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This paper deals with the problem of large number of parameters and complex calculation in video abstract generation of Fully Connected Network and Convolutional Neural Network. At the same time, the training and testing of such model need a lot of time and computer resources. We came up with a deep learning network parameter compression method based on Singular Value Decomposition(SVD) and Trucker Decomposition (TD) is proposed to generate the video summaries. The experiment was compared with other methods on TVSum and SumMe dataset, and the F1 value was 55.3% in TVSum dataset and 46.8% in SumMe dataset. At the same time, the degree of test time shortening under the same data volume is taken as the evaluation basis. The experimental results show that the proposed method achieves 1.04 times of acceleration in the SVD forward calculation, and 1.29 times of acceleration in the TD forward calculation. In a conclusion, the neural network model based on low-rank decomposition can effectively save computer resources and the time consumed by running programs.

At present, the raindrop size distribution (DSD) fitting method and traditional neural network method are the main quantitative precipitation estimation (QPE) algorithms. The former is not enough to express the spatiotemporal variability of the DSDs using generalized $Z-R$ parameter relationship, and the latter is limited by the number of network layers, network structure and computing power. Their performance cannot both meet actual needs. In this research, we propose an alternative approach to dual-polarization radar (DPR) QPE. In particular, a radar-gauges dataset (RGD) is constructed for QPE based deep learning using radar raw data and gauges measurements for landfall typhoon in South China, and a deep learning model is designed and trained using this dataset. The model is applied to radar data to produce rainfall estimates. Preliminary results show the promising performance of this novel method compared to traditional QPE estimators.

with the development of information technology, the data of online teaching resources is also rising rapidly. It is not convenient for students to screen the types of resources in the learning process. The diversified integration of online teaching resources is also the basis of ideological and political course teaching in colleges and universities. Therefore, this paper designs the online teaching resources diversified integration of ideological and political course in colleges and universities under the background of big data. Through the design of a diversified integration model framework, the resource service system and teaching resource platform are integrated to meet the learning needs of college students. The decision tree is introduced for classification and integration. EBP pruning method is used to obtain the data regions that analysts are interested in. The experimental results show that, after using the diversified integration method, the students' theoretical course scores have been significantly improved, which shows that the design of diversified integration method has a certain effectiveness.

In this article, we consider the event-triggered control of discrete-time nondeterministic linear systems, and instead of the continuous feedback strategies, we use the intermittent feedback strategies. Firstly, we find the optimal event generator and controller under the assumption that the closed-loop system is Hurwitz, hence, the state trajectory of the event-triggered system is close to that of the continuous feedback system with arbitrary accuracy. At the same time, a minimum number of states is sent to the controller to reduce the communication between the event generator and the controller. Furthermore, we show that the controllability of the system is sufficient to ensure that the error between the state trajectory of the event-triggered system and the state trajectory of the continuous feedback system can be arbitrarily small.

Considering the difference of emergency response capacity in different geographical regions, a multi-objective route optimization model was constructed with the transportation cost, transportation risk and risk equity in consideration of emergency response capacity. The risk of hazardous materials road transportation was innovatively considered from the perspective of risk equity, and the feasibility of the model was verified through an example analysis. The experimental results show that the multi-objective model can effectively take into account the interests of the government, the transporter and the citizen, and the results are of certain reference value to the decision-makers.

A Geo-SAR staring and tracking imaging mode, which is employed to obtain the hurricane information, is illustrated in this manuscript. Hurricane, as one kind of serious natural hazards, may cause extensive economic losses and severe civil personnel losses in coastal areas. Geo-SAR can adjust its antenna pointing direction to acquire its image when some hurricane changes its location. Due to satellite's little pitch and roll controlling angles, short responding time is needed to follow the hurricane's track for continual imaging. This paper firstly illustrates Geo-SAR staring and tracking imaging mode. Furthermore, attitude controlling expressions and SAR system key parameter designing approach are presented. Finally, a simulation experiment is performed to verify the efficiency and superiority of this approach. The results show that this new mode has a good effect on hurricane observation.

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific "dual--party" secure learning scenario based on two parties -- both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others. To achieve the above goal, we firstly extent -- LightGBM -- a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets., The first two authors contributed equally to the manuscript. The paper has been accepted for publication in IEEE BigData 2019

The prevention and control of crop diseases and insect pests is a key link in agricultural production. Accurate image recognition of diseases and insect pests is significant in agriculture and it has high efficiency, low cost and convenient operation. The traditional recognition method is difficult to recognize the pests relying on manual recognition, and it is easy to misjudge the pests. MobileNet V2 is an important algorithm for image recognition, which has high recognition accuracy and much lower parameter quantity than other algorithms. [1] This paper proposes a new SE-MobileNet V2 target recognition algorithm which improves the MobileNet V2 algorithm. The proposed algorithm embeds SE module between the last pooling layer and convolution layer of MobileNet V2 network, enhancing the receptive field of the network. It combines the advantages of MobileNet V2 and SENet, is more accurate than MobileNet V2 and is more suitable to recognize small target. In this paper, experiments are conducted based on data sets containing 11 kinds of lycium barbarum diseases and pests. Through experimental verification, the proposed algorithm has the highest recognition accuracy and strongest robustness than other algorithm. SE-MobileNet V2 target recognition algorithm improves the accuracy and speed of image recognition, is meaningful for accurate control of lycium barbarum diseases and pests.

This paper is concerned with output feedback control to reduce the bullwhip effect of a dynamic supply chain system based on an interval-like demand prediction scheme. By introducing an interval-like time-varying delayed state signal to predict the order variations of downstream sites and taking into account the parametric perturbations, the dynamic supply chain is modeled as an uncertain linear discrete-time system with interval-like time-delay. Then, output feedback control scheme is proposed to improve the performance of the supply chain system. A sufficient condition to the existence of the output feedback controller is derived. It is found from simulation results that the designed output feedback controller can effectively stabilize the supply chain system with interval-like time-delay and parametric perturbations. Moreover, if the introduced interval-like time-delays are chosen properly, the bullwhip effect of the system can be reduced significantly.

Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. In addition, we implement a general semantic retrieval framework that combines our proposed model and the Approximate Nearest Neighbor (ANN) technology, which enables us to find the most similar question from all available candidates very quickly during online serving. The experiments show the superiority of our proposed method as compared with the existing sentence matching models., IJCNN 2019 - International Joint Conference on Neural Networks, Budapest Hungary, 14-19 July 2019

A human object tracking algorithm fused with depth information, color information and forecast information is presented in this paper, which enables follow-robot to track motion human object robustly. Firstly, Camshift algorithm is expended into three-dimensional space by color information and depth information. Secondly, the interference from complex background is eliminated from the projection drawing by depth image of motion human object in three-dimensional space. Finally, human object’s location information is forecasted by Kalman algorithm and human’s center of mass location is calculated in the corresponding depth image, human object’s tracking is achieved now. Using Kinect sensor to capture RGB image and depth image, and human object tracking algorithm fused with Multi-information is achieved and applied to the follow-robot. The experimental result shows that the algorithm proposed in this paper can track motion human robustly in condition of complex background, uneven ambient light, deformed target and so on.

Mechanical ventilation (MV) is an effective management strategy for neonates with critical congenital heart disease or congenital tracheal stenosis (CTS). However, there is no standard for patient-specific mode selection. This study numerically investigated the aerodynamic effects of tracheal model with severe stenosis when by different levels of ventilator assist during Pressure Support Ventilation (PSV) and Neurally Adjusted Ventilatory Assist (NAVA). Based on medical images, a three-dimensional (3D) tracheal model with insertion of a cuffed endotracheal tube was reconstructed. The technology of Computational Fluid Dynamics (CFD) was applied to simulate the airflow in the trachea. The aerodynamic parameters, including pressure drop (PD), streamlines and rate of energy loss (ELR), were compared to assess the MV effects. The results indicated that high assist level, accompanied by high airflow velocity, should be the main cause of aerodynamic disorders in the airway during MV. Lower PD, ELR and relatively steady velocity of NAVA was observed. Compare with PSV, it was inferred that preserved auto-regulation of respiration during NAVA may have potential advantages for flow rate regulation in patient with CTS. CFD analysis is a potential noninvasive tool for obtaining tracheal aerodynamics, which will be helpful for making decisions of appropriate MV mode.

With the construction and implementation of the government information resources sharing mechanism, the protection of citizens' privacy has become a vital issue for government departments and the public. This paper discusses the risk of citizens' privacy disclosure related to data sharing among government departments, and analyzes the current major privacy protection models for data sharing. Aiming at the issues of low efficiency and low reliability in existing e-government applications, a statistical data sharing framework among governmental departments based on local differential privacy and blockchain is established, and its applicability and advantages are illustrated through example analysis. The characteristics of the private blockchain enhance the security, credibility and responsiveness of information sharing between departments. Local differential privacy provides better usability and security for sharing statistics. It not only keeps statistics available, but also protects the privacy of citizens.

Problem-based learning and entrepreneurially minded learning modules have been developed to include fluid power concepts into undergraduate Mechanical Engineering core courses Thermodynamics and Fluid Mechanics. Modules in Thermodynamics focus on pneumatics and modules in Fluid Mechanics focus on hydraulics. The purpose of this work is to assess the created modules for student awareness of fluid power, knowledge of fluid power concepts, and growth in the entrepreneurial mindset. Both direct and indirect methods were used for assessment. Assessment results indicate that students applied fluid power concepts that are traditionally not covered in these courses. Student surveys also indicate that students demonstrated sample behaviors associated with the entrepreneurial mindset, as defined by the Kern Entrepreneurial Engineering Network framework.

A reversible watermarking algorithm in low distortion is proposed for color images in this paper. Through analyzing the correlation of different color components in color image, and expanding prediction pixels by adaptive prediction operator, the embedding of watermark data and the retrieving of the original image are realized. Experiments show that, this algorithm achieves better effect compared with other traditional algorithms for color image, and it can retrieve the original image losslessly.

Dynamic checking the integrity of software at run-time is always a hot and difficult spot for trusted computing. In this paper we present a lightweight approach, Control-flow checking using branch sequence signatures (CFCBS), to checking violation of control flow integrity at run-time. Our solution assigns a unique branch sequence signature, which enhances the capability of fault coverage, to each basic block at compile time and upgrades the branch sequence signature when the program is executed. Control flow faults can be detected by comparing the run-time signature with the saved one at compile time.

Recent years have seen explosive growth in information security problem. Trusted computing is increasingly used in the fields of information security. The basic idea of trusted computing is different from traditional security method. In other words, system abstracts normal behavior and compare with real-time behavior. Most of the current research focuses on static integrity metrics. However, it is not enough for dynamic measurement in trusted computing environment. In this paper, we present an efficient identification methodology of malicious insider user behavior. We focus on modeling insider user normal behavior in trusted computing networks. Our research methodology provides an approach to construct, assess, and optimize insider user normal behavior model. Our model framework derives from a dynamic Bayesian perspective. The experiment shows efficiency and robustness of our prototype model.

Through K-nearest neighbor algorithm based on k-d tree, the noises in the disordered and three-dimensional point cloud are removed. The value of the k in the K-nearest neighbor algorithm is discussed in detail. By three-dimensional projection, the dimension of k-d tree is reduced. So efficiency of de-noising is improved. The original depth image corresponding with the point cloud data is converted to gray scale images. After binarization and extracting foreground region, the correspondence between the foreground region and the point cloud data helps to remove the noise from specified perspective. By a best perspective projection, three-dimensional point cloud is simplify to two-dimensional point cloud. Using the K-nearest neighbor algorithm to remove the rest of noises. Consequent of de-noising is good.

In order to prevent voltage collapse, lots of voltage stability assessments are proposed to search weak buses. However, these assessments are either only related to network topology or time-consumed. Therefore, voltage stability extended voltage sensitivity factor (EVSF), an improved voltage stability assessment considering voltage regulation of generator and HVDC, is given and derived in this paper. And a case is illustrated and analyzed. Comparing with VSF indicator, EVSF is more considerate and sensitive to discover weak buses with multi-infeed HVDC. It's proved that EVSF is an effectiveness assessment for power system voltage stability analysis.

This paper presents a multi-course problem-based learning (PBL) experience to foster the entrepreneurial mindset in the junior and senior mechanical engineering curriculum. Previous senior project students designed, fabricated, and validated a fluid-powered gantry crane that exhibited areas for improvement. To improve upon the project gantry crane, junior-and senior-level students implemented concurrent problem-based learning activities in three courses: Fluid Mechanics, Heat Transfer, and Mechatronics. Even though the PBLs targeted both technical and entrepreneurial objectives, only the results of entrepreneurial mindset attributes are discussed in this paper. Assessment results including student commentary are detailed and discussed in the paper. Preliminary results indicate extensive student practice of entrepreneurial skills.

Considering the incompleteness of localization information in wireless sensor networks, the sensor network monitoring region was divided into a plurality of small grids. Sensors and targets are randomly dropped in the grids. Defining the targets position information as a sparse vector, a range-free multiple target localization algorithm using compressive sensing theory (MTLCS) was proposed. Only targets number sensed by sensor nodes is needed in the algorithm. It doesn't depend on extra hardware measurements. MTLCS can provide the targets position with sparse detected information. The number of targets detected by sensor nodes was expressed as the product of measurement matrix, sparse matrix and sparse vector in compressive sensing theory. Targets are localized with the sparse signal reconstruction. In order to explore MTLCS performance, BP and OMP are applied to recover targets localization. In case of grid number N=20 × 20, simulation is done with different measurement noise, sensing radius, targets number and sensor numbers. In case of 0.6ale;r/nale;0.8, simulation results show that MTLCS has the localization error smaller than 30% without physical distance measurement. MTLCS can satisfy the requirements of target localization in wireless sensor network in the case of incomplete information.

In this paper, an operator-based robust tracking control design for nonlinear plants with perturbation is proposed by using robust right coprime factorisation approach and operator-based observers. In the real industrial process, most of the controlled systems have nonlinear characteristics, including unknown uncertainties consisting of modelling errors and external disturbances. Considering the nonlinearity and uncertainties, an operator-based robust nonlinear perfect tracking control using robust right coprime factorisation is investigated. That is, first, because the unknown uncertain plant generates difficulties in designing realisable operator controllers to obtain the desired tracking performance, an operator state observer and an operator disturbance observer based on nominal plant properties are designed to compensate the effect of uncertain plant. Second, a realisable operator-perfect tracking controller is designed to improve the control performance and to realise the perfect tracking. Based on the proposed designed scheme, the designed system is robustly stable and perfect output tracking performance can be realised. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.

In this paper, an algorithm about heart sound signals segmentation was described, in which only heart sound signals were used. The algorithm was based on the normalized average Shannon energy of heart sound signals. 20 cases of normal and 20 cases of abnormal heart sound signals of heart valve disease selected randomly from the database of the Institute of Research Clinique of Montreal were used for testing. The features of each component, such as AR spectrum energy in systolic period were extracted after segmentation. The energy of high frequency is generally less than -95db to normal signal. Reversely, the energy of high frequency is generally more than -95db for abnormal signal. This feature is valuable in diagnosis of congenital heart diseases and other cardiac diseases.

A semantic active service model is proposed for the wireless sensor network in intelligent buildings, considering the characteristics of wireless sensor networks such as multi-nodes and intelligent. This paper discusses some key technologies for achieving the model and describes the service with OWL-S semantic ontology description language. It also proposed a timeliness and high self-adaptable method of service matching and composition for the user-oriented active services under the wireless sensor networks.

On the basis of systematic review of the literature of knowledge management in the field of education in China over the past decade, this paper makes a quantitative analysis and qualitative research on 145 articles, which were published in core magazine, and summarize the current situation and development trend of knowledge management in the field of education in China. At last, some questions are pointed out in the application process in educational field of China, and it put forward the corresponding proposes and strategy, so as to provide reference and guidance for the following study.

Water resources are the most important strategic resource in Northwest. Poor water condition is an important reason for fragile ecological environment, economic backwardness, and concentrated poverty population. Deep study of water security is an effective way to promote the protection of the western development strategy. This paper reviews the forefront progress of both international and domestic, conceptualizes water security, constructs DPSIR model, and emphasizes the seriousness of the water security issues caused by the high intensity of human activity, then explores the driving, pressure, influence, status and respond factors of water security. Zhangye region is taken as a case study area, and the AHP and entropy weight method is employed to determine the weight factors. Finally, the gray theory method is used to evaluate water security in 2004–2010 in Zhangye. The conclusions are that: the water security of Zhangye City has increased steadily and it has taken a greater progress since 2004 to 2010. Adverse natural factors are still the main reason for water security issues. In addition, the facts of high intensity of human activities, less developed economic status and the negative system of management further conduce social resources scarcity, which aggravate the water safety problems.

A new Robot Terrain Inclination model (RTI model) extracted from laser scanner data is proposed in this paper. The RTI model can be used to evaluate the terrain traversability or assist localization and mapping for outdoor mobile robot. The paper presents the segmentation of the terrain points and the non-terrain points and the extraction of RTI model. Experiments were carried out to demonstrate the accuracy of RTI model.

This paper deals with an operator based on plant output tracking design problem of time-delays nonlinear systems by using a robust right coprime factorization approach. In details, operator based nonlinear control systems are designed, and sufficient conditions for the designed feedback control systems are obtained. Based on the conditions, robust stability of the nonlinear systems is ensured and output tracking performance is also realized. A simulation example is given to support the proposed design scheme.

Based on the concept lattice theory, this paper studies the cycle of association rule mining about the periodic fluctuations of time-series. First of all, do anti-season pretreatment to time-series, then give the algorithm of the generation of the cycle of association rules, and the pruning to the generated concept within algorithm improve the efficiency of the mining speed. And then we can use the given higher precision model to do anti-season changes calculation to those time series which dissatisfy anti-season pretreatment using the moving average method.