The soft rot infected by pathogenic bacterium Erwinia aroideae Holland is one of the three serious diseases of Chinese cabbage ( Brassica pekinensis Rupr.). By constructing vector system of high frequency transformati...The soft rot infected by pathogenic bacterium Erwinia aroideae Holland is one of the three serious diseases of Chinese cabbage ( Brassica pekinensis Rupr.). By constructing vector system of high frequency transformation mediated by Agrobacterium tunefaciens EHA105, anti-bacterial peptide gene with strong bactericidal action to pathogenic bacteria was introduced into Chinese cabbage AB-81 self-bred line and the transgenic plants were obtained. PCR and Southern blotting detection showed that target gene was integrated into plant genome of Chinese cabbage. The tests of bacteriostasis action of the extract from transgenic plants in vitro, and the assay of disease-resistant of transgenic plantlets in the tube and the pot by perfusing inoculation with pathogenic bacteria showed obvious resistance to soft rot. This resistance can be a stable heredity by genetic analysis of generations of transgenic plants self-bred, separation ratio of its R, was 3:1. The resistance to Km and disease of soft rot was still kept in the R-5. These results indicated the possibility of breeding new varieties of anti-soft rot Chinese cabbage by transgenic plants as parents.展开更多
Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenome...Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.展开更多
In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-s...In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.展开更多
In the separation process with a jig washer, an accurate on-line measurement of loose status of a jigging bed is essential for a successful control of coal quality and loose status is difficult to measure on-line dire...In the separation process with a jig washer, an accurate on-line measurement of loose status of a jigging bed is essential for a successful control of coal quality and loose status is difficult to measure on-line directly in industrial process situations. So a soft-sensor technology is needed for this purpose. The soft-sensor model is developed in the experiment by an adaptive neuro-fuzzy inference system （ANFIS） which has a remarkable ability of learning and generalization. Based on the analysis of the technologic mechanism of jigging bed, the structure of the ANFIS is established to build the soft-sensor model of loose status estimation. The ANFIS is trained by a hybrid learning algorithm. Finally, the simulation results and comparison analysis are presented, which indicate that the ANFIS has better abilities of learning and generalization than the RBF and the BP networks. Thus, it is possible that the loose status of the jigging bed can be estimated on-line bv using ANFIS.展开更多
The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation proce...The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.展开更多
Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,whic...Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.展开更多
In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring...In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.展开更多
To solve the problems that the exception handling code is hard to test and maintain and that it affects the robustness and reliability of software, a method for evaluating the exception handling of programs is present...To solve the problems that the exception handling code is hard to test and maintain and that it affects the robustness and reliability of software, a method for evaluating the exception handling of programs is presented. The exception propagation graph （EPG） that describes the large programs with exception handling constructs is proposed by simplifying the control flow graph and it is applied to a case to verify its validity. According to the EPG, the exception handling code that never executes is identified; the points that are the most critical to controlling exception propagation are found; and the irrational exception handling code is corrected. The constructing algorithm for the EPG is given; thus, this provides a basis for automatically constructing the EPG and automatically correcting the irrational exception handling code.展开更多
The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to ob...The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.展开更多
A computer program called Matrix Generator (MG) was developed for transforming sized DNA fragments into a presence/absence data matrix. Dynamic computation was run to avoid errors introduced using fixed-bin-width arit...A computer program called Matrix Generator (MG) was developed for transforming sized DNA fragments into a presence/absence data matrix. Dynamic computation was run to avoid errors introduced using fixed-bin-width arithmetic. MG can be used with bin sized fragments from AFLP, ISSR, RAPD, RFLP, and other molecular markers. The accuracy of MG was tested using fAFLP data of Abelia and the results show that MG results in higher resolution of taxa and is more reliable than programs of the similar usage.展开更多
WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted ma...WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html展开更多
Based on the characteristics of ATM system and the special requirement of financial transaction, an overall design of hardware and software structure of ATM was made. For software structure, the pattern of modules and...Based on the characteristics of ATM system and the special requirement of financial transaction, an overall design of hardware and software structure of ATM was made. For software structure, the pattern of modules and table? drive is adopted to realize the security of financial transaction and the diagnosis of communication fault. A new method, which is based on the application layer, transport layer and network layer, is used for diagnosing communication fault. Supporting both magnetic card and IC card, the system has been put into use in real financial systems, and has brought about both economic and social effects.展开更多
This paper presents a hybrid soft computing modeling approach for a neurofuzzy system based on rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the inpu...This paper presents a hybrid soft computing modeling approach for a neurofuzzy system based on rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the input dimension increases, the fuzzy rule base increases exponentially. This leads to a huge infrastructure network which results in slow convergence. To solve this problem, rough set theory is used to obtain the reductive rules, which are used as fuzzy rules of the fuzzy system. The number of rules decrease, and each rule does not need all the conditional attribute values. This results in a reduced, or not fully connected, neural network. The structure of the neural network is relatively small and thus the weights to be trained decrease. The genetic algorithm is used to search the optimal discretization of the continuous attributes. The NFRSGA approach has been applied in the practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in a Fluid Catalytic Cracking Unit (FCCU), and satisfying results are obtained.展开更多
Ethernet over SDH/SONET (EOS) is a hotspot in today's data transmission technology for it combines the merits of both Ethernet and SDH/SONET. However, implementing an EOS system on a chip is complex and needs full...Ethernet over SDH/SONET (EOS) is a hotspot in today's data transmission technology for it combines the merits of both Ethernet and SDH/SONET. However, implementing an EOS system on a chip is complex and needs full verifications. This paper introduces our design of Hardware/Software co-verification platform for EOS design. The hardware platform contains a microprocessor board and an FPGA (Field Programmable Gate Array)-based verification board, and the corresponding software includes test benches running in FPGAs, controlling programs for the microprocessor and a console program with GUI (Graphical User Interface) interface for configuration, management and supervision. The design is cost-effective and has been successfully employed to verify several IP (Intellectual Property) blocks of our EOS chip. Moreover, it is flexible and can be applied as a general-purpose verification platform.展开更多
A flexible and controllable movement-assisted software-defined sensor network(MA-SDSN)based on the software-defined network(SDN)and network function virtualization(NFV)is proposed.First,a three-layer fundamental archi...A flexible and controllable movement-assisted software-defined sensor network(MA-SDSN)based on the software-defined network(SDN)and network function virtualization(NFV)is proposed.First,a three-layer fundamental architecture is proposed to overcome the inherent distributed management and rigidity of the conventional wireless sensor networks.Furthermore,the platform for research and development of MA-SDSN is established,and the dumb node(DN),the software-defined node(SN)and the movement-assisted node(MN)are designed and implemented.Then,the southbound application programming interface(API)is designed to provide a series of frames for communication between controllers and sensor nodes.The northbound API is developed and demonstrated overall and in detail.The functions of the controller are presented including topology discovery,dynamic networking,packet processing,mobility management and virtualization.Followed by the MA-SDSN network model,a Markov chain-based movement-assisted weighted relocation(MMWR)topology control algorithm is proposed to redeploy the MNs based on the node status and weight.Simulation results and analysis indicate that the proposed algorithm based on the MA-SDSN extends network lifetime with a lower average power consumption.展开更多
文摘The soft rot infected by pathogenic bacterium Erwinia aroideae Holland is one of the three serious diseases of Chinese cabbage ( Brassica pekinensis Rupr.). By constructing vector system of high frequency transformation mediated by Agrobacterium tunefaciens EHA105, anti-bacterial peptide gene with strong bactericidal action to pathogenic bacteria was introduced into Chinese cabbage AB-81 self-bred line and the transgenic plants were obtained. PCR and Southern blotting detection showed that target gene was integrated into plant genome of Chinese cabbage. The tests of bacteriostasis action of the extract from transgenic plants in vitro, and the assay of disease-resistant of transgenic plantlets in the tube and the pot by perfusing inoculation with pathogenic bacteria showed obvious resistance to soft rot. This resistance can be a stable heredity by genetic analysis of generations of transgenic plants self-bred, separation ratio of its R, was 3:1. The resistance to Km and disease of soft rot was still kept in the R-5. These results indicated the possibility of breeding new varieties of anti-soft rot Chinese cabbage by transgenic plants as parents.
基金Supported by the National Natural Science Foundation of China(61273070)the Foundation of Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.
基金Supported by the National Natural Science Foundation of China （No.60421002） and the New Century 151 Talent Project of Zhejiang Province.
文摘In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.
基金Project 70533050 supported by National Natural Science Foundation of China, 2005037225 by Postdoctoral Science Foundation of China, [2004]300 byPostdoctoral Science Foundation of Jiangsu Province, and OC 4465 bu Young Science Foundation of China University of Mining & Technology
文摘In the separation process with a jig washer, an accurate on-line measurement of loose status of a jigging bed is essential for a successful control of coal quality and loose status is difficult to measure on-line directly in industrial process situations. So a soft-sensor technology is needed for this purpose. The soft-sensor model is developed in the experiment by an adaptive neuro-fuzzy inference system （ANFIS） which has a remarkable ability of learning and generalization. Based on the analysis of the technologic mechanism of jigging bed, the structure of the ANFIS is established to build the soft-sensor model of loose status estimation. The ANFIS is trained by a hybrid learning algorithm. Finally, the simulation results and comparison analysis are presented, which indicate that the ANFIS has better abilities of learning and generalization than the RBF and the BP networks. Thus, it is possible that the loose status of the jigging bed can be estimated on-line bv using ANFIS.
基金Supported by the Natural Science Foundation of Jiangsu Province of China(BK20130531)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD[2011]6)Jiangsu Government Scholarship
文摘The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.
基金Supported by the National Natural Science Foundation of China （21006127）, the National Basic Research Program of China （2012CB720500） and the Science Foundation of China University of Petroleum, Beijing （KYJJ2012-05-28）.
文摘Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.
基金Supported by the National Natural Science Foundation of China(61273160)the Fundamental Research Funds for the Central Universities(14CX06067A,13CX05021A)
文摘In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.
基金The National Natural Science Foundation of China(No60503020)the National Basic Research Program of China (973Program) (No2002CB312000)+1 种基金the Natural Science Foundation of Jiangsu Province (NoBK2006094)the Science Research Foundation of China University of Mining and Technology
文摘To solve the problems that the exception handling code is hard to test and maintain and that it affects the robustness and reliability of software, a method for evaluating the exception handling of programs is presented. The exception propagation graph （EPG） that describes the large programs with exception handling constructs is proposed by simplifying the control flow graph and it is applied to a case to verify its validity. According to the EPG, the exception handling code that never executes is identified; the points that are the most critical to controlling exception propagation are found; and the irrational exception handling code is corrected. The constructing algorithm for the EPG is given; thus, this provides a basis for automatically constructing the EPG and automatically correcting the irrational exception handling code.
基金Supported by the National Science Foundation of China(61622301,61533002)Beijing Natural Science Foundation(4172005)Major National Science and Technology Project(2017ZX07104)
文摘The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.
文摘A computer program called Matrix Generator (MG) was developed for transforming sized DNA fragments into a presence/absence data matrix. Dynamic computation was run to avoid errors introduced using fixed-bin-width arithmetic. MG can be used with bin sized fragments from AFLP, ISSR, RAPD, RFLP, and other molecular markers. The accuracy of MG was tested using fAFLP data of Abelia and the results show that MG results in higher resolution of taxa and is more reliable than programs of the similar usage.
基金Project (No. BFGEN.100B) supported by the Meat and LivestockLtd., Australia (MLA)
文摘WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html
文摘Based on the characteristics of ATM system and the special requirement of financial transaction, an overall design of hardware and software structure of ATM was made. For software structure, the pattern of modules and table? drive is adopted to realize the security of financial transaction and the diagnosis of communication fault. A new method, which is based on the application layer, transport layer and network layer, is used for diagnosing communication fault. Supporting both magnetic card and IC card, the system has been put into use in real financial systems, and has brought about both economic and social effects.
基金Sponsored by the National High Technology Research and Development Program of China (Grant No.G2001 AA413130).
文摘This paper presents a hybrid soft computing modeling approach for a neurofuzzy system based on rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the input dimension increases, the fuzzy rule base increases exponentially. This leads to a huge infrastructure network which results in slow convergence. To solve this problem, rough set theory is used to obtain the reductive rules, which are used as fuzzy rules of the fuzzy system. The number of rules decrease, and each rule does not need all the conditional attribute values. This results in a reduced, or not fully connected, neural network. The structure of the neural network is relatively small and thus the weights to be trained decrease. The genetic algorithm is used to search the optimal discretization of the continuous attributes. The NFRSGA approach has been applied in the practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in a Fluid Catalytic Cracking Unit (FCCU), and satisfying results are obtained.
文摘Ethernet over SDH/SONET (EOS) is a hotspot in today's data transmission technology for it combines the merits of both Ethernet and SDH/SONET. However, implementing an EOS system on a chip is complex and needs full verifications. This paper introduces our design of Hardware/Software co-verification platform for EOS design. The hardware platform contains a microprocessor board and an FPGA (Field Programmable Gate Array)-based verification board, and the corresponding software includes test benches running in FPGAs, controlling programs for the microprocessor and a console program with GUI (Graphical User Interface) interface for configuration, management and supervision. The design is cost-effective and has been successfully employed to verify several IP (Intellectual Property) blocks of our EOS chip. Moreover, it is flexible and can be applied as a general-purpose verification platform.
基金The National Natural Science Foundations of China(No.61471164,61601122)
文摘A flexible and controllable movement-assisted software-defined sensor network(MA-SDSN)based on the software-defined network(SDN)and network function virtualization(NFV)is proposed.First,a three-layer fundamental architecture is proposed to overcome the inherent distributed management and rigidity of the conventional wireless sensor networks.Furthermore,the platform for research and development of MA-SDSN is established,and the dumb node(DN),the software-defined node(SN)and the movement-assisted node(MN)are designed and implemented.Then,the southbound application programming interface(API)is designed to provide a series of frames for communication between controllers and sensor nodes.The northbound API is developed and demonstrated overall and in detail.The functions of the controller are presented including topology discovery,dynamic networking,packet processing,mobility management and virtualization.Followed by the MA-SDSN network model,a Markov chain-based movement-assisted weighted relocation(MMWR)topology control algorithm is proposed to redeploy the MNs based on the node status and weight.Simulation results and analysis indicate that the proposed algorithm based on the MA-SDSN extends network lifetime with a lower average power consumption.
