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.

Synthesis of flexible inter-plant heat exchanger networks:A decomposition method considering intermedium fluid circles

The traditional methods for synthesizing flexible heat exchanger networks(HENs)are not directly applicable to inter-plant HEN challenges,primarily due to the spread of system uncertainty across plants via intermedium fluid circles.This complicates the synthesis process significantly.To tackle this issue,this study proposes a decomposed stepwise methodology to facilitate the flexible synthesis of the interplant HENs performing indirect heat integration.A decomposition strategy is proposed to divide the overall network into manageable sub-networks by dissecting the intermedium fluid circles.To address the variability in intermedium fluid temperatures,a temperature fluctuation analysis approach is developed and a heuristic rule is introduced to maintain the temperature feasibility of the intermedium fluids.To ensure adequate flexibility and cost-effectiveness of the designed networks,flexibility analysis and network retrofit steps are conducted through model-based optimization techniques.The efficacy of the method is demonstrated through two case studies,showing its potential in achieving the desired operational flexibility for inter-plant HENs.

3-D fracture network dynamic simulation based on error analysis in rock mass of dam foundation

Accurate 3-D fracture network model for rock mass in dam foundation is of vital importance for stability,grouting and seepage analysis of dam foundation.With the aim of reducing deviation between fracture network model and measured data,a 3-D fracture network dynamic modeling method based on error analysis was proposed.Firstly,errors of four fracture volume density estimation methods(proposed by ODA,KULATILAKE,MAULDON,and SONG)and that of four fracture size estimation methods(proposed by EINSTEIN,SONG and TONON)were respectively compared,and the optimal methods were determined.Additionally,error index representing the deviation between fracture network model and measured data was established with integrated use of fractal dimension and relative absolute error(RAE).On this basis,the downhill simplex method was used to build the dynamic modeling method,which takes the minimum of error index as objective function and dynamically adjusts the fracture density and size parameters to correct the error index.Finally,the 3-D fracture network model could be obtained which meets the requirements.The proposed method was applied for 3-D fractures simulation in Miao Wei hydropower project in China for feasibility verification and the error index reduced from 2.618 to 0.337.

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.

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.

A quantitative BP neural network analysis of the relationships between ΣREE content and impact factors in the Beibu Gulf

The distribution characteristics of rare earth elements （REE） in bottomsediments are influenced by many factors. Hence, conducting a quantitative analysis isdifficult. A qualitative analysis of the relationships between ΣREE content andprovenance, hydrodynamics, grain size and mineral distribution in the Beibu Gulf showsthat terrestrial rocks control the ΣREE composition. Both weaker hydrodynamics andfiner grain size lead to a higher ΣREE content. Relative curves revealing therelationships between individual impact factors and ΣREE content were obtained fromthe combination of qualitative and quantitative analyses of the BP neural network,which trained the position of samples, gravel content, sand content, silt content, claycontent and clay mineral content. The results are consistent with those of thequantitative analysis. The self-learning algorithm is automatically determined andcalculated quantitatively. The impact of each factor on REEs and how each factorcontrols the ΣREE distribution is identified. Thus, environmental changes and thegeological evolution of the region can be inferred based on curve variation and the geological evolution of the region can be inferred based on curve variation and theactual situation. This method also provides useful theoretical guidance for the analysisof REE enrichment and dispersion.

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.

INTEGRATED ANALYSIS APPROACHES TO ROCK MECHANICS PROBLEMS

In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass and engineering and our obtainable information level at hand,the integrated approaches with intelligent characters are proposed. Many previous standard methods,such as precedent type analysis,rock classification,analytic method stress-based,basic numerical methods (BEM,FEM,DEM,hybrid),and their extended numerical methods (fully coupled) to be developed,can be selected respectively or integrated accordingly. It is alternative to develop basic/fully integrated system,and internet-based approaches. These novel methods can also be selected or integrated each other or with the standard methods to perform rock mechanics analysis. Some key techniques to develop these alternative methods are discussed. It may focus in future on developing fully integrated systems and internet-based approaches. Developing an environmental,virtual facility/space shall be firstly done for this collaborative research on internet.

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.

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.

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。

基于主路径分析的领域研究路径识别研究——以知识图谱为例

[目的/意义]引文网络的主路径分析可以对特定领域的重要文献进行识别,并能实现主流研究线索提取。文章旨在利用主路径分析方法,对知识图谱的研究路径进行分析,梳理其研究发展的脉络。[方法/过程]文章首先从Web of Science平台获取知识图谱领域的研究论文,然后利用HistCite软件生成文献的直接引证网络,再将数据导入到Pajek中,生成该数据集的多条主路径,并结合主路径上论文的内容进行定性分析。[结果/结论]通过主路径分析,可以快速识别出知识图谱领域的一些主路径,如知识图谱的构建、知识图谱在推荐和问答等应用场景中的研究、知识图谱在制造业等特定应用领域的应用研究,折射出知识图谱技术的发展脉络和研究方向。综述性研究在知识图谱领域的发展中扮演了重要角色,为领域内的深入研究提供了基础和方向。

我国省级层面县域医共体政策注意力演变研究

目的:分析省级县域医共体政策注意力演变过程与规律,以期为后续政策的制定与优化提供参考。方法:将省级县域医共体政策划分为探索期和试点期两个阶段,以及东部、中部、西部3个地区,以政策注意力理论为指导,分析各阶段、各地区的县域医共体政策注意力强度与政策注意力配置,并运用PMC指数模型对不同阶段、不同地区的县域医共体政策文本进行政策注意力综合量化评价。结果:省级县域医共体政策注意力存在时空差异。在时间维度表现为省级政策以中央政策为导向,试点期政策注意力强度大于探索期,政策注意力配置随中央政策注意力的侧重而转移;在空间维度上,省级县域医共体政策注意力水平地域差异性较大,东部省份较高,中部地区次之,西部地区较低。结论:省级县域医共体政策出台一方面要坚持中央导向与地方创新的统一,既要在中央政策的指导下进行政策制定,又要根据地域实际情况进行区域特征性政策创新;另一方面要坚持顶层支持与区域合作的统一,既需要由中央对县域医共体政策注意力水平较低的地区进行垂直的专项支持,又要发挥近邻效应与示范效应的作用进行区域间的平行合作,以提升县域医共体政策水平。

我国公立医院“一院多区”建设困境及对策

目的:对公立医院“一院多区”建设发展中存在的问题进行梳理和探讨,为推动公立医院“一院多区”建设提供借鉴。方法:采用文献分析法对相关问题进行梳理,结合社会网络分析法对问题网络进行可视化解读及中心度分析,运用TOPSIS法对问题的中心度测量结果进行综合评价。结果:公立医院“一院多区”在建设发展中存在人员配置不均衡、信息沟通不畅、配套政策不完善等问题;通过TOPSIS综合评价,排名前3位分别为院区定位及学科布局不合理(Ci=1.000)、医疗质量同质化难保障(Ci=0.886)以及医院管理难度加大(Ci=0.725)。结论:创新管理模式,强化质量标准,统筹学科布局,加速推进“一体化管理、同质化医疗、差异化发展”是公立医院“一院多区”建设发展的必要举措。

基于网络(ANP)的口袋公园服务水平研究——以安宁市昆钢口袋公园为例

在我国城市建设过程中,城市角落遗弃的小空间可改造成为口袋公园,口袋公园为市民增加了城市绿地。通过选取代表性口袋公园建设资料研究,综合运用多种分析方法,研究口袋公园的综合服务水平。研究表明,口袋公园选址的小区人口密度,尤其是道路网络的通达性对服务水平影响较大。

Understanding roof deformation mechanics and parametric sensitivities of coal mine entries using the discrete element method

Although conventional coal mine designs are conservative regarding pillar strength,local failures such as roof-falls and pillar bursts still affect mine safety and operations.Previous studies have identified that discontinuous,layered roof materials have some self-supporting capacity.This research is a preliminary step towards understanding these mechanics in coal-measure rocks.Although others have considered broad conceptual models and simplified analogs for mine roof behavior,this study presents a unique numerical model that more completely represents in-situ roof conditions.The discrete element method(DEM)is utilized to conduct a parametric analysis considering a range of in-situ stress ratios,material properties,and joint networks to determine the parameters controlling the stability of single-entries modeled in two-dimensions.Model results are compared to empirical observations of roof-support effectiveness(ARBS)in the context of the coal mine roof rating(CMRR)system.Results such as immediate roof displacement,overall stability,and statistical relationships between model parameters and outcomes are presented herein.Potential practical applications of this line of research include:(1)roof-support optimization for a range of coal-measure rocks,(2)establishment of a relationship between roof stability and pillar stress,and(3)determination of which parameters are most critical to roof stability and therefore require concentrated evaluation.

Numerical analysis on thermoacoustic engine using network method

The network method for modeling thermoacoustic engines is described. Some simulation results on acoustic fields and phases in engine, especially in the thermoacoustic stack are presented and analyzed. The effects of some key factors on performance of stack and engine system are simulated and discussed. These effect factors include the spaces of plates of stack, the position of stack in engine system, the source parameter of stack, and the mean working pressure of the engine system.