A Study on Priority Based ZigBee Network Performance Analysis with Tree Routing Method

The Wireless Sensor Network (WSN) is spatially distributed autonomous sensor to sense special task. WSN like ZigBee network forms simple interconnecting, low power, and low processing capability wireless devices. The ZigBee devices facilitate numerous applications such as pervasive computing, security monitoring and control. ZigBee end devices collect sensing data and send them to ZigBee Coordinator. The Coordinator processes end device requests. The effect of a large number of random unsynchronized requests may degrade the overall network performance. An effective technique is particularly needed for synchronizing available node’s request processing to design a reliable ZigBee network. In this paper, region based priority mechanism is implemented to synchronize request with Tree Routing Method. Riverbed is used to simulate and analyze overall ZigBee network performance. The results show that the performance of the overall priority based ZigBee network model is better than without a priority based model. This research paves the way for further designing and modeling a large scale ZigBee network.

基于CNN-Swin Transformer Network的LPI雷达信号识别

针对在低信噪比(SNR)条件下,低截获概率雷达信号调制方式识别准确率低的问题,提出一种基于Transformer和卷积神经网络(CNN)的雷达信号识别方法。首先,引入Swin Transformer模型并在模型前端设计CNN特征提取层构建了CNN+Swin Transformer网络(CSTN),然后利用时频分析获取雷达信号的时频特征,对图像进行预处理后输入CSTN模型进行训练,由网络的底部到顶部不断提取图像更丰富的语义信息,最后通过Softmax分类器对六类不同调制方式信号进行分类识别。仿真实验表明:在SNR为-18 dB时,该方法对六类典型雷达信号的平均识别率达到了94.26%,证明了所提方法的可行性。

基于ANP-FCE的矿井风险防控系统研发

为加强煤矿安全风险防控能力,采用ANP-FCE相结合的方法构建矿井风险防控系统。首先,基于事故致因理论对矿井生产体系进行调研,并建立矿井风险防控指标体系;其次,运用ANP分析主要影响因素,并采用FCE建立矿井风险防控评价模型;最后,利用Java平台,依托JSP+SSM框架与MySQL数据库联合开发矿井风险防控系统,并在某煤矿试运行。结果表明:该系统可以确定矿井安全生产工作的重点,实现矿井生产数据实时显示,并具有重点作业监控和预警功能。系统的开发有利于企业各级领导及时掌握各种数据信息,提高企业自身的风险防控和预警能力,为煤矿企业的运营和发展提供安全保障。

基于ISM-ANP的绿色包装灰色评价模型研究

目的 对绿色包装设计方案进行多维度综合评价,提供具体的包装优化依据,解决过度包装、废弃包装带来的环境问题。方法 首先建立涵盖环境、产品性能、创新性、经济性、用户体验感五大属性的评价指标体系,以ISM分析指标之间的内在关联情况并利用ANP确定权重值;其次通过灰色模糊评价对包装等级进行划分;最后以综合评价方法求得包装的绿色度评分。结果 将指标权重值与传统AHP方法得出的结果进行比较,发现本研究模型更科学合理。以一项小麦壳制保鲜包装设计为例,运用该综合评价模型进行运算后得出该方案评价值为5.043,再根据指标的得分数据有针对性地提出包装优化方向。结论 研究模型适用于各类绿色包装设计方案的筛选与优化,有助于包装设计与生产相关人员进行决策,引导我国包装行业更好地向绿色模式转变。

基于ANP动火作业事故应急处置影响因素研究

为提高企业对动火作业突发事件的应急处置能力,利用ANP网络层次分析法构建评价指标体系。根据法律法规、标准文件以及对事故案例分析,结合应急处置工作闭环原则确定4个一级指标和13个二级指标,借助yaanp辅助软件计算各影响因素权重,得到动火作业应急处置能力影响因素的重要度排序。结果表明,4个一级指标的权重值分别为0.22704、0.22704、0.42359、0.12232;13个二级指标中,动火作业应急处置能力影响因素最终排序为应急保障、处置情况评估与改进、应急救援,权重值分别为0.243885、0.131847、0.039.725。

Fault location method for petal-shaped distribution network with inverter-interfaced distributed generators

In this paper,a fault location method for the petal-shaped distribution network(PSDN)with inverter-interfaced distributed generators(IIDGs)is proposed to shorten the time of manual inspection.In order to calculate the fault position,the closed-loop structure of the PSDN is skillfully exploited,and the common control strategies of IIDGs are considered.For asymmetrical faults,a fault line identification formula based on the negative-sequence current phase differences is presented,and a fault location formula only utilizing the negative-sequence current amplitudes is derived to calculated the fault position.For symmetrical faults,the positive-sequence current at both ends of lines and the current output from IIDGs are used to identify the fault line,and the positive-sequence current on multiple lines are used to pinpoint the fault position.In this method,corresponding current phasors are separated into amplitudes and phases to satisfy the limitation of communication level.The simulation results show that the error is generally less than 1%,and the accuracy of the proposed method is not affected by the fault type,fault position,fault resistance,load current,and the IIDG penetration.

A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.

Sensitivity analysis of distributed parameter elements in high-speed circuit networks

This paper presents an analysis method, based on MacCormack＇s technique, for the evaluation of the time domain sensitivity of distributed parameter elements in high-speed circuit networks. Sensitivities can be calculated from electrical and physical parameters of the distributed parameter elements. The proposed method is a direct numerical method of time-space discretization and does not require complicated mathematical deductive process. Therefore, it is very convenient to program this method. It can be applied to sensitivity analysis of general transmission lines in linear or nonlinear circuit networks. The proposed method is second-order-accurate. Numerical experiment is presented to demonstrate its accuracy and efficiency.

Reinforcing a Dangerous Rock Mass Using the Flexible Network Method

Because the main failure type of a dangerous rock mass is collapse, the treatment of such a mass should focus on controlling collapse failure. When treating dangerous rock masses, disturbing the mass （e. g. by blasting） needs to be avoided, as this new damage could cause collapse. So the self-bearing capacity of the mountain mass must be used to treat the dangerous rock mass. This article is based on a practical example of the control of a dangerous rock mass at Banyan Mountain, Huangshi, Hubei Province. On the basis of an analysis of damage mechanism and the stability of the dangerous rock mass, a flexible network reinforcement method was designed to prevent the collapse of the rock mass. The deformations of section Ⅱ w of the dangerous rock mass before and after the flexible network reinforcement were calculated using the two-dimensional finite element method. The results show that the maximum deformation reduced by 55 % after the application of the flexible network reinforcement, from 45.99 to 20.75 ram, which demonstrates that the flexible network method is effective, and can provide some scientific basis for the treatment of dangerous rock masses.

Quantitative analysis modeling for the Chem Cam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network

Laser-induced breakdown spectroscopy(LIBS)has been applied to many fields for the quantitative analysis of diverse materials.Improving the prediction accuracy of LIBS regression models is still of great significance for the Mars exploration in the near future.In this study,we explored the quantitative analysis of LIBS for the one-dimensional Chem Cam(an instrument containing a LIBS spectrometer and a Remote Micro-Imager)spectral data whose spectra are produced by the Chem Cam team using LIBS under the Mars-like atmospheric conditions.We constructed a convolutional neural network(CNN)regression model with unified parameters for all oxides,which is efficient and concise.CNN that has the excellent capability of feature extraction can effectively overcome the chemical matrix effects that impede the prediction accuracy of regression models.Firstly,we explored the effects of four activation functions on the performance of the CNN model.The results show that the CNN model with the hyperbolic tangent(tanh)function outperforms the CNN models with the other activation functions(the rectified linear unit function,the linear function and the Sigmoid function).Secondly,we compared the performance among the CNN models using different optimization methods.The CNN model with the stochastic gradient descent optimization and the initial learning rate?=?0.0005 achieves satisfactory performance compared to the other CNN models.Finally,we compared the performance of the CNN model,the model based on support vector regression(SVR)and the model based on partial least square regression(PLSR).The results exhibit the CNN model is superior to the SVR model and the PLSR model for all oxides.Based on the above analysis,we conclude the CNN regression model can effectively improve the prediction accuracy of LIBS.

Network Reliability Analysis as a Tool to Guide Investment Decisions in Distributed Generation

Distributed Generation (DG) in any quantity is relevant to supplement the available energy capacity based on various locations, that is, whether a site specific or non-site specific energy technology. The evacuation infrastructure that delivers power to the distribution grid is designed with appropriate capacity in terms of size and length. The evacuation lines and distribution network however behave differently as they possess inherent characteristics and are exposed to varying external conditions. It is thus feasible to expect that these networks behave stochastically due to fault conditions and variable loads that destabilize the system. This in essence impacts on the availability of the evacuation infrastructure and consequently on the amount of energy delivered to the grid from the DG stations. Reliability of the evacuation point of a DG is however not a common or prioritized criteria in the decision process that guides investment in DG. This paper reviews a planned solar based DG plant in Uganda. Over the last couple of years, Uganda has seen a significant increase in the penetration levels of DG. With a network that is predominantly radial and experiences low reliability levels, one would thus expect reliability analysis to feature significantly in the assessment of the proposed DG plants. This is however not the case. This paper, uses reliability analysis to assess the impact of different evacuation options of the proposed DG plant on its dispatch levels. The evacuation options were selected based on infrastructure options in other locations with similar solar irradiances as the planned DG location. Outage data were collected and analyzed using the chi square method. It was found to be variable and fitting to different Probability Distribution Functions (PDF). Using stochastic methods, a model that incorporates the PDFs was developed to compute the reliability indices. These were assessed using chi square and found to be variable and fitting different PDFs as well. The viability of the project is reviewed based on Energy Not Supplied (ENS) and the anticipated project payback periods for any considered evacuation line. The results of the study are also reviewed for the benefit of other stakeholders like the customers, the utility and the regulatory body.

Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al2O3, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.

Systematic Review of Graphical Visual Methods in Honeypot Attack Data Analysis

Mitigating increasing cyberattack incidents may require strategies such as reinforcing organizations’ networks with Honeypots and effectively analyzing attack traffic for detection of zero-day attacks and vulnerabilities. To effectively detect and mitigate cyberattacks, both computerized and visual analyses are typically required. However, most security analysts are not adequately trained in visualization principles and/or methods, which is required for effective visual perception of useful attack information hidden in attack data. Additionally, Honeypot has proven useful in cyberattack research, but no studies have comprehensively investigated visualization practices in the field. In this paper, we reviewed visualization practices and methods commonly used in the discovery and communication of attack patterns based on Honeypot network traffic data. Using the PRISMA methodology, we identified and screened 218 papers and evaluated only 37 papers having a high impact. Most Honeypot papers conducted summary statistics of Honeypot data based on static data metrics such as IP address, port, and packet size. They visually analyzed Honeypot attack data using simple graphical methods (such as line, bar, and pie charts) that tend to hide useful attack information. Furthermore, only a few papers conducted extended attack analysis, and commonly visualized attack data using scatter and linear plots. Papers rarely included simple yet sophisticated graphical methods, such as box plots and histograms, which allow for critical evaluation of analysis results. While a significant number of automated visualization tools have incorporated visualization standards by default, the construction of effective and expressive graphical methods for easy pattern discovery and explainable insights still requires applied knowledge and skill of visualization principles and tools, and occasionally, an interdisciplinary collaboration with peers. We, therefore, suggest the need, going forward, for non-classical graphical methods for visualizing attack patterns and communicating analysis results. We also recommend training investigators in visualization principles and standards for effective visual perception and presentation.

Thermogram-based estimation of foot arterial blood flow using neural networks

The altered blood flow in the foot is an important indicator of early diabetic foot complications.However,it is challenging to measure the blood flow at the whole foot scale.This study presents an approach for estimating the foot arterial blood flow using the temperature distribution and an artificial neural network.To quantify the relationship between the blood flow and the temperature distribution,a bioheat transfer model of a voxel-meshed foot tissue with discrete blood vessels is established based on the computed tomography(CT)sequential images and the anatomical information of the vascular structure.In our model,the heat transfer from blood vessels and tissue and the inter-domain heat exchange between them are considered thoroughly,and the computed temperatures are consistent with the experimental results.Analytical data are then used to train a neural network to determine the foot arterial blood flow.The trained network is able to estimate the objective blood flow for various degrees of stenosis in multiple blood vessels with an accuracy rate of more than 90%.Compared with the Pennes bioheat transfer equation,this model fully describes intra-and inter-domain heat transfer in blood vessels and tissue,closely approximating physiological conditions.By introducing a vascular component to an inverse model,the blood flow itself,rather than blood perfusion,can be estimated,directly informing vascular health.

基于ANP分析法的冀西北地区乡村空间形态评价研究

为了解乡村空间形态建设的影响因素并优化乡村空间形态,提升乡村人居环境质量,从乡村空间使用者的需求出发,以冀西北地区部分具有地域普适性的乡村为研究对象进行实地调研,分析使用者对于乡村空间形态建设过程中各类影响因素的需求度。确定20项影响因素并将其归纳为自然环境因素、产业经济因素、历史文化因素、公共政策因素及规划设计因素五种类型。运用ANP分析法对各影响因素进行量化分析,以其为指标构建冀西北地区乡村空间形态建设影响因素的评价体系,并计算得出各指标权重值。基于以上研究成果,提出针对冀西北地区及其周边相似环境区域的乡村空间形态优化建设策略,推动乡村的优化建设及人居环境质量的提升。

FIXED/PREASSIGNED-TIME SYNCHRONIZATION OF QUATERNION-VALUED NEURAL NETWORKS INVOLVING DELAYS AND DISCONTINUOUS ACTIVATIONS: A DIRECT APPROACH

The fixed-time synchronization and preassigned-time synchronization are investigated for a class of quaternion-valued neural networks with time-varying delays and discontinuous activation functions. Unlike previous efforts that employed separation analysis and the real-valued control design, based on the quaternion-valued signum function and several related properties, a direct analytical method is proposed here and the quaternion-valued controllers are designed in order to discuss the fixed-time synchronization for the relevant quaternion-valued neural networks. In addition, the preassigned-time synchronization is investigated based on a quaternion-valued control design, where the synchronization time is preassigned and the control gains are finite. Compared with existing results, the direct method without separation developed in this article is beneficial in terms of simplifying theoretical analysis, and the proposed quaternion-valued control schemes are simpler and more effective than the traditional design, which adds four real-valued controllers. Finally, two numerical examples are given in order to support the theoretical results.

Evolution of economic linkage network of the cities and counties on the northern slope of the Tianshan Mountains,China

The exchanges between cities and counties in the northern slope economic belt of Tianshan Mountains(NSEBTM)are increasingly frequent and the economic linkages are increasingly close,but the spatial distribution of economic development and linkages among the cities and counties within NSEBTM is uneven.Therefore,it is of great significance to study the evolution of spatial-temporal pattern of the economic linkage network of cities and counties on NSEBTM to promote the coordinated and integrated development of the regional economy on NSEBTM.In this study,we used the modified gravity model and social network analysis method to analyze the spatio-temporal evolution characteristics of the economic linkage network structure of cities and counties on NSEBTM in 2000,2010,and 2020.The results showed that the comprehensive development quality level of cities and counties on NSEBTM increased from 2000 to 2020,its growth rate also increased,and its gap between cities and counties continued expanding.Both the spatial distribution patterns of the comprehensive development quality level of cities and counties on NSEBTM in 2000 and 2010 were presented as“high in the middle and low at both ends”,while the spatial distribution pattern of 2020 was exhibited as“high value and low value staggered”.The total amount of external economic linkages of cities and counties on NSEBTM showed an obvious upward trend,and its gap between cities and counties continued expanding,presenting a pattern of“a strong middle section and weak ends”.The direction of economic linkages of NSEBTM existed obvious central orientation and geographical proximity.The density of economic linkage network of NSEBTM increased from 2000 to 2020,and the structure of economic linkage network changed from single-core structure centered with Urumqi City to multicore structure centered with Urumqi City,Karamay City,Shihezi City,and Changji City,shifting from unbalanced development to balanced development.In the future,we should accelerate the construction of urban agglomeration on NSEBTM,cultivate a modern Urumqi metropolitan area,improve comprehensive development quality of the cities and counties at the eastern and western ends,strengthen the intensity of economic linkages between cities and counties,optimize the economic linkage network,and promote the coordinated and integrated development of regional economy.

SAM-FFTA-ANP在高校实验室中的应用

为了减少或避免实验室火灾事故带来的一系列损失,提出了一种基于相似度聚合法的模糊故障树分析方法(similarity aggregation method-fuzzy fault tree analysis, SAM-FFTA),并与网络层次分析法(analytic network process, ANP)相结合,完善了故障树中各原因事件相互依赖的关系,有效地弥补使用单一方法时的不足。首先基于危机生命周期理论分析得出高校实验室火灾演化过程;其次,根据SAM-FFTA合理聚合多位专家的不同意见,得到故障树中各基本事件的失效概率;最后,采用ANP建立了高校实验室火灾评价结构模型,进一步研究致因中影响性较大的事件。结果表明,电气故障是导致实验室火灾发生的最主要因素,同时,高温热源和化学品的管理也对实验室火灾影响显著。

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

基于ANP-物元可拓法的中小餐饮场所火灾风险评价

为有效预防中小餐饮场所火灾事故的发生,运用事故树分析法辨识中小餐饮场所火灾风险因素,并从人为因素、设备因素、环境因素、管理因素4个方面建立中小餐饮场所火灾风险评价指标体系;通过网络层次分析法分析指标间的影响关系并确定各评价指标的权重;构建基于ANP-物元可拓法的中小餐饮场所火灾风险评价模型,并选取某中小餐饮场所进行实例研究。评价结果显示:该餐饮场所的火灾风险等级为Ⅱ级,处于风险较低的状态,与该餐饮场所的实际相符合,验证了该方法的合理性和可靠性,为中小餐饮场所火灾风险评价提供一种新方法。