the problem of weak line detection, two models are proposed. Fuzzy LDA enhancement model is used to enhance the contrast of lane area, another brightness contrast saliency model can be used for robust Lane extraction. Then, two models are applied to lane detection, a two-stage lane detection method is proposed and a blind area vehicle detection method is designed. Firstly, the vehicle area is roughly extracted based on road gray statistics, and then the typical vehicle features are screened finely. Finally, the extracted features and SVM classifiers are used to confirm the candidate regions. Experiments show that: The proposed method can detect the vehicle in the blind area very well and is insensitive to the shape distortion and size change of the vehicle.

A visible human body slice segmentation method framework based on OneCut and adjacent image geometric features

As a recent research hot issue, obtaining the accurate 3 D organ models of Visible Human Project (VHP) has many significances. Therefore, how to extract the organ regions of interest (ROI) in the large-scale color slice image data set has become an urgent issue to be solved. In this paper, we propose a method framework based on OneCut algorithm and adjacent image geometric features to continuously extract the main organ regions is proposed. This framework mainly contains two parts: firstly, the OneCut algorithm is used to segment the ROI of target organ in the current image; secondly, the foreground image (obtained ROI) is corroded into several seed points and the background image (other region except for ROI) is refined into a skeleton. Then the obtained seed points and skeleton can be transmitted and mapped onto the next image as the input of OneCut algorithm. Thereby, the serialized slice images can be processed continuously without manual delineating. The experimental results show that the extracted VHP organs are satisfactory. This method framework may provide well technic foundation for other related application.

A new container scheduling algorithm based on multi-objective optimization

Docker container has been used in cloud computing at a rapid rate in the past 2 years, and Docker container resource scheduling problem has gradually become a research hot issue. It is NP-complete as the optimization criteria is to minimize the overall processing time of all the tasks. Nevertheless, minimization of makespan does not equate to customers’ satisfaction. Aiming at the performance optimization of Docker container resource scheduling, the authors propose a multi-objective container scheduling algorithm, namely Multiopt. The algorithm considers five key factors: CPU usage of every node, memory usage of every node, the time consumption transmitting images on the network, the association between containers and nodes, the clustering of containers, which affect the performance of applications in containers. To select the most suitable node to deploy containers needed to be allocated in the scheduling process, the authors define a metric method for every key factor and establish a scoring function for each one and then combine them into a composite function. The experimental results show that compared with the other three well-known algorithms: Spread, Binpack, and Random, Multiopt increases the maximum TPS by 7% and reduces the average response time per request by 7.5% while consuming roughly same allocation time.

A Literature Research on the Hot Issues in Public-Private Partnership Projects: Based on the Classified Statistics of Literatures During 2008–2016

As the extensively use of PPP scheme, the lack on the theory guidance for system establishment are gradually getting more noticed. Based on the year and research topics, the paper classified PPP project relevant literatures in recent eight years. It explored the current research hot issues of PPP projects and summarized the corresponding research methods and conclusions. Hope provide theoretical basis to establish the system.

An Approach of Rough Set for Data Fusion

Data fusion is an important research hot issue due to rapidly growing networks. Rough set theory is an effective tool for process imperfect information. In this paper, we suggest an approach for fusing attribute information from multiple sources into a decision system based on rough set model. A numerical example is employed to illustrate how to fuse information that we take interesting in and to validate the feasibility of the approach.

Robust Visual Tracking Based on Support Vector Machine and Weighted Sampling Method

Abstract Visual tracking algorithm based on binary classification has become the research hot issue. The tracking algorithm firstly constructs a binary classifier between object and background, then to determine the object’s location by the probability of the classifier. However, such binary classification may not fully handle the outliers, which may cause drifting. To improve the robustness of these tracking methods, a novel object tracking algorithm is proposed based on support vector machine (SVM) and weighted multi-sample sampling method. Our method constructs a classifier by sampling positive and negative samples and then to find the best candidate that has the largest response using SVM classifier. What’s more, the proposed method integrates weighted multi-instance sampling method, which can consider the sample importance by the different weights. The experimental results on many sequences show the robustness and accuracy of the improved method. The proposed target tracking algorithm in video target tracking with a variety of classic popular tracking algorithm, better able to achieve robust target tracking, but also in the infrared video, the infrared target tracking is also has the advantages of stable and accurate.

Analysis of projects received and funded in fields of emergency and intensive care medicine/trauma/burns/plastic surgery from National Natural Science Foundation of China during 2010-2013

To summarized the projects received and funded in the fields of emergency and intensive care medicine/trauma/burns/plastic surgery from National Natural Science Foundation of China (NSFC) during 2010-2013, put forward the thinking and perspective of this future trend in these fields. The number of the funded project and total funding in the fields of emergency and intensive care medicine/trauma/burns/plastic surgery from NSFC during 2010-2013 had been statistical analyzed, in the meantime, the overview situation of various branches in basic research and further preliminary analysis the research frontier and hot issues have been analyzed. (1) The number of funded project were 581 in H15 of NSFC during 2010-2013, total funding reached to 277.13 million RMB, including 117 projects in H1511 (emergency and intensive care medicine/trauma/burns/plastic surgery and other science issue), 96 projects in H1507 (wound healing and scar), 88 projects in H1502 (multi-organ failure), 71 projects in H1505 (burn), 61 projects in H1504 (trauma). (2) The top 10 working unit for project funding in the field of emergency and intensive care medicine/trauma/burns/plastic surgery present as Third Military Medical University (70), Shanghai Jiao tong University (69), Second Military Medical University (40), Chinese PLA General Hospital (36), Forth Military Medical University (35), Zhejiang University (22), Sun Yat-Sen University (18), Southern Medical University (14), China Medical University (11), Capital Medical University (11) respectively, the number of funded project positive correlated with funding. (3) The funded research field in H15 covered almost all important organs and system injury or repair research, our scientists reached a fairly high level in some research field, for example, sepsis, trauma, repair, et al. "Sepsis" was funded 112 projects in H15 for 4 years, the growth rate became rapid and stable comparing to shock, burns and cardiopulmonary resuscitation funded projects' number. "Emergency and intensive care medicine/trauma/burns" research fields related to heart, lung, bone/cartilage/muscle, stomach/intestinal/liver, brain/spinal cord/peripheral nerve and other tissues/organs. The number of funded projects in plastic surgery related research fields in angioma and flap related projects were down below to 3 projects, but the number of funded project in wounds, scar repair related research field were more than other fields relatively. (4) In frontier and research hot issue, the funded rate represent as 23.8%, 21.4%, 19.0% and 23.9% in stem cell related research fields in 4 years respectively. The funded rate average to 20.9% in epigenetic related research fields for four years, the funded rate achieved to break through "zero" in autophagy related research fields, the total rate raised to 32.6% from 2011 to 2013. The funded number and funding were raised rapidly in the fields of emergency and intensive care medicine/trauma/burns/plastic surgery from NSFC. The application for each proposal should be focus on concise or upgrade the scientific issues to improve the quality. The depth or systematic in content and interdisciplinary research fields (e.g. immunology) should be paid attention to. Sepsis, trauma and burns will be the main stream direction in future in the fields of emergency and intensive care medicine/trauma/burns/plastic surgery. The fields of wound healing and scar, surface organ defects, damage, repair and regeneration, surface tissue/organ transplantation and reconstruction, craniofacial deformities and correction are important develop directions in future work.

Video Object Detection Algorithm Based on Gaussian Mixture Models

The moving object detection of the video image is the basis of sequence image analysis, and it is the research hot issue of today’s foreign and domestic scholars. For detecting the moving object from the scene image in time, a detection algorithm of video moving object based on Gaussian mixture models is proposed in the paper. The pixel values are seen as the combination of the foreground Gaussian distribution and the background Gaussian distribution, and the background estimation and the adaptive background update will be put up. The statistical number of the foreground pixel of the current frame determines whether the light has a larger change, and it combines with the frame-difference method to detect moving object. The experimental results show that the algorithm can quickly and accurately establish the background model and accurately segment the foreground object.

Research on Component Retrieval Methods

With the development of the component technology and the expansion of component library, representing and retrieving components, the two core technologies of component library management have been a focus on research hot issues in researches. Based on the present widely-used component faceted classification description method and its features, and from the different requirements and ways of component retrieval that were used by component re-users, three component retrieval ways based on browser, facet and term, the five corresponding matching models and two matching algorithms are proposed. The theoretical analysis and experimental results show that the three kinds of retrieval methods used in large-scale component library component retrieval, will meet the component reuse of various searching requirements and that these ways are feasible in retrieval efficiency..