The main source of water in Gaza Strip is the shallow coastal aquifer. It is extremely deteriorated in terms of salinity which influenced by many variables. Studying the relation between these variables and salinity i...The main source of water in Gaza Strip is the shallow coastal aquifer. It is extremely deteriorated in terms of salinity which influenced by many variables. Studying the relation between these variables and salinity is often a complex and nonlinear process, making it suitable to model by Artificial Neural Networks (ANN). Initially, it is assumed that the salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate, abstraction, abstraction average rate, life time and aquifer thickness. Data were extracted from 56 municipal wells, covering the area of Gaza Strip. After a number of modeling trials, the best neural network was determined to be Multilayer Perceptron network (MLP) with four layers: an input layer of 6 neurons, first hidden layer with 10 neurons, second hidden layer with 7 neurons and the output layer with 1 neuron which gives the final chloride concentration. The ANN model generated very good results depending on the high correlation between the observed and simulated values of chloride concentration. The correlation coefficient (r) was 0.9848. The high value of (r) showed that the simulated chloride concentration values using the ANN model were in very good agreement with the observed chloride concentration which mean that ANN model is useful and applicable for groundwater salinity modeling. ANN model was successfully utilized as analytical tool to study influence of the input variables on chloride concentration. It proved that chloride concentration in groundwater is reduced by decreasing abstraction, abstraction average rate and life time. Furthermore, it is reduced by increasing recharge rate and aquifer thickness.展开更多
In the field of bioinformatics, the size of a biological network is usually very big. Without any help, it’s extremely hard to analyze the network. If it is shown as a visualized picture, things will be easier. So it...In the field of bioinformatics, the size of a biological network is usually very big. Without any help, it’s extremely hard to analyze the network. If it is shown as a visualized picture, things will be easier. So it’s very important to convert the biological network into a picture. However, there are a lot of software tools to be used to visualize the network. They use different file formats and do not support the transfer from one format to another. Sometimes it’s really hard to deal with it. So I analyzed three text file formats of them (“.dl”, “.net” and “.vna”) and developed a program to do this work automatically. The result of execution is very well and the efficiency is also impressive.展开更多
In this paper, a Supervised Linear Feature Mapping(SLFM) algorithm, as a modification of the Kohonen Self Organizing Mapping (SOM),is proposed. The applications in cutting tool wear estimation and quality control and...In this paper, a Supervised Linear Feature Mapping(SLFM) algorithm, as a modification of the Kohonen Self Organizing Mapping (SOM),is proposed. The applications in cutting tool wear estimation and quality control and the comparison with a back propagation (BP) algorithm are discussed. The results show that the SLFM algorithm requires less training time and has higher accuracy compared with the BP algorithm. It might be a great potential approach to integrate multi sensor information in process control.展开更多
This paper presents a fuzzy neural network used for monitoring breakage and wear of tools by vibration sig-nal. Which describes the relationship betwee too conditons and the monitoring indices and expermental results ...This paper presents a fuzzy neural network used for monitoring breakage and wear of tools by vibration sig-nal. Which describes the relationship betwee too conditons and the monitoring indices and expermental results indi-cate it is feasible to vibration signal for on-line drilling condition monitoring.展开更多
In a drilling process, the power spectrum of the drilling force is related tothe tool wear and is widely applied in the monitoring of tool wear. But the feature extraction andidentification of the power spectrum have ...In a drilling process, the power spectrum of the drilling force is related tothe tool wear and is widely applied in the monitoring of tool wear. But the feature extraction andidentification of the power spectrum have always been an unresolved difficult problem. This papersolves it through decomposition of the power spectrum in multilayers using wavelet transform andextraction of the low frequency decomposition coefficient as the envelope information of the powerspectrum. Intelligent identification of the tool wear status is achieved in the drilling processthrough fusing the wavelet decomposition coefficient of the power spectrum by using a BP (BackPropagation) neural network. The experimental results show that the features of the power spectrumcan be extracted efficiently through this method, and the trained neural networks show highidentification precision and the ability of extension.展开更多
The objective of this contribution is to present expositive review content on currently available experimental tools/services/concepts used for most emerging field Wireless Sensor Network that has capability to change...The objective of this contribution is to present expositive review content on currently available experimental tools/services/concepts used for most emerging field Wireless Sensor Network that has capability to change many of the Information Communication aspects in the upcoming era. Currently due to high cost of large number of sensor nodes most researches in wireless sensor networks area is performed by using these experimental tools in various universities, institutes, and research centers before implementing real one. Also the statistics gathered from these experimental tools can be realistic and convenient. These experimental tools provide the better option for studying the behavior of WSNs before and after implementing the physical one. In this contribution 63 simulators/simulation frameworks, 14 emulators, 19 data visualization tools, 46 testbeds, 26 debugging tools/services/concepts, 10 code-updation/reprogramming tools and 8 network monitors has been presented that are used worldwide for WSN researches.展开更多
软件定义网络(Software Defined Network, SDN)是一种新型的网络体系架构,其原理是采用OpenFlow对网络结构进行自定义的扩展与管理。为进一步研究SDN架构的特性并实现网络负载均衡,文章基于Mininet仿真工具对SDN的网络负载均衡进行深入...软件定义网络(Software Defined Network, SDN)是一种新型的网络体系架构,其原理是采用OpenFlow对网络结构进行自定义的扩展与管理。为进一步研究SDN架构的特性并实现网络负载均衡,文章基于Mininet仿真工具对SDN的网络负载均衡进行深入研究,分析了OpenFlow协议技术及其原理,验证了SDN架构的功能和基本工作流程,构建了网络负载均衡仿真研究的实验环境,并在Mininet中进行了模拟实现,为进一步研究SDN架构提供了有效的技术支撑。展开更多
文摘The main source of water in Gaza Strip is the shallow coastal aquifer. It is extremely deteriorated in terms of salinity which influenced by many variables. Studying the relation between these variables and salinity is often a complex and nonlinear process, making it suitable to model by Artificial Neural Networks (ANN). Initially, it is assumed that the salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate, abstraction, abstraction average rate, life time and aquifer thickness. Data were extracted from 56 municipal wells, covering the area of Gaza Strip. After a number of modeling trials, the best neural network was determined to be Multilayer Perceptron network (MLP) with four layers: an input layer of 6 neurons, first hidden layer with 10 neurons, second hidden layer with 7 neurons and the output layer with 1 neuron which gives the final chloride concentration. The ANN model generated very good results depending on the high correlation between the observed and simulated values of chloride concentration. The correlation coefficient (r) was 0.9848. The high value of (r) showed that the simulated chloride concentration values using the ANN model were in very good agreement with the observed chloride concentration which mean that ANN model is useful and applicable for groundwater salinity modeling. ANN model was successfully utilized as analytical tool to study influence of the input variables on chloride concentration. It proved that chloride concentration in groundwater is reduced by decreasing abstraction, abstraction average rate and life time. Furthermore, it is reduced by increasing recharge rate and aquifer thickness.
文摘In the field of bioinformatics, the size of a biological network is usually very big. Without any help, it’s extremely hard to analyze the network. If it is shown as a visualized picture, things will be easier. So it’s very important to convert the biological network into a picture. However, there are a lot of software tools to be used to visualize the network. They use different file formats and do not support the transfer from one format to another. Sometimes it’s really hard to deal with it. So I analyzed three text file formats of them (“.dl”, “.net” and “.vna”) and developed a program to do this work automatically. The result of execution is very well and the efficiency is also impressive.
文摘In this paper, a Supervised Linear Feature Mapping(SLFM) algorithm, as a modification of the Kohonen Self Organizing Mapping (SOM),is proposed. The applications in cutting tool wear estimation and quality control and the comparison with a back propagation (BP) algorithm are discussed. The results show that the SLFM algorithm requires less training time and has higher accuracy compared with the BP algorithm. It might be a great potential approach to integrate multi sensor information in process control.
文摘This paper presents a fuzzy neural network used for monitoring breakage and wear of tools by vibration sig-nal. Which describes the relationship betwee too conditons and the monitoring indices and expermental results indi-cate it is feasible to vibration signal for on-line drilling condition monitoring.
文摘In a drilling process, the power spectrum of the drilling force is related tothe tool wear and is widely applied in the monitoring of tool wear. But the feature extraction andidentification of the power spectrum have always been an unresolved difficult problem. This papersolves it through decomposition of the power spectrum in multilayers using wavelet transform andextraction of the low frequency decomposition coefficient as the envelope information of the powerspectrum. Intelligent identification of the tool wear status is achieved in the drilling processthrough fusing the wavelet decomposition coefficient of the power spectrum by using a BP (BackPropagation) neural network. The experimental results show that the features of the power spectrumcan be extracted efficiently through this method, and the trained neural networks show highidentification precision and the ability of extension.
文摘The objective of this contribution is to present expositive review content on currently available experimental tools/services/concepts used for most emerging field Wireless Sensor Network that has capability to change many of the Information Communication aspects in the upcoming era. Currently due to high cost of large number of sensor nodes most researches in wireless sensor networks area is performed by using these experimental tools in various universities, institutes, and research centers before implementing real one. Also the statistics gathered from these experimental tools can be realistic and convenient. These experimental tools provide the better option for studying the behavior of WSNs before and after implementing the physical one. In this contribution 63 simulators/simulation frameworks, 14 emulators, 19 data visualization tools, 46 testbeds, 26 debugging tools/services/concepts, 10 code-updation/reprogramming tools and 8 network monitors has been presented that are used worldwide for WSN researches.
文摘软件定义网络(Software Defined Network, SDN)是一种新型的网络体系架构,其原理是采用OpenFlow对网络结构进行自定义的扩展与管理。为进一步研究SDN架构的特性并实现网络负载均衡,文章基于Mininet仿真工具对SDN的网络负载均衡进行深入研究,分析了OpenFlow协议技术及其原理,验证了SDN架构的功能和基本工作流程,构建了网络负载均衡仿真研究的实验环境,并在Mininet中进行了模拟实现,为进一步研究SDN架构提供了有效的技术支撑。
