不同月相中健康人群胃经经络值变化规律

【目的】通过观察1个月相周期中不同时间胃经经络值的变化规律,了解健康人群胃经气血与月相变化的相关性。【方法】在1个月相周期的朔月、上弦月、望月、下弦月4个日期使用DMS中医经络检测仪对70例健康受试者双侧胃经原穴冲阳进行穴位电阻测量,经DMS配套基础病理数据库分析转化为双侧胃经经络值,观察4个时间双侧胃经经络值的变化。【结果】双侧胃经平均经络值上弦月和望月较高,下弦月和朔月较低,差异有统计学意义(P<0.05或P<0.01),左右两侧胃经经络值变化不完全一致。【结论】总体上,胃经经络值上半月增加,下半月减少,但增加和减少并非匀速;反映胃经气血随月相发生规律性变化且总体上与《内经》月相与气血相关性的描述一致。提示在临床上应当参考月相盈亏的变化,评判人体经络气血盛衰之变化,以评估病情(虚实),从而拟定更为合理的治疗方案,勿犯虚虚实实之戒。

慢性荨麻疹患者肺经和心经经络值检测及其意义

目的:论证中医"肺主皮毛"、"阴阳对立"等基本理论的科学性及经络辨证在慢性荨麻疹诊疗中的理论和临床价值。方法:采用德国Mora-Super生物共振治疗仪(EAP-PC程序)对60例慢性荨麻疹患者(发作期和缓解期各30例)进行肺经和心经经络值的检测,再选取30例健康者作为对照组,比较患者和健康者之间肺和心经经络值的差异。结果:发作期组肺经值高于健康组,差异具有显著性,心经值间比较无统计学差异;缓解期组肺经值低于健康组,差异具有显著性,心经值间比较无统计学差异;发作期组患者肺经值高于缓解期组,差异具有显著性,心经值间比较无统计学差异。结论:慢性荨麻疹发病与肺经密切相关而与心经无明显关系,中医"肺主皮毛"理论及经络辨证论治具有一定的科学依据。

痰湿型高血压病患者经络能量值与血脂BMI指数的相关性研究

目的 研究痰湿型高血压病患者经络能量值与BMI指数、血脂指标的相关性。方法 选取2021年1月—2022年5月在江门市中心医院中医科门诊体检及住院的痰湿型高血压病患者及平和体质健康体检者各80例,检测其经络能量值、BMI指数及血脂指标进行相关性分析,并比较2组间的差异。结果 健康组及高血压组经络能量与BMI指数、三酰甘油(TC)、胆固醇(TG)、低密度脂蛋白(LDL-C)均存在相关性,且均呈负相关。结论 经络能量的低下,可能预示着患者BMI指数、血脂水平偏高,存在代谢问题,可适当运用中医药改善痰湿型高血压病患者的脏腑功能及气血情况,纠正痰湿体质,减少并发症的发生。

高血压患者经络良导络值的分析研究

目的:通过检测收集高血压患者的经络良导络值,探索人体经络数值的变化规律,分析其对临床的指导意义。方法:运用经络检测仪,以太渊、大陵、神门、腕骨、阳池、阳溪、太白、太冲、太溪、束骨、丘墟、冲阳,左右各12条经络的原穴或腧穴为主,采集左右24条经脉的经络良导络值,录入统计软件进行聚类分析以及相关性等方面的分析研究。结果:275例高血压患者的肝经、膀胱经、心经、脾经、小肠经明显处于高能量的太过、实证状态,胆经、三焦经、大肠经、肾经处于低能量不足的虚证状态,心包经、肺经、胃经则出现右高左低的状态。结论:高血压病变时经络的异常变化有一定规律可循。

脂肪肝患者经络良导络值分析研究

目的:通过检测脂肪肝患者的经络良导络值,探索人体经络的变化规律,分析其对临床的指导意义。方法:运用经络检测仪,以太渊、大陵、神门、腕骨、阳池、阳溪、太白、太冲、太溪、束骨、丘墟、冲阳,左右各12条经络的原穴或输穴为主,采集24条经脉的经络良导络值,录入软件进行聚类、相关性的分析研究。结果:本次共检测收集85例脂肪肝患者经络良导络值数据,通过统计分析,得出左侧的三焦经、大肠经、肾经,右侧三焦经等明显处于低能量状态,右侧的小肠经、心经、心包经、肺经等明显处于高能量状态。结论:经脉所对应脏腑呈现下虚上实的病理特点,对临床诊治脂肪肝有指导意义。

基于经络检测仪探讨阳虚体质经络能量分布特点

目的:采用经络检测仪检测经络能量值,从客观指标上研究阳虚体质经络能量分布特点。方法:依据九种常见体质分型,对纳入的261例研究对象进行体质判别分型,将偏颇体质中占比最多的98例阳虚质与100例平和质进行比较,经卡方检验,发现阳虚质人群经络能量分布特点,同时将其他体质(阴虚质)与平和质进行比较,从而探讨阳虚质人群经络能量分布的特异性。结果:体质为阳虚质者大肠经、三焦经、胃经能量值偏低,肝经、小肠经、脾经能量值偏高,与平和质相比较,差异具有统计学意义(P<0.01);与其他体质经络能量分布进行对比,阳虚体质人群的经络能量分布具有特异性。结论:中医经络检测仪对于临床上客观化诊断阳虚体质具有重要的参考意义。本研究发现阳虚质人群以阳气虚为本,同时内生积滞,致使相关脏腑经络出现偏低或偏高,从而导致相关病变。

60例未治疗干预无症状期HIV感染(气虚)经络虚实及相关因素回顾性研究

[目的]回顾研究60例未干预无症状期HIV感染(气虚)经络虚实及相关因素。[方法]使用回顾性研究方法,60例干预前无症状期HIV感染(气虚)患者病历,经络检测仪检测经络虚实表现,从左至右,检测棒依次点按肺、肾、肝、胆、脾经24个穴位。多元逐步回归分析(前进法)剔除变量F值=2,得到局部最优方程。判读经络检测系统值及性别、年龄等。观测体能值、阴经/阳经值(简称阴/阳)、上肢/下肢经络值(简称上/下)。[结果]肺、肾经虚,肝、胆、脾实51例(85%);肺经虚,肝、脾经实6例(10%);肾经虚,肝经实3例(5%)。病程差异显著:病程每增加1个月,阴经/阳经值增加0.0145,同步递增;年龄差异显著:年龄每增加1个单位,经络上/下值就增加-0.001,同步递减。[结论]无症状期HIV感染(气虚)肺肾经络虚表现为主,伴肝胆脾经实证为辅,阴/阳、上/下值受病程、年龄因素影响,呈"量效关系(同步递增/递减)"。病程越长,经络阴阳平衡越容易破坏;年龄越大,心肾气血平衡及心态平和调整越好。早养生干预可据补虚泻实原则,补肺肾,泻肝胆脾经。

中医干预无症状期HIV感染(气虚)随机平行对照研究

[目的]观察募合配穴联合胃通汤对糖尿病胃轻瘫的临床疗效。[方法]采用随机平行对照的研究方法,将60例符合纳入标准的住院患者按随机数字法简单随机分为两组。对照组30例安慰剂(甘草代茶),6 g/次,3次/d。治疗组30例自拟中药茶(黄芪、党参、白术等)+耳针治疗+穴位自我按摩,3次/d,连续4周为一疗程,连续使用12个疗程。观测CD4+、CD8+细胞计数,血常规、肝肾功能、及经络检测、不良反应。连续治疗4周(1疗程),判定疗效。[结果]能改善患者中医临床乏力、气短症状(P <0. 05)。治疗组乏力好转30例(100%),总有效率100%;治疗组气短症状改善显效24例,有效4例,总有效率93. 33%。可以改善患者的经络检测结果,提升患者的体能值,在一定程度上提高患者的抵抗力;平衡治疗组的阴经/阳经值(简称阴/阳),使之更趋于正常值,阴阳经络气血平衡,身体代谢正常;上肢/下肢经络值(简称上/下)趋于正常值,代表上下平衡情况好,即心肾气血平稳,心态平和;左侧经络/右侧经络值(简称左/右)也趋于正常值,表示身体内部的肌肉骨骼系统有良好的运作能力)(P <0. 01)。实验室检查结果对CD4细胞计数、CD8细胞计数、肝功能、肾功能无明显改善效果。[结论]自拟中药茶配合耳针治疗+穴位自我按摩调理能改善无症状期HIV感染(气虚)疗效确切,值得临床推广运用。

Projectile impact point prediction method based on GRNN

In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.

Spatial interpolation method based on integrated RBF neural networks for estimating heavy metals in soil of a mountain region

A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.

A Low Resolution Image Restoration Method based on BP Neural Network

In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures, getting the restructuring matrix. The characteristic block with the best restoration effect was determined by analyzing the pixel difference of the common information of each image at the same position. Then the characteristic blocks and their original blocks were used to build and train neural network. Finally, images were restored by the neural network and the differences between pictures were reduced. Experimental results showed that this method could significantly improve the efficiency and precision of algorithm.

Solving a Model of Inversion Layer Quantization Effects in Deep Submicron MOSFETs with Artificial Neural Networks

Approximate the solution of a model for inversion layer quantization effects in deep submicron MOSFETs with feed-forward artificial neural networks (ANNs) is proposed.To realize this,the solution of eigenvalue problems actually need to be considered for differential and integrodifferential operators,using ANNs.To validate the method and verify its accuracy,it is applied to the Schr o ¨dinger equation for the Morse potential problem that has an analytically known solution.Then a model is proceeded with which approximates the Schr o ¨dinger equation and the Poisson equation problem called the triangular-potential approximation.In conclusion,the presented method is simple to implement,and have several verification applications.

Application of BPANN in spinning deformation of thin-walled tubular parts with longitudinal inner ribs

Back-propagation artificial neural network (BPANN) is used in ball backward spinning in order to form thin-walled tubular parts with longitudinal inner ribs. By selecting the process parameters which have a great influence on the height of inner ribs as well as fish scale on the surface of the spun part, a BPANN of 3-8-1 structure is established for predicting the height of inner rib and recognizing the fish scale defect. Experiments data have proved that the average relative error between the measured value and the predicted value of the height of inner rib is not more than 5%. It is evident that BPANN can not only predict the height of inner ribs of the spun part accurately, but recognize and prevent the occurrence of the quality defect of fish scale successfully, and combining BPANN with the ball backward spinning is essential to obtain the desired spun part.

A Method for Reducing Noise Radiated from Structures with Vibration Absorbers by Using an Accelerated Neural Network

A method for reducing noise radiated from structures by vibration absorbers is presented. Since usual design method for the absorbers is invalid for noise reduction, the peaks of noise power in the frequency domain as cost functions are applied. Hence, the equations for obtaining optimal parameters of the absorbers become nonlinear expressions. To have the parameters, an accelerated neural network procedure has been presented. Numerical calculations have been carried out for a plate type cantilever beam with a large width, and experimental tests have been also performed for the same beam. It is clarified that the present method is valid for reducing noise radiated from structures. As for the usual design method for the absorbers, model analysis has been given, so the number of absorbers should be the same as that of the considered modes. While the nonlinear problem can be dealt with by the present method, there is no restriction on the number of absorbers or the model number.

APPROXIMATION MULTIDIMENSION FUCTION WITH FUNCTIONAL NETWORK

The functional network was introduced by E.Catillo, which extended the neural network. Not only can it solve the problems solved, but also it can formulate the ones that cannot be solved by traditional network. This paper applies functional network to approximate the multidimension function under the ridgelet theory. The method performs more stable and faster than the traditional neural network. The numerical examples demonstrate the performance.

Approach to Generalized Synchronization with Application to Chaos-Based Secure Communication

A constructive theorem is established for generalized synchronization (GS) related to C1 diffeomorphic transformations of unidirectionally coupled dynamical arrays. The theorem provides some interpretations about the underlying mechanism of various GS phenomena in nature. As a direct application of the theorem, a chaos-based secure Internet communication scheme is proposed. Moreover, a cellular neural network (CNN) of Chen's chaotic circuits with GS property is designed and studied. Numerical simulation shows that this Chen's CNN has high security and is fast and reliable for secure Internet communications.

RESEARCH AND APPLICATION OF A NEURAL NETWORK CLASSIFIER BASED ON DYNAMIC THRESHOLD

In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.

A NOVEL MULTI-VALUED BAM MODEL WITH IMPROVED ERROR-CORRECTING CAPABILITY

A Hyperbolic Tangent multi-valued Bi-directional Associative Memory (HTBAM) model is proposed in this letter. Two general energy functions are defined to prove the stability of one class of multi-valued Bi-directional Associative Mernorys(BAMs), with HTBAM being the special case. Simulation results show that HTBAM has a competitive storage capacity and much more error-correcting capability than other multi-valued BAMs.

Multiwavelets domain singular value features for image texture classification

A new approach based on multiwavelets transformation and singular value decomposition (SVD) is proposed for the classification of image textures. Lower singular values are truncated based on its energy distribution to classify the textures in the presence of additive white Gaussian noise (AWGN). The proposed approach extracts features such as energy, entropy, local homogeneity and max-min ratio from the selected singular values of multiwavelets transformation coefficients of image textures. The classification was carried out using probabilistic neural network (PNN). Performance of the proposed approach was compared with conventional wavelet domain gray level co-occurrence matrix (GLCM) based features, discrete multiwavelets transformation energy based approach, and HMM based approach. Experimental results showed the superiority of the proposed algorithms when compared with existing algorithms.

Color space lip segmentation for drivers＇ fatigue detection

to the chroma distribution diversity （CDD） between lip color and skin color, the lip color area is segmented by the back propagation neural network （BPNN） with three typical color features. Isolated noisy points of the lip color area in binary image are eliminated by a proposed re- gion connecting algorithm. An improved integral projection algorithm is presented to locate the lip boundary. Whether a driver is fatigued is recognized by the ratio of the frame number of the images with mouth opening continuously to the total image frame number in every 20s. The experiments show that the proposed algorithm provides higher correct rate and reliability for fatigue driving detec- tion, and is superior to the single color feature-based method in the lip color segmention. Besides, it improves obviously the accuracy and speed of the lip boundary location compared with the traditional integral projection algrothm.