Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most en...Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.展开更多
In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is propose...In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.展开更多
This paper presents the development of a network based real time condition monitoring system of rotating machinery. The system is built up in a double net structure consisting of local net (including client and server...This paper presents the development of a network based real time condition monitoring system of rotating machinery. The system is built up in a double net structure consisting of local net (including client and server) and intranet. The client serves as a field data collector and processor that samples the vibration signals and process parameters of a machine monitored in the net and processes the sampled data. The data collected by the client are transmitted to the server that processes the data further and provides the results of the diagnosis of each machine to any distant terminals through intranet or internet. Such a structure of the monitoring system is advantageous in safety, reliability and reasonably shares the existing net resources. In order to ensure real time transmission of the data, two procedures of data transmission, virtual channel and data pool, are developed and applied in the monitoring system. The experimental results show that the monitoring system works well and is suitable to monitor a large group of rotating machines.展开更多
Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of r...Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better.展开更多
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and ...CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.展开更多
This paper discusses a 40-Gbit/s transparent optical network focusing on the optical transport performance. We show 1200-km transmission with two WSOXC' sspaced by 400 km. In addition, network control issues are b...This paper discusses a 40-Gbit/s transparent optical network focusing on the optical transport performance. We show 1200-km transmission with two WSOXC' sspaced by 400 km. In addition, network control issues are briefly addressed.展开更多
Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective funct...Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.展开更多
By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power pla...By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.展开更多
Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to dem...Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to demonstrate the feasibility of producing a tool that can provide a performance evaluation of various eonstellation access techniques and routing policies. The architecture and design of the simulation system are explored. The algorithm of data routing and instrument scheduling in this project is described. Besides these, the key methodologies of simulating the inter-satellite link features in the data transmissions are also discussed. The performance of both instrument scheduling algorithm and routing schemes is evaluated and analyzed through extensive simulations under a typical scenario.展开更多
Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distribu...Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.展开更多
It is a difficult problem to improve network performance and resource utilization efficiency in wireless communications. As a standard of broadband wireless access systems, IEEE 802.16 adopts Orthogonal Frequency Divi...It is a difficult problem to improve network performance and resource utilization efficiency in wireless communications. As a standard of broadband wireless access systems, IEEE 802.16 adopts Orthogonal Frequency Division Multiplexing (OFDM) and multi-modulation/coding techniques at the physical layer, combines contend and pre-contract mechanisms at the Medium Access Control (MAC) layer. Based on the QoS-related concepts such as connection and service flow, IEEE 802.16 optimizes network entry and initialization, and frame format in order to improve network throughput, reduce network delay, and increase the flexibility of network configuration. Based on IEEE 802.16, WiMAX adopts Point-to-Multipoint (PMP) network topology to realize flexible networking. It is a typical application technology of broadband wireless access systems.展开更多
The detection system integrates control technology, network technology, video encoding and decoding, video transmiss-ion, multi-single chip microcomputer communication, dat-abase technology, computer software and robo...The detection system integrates control technology, network technology, video encoding and decoding, video transmiss-ion, multi-single chip microcomputer communication, dat-abase technology, computer software and robot technology. The robot can adaptively adjust its status according to diameter (from 400 mm to 650 mm) of pipeline. The maximum detection distance is up to 1 000 m. The method of video coding in the system is based on fractal transformation. The experiments show that the coding scheme is fast and good PSNR. The precision of on-line detection is up to 3% thickness of pipeline wall. The robot can also have a high precision of location up to 0.03 m. The control method is based on network and characterized by on-line and real-time. The experiment in real gas pipeline shows that the performance of the detection system is good.展开更多
Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility ...Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.展开更多
The End-to-End Reconfigurability (E2R) project aims at realizing the convergence of the heterogeneous radio networks and the optimal utilization of the radio resources. With the continuous development of E2R technolog...The End-to-End Reconfigurability (E2R) project aims at realizing the convergence of the heterogeneous radio networks and the optimal utilization of the radio resources. With the continuous development of E2R technology and cognitive theory, the evolution from existing radio networks to future reconfigurable radio networks with the cognitive ability becomes possible. Nowadays the research aspects of E2R include the system architecture of reconfigurable radio networks and some key technologies for their evolution.展开更多
In the era of big data rich inWe Media,the single mode retrieval system has been unable to meet people’s demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and un...In the era of big data rich inWe Media,the single mode retrieval system has been unable to meet people’s demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes:A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network(CMHR-DRN).The model construction is divided into two stages:The first stage is the feature extraction of different modal data,including the use of Deep Residual Network(DRN)to extract the image features,using the method of combining TF-IDF with the full connection network to extract the text features,and the obtained image and text features used as the input of the second stage.In the second stage,the image and text features are mapped into Hash functions by supervised learning,and the image and text features are mapped to the common binary Hamming space.In the process of mapping,the distance measurement of the original distance measurement and the common feature space are kept unchanged as far as possible to improve the accuracy of Cross-Modal Retrieval.In training the model,adaptive moment estimation(Adam)is used to calculate the adaptive learning rate of each parameter,and the stochastic gradient descent(SGD)is calculated to obtain the minimum loss function.The whole training process is completed on Caffe deep learning framework.Experiments show that the proposed algorithm CMHR-DRN based on Deep Residual Network has better retrieval performance and stronger advantages than other Cross-Modal algorithms CMFH,CMDN and CMSSH.展开更多
On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Mal...On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.展开更多
Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and c...Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.展开更多
The factors, such as the network optimization or the network amelioration by fixed telecommunication network operators, the convergence of the Personal Handy-phone System (PHS) network and the Public Switched Telephon...The factors, such as the network optimization or the network amelioration by fixed telecommunication network operators, the convergence of the Personal Handy-phone System (PHS) network and the Public Switched Telephone Network (PSTN), the integration of PSTN and the Third Generation Mobile Communication (3G) Network, the broadband and multimedia based communication networks, causes the requirement for fixed network’s intelligentization. The Softswitch is a feasible approach to meet this kind of requirement. The solution to make the network comprehensively intelligent based on Softswitch is highly advantageous, which enriches communication services and promotes Fixed and Mobile Convergence (FMC).展开更多
文摘Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.
基金supported by the Science&Technology Department of Sichuan Province under Grant No.2020YJ0044。
文摘In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.
文摘This paper presents the development of a network based real time condition monitoring system of rotating machinery. The system is built up in a double net structure consisting of local net (including client and server) and intranet. The client serves as a field data collector and processor that samples the vibration signals and process parameters of a machine monitored in the net and processes the sampled data. The data collected by the client are transmitted to the server that processes the data further and provides the results of the diagnosis of each machine to any distant terminals through intranet or internet. Such a structure of the monitoring system is advantageous in safety, reliability and reasonably shares the existing net resources. In order to ensure real time transmission of the data, two procedures of data transmission, virtual channel and data pool, are developed and applied in the monitoring system. The experimental results show that the monitoring system works well and is suitable to monitor a large group of rotating machines.
基金supported by the National Key Research and Development Program of China (No.2022YFE0196000)the National Natural Science Foundation of China (No.61502429)。
文摘Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better.
文摘Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
文摘CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.
文摘This paper discusses a 40-Gbit/s transparent optical network focusing on the optical transport performance. We show 1200-km transmission with two WSOXC' sspaced by 400 km. In addition, network control issues are briefly addressed.
基金Supported by the National Natural Science Foundation of China under Grant No 61671142the Fundamental Research Funds for the Central Universities under Grant No 02190022117021
文摘Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.
基金supported by the project of "SDUST Qunxing Program"(No.qx0902075)
文摘By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.
基金This project was supported by the National "863" High-Tech Research and Development Program of China(2002AA7170)
文摘Ongoing research is described that is focused upon modelling the space base information network and simulating its behaviours： simulation of spaced based communications and networking project. Its objective is to demonstrate the feasibility of producing a tool that can provide a performance evaluation of various eonstellation access techniques and routing policies. The architecture and design of the simulation system are explored. The algorithm of data routing and instrument scheduling in this project is described. Besides these, the key methodologies of simulating the inter-satellite link features in the data transmissions are also discussed. The performance of both instrument scheduling algorithm and routing schemes is evaluated and analyzed through extensive simulations under a typical scenario.
文摘Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.
文摘It is a difficult problem to improve network performance and resource utilization efficiency in wireless communications. As a standard of broadband wireless access systems, IEEE 802.16 adopts Orthogonal Frequency Division Multiplexing (OFDM) and multi-modulation/coding techniques at the physical layer, combines contend and pre-contract mechanisms at the Medium Access Control (MAC) layer. Based on the QoS-related concepts such as connection and service flow, IEEE 802.16 optimizes network entry and initialization, and frame format in order to improve network throughput, reduce network delay, and increase the flexibility of network configuration. Based on IEEE 802.16, WiMAX adopts Point-to-Multipoint (PMP) network topology to realize flexible networking. It is a typical application technology of broadband wireless access systems.
基金Supported by the National High technology Research and Development Program of China (No.2002 AA442110)
文摘The detection system integrates control technology, network technology, video encoding and decoding, video transmiss-ion, multi-single chip microcomputer communication, dat-abase technology, computer software and robot technology. The robot can adaptively adjust its status according to diameter (from 400 mm to 650 mm) of pipeline. The maximum detection distance is up to 1 000 m. The method of video coding in the system is based on fractal transformation. The experiments show that the coding scheme is fast and good PSNR. The precision of on-line detection is up to 3% thickness of pipeline wall. The robot can also have a high precision of location up to 0.03 m. The control method is based on network and characterized by on-line and real-time. The experiment in real gas pipeline shows that the performance of the detection system is good.
文摘Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users＇ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS （global positioning system） datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users＇ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users＇ scores and city＇s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.
基金supported by the National Natural Science Foundation of China under Grant No. 60632030the E3 Project(FP7-ICT-2007-216248) with in Community’s Seventh Framework Program.
文摘The End-to-End Reconfigurability (E2R) project aims at realizing the convergence of the heterogeneous radio networks and the optimal utilization of the radio resources. With the continuous development of E2R technology and cognitive theory, the evolution from existing radio networks to future reconfigurable radio networks with the cognitive ability becomes possible. Nowadays the research aspects of E2R include the system architecture of reconfigurable radio networks and some key technologies for their evolution.
文摘In the era of big data rich inWe Media,the single mode retrieval system has been unable to meet people’s demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes:A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network(CMHR-DRN).The model construction is divided into two stages:The first stage is the feature extraction of different modal data,including the use of Deep Residual Network(DRN)to extract the image features,using the method of combining TF-IDF with the full connection network to extract the text features,and the obtained image and text features used as the input of the second stage.In the second stage,the image and text features are mapped into Hash functions by supervised learning,and the image and text features are mapped to the common binary Hamming space.In the process of mapping,the distance measurement of the original distance measurement and the common feature space are kept unchanged as far as possible to improve the accuracy of Cross-Modal Retrieval.In training the model,adaptive moment estimation(Adam)is used to calculate the adaptive learning rate of each parameter,and the stochastic gradient descent(SGD)is calculated to obtain the minimum loss function.The whole training process is completed on Caffe deep learning framework.Experiments show that the proposed algorithm CMHR-DRN based on Deep Residual Network has better retrieval performance and stronger advantages than other Cross-Modal algorithms CMFH,CMDN and CMSSH.
基金Supported by Brilliant Youth Fund in Hebei Province
文摘On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
文摘Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.
文摘The factors, such as the network optimization or the network amelioration by fixed telecommunication network operators, the convergence of the Personal Handy-phone System (PHS) network and the Public Switched Telephone Network (PSTN), the integration of PSTN and the Third Generation Mobile Communication (3G) Network, the broadband and multimedia based communication networks, causes the requirement for fixed network’s intelligentization. The Softswitch is a feasible approach to meet this kind of requirement. The solution to make the network comprehensively intelligent based on Softswitch is highly advantageous, which enriches communication services and promotes Fixed and Mobile Convergence (FMC).
