《易传》“神”字的科学蕴涵

通过对《易传》“神”字的剖析,可揭示出其中包括的天地化生的自然规律和阴阳变易的哲学蕴涵,折射出其朴素的唯物论和辩证法思想。在一定程度上廓清了笼罩在《周易》上面的神秘色彩和人们对它的误解。

“神”字穴临床应用规律探析

"神"字穴是指穴名中含有"神"字的腧穴,即神门、神庭、神阙、本神、神藏、神封、神道、神堂、四神聪九穴,通过近年来与"神"字穴相关文献报道,试图探求其与中医学概念中"神"的联系,归纳其穴性特点以及临床应用于治疗与"神"相关疾病的规律。

“神”字穴命名规律及其作用刍议

神门、神阙、神道、神堂、神封、神藏、神庭、本神、四神聪等9个含有"神"字的腧穴具有以下特点:(1)诸"神"字穴与心、脑关系密切;(2)诸"神"字穴在人体的分布符合中国古建筑特征,即中轴对称,且具有方正严整的群体组合与布局;(3)诸"神"字穴均以"神"命名,与人体的精神意志密切相关。

取类比象思维运用“神”字穴临证体会

"神"在历代医家施针遣方中占有重要地位。《黄帝内经》中共有9个含有"神"字的腧穴,均有调理人体之"神"的作用。该文结合取类比象思维,浅析"神"字穴的穴位名称,并总结其临床运用体会。

Digital watermarking algorithm based on neural network in multiwavelet domain

A novel blind digital watermarking algorithm based on neural networks and multiwavelet transform is presented. The host image is decomposed through multiwavelet transform. There are four subblocks in the LL- level of the multiwavelet domain and these subblocks have many similarities. Watermark bits are added to low- frequency coefficients. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results demonstrate that the new algorithm is robust against a variety of attacks, especially, the watermark extraction does not require the original image.

Wireless location algorithm using digital broadcasting signals based on neural network

In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.

Improvement and Realization of Neuron PSD Control in Servo-Control System

Neuron PSD (proportion, sum and differentiation) controller has the ability of on-line change of weights to reach the purpose of regulating parameters of PID using neuron’s self-studying and self-organization and the change of controlled plant, which overcomes the disadvantages of affecting a conventional PID’s accurate regulation because of the change of load, model and non-linearity. LF2407 DSP can reach the purpose of parallel running by using multi-sets of bus. So it can greatly increase operation speed and offer a set of flexible instruction system. The realization of PSD control on DSP can build an ideal electrical machine controller.

Pseudo Random Number Generator Based on Back Propagation Neural Network

Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network(BPNN) is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.

Identification of rice seed varieties using neural network

A digital image analysis algorithm based color and morphological features was developed to identify the six varieties （ey7954, syz3, xsl 1, xy5968, xy9308, z903） rice seeds which are widely planted in Zhejiang Province. Seven color and fourteen morphological features were used for discriminant analysis, Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties. When the model was tested on the test data set, the identification accuracies were 90.00%, 88.00%, 95.00%, 82.00%, 74.00%, 80.00% for ey7954, syz3, xsl1, xy5968, xy9308, z903 respectively.

Wavelet neural network based watermarking technology of 2D vector maps

A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.

Four optimal design approaches of high-order finite-impulse response filters based on neural network

Four optimal approaches of high-order finite-impulse response(FIR) digital filters were developed for designing four types filters using neural network algorithms. The solutions were presented as parallel algorithms to approximate the desired frequency response specification. Therefore, these methods avoid matrix inversion, and make a fast calculation of the filter’s coefficients possible. The convergence theorems of these proposed algorithms were presented and proved to illustrate them stable, and the implementation of these methods was described together with some design guidelines. The simulation results show that the ripples of the designed FIR filters are significantly little in the pass-band and stop-band, and the proposed algorithms are of fast convergence.

Offline Handwritten Characters Recognition Using Moments Features and Neural Networks

In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.

Simulation and experimental research of digital valve control servo system based on CMAC-PID control method

Digital valve control servo system is studied in this paper. In order to solve the system problems of poor control precision and slow response time,a CMAC-PID( cerebellar model articulation controller-PID) compound control method is proposed. This compound controller consists of two components: one is a traditional PID for the feedback control to guarantee stability of the system; the other is the CMAC control algorithm to form a feed-forward control for achieving high control precision and short response time of the controlled plant. Then the CMAC-PID compound control method is used in the digital valve control servo system to improve its control performance. Through simulation and experiment,the proposed CMAC-PID compound control method is superior to the traditional PID control for enhancing stability and robustness,and thus this compound control can be used as a new control strategy for the digital valve control servo system.

Shadow detection combining characters of human vision

A shadow detection method using pulse couple neural network inspired by the characters of human visual system is proposed.More precisely,lateral inhibition of human vision and coefficient of variation are combined together to improve the pulse couple neural network.Shadow detection is considered to be a shadow region segmentation problem.Experiment shows that the presented method is consistent with human vision compared to shadow detection methods based on HSV and pulse couple neural network(PCNN) by both subjective and objective assessments.

Application of MBAM Neural Network in CNC Machine Fault Diagnosis

In order to improve the bidirectional associative memory(BAM) performance, a modified BAM model(MBAM) is used to enhance neural network(NN)’s memory capacity and error correction capability, theoretical analysis and experiment results illuminate that MBAM performs much better than the original BAM. The MBAM is used in computer numeric control(CNC) machine fault diagnosis, it not only can complete fault diagnosis correctly but also have fairly high error correction capability for disturbed Input Information sequence.Moreover MBAM model is a more convenient and effective method of solving the problem of CNC electric system fault diagnosis.

Chinese word segmentation with local and global context representation learning

A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.

Discussion on the Current Situation and Artistic Characteristics of the 4 Intangible Cultural Heritage Relics Worldwide

Nowadays, with the rapid economic and technological development, people＇s material and spiritual requirements has got great satisfaction. In the course of historical development, ancestors of human beings created incomparable cultural heritage relics and spiritual civilization with their diligent hands and the great wisdom left us countless cultural heritage relics, many of them have become the famous wonder of the world. These civilizations and cultural heritage are located in Asia, Europe and America, Africa, for example, the Egyptian Pyramids, Acropolis in Athens. Mayan Civilization in Mexico, Mogao Grottoes of Dun Huang of China, Qin Terra-Cotta Warriors and Horses Figurines, Angkor Wat-Cambodia, Venice-ltaly, the Buddhas of Bamiyan in Afghanistan. The essay will mainly choose the 4 most famous representative cultural heritages worldwide to discuss their artistic characteristics, current situation and damage level and effective protective measures.

Chemistry, Theater, Information and Communications Technology

Theater play that shows how textiles, make up, hair dyes and music players has changed along the last six decades, especially due to polymers. It is a play that has a historic, chemical and musical side that introduces the use of ICT （information and communications technology）, such as Faeebook, Moodle platform, Web sites, E-mail, Word, Publisher, PowerPoint, Videos and Digital music. Results in the conduct of the students that participated： assertiveness, self-esteem, tolerance, cooperation, responsibility and teamwork, all of them significantly contributed a meaningful learning about the polymers.

Research on Recognition Method of Handwritten Numerals Segmentation based on B-P Neural Network

We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary, image of zip code box and message of the two characters binary image： analyze the image processing, which includes code frame edge detection and separation of the image binarization, denoising smoothing, tilt correction, the extraction code number, position, normalization processing, digital image thinning, character recognition feature extraction. Through testing, the recognition rate of this method can be over 90%. The recognition time of characters for character is less than 1.3 second, which means the method is of more effective recognition ability and can better satisfy the real system requirements.

Research on Recognition Method of Handwritten Numerals Segmentation based on B-P Neural Network

We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary image of zip code box and message of the two characters binary image; analyze the image processing, which includes code frame edge detection and separation of the image binarization, denoising smoothing, tilt correction, the extraction code number, position, normalization processing, digital image thinning, character recognition feature extraction. Through testing, the recognition rate of this method can be over 90%. The recognition time of characters for character is less than 1.3 second, which means the method is of more effective recognition ability and can better satisfy the real system requirements.