In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasib...In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasible owing to limited resources.In this paper,a physical layer authentication scheme is pro-posed to detect whether there is anomalous access by the attackers disguised as legitimate users.Explicitly,channel state information(CSI)is used as a form of fingerprint to exploit spatial discrimination among de-vices in the wireless network and machine learning(ML)technology is employed to promote the improve-ment of authentication accuracy.Considering that the falsified messages are not accessible for authenticator during the training phase,deep support vector data de-scription(Deep SVDD)is selected to solve the one-class classification(OCC)problem.Simulation results show that Deep SVDD based scheme can tackle the challenges of physical layer authentication in wireless communication environments.展开更多
Switzerland is one of the most desirable European destinations for Chinese tourists;therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leadi...Switzerland is one of the most desirable European destinations for Chinese tourists;therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform—Sina Weibo, a hybrid of Twitter and Facebook—has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. In order to offer a better decision support platform to tourism destination managers as well as Chinese tourists, we proposed a framework using linked data on Sina Weibo. Linked Data is a term referring to using the Internet to connect related data. We will show how it can be used and how ontology can be designed to include the users’ context (e.g., GPS locations). Our framework will provide a good theoretical foundation for further understand Chinese tourists’ expectation, experiences, behaviors and new trends in Switzerland.展开更多
There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because the...There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.展开更多
An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The metho...An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The method first discovers the routes between the data sensors and the sink node.Several factors are considered for each sensor node along the route,including energy,number of neighbours,previous transmissions,and energy depletion ratio.Considering all these variables,the Sink Reachable Support Measure and the Secure Communication Support Measure,the method evaluates two distinct measures.The method calculates the data carrier support value using these two metrics.A single route is chosen to collect data based on the value of data carrier support.It has contributed to the design of Secure Communication Support(SCS)Estimation.This has been measured according to the strategy of each hop of the route.The suggested method improves the security and efficacy of data collection in wireless sensor networks.The second stage uses the two-fish approach to build a trust model for secure data transfer.A sim-ulation exercise was conducted to evaluate the effectiveness of the suggested framework.Metrics,including PDR,end-to-end latency,and average residual energy,were assessed for the proposed model.The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework.展开更多
In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classif...In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.展开更多
In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ...In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.展开更多
On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them an...On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them and the necessity of improving them. The structure, models and data organizing schema of the material management decision support system are investigated based on a new data management technology (data warehousing technology).展开更多
Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechani...Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechanism. Moreover, the managers of company need wide-range and comprehensive decision information more and more urgently which is the character of information explosion era. The needs of users become harsher and harsher in the design of MIS, and these needs have brought new problems to the general designers of MIS. Furthermore, the current method of traditional database development can't solve so big and complex problems of wide-range and comprehensive information processing. This paper proposes the adoption of parallel processing mode, the built of new decision support system (DSS) is to discuss and analyze the problems of information collection, processing and the acquirement of full-merit information with cross-domain and cross-VLDB (very-large database).展开更多
The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum ass...The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.展开更多
<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process...<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.</span>展开更多
Decision Support Systems(DSS)are man-machine interaction systems,which support the de-cision-makers to solve the unstructured and semi-structured decisions,this paper advances that thefunction of problem-oriented info...Decision Support Systems(DSS)are man-machine interaction systems,which support the de-cision-makers to solve the unstructured and semi-structured decisions,this paper advances that thefunction of problem-oriented information retrieval DSS can meet the needs of enterprise’s topmanagement effectively in comparison with other information retrieval functions,in accordancewith the features of supporting information for decision.An architecture of this system is presented,which dissolves a problem put forward or recognized by the user into the problem recognized by thecomputer,forming retrieval tactics and searching the data the user needs.Designed and developedaccording to the architecture of this system,a prototype system is introduced,which is CF Econom-ic Environment Information Retrieval DSS.展开更多
The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibi...The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.展开更多
Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be...Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be seen as an index for regular data support.As a type of change of scale,data refinement is useful for many scenarios where spatial scales of existing data,desired analyses,or specific applications need to be made commensurate and refined.As spatial data are related to certain data support,they can be conceived of as support-specific realizations of random fields,suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finerresolution ones.In this paper,geostatistical methods for downscaling are described,and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China,with the latter’s majority grid cells used as surrogate reference data.It was found that proper structural modeling is important for achieving increased accuracy in data refinement;here,structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter.It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.展开更多
A layered architecture of muhisensory integration gripper system is first developed, which includes data acquisition layer, data processing layer and network interface layer. Then we propose a novel support-vector-mac...A layered architecture of muhisensory integration gripper system is first developed, which includes data acquisition layer, data processing layer and network interface layer. Then we propose a novel support-vector-machine-based data fusion algorithm and also design the gripper system by combining data fusion with CAN bus and CORBA technology, which provides the gripper system with outstanding characteristics such as modularization and intelligence. A multisensory integration gripper test bed is finally built on which a circuit board replacement job based on Internet-based teleoperation is achieved. The experimental results verify the validity of this gripper system design.展开更多
基金partially supported by the National Key Research and Development Project under Grant2020YFB1806805Social Development Projects of Jiangsu Science and Technology Department under Grant No.BE2018704
文摘In wireless communication,the problem of authenticating the transmitter’s identity is challeng-ing,especially for those terminal devices in which the security schemes based on cryptography are approxi-mately unfeasible owing to limited resources.In this paper,a physical layer authentication scheme is pro-posed to detect whether there is anomalous access by the attackers disguised as legitimate users.Explicitly,channel state information(CSI)is used as a form of fingerprint to exploit spatial discrimination among de-vices in the wireless network and machine learning(ML)technology is employed to promote the improve-ment of authentication accuracy.Considering that the falsified messages are not accessible for authenticator during the training phase,deep support vector data de-scription(Deep SVDD)is selected to solve the one-class classification(OCC)problem.Simulation results show that Deep SVDD based scheme can tackle the challenges of physical layer authentication in wireless communication environments.
文摘Switzerland is one of the most desirable European destinations for Chinese tourists;therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform—Sina Weibo, a hybrid of Twitter and Facebook—has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. In order to offer a better decision support platform to tourism destination managers as well as Chinese tourists, we proposed a framework using linked data on Sina Weibo. Linked Data is a term referring to using the Internet to connect related data. We will show how it can be used and how ontology can be designed to include the users’ context (e.g., GPS locations). Our framework will provide a good theoretical foundation for further understand Chinese tourists’ expectation, experiences, behaviors and new trends in Switzerland.
基金Project(61374140)supported by the National Natural Science Foundation of China
文摘There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.
文摘An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The method first discovers the routes between the data sensors and the sink node.Several factors are considered for each sensor node along the route,including energy,number of neighbours,previous transmissions,and energy depletion ratio.Considering all these variables,the Sink Reachable Support Measure and the Secure Communication Support Measure,the method evaluates two distinct measures.The method calculates the data carrier support value using these two metrics.A single route is chosen to collect data based on the value of data carrier support.It has contributed to the design of Secure Communication Support(SCS)Estimation.This has been measured according to the strategy of each hop of the route.The suggested method improves the security and efficacy of data collection in wireless sensor networks.The second stage uses the two-fish approach to build a trust model for secure data transfer.A sim-ulation exercise was conducted to evaluate the effectiveness of the suggested framework.Metrics,including PDR,end-to-end latency,and average residual energy,were assessed for the proposed model.The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework.
基金supported by National Natural Science Foundation of China(62371098)Natural Science Foundation of Sichuan Province(2023NSFSC1422)+1 种基金National Key Research and Development Program of China(2021YFB2900404)Central Universities of South west Minzu University(ZYN2022032).
文摘In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.
文摘In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.
文摘On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them and the necessity of improving them. The structure, models and data organizing schema of the material management decision support system are investigated based on a new data management technology (data warehousing technology).
文摘Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechanism. Moreover, the managers of company need wide-range and comprehensive decision information more and more urgently which is the character of information explosion era. The needs of users become harsher and harsher in the design of MIS, and these needs have brought new problems to the general designers of MIS. Furthermore, the current method of traditional database development can't solve so big and complex problems of wide-range and comprehensive information processing. This paper proposes the adoption of parallel processing mode, the built of new decision support system (DSS) is to discuss and analyze the problems of information collection, processing and the acquirement of full-merit information with cross-domain and cross-VLDB (very-large database).
基金Supported by the National Science & Technology Pillar Program(2006BAJ07B07)
文摘The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.
文摘<span style="font-family:Verdana;">The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.</span>
文摘Decision Support Systems(DSS)are man-machine interaction systems,which support the de-cision-makers to solve the unstructured and semi-structured decisions,this paper advances that thefunction of problem-oriented information retrieval DSS can meet the needs of enterprise’s topmanagement effectively in comparison with other information retrieval functions,in accordancewith the features of supporting information for decision.An architecture of this system is presented,which dissolves a problem put forward or recognized by the user into the problem recognized by thecomputer,forming retrieval tactics and searching the data the user needs.Designed and developedaccording to the architecture of this system,a prototype system is introduced,which is CF Econom-ic Environment Information Retrieval DSS.
文摘The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.
基金Research reported in this paper is supported by the National Natural Science Foundation of China(grant numbers 41171346,41471375).
文摘Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be seen as an index for regular data support.As a type of change of scale,data refinement is useful for many scenarios where spatial scales of existing data,desired analyses,or specific applications need to be made commensurate and refined.As spatial data are related to certain data support,they can be conceived of as support-specific realizations of random fields,suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finerresolution ones.In this paper,geostatistical methods for downscaling are described,and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China,with the latter’s majority grid cells used as surrogate reference data.It was found that proper structural modeling is important for achieving increased accuracy in data refinement;here,structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter.It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.
文摘A layered architecture of muhisensory integration gripper system is first developed, which includes data acquisition layer, data processing layer and network interface layer. Then we propose a novel support-vector-machine-based data fusion algorithm and also design the gripper system by combining data fusion with CAN bus and CORBA technology, which provides the gripper system with outstanding characteristics such as modularization and intelligence. A multisensory integration gripper test bed is finally built on which a circuit board replacement job based on Internet-based teleoperation is achieved. The experimental results verify the validity of this gripper system design.
