The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time...The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.展开更多
With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization p...With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.展开更多
Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this pap...Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.展开更多
A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stab...A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.展开更多
With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord m...With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.展开更多
Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents...Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.展开更多
Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wi...Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.展开更多
For rechargeable wireless sensor networks,limited energy storage capacity,dynamic energy supply,low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanentl...For rechargeable wireless sensor networks,limited energy storage capacity,dynamic energy supply,low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario.Therefore,before data delivery,a sensor has to update its waking schedule continuously and share them to its neighbors,which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets.In this work,we propose the maximum data generation rate routing protocol based on data flow controlling technology.For a sensor,it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors.Hence,the energy consumption for time synchronization,location information and waking schedule shared will be reduced significantly.The saving energy can be used for improving data collection rate.Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.展开更多
A reinforcemen based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the ...A reinforcemen based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart pole balancing system and simulation result shows significant improvements on the rule generation.展开更多
Network traffic is very important for testing network equipment, network services, and security products. A new method of generating traffic based on statistical packet-level characteristics is proposed. In every time...Network traffic is very important for testing network equipment, network services, and security products. A new method of generating traffic based on statistical packet-level characteristics is proposed. In every time unit, the generator determines the sent packets number, the type and size of every sent packet according to the statistical characteristics of the original traffic. Then every packet, in which the protocol headers of transport layer, network layer and ethernet layer are encapsulated, is sent via the responding network interface card in the time unit. The results in the experiment show that the correlation coefficients between the bandwidth, the packet number, packet size distribution, the fragment number of the generated network traffic and those of the original traffic are all more than 0.96. The generated traffic and original traffic are very highly related and similar.展开更多
Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactio...Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.展开更多
Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element i...Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.展开更多
Next Generation Network(NGN)is not a single architecture but a setof architectures with a common set of principles and hence varies byservice provider history,target applications and assets.The paperintroduces NGN fun...Next Generation Network(NGN)is not a single architecture but a setof architectures with a common set of principles and hence varies byservice provider history,target applications and assets.The paperintroduces NGN functional requirements,NGN services and NGNarchitectural features.It also discusses why NGN is needed,whenNGN is targeted,NGN trends and NGN deployment.It concludes thatit is no longer a case whether NGN is needed but rather when andat what speed of the evolution.展开更多
We are developing a novel technology for the next generation optical access network. The proposed archi-tecture provides FTTX high bandwidth which enables to give out 10Gbit/s per end-user. Increasing the subscribers ...We are developing a novel technology for the next generation optical access network. The proposed archi-tecture provides FTTX high bandwidth which enables to give out 10Gbit/s per end-user. Increasing the subscribers in the future will cause massive congestion in the data transferred along the optical network. Our solution is using the wavelength division multiplexing PON (CWDM-PON) technology to achieve high bandwidth and enormous data transmission at the network access. Physical layer modifications are used in our model to provide satisfactory solution for the bandwidth needs. Thus high data rates can be achieved throughout the network using low cost technologies. Framework estimations are evaluated to prove the intended model success and reliability. Our argument that: this modification will submit a wide bandwidth suitable for the future Internet.展开更多
It is noted that the revolutionary development of technologies,fundamentalchange of traffic composition,trend of network convergence as well asmarket opening and competition have become the driving forces to developNe...It is noted that the revolutionary development of technologies,fundamentalchange of traffic composition,trend of network convergence as well asmarket opening and competition have become the driving forces to developNext Generation Networks (NGN).After introducing the concepts andcharacteristics of NGN,the paper details its 5 strategic developmentdirections:evolution to softswitch-based next generation switching network,evolution to next generation mobile communication network represented by3G,evolution to IPv6-based next generation Internet,evolution to diversifiedbroadband access network,and evolution to next generation transportnetwork based on optical networking.Finally,it briefs the strategic thinkingon NGN of China Telecom,the largest fixed network carrier in the world.展开更多
Softswitch technology integrates the su-periorities of both an intelligence net-work and the Internet, which embodiesits maturity and advancement. With ahierarchical network model, it effectivelysolves problems of evo...Softswitch technology integrates the su-periorities of both an intelligence net-work and the Internet, which embodiesits maturity and advancement. With ahierarchical network model, it effectivelysolves problems of evolution and convergenceof current communication networks. It also fol-展开更多
The convergence of communication services becomes a focus in the industry along with the requirement for full-service operation and technical development. Service convergence includes two aspects: The convergence of t...The convergence of communication services becomes a focus in the industry along with the requirement for full-service operation and technical development. Service convergence includes two aspects: The convergence of the fixed and mobile networks; and the convergence of traditional communications and Internet services. This requires balancing the conflicts between the openness and operationability of terminal capability and network convergence. Unified authentication and authorization are the basis for service convergence in terms of operationability. Modular network and open terminal are technical solutions for the service convergence.展开更多
Distributed Generation (DG) in any quantity is relevant to supplement the available energy capacity based on various locations, that is, whether a site specific or non-site specific energy technology. The evacuation i...Distributed Generation (DG) in any quantity is relevant to supplement the available energy capacity based on various locations, that is, whether a site specific or non-site specific energy technology. The evacuation infrastructure that delivers power to the distribution grid is designed with appropriate capacity in terms of size and length. The evacuation lines and distribution network however behave differently as they possess inherent characteristics and are exposed to varying external conditions. It is thus feasible to expect that these networks behave stochastically due to fault conditions and variable loads that destabilize the system. This in essence impacts on the availability of the evacuation infrastructure and consequently on the amount of energy delivered to the grid from the DG stations. Reliability of the evacuation point of a DG is however not a common or prioritized criteria in the decision process that guides investment in DG. This paper reviews a planned solar based DG plant in Uganda. Over the last couple of years, Uganda has seen a significant increase in the penetration levels of DG. With a network that is predominantly radial and experiences low reliability levels, one would thus expect reliability analysis to feature significantly in the assessment of the proposed DG plants. This is however not the case. This paper, uses reliability analysis to assess the impact of different evacuation options of the proposed DG plant on its dispatch levels. The evacuation options were selected based on infrastructure options in other locations with similar solar irradiances as the planned DG location. Outage data were collected and analyzed using the chi square method. It was found to be variable and fitting to different Probability Distribution Functions (PDF). Using stochastic methods, a model that incorporates the PDFs was developed to compute the reliability indices. These were assessed using chi square and found to be variable and fitting different PDFs as well. The viability of the project is reviewed based on Energy Not Supplied (ENS) and the anticipated project payback periods for any considered evacuation line. The results of the study are also reviewed for the benefit of other stakeholders like the customers, the utility and the regulatory body.展开更多
ZTE Softswitch supports the interoperability and convergence oflegacy PSTN/ISDN, PLMN, IN, and the Internet, allowing operatorsor service providers to offer diversified services to any subscriber atany time on a ZTE S...ZTE Softswitch supports the interoperability and convergence oflegacy PSTN/ISDN, PLMN, IN, and the Internet, allowing operatorsor service providers to offer diversified services to any subscriber atany time on a ZTE Softswitch network.With powerful C4 and C5 features, ZTE Softswitch effectivelysolves the evolution problems in the existing networks, protectinglegacy network investment and reducing future investment to a prof-itable level for providers.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.62276274)Shaanxi Natural Science Foundation(Grant No.2023-JC-YB-528)Chinese aeronautical establishment(Grant No.201851U8012)。
文摘The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.
基金This research is supported by the Science and Technology Program of Gansu Province(No.23JRRA880).
文摘With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.
基金funded by the Artificial Intelligence Technology Project of Xi’an Science and Technology Bureau in China(No.21RGZN0014)。
文摘Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.
文摘A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.
基金National Natural Science Foundation of China (No.61801106)。
文摘With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.
基金supported by the Science and Technology Project of Central China Branch of State Grid Corporation of China under 5214JS220010.
文摘Along with the increasing integration of renewable energy generation in AC-DC power networks,investigating the dynamic behaviors of this complex system with a proper equivalent model is significant.This paper presents an equivalent modeling method for the AC-DC power networks with doubly-fed induction generator(DFIG)based wind farms to decrease the simulation scale and computational burden.For the AC-DC power networks,the equivalent modeling strategy in accordance with the physical structure simplification is stated.Regarding the DFIG-based wind farms,the equivalent modeling based on the sequential identification of multi-machine parameters using the improved chaotic cuckoo search algorithm(ICCSA)is conducted.In light of the MATLAB simulation platform,a two-zone four-DC interconnected power grid with wind farms is built to check the efficacy of the proposed equivalentmodelingmethod.Fromthe simulation analyses and comparative validation in different algorithms and cases,the proposed method can precisely reflect the steady and dynamic performance of the demonstrated system under N-1 and N-2 fault scenarios,and it can efficiently achieve the parameter identification of the wind farms and fulfill the equivalent modeling.Consequently,the proposed approach’s effectiveness and suitability are confirmed.
基金National Key R&D Program of China"Study on impact assessment of ecological climate and environment on the wind fann and photovoltaic plants"(2018YFB1502800)Science and Technology Project of State Grid Hebei Electric Power Company"Research and application of medium and long-term forecasting technology for regional wind and photovoltaic resources and generation capacity",(5204BB170007)Special Fund Project of Hebei Provincial Government(19214310D).
文摘Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.
基金This work was supported by The National Natural Science Fund of China(Grant No.31670554)The Natural Science Foundation of Jiangsu Province of China(Grant No.BK20161527)+1 种基金We also received three Projects Funded by The Project funded by China Postdoctoral Science Foundation(Grant Nos.2018T110505,2017M611828)The Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions.The authors wish to express their appreciation to the reviewers for their helpful suggestions which greatly improved the presentation of this paper.
文摘For rechargeable wireless sensor networks,limited energy storage capacity,dynamic energy supply,low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario.Therefore,before data delivery,a sensor has to update its waking schedule continuously and share them to its neighbors,which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets.In this work,we propose the maximum data generation rate routing protocol based on data flow controlling technology.For a sensor,it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors.Hence,the energy consumption for time synchronization,location information and waking schedule shared will be reduced significantly.The saving energy can be used for improving data collection rate.Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.
文摘A reinforcemen based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart pole balancing system and simulation result shows significant improvements on the rule generation.
基金supported in part by national science and technology major project of the ministry of science and technology of China No. 2012BAH45B01Fundamental Research Funds for the Central Universities No. 2014ZD03-03
文摘Network traffic is very important for testing network equipment, network services, and security products. A new method of generating traffic based on statistical packet-level characteristics is proposed. In every time unit, the generator determines the sent packets number, the type and size of every sent packet according to the statistical characteristics of the original traffic. Then every packet, in which the protocol headers of transport layer, network layer and ethernet layer are encapsulated, is sent via the responding network interface card in the time unit. The results in the experiment show that the correlation coefficients between the bandwidth, the packet number, packet size distribution, the fragment number of the generated network traffic and those of the original traffic are all more than 0.96. The generated traffic and original traffic are very highly related and similar.
基金support provided in part by the National Key Research and Development Program of China (No.2020YFB1005804)in part by the National Natural Science Foundation of China under Grant 61632009+1 种基金in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01in part by the NCRA-017,NUST,Islamabad.
文摘Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.
文摘Wind power is a kind of clean energy promising significant social and environmental benefits, and in The Peoples Republic of China, the government supports and encourages the development of wind power as one element in a shift to renewable energy. In recent years however, maritime safety issues have arisen during offshore wind power construction and attendant production processes associated with the rapid promotion and development of offshore wind farms. Therefore, it is necessary to carry out risk assessment for phases in the life cycle of offshore wind farms. This paper reports on a risk assessment model based on a Dynamic Bayesian network that performs offshore wind farms maritime risk assessment. The advantage of this approach is the way in which a Bayesian model expresses uncertainty. Furthermore, such models permit simulations and reenactment of accidents in a virtual environment. There were several goals in this research. Offshore wind power project risk identification and evaluation theories and methods were explored to identify the sources of risk during different phases of the offshore wind farm life cycle. Based on this foundation, a dynamic Bayesian network model with Genie was established, and evaluated, in terms of its effectiveness for analysis of risk during different phases of the offshore wind farm life cycle. Research results show that a dynamic Bayesian network method can perform risk assessments effectively and flexibly, responding to the actual context of offshore wind power construction. Historical data and almost real-time information are combined to analyze the risk of the construction of offshore wind power. Our results inform a discussion of security and risk mitigation measures that when implemented, could improve safety. This work has value as a reference and guide for the safe development of offshore wind power.
文摘Next Generation Network(NGN)is not a single architecture but a setof architectures with a common set of principles and hence varies byservice provider history,target applications and assets.The paperintroduces NGN functional requirements,NGN services and NGNarchitectural features.It also discusses why NGN is needed,whenNGN is targeted,NGN trends and NGN deployment.It concludes thatit is no longer a case whether NGN is needed but rather when andat what speed of the evolution.
文摘We are developing a novel technology for the next generation optical access network. The proposed archi-tecture provides FTTX high bandwidth which enables to give out 10Gbit/s per end-user. Increasing the subscribers in the future will cause massive congestion in the data transferred along the optical network. Our solution is using the wavelength division multiplexing PON (CWDM-PON) technology to achieve high bandwidth and enormous data transmission at the network access. Physical layer modifications are used in our model to provide satisfactory solution for the bandwidth needs. Thus high data rates can be achieved throughout the network using low cost technologies. Framework estimations are evaluated to prove the intended model success and reliability. Our argument that: this modification will submit a wide bandwidth suitable for the future Internet.
文摘It is noted that the revolutionary development of technologies,fundamentalchange of traffic composition,trend of network convergence as well asmarket opening and competition have become the driving forces to developNext Generation Networks (NGN).After introducing the concepts andcharacteristics of NGN,the paper details its 5 strategic developmentdirections:evolution to softswitch-based next generation switching network,evolution to next generation mobile communication network represented by3G,evolution to IPv6-based next generation Internet,evolution to diversifiedbroadband access network,and evolution to next generation transportnetwork based on optical networking.Finally,it briefs the strategic thinkingon NGN of China Telecom,the largest fixed network carrier in the world.
文摘Softswitch technology integrates the su-periorities of both an intelligence net-work and the Internet, which embodiesits maturity and advancement. With ahierarchical network model, it effectivelysolves problems of evolution and convergenceof current communication networks. It also fol-
文摘The convergence of communication services becomes a focus in the industry along with the requirement for full-service operation and technical development. Service convergence includes two aspects: The convergence of the fixed and mobile networks; and the convergence of traditional communications and Internet services. This requires balancing the conflicts between the openness and operationability of terminal capability and network convergence. Unified authentication and authorization are the basis for service convergence in terms of operationability. Modular network and open terminal are technical solutions for the service convergence.
文摘Distributed Generation (DG) in any quantity is relevant to supplement the available energy capacity based on various locations, that is, whether a site specific or non-site specific energy technology. The evacuation infrastructure that delivers power to the distribution grid is designed with appropriate capacity in terms of size and length. The evacuation lines and distribution network however behave differently as they possess inherent characteristics and are exposed to varying external conditions. It is thus feasible to expect that these networks behave stochastically due to fault conditions and variable loads that destabilize the system. This in essence impacts on the availability of the evacuation infrastructure and consequently on the amount of energy delivered to the grid from the DG stations. Reliability of the evacuation point of a DG is however not a common or prioritized criteria in the decision process that guides investment in DG. This paper reviews a planned solar based DG plant in Uganda. Over the last couple of years, Uganda has seen a significant increase in the penetration levels of DG. With a network that is predominantly radial and experiences low reliability levels, one would thus expect reliability analysis to feature significantly in the assessment of the proposed DG plants. This is however not the case. This paper, uses reliability analysis to assess the impact of different evacuation options of the proposed DG plant on its dispatch levels. The evacuation options were selected based on infrastructure options in other locations with similar solar irradiances as the planned DG location. Outage data were collected and analyzed using the chi square method. It was found to be variable and fitting to different Probability Distribution Functions (PDF). Using stochastic methods, a model that incorporates the PDFs was developed to compute the reliability indices. These were assessed using chi square and found to be variable and fitting different PDFs as well. The viability of the project is reviewed based on Energy Not Supplied (ENS) and the anticipated project payback periods for any considered evacuation line. The results of the study are also reviewed for the benefit of other stakeholders like the customers, the utility and the regulatory body.
文摘ZTE Softswitch supports the interoperability and convergence oflegacy PSTN/ISDN, PLMN, IN, and the Internet, allowing operatorsor service providers to offer diversified services to any subscriber atany time on a ZTE Softswitch network.With powerful C4 and C5 features, ZTE Softswitch effectivelysolves the evolution problems in the existing networks, protectinglegacy network investment and reducing future investment to a prof-itable level for providers.
