—In this paper, Taylor series expansion is applied to calculate the modeling error of Archimedes gear hob. The solution of hob modeling error is changed from the original transcendental equation to the power series equation. Then it makes the calculation formula simple. Further clarification of the relationship between the parameters, easy to program calculation and computer optimization design. On this basis, a new formula for calculating Archimimedes helical tooth angle is proposed. It also has wide application value in Archimedes gear machining.
The precision air conditioner accounts for 10% of energy consumption in data center. It is important to study how to adjust precision air conditioner to reduce energy consumption. Computational Fluid Dynamics (CFD) model is the most common method to simulate the temperature field in computer room, but the simulation time of existing software is large and the efficiency is low. In this paper, the proper orthogonal decomposition (POD) method is adopted. By taking the temperature field of the computer room under discrete configuration parameters as input, the proper orthogonal decomposition of each temperature field is performed to obtain the proper orthogonal basis of the temperature field, and then the approximate value of the temperature field of the computer room is obtained. Simulink is used to build air conditioning model and computer room model, and the temperature field under random load level is simulated. The results show that POD method has a great advantage in computing speed under the premise of little difference in accuracy.
—Rich media is introduced and analyzed from the concept itself, typical applications in different fields, the characteristics and influence in two sizes. Knowledge organization and traditional knowledge organization systems are also analyzed and most of them are suitable for rich media. In this paper, 9 typical applications are reorganized from four dimensions of contents, time, space and human. This paper also points out the feature representation and mapping problems in rich media knowledge organization work.
In order to solve the problem of frequent leakage of personal privacy information, this paper proposes a personal privacy protection scheme based on blockchain technology. Targeting at the existing problems and combining the advantages of decentralization, trust removal and data unchangeable of blockchains, a framework model is proposed. The objects of the model are designed and explained, and the hash function in blockchain cryptography is used to encrypt and decrypt personal information. The specific processes including uploading information, obtaining user’s personal information, reading access record and revoking permission are described. The feasibility test and performance analysis of the scheme are carried out. It is shown that the proposed blockchain based scheme can meet the needs of personal privacy protection and enable users to have strong control over their private information, and it can effectively avoid common network attacks.
From the perspective of the experience of the learners of the MOOC, it provides a reference for the development of the MOOC and the improvement of the learning effect. Through the questionnaire survey, the differences in the experience of the learners in the MOOC are studied. The results show that there are significant differences in the experience of the learners of different classes of MOOCs. The enlightenment of the learners and the public learners is in the flow of experience. There are significant differences in the state dimension, and the learning attitude and learning effect of the high heart flow experience group are significantly higher than the low heart flow experience group. The gender, personal attitude and learning effect of the learners are the important influencing factors of the learner's experience.
As cloud computing becomes widespread, more and more users prefer to outsource their local sensitive data into the cloud. In order to protect data privacy, these sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very difficult task. Although traditional searchable encryption techniques allow users to securely search over encrypted cloud data, they only support exact single keyword search, i.e. they do not allow any minor spelling errors or format inconsistencies. Besides, these traditional schemes only support Boolean search, without capturing any relevance of data files and rarely sort the search result. Recently, fuzzy keyword search over encrypted data techniques are introduced to resolve the problem of spelling errors and format inconsistencies. But these methods may incur large index size, search result inaccuracy and high search complexity, which greatly reduce the system usability and efficiency. This paper proposes the solution for privacy preserving ranked fuzzy keyword search over encrypted cloud data with small index. K-grams and Jaccard coefficient are utilized to construct fuzzy keyword set and produce fuzzy results, and an efficient relevance criteria is also provided to capture the relevance between data files and search requests. Extensive experimental results show the efficiency of our proposed method.
Since the rapid development of the vehicular networking and cloud computing, a new-type hybrid cloud, Vehicular Cloud Computing (VCC), has emerged. However, the new emerging security and privacy issues need to be addressed prior to a widespread deployment. This paper characterizes VCC in an overall framework, and categorizes VCC into static, dynamic and hybrid in terms of the applicable scenario. We simply analyze the security and privacy challenges of VCC underlying each layer and each type. Some feasible solutions to the focused security issues are designed and proposed for an applicable implementation of VCC. Finally, we provide several promising open topics.
The method analysis the topological properties of Liquefied Natural Gas Waterway terminal Networks is studied based on the betweenness centrity in this paper. LNG shipping systems are vital to the economic development of our country. And it is critical for Chinese economic growth. Furthermore, the Chinese liquefied natural gas waterway terminal network hub degree is combined by the local economic growth. Especially, the shipping volume, degrees, weights characteristics and modular properties are mainly studied in Chinese Liquefied Natural Gas Waterway terminal Networks. This research on Liquefied Natural Gas Waterway terminal hub degree and weight characteristics can improve economic benefits and the hub waterway terminals location. Here, the betweenness centrity of LNG shipping volume information is used during the year 2010-2018. Furthermore, we analysis the network of links between liquefied natural gas waterway terminals with the rationality. The network has several features that set it apart from other transportation networks are shown.
Traditional wavelength division multiplexing networks use fixed grids to meet the needs of today's rapidly evolving networks. The elastic optical network with OFDM as the core can divide the spectrum resources into finer granularity, thereby improving the utilization of network resources and reducing the bandwidth blocking rate in the network. this paper considers risk avoidance to improve the elastic optical network routing and wavelength assignment algorithm based on spectrum coloring theory: First, define the network The routing risk coefficient, starting from the power communication service flow, analyzes the resource status of nodes and links, and establishes a route optimization model. Secondly, based on the characteristics of power communication network, based on the graph coloring theory and considering the spectrum fragmentation problem, the risk equilibrium model is designed. Finally, the joint transfer hops and the network risk value are jointly balanced, and a mixed integer linear programming model of the RSA problem with the minimum transfer hop and the lowest risk balance is jointly established. The spectrum is based on the coloring number. Equally divided blocks, the spectrum is allocated by block. The simulation results show that compared with the typical algorithm, the proposed algorithm alleviates the problem of uneven distribution of risk and spectrum fragmentation in the power communication network in the prior art, and comprehensively considers routing and spectrum, and balances the number of hops. Balance the risk with the effect of improving resource utilization.
Due to the open nature of wireless networks, power wireless private networks (PWPNs), as the carrier of smart grid service, must provide highly reliable and secure communication capabilities. The security risks of terminals, wireless channels, base stations, core networks, host systems and data applications are analyzed. Aiming at the multi-service characteristics of smart grid, a security architecture based on service isolation is proposed in PWPNs. The architecture implements end-to-end service security isolation and can effectively combat different security threats from outside.
Musical contextual factors can affect the users’ preferences for music greatly, so it is necessary to take the users’ current contextual factors into account when making music recommendations to the user. However, we face two critical challenges: how to get the users’ contextual information and how to integrate the contextual information into the recommender systems. In this paper, a method is proposed for extracting contextual factors using the word2vec algorithm which is concerned to context-aware information. The neural network model is used to obtain distributed representation of the music pieces. According to the learned distributed representation, the users’ long-term and the short-term music preferences can be predicted. Then, a word embedding model is presented, which can incorporate the contextual information into the recommender system with the cosine similarity between the representations of tracks and the users. Finally, the most similar tracks are recommended to the target users.
The authority management module is an important part of the broadcast TV monitoring system. In order to ensure that the important and key functions of the system are implemented by the users who meet the requirements, this paper introduces the static trust value and dynamic trust value based on the original access control model of the monitoring system, and proposes a dynamic access control model based on user trust. Static trust value and dynamic trust value are derived from Bayesian estimation theory. The model uses the user's role and static trust value as the basis for obtaining permissions, and then calculates the user's dynamic trust value in real time in combination with user behavior and device state, and grants the user specific permissions in the actual operation. Finally, an application example and the model security analysis are given. The results show that the model can realize dynamic authorization and satisfy the principle of least privilege.
This paper reviews glowworm swarm optimization algorithm (GSO), which is a meta-heuristic swarm intelligence algorithm. The GSO algorithm is applied for solving optimization problems. Shortcoming of the GSO algorithm has been identified with the introduction and discussion of the improvement taken place in recent years. Adaptive step size and new movement rules have been widely used in the improvement of GSO algorithm. The application of GSO including clustering techniques is also presented. Very promising GSO clustering versions use MapReduce framework to improve the computational efficiency when the clustered data set is large and thereby reducing the time complexity.
Fly ash, sodium carbonate, potassium hydrochloric acid solution and high-speed rail are the main raw material, prepared polysilicate aluminum ferrite coagulant (PSAF) and used for dyeing wastewater treatment simulation. This paper discusses homemade coagulation effect, at the same time, the introduction of an important parameter to characterize──floc fractal fractal dimension fractal structures from microscopic floc morphology studies floc angle between the macroscopic relationship with coagulation effect, is how to get the structure of dense settlement a good batting performance body, and it provides a research method. In addition, the application of computer simulation technology to build floc growth model, fractal floc formation under different coagulation conditions were simulated by comparing the two types of floc morphology and appearance of fractal dimension. This paper discusses the simulation model established in the coagulation test the feasibility of the application.
With the development of lunar exploration, researches have proposed a large number of techniques for In-Situ Resource Utilization (ISRU). Due to microgravity in the Moon, traditional metallurgy such as cold hearth melting is hard to refining metals or alloys. Hence, this paper proposed a new zone refining technique using solar-pump laser as heat source, which would be a potential titanium metallurgical technique in the lunar in future. Numerical simulation and thermodynamic equilibrium distribution calculation were conducted in order to investigate the theoretical feasibility of titanium zone refining using laser heating. The results show that laser zone refining is capable of segregation of titanium and iron in theory. Thus, the design of zone refining of titanium in lunar implementing appropriate laser parameters is possible to be successful in the near future.
This paper designed and implemented a direct memory access (DMA) architecture of PCI-Express (PCIe) between Xilinx field programmable gate array (FPGA) and Freescale PowerPC. The DMA architecture based on FPGA is compatible with the Xilinx PCIe core while the DMA architecture based on POWERPC is compatible with VxBus of VxWorks. The solutions provide a high-performance and low-occupancy alternative to commercial products. In order to maximize the PCIe throughput while minimizing the FPGA resources utilization, a novel strategy for the DMA engine is adopted, where the DMA register list is stored not only inside the FPGA during initialization phase but also in the central memory of the host CPU. The FPGA design package is complemented with simple register access to control the DMA engine by a VxWorks driver. The design is compatible with Xilinx FPGA Kintex Ultrascale Family, and operates with the Xilinx PCIe endpoint Generation 1 with lane configurations x8. A data throughput of more than 666 MBytes/s (memory write with data from FPGA to PowerPC) has been achieved with the single PCIe Gen1 x8 lanes endpoint of this design.
Intelligent distribution network is one of the key technologies and the important part of smart grid. It has great significance to the high-quality electric energy and the promotion of new energy revolution which can promote environmental protection and sustainable development. At present, our country’s smart distribution network is still in its initial stage which is relatively weak. It cannot solve the problem of a large number of renewable energy accesses to the power grid and achieve the optimal operation of distribution network and itself healing control. With the gradual deepening of the construction of smart distribution network, the future power grid will be more complex, and its operation and control is bound to be more highly dependent on digital simulation tools. At first, the three aspects of the distribution network simulation platform is analyzed in this paper. Secondly, the model and algorithm of the function module of the simulation platform are analyzed in the two parts of the reactive power optimization and fault analysis. Reactive power optimization part of the column writes the objective function, equality and inequality constraints. The function of reactive power optimization is realized by using particle swarm optimization algorithm. Fault analysis section describes the module data relations, three-phase short-circuit fault and asymmetric fault processing flow. Finally, the line model is built in the simulation platform, which verifies the correctness of the simulation platform.
In this paper, a control method is proposed for spacecraft with unknown disturbances. For attitude subsystem, an adaptive estimator is designed to deal with the unknown disturbances. For angular velocity subsystem, the actual control input is designed. If the designed control law is added to angular velocity subsystem, a nonlinear closed-loop system of angular velocity error is arrived. The convergence of closed-loop system is proved by input-output stability theory. The derivative of virtual control and unknown disturbances in the actual control are also estimated using adaptive estimator. It can be proved that, with the proposed controller, the attitude can converge to a small-neighbourhood of the designed attitude. Theoretical results are illustrated by numerical simulation.
Due to the existence of flexible link in fast steering mirror (FSM), the uncertainty of the structural stiffness of the flexible-supported FSM is greatly increased when FSM works. This leads to a higher requirement for the robustness of control system. Aimed at this problem, a generalized error fast differential based adaptive controller is proposed by combining the adaptive control theory with the tracking differential theory. The controller can make appropriate adjustments to track the given reference model if the structure changes. Firstly, the mathematical model of the FSM control system is optimized to match the mathematical model of the tracking differentiator. Secondly, according to the input and output data of the FSM, the adaptive control system is designed based on Lyapunov stabilized theory. Finally, the denoised system's generalized error signal and its differential signal are obtained by using a fast differentiator. The signals are used in adaptive control system immediately. The numerical simulations results show that the proposed algorithm can both suppress the influence of the change of structural stiffness and the sensor’s noise.
Due to the complex and diverse indoor environment, the traditional positioning algorithm used indoors will greatly reduce the positioning accuracy. Aiming at the ever-increasing demands of accuracy and stability in indoor positioning, an indoor micro-positioning system based on Bluetooth technology is designed. The low-power Bluetooth wireless data transmission module RL-CC2541-S3 is used as the signal acquisition unit, and the STM32F103ZET6 high-performance microprocessor is used as the core to complete the data processing and positioning calculation. An indoor positioning algorithm based on Gaussian filtering and Elman neural network is designed to improve the positioning accuracy of the system. Firstly, the Pauta criterion is used to eliminate the outliers of the sampled data to ensure the validity of the data. Then Gaussian filtering is used to remove the interference of Gaussian noise and the least squares method is used for curve fitting to further improve the accuracy of ranging. Finally, the nonlinear approximation of Elman neural network is used to achieve the target location. The experimental results show that the absolute error of indoor positioning is close to 0.3m, which is better than the traditional Chan algorithm, LS algorithm and Taylor series positioning algorithm, which can meet the needs of general indoor micro-positioning.
