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Due to the huge difference in the representation of sketches and 3D models, sketch-based 3D model retrieval is a challenging problem in the areas of graphics and computer vision. Some state-of-the-art approaches usually extract features from 2D sketches and produce multiple projection views of 3D models, and then select one view of 3D models to match sketch. It's hard to find “the best view” and views from different perspectives of a 3D model may be completely different. Other methods apply learning features to retrieve 3D models based on 2D sketch. However, sketches are abstract images and are usually drawn subjectively. It is difficult to be learned accurately. To address these problems, we propose cross-domain correspondence method for sketch-based 3D model retrieval based on manifold ranking. Specifically, we first extract learning features of sketches and 3D models by two-parts CNN structures. Subsequently, we generate cross-domain undirected graphs using learning features and semantic labels to create correspondence between sketches and 3D models. Finally, the retrieval results are computed by manifold ranking. Experimental results on SHREC 13 and SHREC 14 datasets show the superior performance in all 7 standard metrics, compared to the state-of-the-art approaches.

As one of the most important components of plants, the reconstruction of high-precision leaf models is a critical step for building tree models. According to the morphological structure of leaves, this paper proposes a leaf reconstruction method based on the laser point cloud data. The moving least squares method is used to fit the leaf surface to extract the complex profile information of the leaf, and the delaunay triangulation algorithm is used to reconstruct the three-dimensional model of the fitted leaf point cloud data. The results show that the proposed method can not only realize the three-dimensional reconstruction of the leaf, but also reflect the morphological characteristics of real leaf.

The flow direction calculation over flat surfaces in a digital elevation model (DEM) is vital for hydrological analysis. The Garbrecht and Martz (G&M) algorithm is a widely used algorithm to obtain correct hydrological flow patterns over flat surfaces. In this study, we propose an efficient variant over the fastest variant of the G&M algorithm. The proposed variant introduces three improvements and significantly reduces redundant computation. For all tested DEMs, the running time of our proposed variant is 26% to 49% shorter than that of the fastest variant. The proposed variant can be used to obtain correct hydrological flow patterns over flat surfaces with less time.

The fidelity of wind tunnel data often cannot meet the fidelity requirement of engineering simulator. Based on parameter decoupling principle, a step-by-step longitudinal parameter estimation method and a flight test procedure aiming at fly-by-wire aircraft was designed to avoid the disturb to estimation coming from the flight control system and decrease the collinearity of the flight data, as a result, improving the fidelity of estimation. The result of the parameter estimation reveals the response of dynamic model established on the basis of estimated parameter satisfied the tolerance of CCAR60, which means it meets the fidelity requirement of the engineering simulator. It also proves that the longitudinal parameter estimation method and the flight test procedure are capable of gaining a sound estimation.

To a great extent, the quality of teaching determines the level of trained talents. Now it has entered the era of intelligent education, coupled with the rapid development of the Internet, has produced a large number of teaching data, which also makes the monitoring and evaluation of teaching quality become particularly difficult. In view of the above problems, this paper proposes the construction of big data monitoring platform for teaching quality under intelligent education. In this paper, the OPC UA unified architecture is used for communication between devices, and the information configuration is based on the spring boot framework to achieve data collection. Then, data processing is based on GRU neural network, and spark distributed computing framework is used to improve the efficiency of data operation. Finally, the monitoring effect is realized by constructing the evaluation system.

Ensemble learning can deal well with the concept drift problem in industrial process data. However, the accuracy and diversity of base learners have a great impact on the generalization performance of the ensemble model. In order to further improve the ability of ensemble model to cope with concept drift, this paper proposes a selective ensemble modeling method based on Random Vector Functional Link network (RVFL) and game theory. RVFL network is used as base learner, the accuracy of the base learner and the contribution rate of the base learner to the diversity of the ensemble model are regarded as the two sides of the game. Game theory is used to solve the optimal selection scheme for the accuracy and diversity of the ensemble model. The rationality and effectiveness of the proposed algorithm are verified by using open data sets and real industrial data.

As a data-driven management mechanism, the safety management system (SMS) emphasizes that all safety decisions should be made based on objective data. The safety performance management (SPM), as an element of SMS, is considered of great importance to evaluate the effectiveness of SMS. The standard deviation principle (STDEVP) recommended by International Civil Aviation organization (ICAO) offers a way to set the criteria values that serve to trigger the evaluation, decision, adjustment or intervention relate to particular safety performance indicator (SPI). However, more specific operational guidance on how to determine reasonable safety triggers based on the historical data is yet to offer. This paper proposed a practical way of designing safety triggers based on STDEVP using linear optimization. The objective of the optimization is to reduce the cost of safety management while maintain an acceptable safety margin, and constraints are based on historical data of the operation. The optimized safety margin can be determined by solving the constraint conditions, which will serve as the reference of designing safety triggers. The applicability of the method is verified by applying it to monitor the SPIs in airline companies.

Aflatoxin B1 and B2 are toxic to humans and widespread in agricultural and food products. Thus, rapid and accurate determination of these substances is essential for the safe consumption of agricultural products and prevention of further detriment to human health. Detection via conventional methods has proven to be time-consuming and complex. Here, we present that a terahertz (THz) metamaterial (MM)-based biosensor shows promising potential for high-sensitivity, quantitative identification of aflatoxin B1 and B2 with extremely small amounts. In addition, the sensing of analyte was demonstrated as a function of content, thickness, and dielectric constant by detailed measurement and simulation.

Utility pattern mining is an important data mining technology that can find patterns that are both statistically significant and in accordance with users' expectations and objectives, which emerged recently to address the limitation of frequent pattern mining. To the best of our knowledge, utility pattern mining algorithms proposed so far however are all sequential ones that are inefficient and not scalable when dealing with huge amounts of data. To address the scalability and efficiency challenge, this paper proposes a parallel utility pattern mining algorithm based on Spark, a parallel programming model that uses in-memory storage for data sharing. The contributions include an improved vertical data structure, a three-phase parallel mining framework, and an efficient algorithm. Extensive experiments on both artificial and real-world data show that the proposed parallel algorithm is scalable and efficient.

Fly ashes, as a type of solid wastes, has been widely recycled recently. Its characteristic is directly related to the temperature of the hearth in which it has been heated. The constituents and forms of the fly ashes may vary in the specific reaction atmosphere and temperature. In this paper, the X-ray diffraction (Xrd) and the scanning electron microscope (SEM) were applied to observe the mineral form and crystal components of the fly ashes generated by the combustion of tobacco rod and lignite. The results showed that: the fusion temperature of the fly ashes rised with the increase of the CaO content and the fusion temperature of the fly ashes in the oxidation atmosphere was about 50°C higher than that in the reducing atmosphere. Through the entire process, the fusion temperature of the fly ashes initially rises with the increase in the percentage of the residual carbon, then decreases and in the end it levels off. The results from this research work can be used as the guidelines for the reutilization of fly ashes in industrial production.

Existing Online to Offine (O2O) take-out food ordering systems expose the defects that the deliveryman is responsible for delivering limited orders at a time. The inefficiency of this method results in high delivery fee. In order to improve the efficiency of delivery, we proposed the FoodCarpool, a negotiation-based carpooling system for take-out food delivery. Comparing to existing take-out food delivery system which is both time and resource intensive, theoretical analysis ensures that our negotiation mechanism based system model will always increase users' utilities. Finally, we conduct a set of experiments to evaluate the performance of our system. The experimental results show that comparing to the existing scheme, our approach improves total users' utilities and ensures the satisfactions of users.

The civil aviation transportation industry is dynamic and open. Its internal operation and external environment factors are constantly changing. These changes can result in safety risks, which could trigger unsafe events. Adopting scientific and effective methods in advance to carry out change risk management and effective risk control measures is significant in the prevention of unsafe events after the implementation of a change. The problems and deficiencies in change risk management in civil aviation service providers were analyzed. The object and content of change risk management in civil aviation service providers were proposed based on the operational content and main risks of civil aviation service providers. There were 6 types and 30 typical changes, covering typical safety related changes for civil aviation service providers. The process and method of change risk management in civil aviation were established according to the operational characteristics and practical work of civil aviation service providers, which included system and job analysis, hazard identification, risk assessment, risk control and continuous risk monitoring. Finally, an airline using an A320 to fly into Airport A was used as an example, and change risk management was implemented into the Flight Operation Department. This example demonstrated that the proposed method was successful.

Thin film selector is an indispensable part for high density memory and memristor arrays to avoid the sneak path problem in crosspoint architecture. Although significant efforts have been devoted to developing novel thin-film selectors, it remains a great challenge in achieving good switching performance in the selectors to satisfy diverse memory elements. In this work, we utilized high-defect-density chalcogenide glass in conjunction with high mobility metal element to achieve a super nonlinear selective switching. The proposed thin-film selector based on the mixed ionic electronic conducting material (MIEC) exhibits superior selecting performance including excellent nonlinearity (< 5 mV/dev), ultra-low leakage (< 10 fA), and bidirectional operation. A switching model is proposed, and the in-depth understanding of the selective switching mechanism offers an insight of switching dynamics for MIEC thin-film selector. This work opens a new direction of selector design by combining high mobility elements and high-defect-density chalcogenide glasses, which can be extended to other materials with similar properties.

Network intrusion is one of the most serious threats to network infrastructure and digital assets. Intrusion-detection systems detects un-authorized network access which violates the permissions settings of computer systems, networks, and information systems. Data mining is the one of the businesslike techniques amongst which are available for intrusion detection. Many data mining methods, including decision trees and rough set theory, would work better on discrete or binarized data. In data sets, on account of the existence in very large numbers, data discretization is deemed to be the data pretreatment method and such technology is very important. In this paper, the discretization methods of DFSFDP, DBSCAN, CAIM and K-means are implemented and applied to the KDD intrusion detection data set. Then four evaluation indexes are used to evaluate the effect of discretization. Moreover, with regard to the four discretization methods, their performance and accuracy are put forward and contrasted. The results of experiment acquired through using these methods on NSL-KDD dataset prove that used DFSFDP which is selected by the index of inconsistency measure performs well in terms of classification accuracy.

In a crowdsourcing system, security is a critical issue which affects the participation willingness of users. To motivate users' participation, most of existing work provide additional reward to compensate their loss due to security issues. However, more efficient way is to motivate the users to arm with higher security capability, to reduce the infection probability from the attackers and malicious software. In this paper, we propose an insurance-based incentive framework to motivate the users to upgrade to a higher security level. The framework can be formed as a Stackelberg game, where crowdsourcing platform is the leader and the users are followers. Through backward induction, we found that a Nash Equilibrium exists in the Stackelberg game. Simulation result shows that the proposed mechanism can enhance both social welfare, platform utility and users' utility in the crowdsourcing system.

Commonly, the data used in the real-world applications is composed by two types, the continuous data and the discrete data. The continuous data represents a range of values, while the discrete data refers to the information that share certain commonality. Since the discretized data always enjoys the general and simple usability, many data mining methods such as rough set theory and decision tree are designed to deal with discrete data. Due to the abundant existence of continuous attributes in data sets, data discretization is required as an important data processing method. In this paper, a density-based clustering algorithm is used to generate a discretization method. Specifically, in order to automatically seek out the proper number of clusters, the clustering method is employed to divide data set into clusters by fast search and find of density peaks. Then a top-down splitting strategy is utilized to discretize the interval of attributes. Furthermore, a novel probabilistic inconsistency measure is proposed to evaluate the results of discretization method. The experimental results demonstrate that the discretization methods with higher classification accuracy selected by inconsistency measure is better than the other methods. Therefore, the inconsistency measure can be used as an evaluation indicator.

We propose a systematic code design method for constructing efficient spectrum shaping k constrained codes for high-density data storage systems. Simulation results demonstrate that the designed codes can achieve significant spectrum shaping effect with only around 1% code rate loss and reasonable computational complexity.

In this paper, we present a wideband implantable antenna operating at ISM band. To obtain directional radiation pattern, five PEC planes are arranged at the bottom layer and on the four sides of the antenna to limit antenna radiation. The proposed antenna has a wide −10 dB bandwidth of 2.18–2.69 GHz covering ISM band and the back-to-front ratio of the antenna is 22 dBi on xoz plane. The directional antenna has a compact size of 11 mm×12 mm×10 mm.

Based on the dimension theory, this paper deduces the similarity relationships of prototype aircraft and scaled-model aircraft. American army standard AR-40A is an automatic carrier landing system design criterion generally used but not applicable for scaled-model aircraft any more. The essay proposes the adjustment method of frequency-domain characteristics boundary for AR-40A to meet the requirement of scaled-model aircraft based on comparisons of frequency-domain characteristics between prototype aircraft and scaled-model aircraft. Automatic carrier landing system for scaled-model aircraft with scale ratio of 4 referring to adjusted AR-40A and its corresponding prototype aircraft are designed. The carrier landing simulation results show that the scaled model aircraft ACLS designed by referring to the adjusted AR40A criteria is successful, which proves that the adjustment method of automatic carrier landing system design criterion proposed in this paper is correct.

This paper proposes an event-triggered course-tracking control approach of marine surface vessels based on output reference tracking method. Throughout this work, a reference system, which consists in the continuously controlled version of the system understudy, is employed. Based on the difference between the state of the event-triggered system and that of the reference system, a Lyapunov-like function is defined to guarantee the boundedness of the tracking error. The proposed strategy reduces the amount of energy consumption and computation, thus results in less controller executions, and its feasibility is further verified by the exclusion of Zeno behavior. Effectiveness of the proposed algorithm is further illustrated by simulation results.

Non-orthogonal multiple access as a promising technology is expected to support massive connectivity in the next generation. In this paper, we propose a novel perfect energy spread transform (P-EST) based multiple access scheme, which perfectly spreads a symbol energy all over the frequency-domain and time-domain increasing the diversity gain of the system. The parameters of the proposed scheme can be adjusted to provide different levels of overloading, thus suitable to meet the diverse traffic connectivity requirements. We then develope linear minimum mean square error (LMMSE) detector for the P-EST multiple access system. The LMMSE detector shows a robust performance in terms of block error rate (BLER), but the high complexity makes it infeasible when the number of users becomes large. To further reduce the computational complexity, we propose the approximate message passing algorithm (AMP) detector. Due to the energy spreading feature of P-EST, the AMP algorithm is able to perform with excellent convergence property and reduce the complexity to a great extent compared to LMMSE.