Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performan...Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.展开更多
A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the paramet...A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.展开更多
This study examined the spatio-temporal trajectories of the international freight forwarding service(IFFS) in the Yangtze River Delta(YRD) and explored the driving mechanisms of the service. Based on a bipartite netwo...This study examined the spatio-temporal trajectories of the international freight forwarding service(IFFS) in the Yangtze River Delta(YRD) and explored the driving mechanisms of the service. Based on a bipartite network projection from an IFFS firm-city data source, we mapped three IFFS networks in the YRD in 2005, 2010, and 2015. A range of statistical indicators were used to explore changes in the spatial patterns of the three networks. The underlying influence of marketization, globalization, decentralization, and integration was then explored. It was found that the connections between Shanghai and other nodal cities formed the backbones of these networks. The effects of a city's administrative level and provincial administrative borders were generally obvious. We found several specific spatial patterns associated with IFFS. For example, the four non-administrative centers of Ningbo, Suzhou, Lianyungang, and Nantong were the most connected cities and played the role of gateway cities. Furthermore, remarkable regional equalities were found regarding a city's IFFS network provision, with notable examples in the weakly connected areas of northern Jiangsu and southwestern Zhejiang. Finally, an analysis of the driving mechanisms demonstrated that IFFS network changes were highly sensitive to the influences of marketization and globalization, while regional integration played a lesser role in driving changes in IFFS networks.展开更多
The Space-Air-Ground Integrated Network(SAGIN) realizes the integration of space, air,and ground networks, obtaining the global communication coverage.Software-Defined Networking(SDN) architecture in SAGIN has become ...The Space-Air-Ground Integrated Network(SAGIN) realizes the integration of space, air,and ground networks, obtaining the global communication coverage.Software-Defined Networking(SDN) architecture in SAGIN has become a promising solution to guarantee the Quality of Service(QoS).However, the current routing algorithms mainly focus on the QoS of the service, rarely considering the security requirement of flow. To realize the secure transmission of flows in SAGIN, we propose an intelligent flow forwarding scheme with endogenous security based on Mimic Defense(ESMD-Flow). In this scheme, SDN controller will evaluate the reliability of nodes and links, isolate malicious nodes based on the reliability evaluation value, and adapt multipath routing strategy to ensure that flows are always forwarded along the most reliable multiple paths. In addition, in order to meet the security requirement of flows, we introduce the programming data plane to design a multiprotocol forwarding strategy for realizing the multiprotocol dynamic forwarding of flows. ESMD-Flow can reduce the network attack surface and improve the secure transmission capability of flows by implementing multipath routing and multi-protocol hybrid forwarding mechanism. The extensive simulations demonstrate that ESMD-Flow can significantly improve the average path reliability for routing and increase the difficulty of network eavesdropping while improving the network throughput and reducing the average packet delay.展开更多
In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data ...In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.展开更多
This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Sp...This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.展开更多
In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding a...In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding algorithm is proposed. In this algorithm, according to the distance between data carriers （nodes） and the destination, some intermediate nodes are selected to relay the data. Especially, the forwarded copies can be increased when the delay reaches a threshold, to guarantee the required delivery ratio. The simulation results show that the proposed algorithm can effectively reduce the storage occupancies of nodes and forwarding delay, and guarantee the delivery ratio simultaneously.展开更多
A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluct...A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.展开更多
The existing physical-layer network coding(PNC) can be grouped into three generic schemes,which are XOR-based PNC,superposition-based PNC,and denoising-and-forward(DNFbased) PNC.Generally speaking,DNF-based PNC has be...The existing physical-layer network coding(PNC) can be grouped into three generic schemes,which are XOR-based PNC,superposition-based PNC,and denoising-and-forward(DNFbased) PNC.Generally speaking,DNF-based PNC has better performance of rate pair region compared with the other two schemes when the transmission is symmetric.When the transmission is asymmetric,its performance is degraded severely.However,superposition-based PNC does not have that limitation even if its rate pair region performance is inferior to that of DNF-based PNC and XOR-based PNC.In this paper,we focus on the combined use of the two PNC schemes,superposition-based PNC and DNFbased PNC,and present a novel PNC scheme called joint superposition and DNF physical-layer network coding(JSDNF-based PNC) as well as the information theory analysis of the achievable rate pair region.At the same time,in the proposed scheme,an adaptive power allocation factor is introduced.By changing the power factor,the system can adapt its rate pair region flexibly.The numerical results show that the proposed scheme achieves the largest rate pair region when the rate difference of two source signals is very large.At the same time,the support on asymmetric transmission is also an important profit of the scheme.展开更多
This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigati...This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv- ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe how to take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks. The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net- works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectory in the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, we explain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net- work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to- end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu- lar traffic statistics in a privacy-preserving manner.展开更多
In the paper, we concentrate on the infl uence of heterogeneity on the performance of forwarding algorithms under opportunistic networks. Therefore, we first describe two different heterogeneous network models, and ca...In the paper, we concentrate on the infl uence of heterogeneity on the performance of forwarding algorithms under opportunistic networks. Therefore, we first describe two different heterogeneous network models, and capture the heterogeneity which concern mobile nodes' contact dynamics under the individual models and the spatial models. Then we investigate inter-contact time is not fully follow exponential distribution and compare the performance of the delivery delay between direct forwarding protocol and three-hop forwarding protocol under three network models. We illustrate the performance of message delivery delay under the spray and wait protocol and prophet protocol from simulation results. Our simulation results show that the heterogeneity should be considered for the performance of forwarding protocols.展开更多
This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in th...This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.展开更多
In this paper, we present a special spectrum sharing scheme that is a joint optimization of relay selection and power allocation at the secondary transmitter, where the primary user is incapable of supporting its targ...In this paper, we present a special spectrum sharing scheme that is a joint optimization of relay selection and power allocation at the secondary transmitter, where the primary user is incapable of supporting its target signal-to-noise ratio(SNR). Specifically, the selected secondary transmitter assists the primary user with achieving its target SNR via two-phase cooperative amplify-and-forward relaying. By searching for the candidate secondary transmitters which have already satisfied the primary user's target SNR, we can select the optimal secondary transmitter. This optimal secondary transmitter not only satisfies the primary user's target SNR,but also maximizes the throughput of the secondary user. We study this joint optimization problem such that the secondary user's throughput is maximized under the constraint that satisfies the primary user's target SNR.Numerical results show that our scheme can maximize the throughput of the secondary user, and can obtain the win-win solution for the primary and secondary systems.展开更多
In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other...In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.展开更多
Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutio...Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutions to the design of optical planar devices.Here,an approach is proposed to explore the use of generative adversarial networks(GANs)to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network,where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method,the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses,while also highlighting the potential of deep learning in optical device design.展开更多
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008200)the Independent Research Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.SML2022SP505)。
文摘Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.
基金The National Natural Science Foundation of China(No.60621002)the National High Technology Research and Development Pro-gram of China(863 Program)(No.2007AA01Z2B4).
文摘A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.
基金National Natural Science Foundation of China(No.41671132,41771139)Natural Science Foundation of Jiangsu Province(No.BK20171516)
文摘This study examined the spatio-temporal trajectories of the international freight forwarding service(IFFS) in the Yangtze River Delta(YRD) and explored the driving mechanisms of the service. Based on a bipartite network projection from an IFFS firm-city data source, we mapped three IFFS networks in the YRD in 2005, 2010, and 2015. A range of statistical indicators were used to explore changes in the spatial patterns of the three networks. The underlying influence of marketization, globalization, decentralization, and integration was then explored. It was found that the connections between Shanghai and other nodal cities formed the backbones of these networks. The effects of a city's administrative level and provincial administrative borders were generally obvious. We found several specific spatial patterns associated with IFFS. For example, the four non-administrative centers of Ningbo, Suzhou, Lianyungang, and Nantong were the most connected cities and played the role of gateway cities. Furthermore, remarkable regional equalities were found regarding a city's IFFS network provision, with notable examples in the weakly connected areas of northern Jiangsu and southwestern Zhejiang. Finally, an analysis of the driving mechanisms demonstrated that IFFS network changes were highly sensitive to the influences of marketization and globalization, while regional integration played a lesser role in driving changes in IFFS networks.
基金supported by the National Key Research and Development Program of China under Grant 2020YFB1804803the National Natural Science Foundation of China under Grant 61872382the Research and Development Program in Key Areas of Guangdong Province under Grant No.2018B010113001。
文摘The Space-Air-Ground Integrated Network(SAGIN) realizes the integration of space, air,and ground networks, obtaining the global communication coverage.Software-Defined Networking(SDN) architecture in SAGIN has become a promising solution to guarantee the Quality of Service(QoS).However, the current routing algorithms mainly focus on the QoS of the service, rarely considering the security requirement of flow. To realize the secure transmission of flows in SAGIN, we propose an intelligent flow forwarding scheme with endogenous security based on Mimic Defense(ESMD-Flow). In this scheme, SDN controller will evaluate the reliability of nodes and links, isolate malicious nodes based on the reliability evaluation value, and adapt multipath routing strategy to ensure that flows are always forwarded along the most reliable multiple paths. In addition, in order to meet the security requirement of flows, we introduce the programming data plane to design a multiprotocol forwarding strategy for realizing the multiprotocol dynamic forwarding of flows. ESMD-Flow can reduce the network attack surface and improve the secure transmission capability of flows by implementing multipath routing and multi-protocol hybrid forwarding mechanism. The extensive simulations demonstrate that ESMD-Flow can significantly improve the average path reliability for routing and increase the difficulty of network eavesdropping while improving the network throughput and reducing the average packet delay.
基金Heilongjiang Natural Science Foundation (F0318).
文摘In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.
文摘This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.
基金supported by the National Natural Science Foundation of China under Grants No.61373139Postdoctoral Science Foundation of China under Grant No.2014M560379 and No.2015T80484Natural Science Foundation of Jiangsu Province under Grant No.BK2012833
文摘In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding algorithm is proposed. In this algorithm, according to the distance between data carriers （nodes） and the destination, some intermediate nodes are selected to relay the data. Especially, the forwarded copies can be increased when the delay reaches a threshold, to guarantee the required delivery ratio. The simulation results show that the proposed algorithm can effectively reduce the storage occupancies of nodes and forwarding delay, and guarantee the delivery ratio simultaneously.
文摘A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.
基金supported in part by National Natural Science Foundation of China under Grant No. 61071090Postgraduate Innovation Program of Scientific Research of Jiangsu Province under Grant No. CX10B -184Z
文摘The existing physical-layer network coding(PNC) can be grouped into three generic schemes,which are XOR-based PNC,superposition-based PNC,and denoising-and-forward(DNFbased) PNC.Generally speaking,DNF-based PNC has better performance of rate pair region compared with the other two schemes when the transmission is symmetric.When the transmission is asymmetric,its performance is degraded severely.However,superposition-based PNC does not have that limitation even if its rate pair region performance is inferior to that of DNF-based PNC and XOR-based PNC.In this paper,we focus on the combined use of the two PNC schemes,superposition-based PNC and DNFbased PNC,and present a novel PNC scheme called joint superposition and DNF physical-layer network coding(JSDNF-based PNC) as well as the information theory analysis of the achievable rate pair region.At the same time,in the proposed scheme,an adaptive power allocation factor is introduced.By changing the power factor,the system can adapt its rate pair region flexibly.The numerical results show that the proposed scheme achieves the largest rate pair region when the rate difference of two source signals is very large.At the same time,the support on asymmetric transmission is also an important profit of the scheme.
基金supported by Faculty Research Fund,Sungkyunkwan University,2013 and by DGIST CPS Global Centerpartly supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF)+1 种基金funded by the Ministry of Science,ICT & Future Planning(No.2012033347)the ITR & D program of MKE/KEIT(10041244,SmartTV 2.0 Software Platform)
文摘This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv- ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe how to take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks. The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net- works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectory in the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, we explain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net- work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to- end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu- lar traffic statistics in a privacy-preserving manner.
基金supported by the National Natural Science Foundation of China under Grant No.61171097
文摘In the paper, we concentrate on the infl uence of heterogeneity on the performance of forwarding algorithms under opportunistic networks. Therefore, we first describe two different heterogeneous network models, and capture the heterogeneity which concern mobile nodes' contact dynamics under the individual models and the spatial models. Then we investigate inter-contact time is not fully follow exponential distribution and compare the performance of the delivery delay between direct forwarding protocol and three-hop forwarding protocol under three network models. We illustrate the performance of message delivery delay under the spray and wait protocol and prophet protocol from simulation results. Our simulation results show that the heterogeneity should be considered for the performance of forwarding protocols.
文摘This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.
基金the National High Technology Research and Development Program(863) of China(No.2014AA01A706)the National Major Science and Technology Special Project of China(Nos.2012ZX03001021 and 2012ZX03005008)+3 种基金the National Natural Science Foundation of China(No.61379159)the Chongqing City College Innovation Team(2013)the Chongqing Municipal Education Commission Science and Technology Research Project(No.KJ130513)the Chongqing Basic and Cutting-Edge Project(No.cstc2013jcyj A40020)
文摘In this paper, we present a special spectrum sharing scheme that is a joint optimization of relay selection and power allocation at the secondary transmitter, where the primary user is incapable of supporting its target signal-to-noise ratio(SNR). Specifically, the selected secondary transmitter assists the primary user with achieving its target SNR via two-phase cooperative amplify-and-forward relaying. By searching for the candidate secondary transmitters which have already satisfied the primary user's target SNR, we can select the optimal secondary transmitter. This optimal secondary transmitter not only satisfies the primary user's target SNR,but also maximizes the throughput of the secondary user. We study this joint optimization problem such that the secondary user's throughput is maximized under the constraint that satisfies the primary user's target SNR.Numerical results show that our scheme can maximize the throughput of the secondary user, and can obtain the win-win solution for the primary and secondary systems.
文摘In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.
基金National Natural Science Foundation of China(No.61975029)。
文摘Multifocal metalenses are of great concern in optical communications,optical imaging and micro-optics systems,but their design is extremely challenging.In recent years,deep learning methods have provided novel solutions to the design of optical planar devices.Here,an approach is proposed to explore the use of generative adversarial networks(GANs)to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network,where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method,the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses,while also highlighting the potential of deep learning in optical device design.
