目的 为解决传统节日文化传播与大众文化需求、文化信息接收方式之间的矛盾,优化传统节日文化传播的途径,进行理论及设计实践研究.方法 针对传统文化传播出现的问题进行文献资料研究.针对传统节日文化及其传播的满意度进行用户问卷调研及用户访谈.结果 针对传统节日文化内容,运用服务设计的方法,从用户体验和用户文化需求的角度出发,探讨其传播现状的问题及对策.提出建设集多种形式传统节日文化内容于一体的文化传播移动端应用,以满足大众多样化的节日文化需求的设计构思及实践.结论 传统节日文化及其传播方式与人民生活方式和文化需求的高度匹配,是解决传统节日文化传播和传承的重要途径之一.采用服务设计方法提升用户体验满意度,为传统节日文化的传播和传承提供新思路.

High resolution range profile (HRRP) target recognition based on deep learning methods is mainly dedicated to changing the 2-Dimensional (2-D) convolutional neural network (CNN) framework into a 1-Dimensional (1-D) feature extractor. In this paper, a new algorithm called triple gramian angular field with CNN (TGAF-CNN) is proposed for HRRP recognition in the low signal-to-noise (SNR) condition. Di...

Since the sparse representation coefficients of synthetic aperture radar (SAR) images often appear in clusters with intrinsic structure, traditional sparse representation theory cannot capture this property. In this paper, the concept of group sparse representation (GSR) is utilized to exploit the intrinsic structure of SAR images. Different from traditional patch-based sparse representation theory, GSR is able to sparsely represent images in the domain of group which contains the image patches with similar structure. Based on the multiplicative speckle noise model, a novel dictionary learning algorithm based on GSR (GSR-DL) for SAR image despeckling is proposed. The proposed algorithm mainly consists of three steps. First, in order to realize the recovery of despeckled SAR image by the GSR model, a mean filter is included in the modeling process. Second, the proposed GSR-DL algorithm is used to calculate the optimal dictionary and group sparse representation coefficients. Third, the despeckled SAR image is reconstructed by the learned dictionary and coefficients. The experimental results on SAR images manifest that the proposed GSR-DL algorithm achieves a better performance than other state-of-the-art despeckling algorithms.

For satisfying both the psychological demand of personalization and the low price demand of customers, there should be a method of personalize design based on mass production.Based on the theory of modular de-sign,it classifies the previous product into certain modules according to the disassemble effort index,establishes the evaluation of modules,shows the detail about exporting the product chain of personalized product and build -ing up a system of personalized production based on mass production.

In this letter, we explore the concept of group sparse representation (GSR) to exploit the intrinsic structure of synthetic aperture radar (SAR) image. Noting that dictionary design is a crucial factor in GSR performance, we propose an over-complete dictionary to better fit the SAR image despeckling problem. This over-complete dictionary consists of the prespecified dictionaries and learned dictionary. Different kinds of dictionaries emulate the image from different angles. In this way, we can simultaneously obtain better performance on speckle noise suppression and image detail preservation. The experimental results on real SAR images demonstrate that the proposed over-complete dictionary based on GSR can achieve more effective speckle reduction as well as image detail preservation.

According to the four degrees of freedom kinematics theory and nationalstandard,it establishes the vehicle model and the pulse virtual proving ground pavement road in Carmaker,analyzes the vehicle comfort and the influence of spring mass to vehicle smoothness under pulse input.The results show that with the increase of the quality of each wheel,the maximum acceleration of front wheel and the body center decrease gradually,and the maximum acceleration of the body center does not affect people's health,but the main vibration frequency of center of the body falls within the scope of the human body sensitive frequency if the additional mass reaches 30kg,which makes human body uncomfortable.

In this paper, a noncircular PARAFAC (NCPARAFAC)-based algorithm is developed for joint angle and delay estimation (JADE) in a multipath communication scenario. To utilize the inherent multidimensional structure in the received data, a three-order tensor is formulated which linked the estimations of angle and delay to the PARAFAC model. To further exploit the property of noncircular signals, an expanded PARAFAC decomposition algorithm is presented. The proposed algorithm doubles the array aperture, thus it has better JADE accuracy than the existing methods. The proposed algorithm does not require spectral peak searching or eigenvalue decomposition of the received signal covariance matrix, and it can achieve automatic pairing of the estimated parameters. Extensive numerical experiments verify the effectiveness and improvement of our algorithm.

This paper discusses the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input multiple-output radar. A three-order tensor model is formulated to exploit the multidimensional structure inherent the received data. To obtain an accurate angle estimation in low signal-to-noise ratio scene, the high-dimensional tensor data is then compressed to core tensor. Joint DOD and DOA estimation is linked to PARAFAC decomposition of the core tensor data. The proposed algorithm can achieve automatic pairing of the estimated angles and outperform the existing methods. The experimental results demonstrate the effectiveness of the proposed method.

This paper discusses the problem of twodimensional direction-of-arrival (2D-DOA) estimation for uniform rectangular array (URA). A noncircular high-order singular value decomposition (NC-HOSVD) is developed for 2DDOA estimation. By employing the NC property of the source matrix as well as the multidimensional structure of the received data, an expanded three-order tensor model is formulated, which doubles the number of detectable sources. A signal subspace method is constructed to obtain 2D-DOA with the HOSVD method. The proposed NC-HOSVD method outperforms the ESPRIT and the HOSVD methods. The experimental results demonstrate the effectiveness of the proposed method.

Euler-Principal Component Analysis (e-PCA) has been recently proposed and successfully applied to the classification frame works. By utilizing the robust dissimilarity measure e-PCA demonstrates better performance than standard PCA while dealing with nonlinear component analysis and suppressing outliers. In this letter, we define a two-Dimensional Euler-Principal Component Analysis (e-2DPCA) framework for SAR image processing. e-2DPCA is based on 2D image matrixes rather than 1D vector which could understand two dimensional (2D) images better and get rid of high dimensional data processing. Furthermore, we applied this algorithm to SAR target recognition. Finally, experiments on MSTAR database perform the usefulness of our method in robust classification towards different situation.

With the combined multiscale Gaussian kernel and Morlet wavelet kernel, two multiscale kernel sparse coding-based classifiers (MKSCCs) are proposed for radar target recognition using high-resolution range profiles (HRRPs). The kernel trick can make samples more clustered in higher-dimensional space. Moreover, the multiscale kernels at different scales have advantages of good generalisation and primary signature capturing ability for target's HRRP, which are helpful to improve the target recognition accuracy and robustness of MKSCC further. Numerous experiments are conducted on five types of ground vehicles' HRRP data and the authors also make comparisons with the KSCC and some related recognition methods. The results demonstrate the effectiveness of the proposed method.

Weak relatedness among tasks leads to failure of regularized multi-task sparse representation (RMTSR) model to handle target recognition in synthetic aperture radar (SAR) imagery. Therefore, it is vital to measure task relationship not only in order to obtain desired model but shrink the size of dictionary and the training time. In this paper, sparse representation under each feature modality is considered as a single task in RMTSR. A nonlinear sparsity correlation index (NSCI) is presented. Furthermore, nonlinear correlation information entropy (NCIE) deduced from NSCI is utilized to quantify the relatedness among tasks from view of information theory. Experiments conducted on MSTAR demonstrate the outperformance and effectiveness of RMTSR even in the case of limited training resource. Moreover, NCIE is efficient to measure the generalization of model and select appropriate feature set to reduce complexity.

Firstly ,the three dimensional model of a car body engine frame was established in CATIA .Then ,the finite element model of the engine frame was established by Hypermesh .Finally ,Patran software was applied to the engine frame modal analysis ,and the natural frequency and the corresponding mode of vibration were got .The results show that this type of engine frame will not generate resonance .

智能推理是CAPP系统的核心功能,针对轴类零件研究了CAPP系统的工艺推理及决策方法.首先阐述了零件工艺知识的知识模型和数据结构,并进行符号化,便于计算机进行处理和识别;然后,建立了工艺数据/知识库,并基于正向推理策略,应用工艺规则推理机实现了工序优先级数决策和零件加工路线的自动排序.该系统在.Net环境下利用Sql Server 2008数据库开发完成,具有很强的实用性和可移植性,适合在中小轴类企业推广应用.

Distributed compressive sensing (DCS) has been used in multiple-input multiple-output (MIMO) radar system. This application has led to substantial improvements over existing methods in MIMO radar. But there are also some challenges that should be resolved in order to benefit the most from DCS-based MIMO radar, such as radar signal with low signal to noise ratio and optimizing measurement matrix design. In distributed DCS-based MIMO radar context, this paper presents a cognitive mechanism for optimizing transmit and receive gain by applying the optimization guideline which based on the coherence of the sensing matrix (CSM) and signal-to-noise ratio. This paper proposed two kinds of method: the first one is to optimize transmit gain with the aim to maximize SNR, and the second one is to minimize CSM by adjusting receive gain. Simulations show that the proposed methods obtain significant better recovery performance than traditional DCS-based MIMO radar systems.

With the development of wideband and ultra wideband communications,huge challenges will ap-pear in the traditional Nyquist sampling theorem based signal collection,transmission,storage and processing system.The analog-to-information convertor(AIC)may be an effective way to overcome these challenges.As a research hotspot in recent years,AIC takes compressive sensing as its theoretical base and breaks through the restrictions of the Nyquist sampling theorem.The AIC system allows accurate reconstruction of signals sampled at a rate many times less than that of the conventional Nyquist frequency,making it an effective way of signal conversion.This paper reviews the recent research progress in sparse signal acquisition and introduces the typi-cal schemes of AIC design and implementation.The advantages and disadvantages of different approaches have been analyzed,and research development at home and abroad has been described.Conclusions and future re-search directions of AIC are given.

鉴于安全生产中事故时常在无意识情况下发生,通过研究无意识行为本质及特点,并分别以安全人机工程中的人、机、环境三要素为导向无意识行为,从而为设计寻求新的契机,探索得出基于无意识行为之下的安全人机系统的设计方法.

As an alternative paradigm to the Shannon-Nyquist sampling theorem, compressive sensing enables sparse signals to be acquired by sub-Nyquist analog-to-digital converters thus may launch a revolution in signal collection, transmission and processing. In the practical compressive sensing applications, the sparse signal is always affected by noise and interference, and therefore the recovery performance reduces based on the conventional compressive sensing, especially in the low signal-to-noise scene, the sparse recovery is usually unavailable. In this paper, the influence of noise on recovery performance is analyzed, so as to provide the theoretical basis for the noise folding phenomenon in compressive sensing. From the analysis, we find that the expected noise gain in the random measure process is closely related to the row and column of the measurement matrix. However, only those columns corresponding to the support for the sparse signal contribute to the sparse vector. In the traditional Shannon-Nyquist sampling system, an antialiasing filter is applied before the sampling process, so as to filter the noise beyond the passband of interest. Inspired by the necessity of antialiasing filtering in bandpass signal sampling, we propose a selective measurement scheme, namely adapted compressive sensing, whose measurement matrix can be updated according to the noise information fed back by the processing center. The measurement matrix is specially designed, and the sensing matrix has directivity so that the signal noise can be suppressed. The measurement matrix senses only the spectrum of interest, where the sparse spectrum is most likely to lie. Moreover, we compare the recovery performance of the proposed adaptive scheme with those of the non-adaptive orthogonal matching pursuit algorithm, FOCal underdetermined system solver algorithm, and sarse Bayesian learning algorithm. Extensive numerical experiments show that the proposed scheme has a better improvement in the performance of the sparse signal recovery. From the viewpoint of implementation, the measurement noise should be taken into consideration in the system, and more efficient algorithms will be developed for source pre-estimation at lower signal-to-noise ratio.