The fasting evolving information technology has driven the global industry into a new period of development where integration of informatization and industrialization became the focus of all countries around the world...The fasting evolving information technology has driven the global industry into a new period of development where integration of informatization and industrialization became the focus of all countries around the world,and a key trend for thousands of enterprises to follow.This now plays a decisive role on the evolution of world’s industrial development as well as the international competition.展开更多
In the evening of October 17, 2012, the vessel Hangjun 12 of Changhang Wuhan Engineering Bureau was struck by lightning when working in Malaysia red ridge channel. In the lightning accident, the weather instrument of ...In the evening of October 17, 2012, the vessel Hangjun 12 of Changhang Wuhan Engineering Bureau was struck by lightning when working in Malaysia red ridge channel. In the lightning accident, the weather instrument of the ship was destroyed by the lightning to the ground, and the electronic information equipment in the vessel was influenced by induction lightning, which had damaged the electronic information system and elec- trical equipment. According to the Code for Design Protection of Structures against Lightning （ GB 50057-2010）, this lightning accident suffered by Hangjun 12 was analyzed theoretically from aspects of the protection scope of the two lightning reds, main measures against lightning electromag- netic impulse, and specific reasons for the lightning accident. Finally, some measures to prevent the ship from being struck by lightning were put forward, such as improving countermeasures against direct lightning flash, enhancing equipotential bonding and shielding measures and so forth.展开更多
多频高精度定位中需要考虑新频率对定位性能的影响。传统相位频间钟偏差(inter frequency clock bias,IFCB)处理方法受制于参考站数量,且与接收机和卫星相关的硬件延迟相关,影响多频精密单点定位(precision point positioning,PPP)的可...多频高精度定位中需要考虑新频率对定位性能的影响。传统相位频间钟偏差(inter frequency clock bias,IFCB)处理方法受制于参考站数量,且与接收机和卫星相关的硬件延迟相关,影响多频精密单点定位(precision point positioning,PPP)的可靠性和准确性。针对IFCB对多全球导航卫星系统(global navigation satellite system,GNSS)多频PPP的影响,本文提出了基于测站IFCB观测值提取功率谱密度(power spectral density,PSD)的算法,进一步构建IFCB参数时变特性约束的多频PPP算法,并全面分析了IFCB的时变特性和不同IFCB模型对非差非组合PPP性能的影响。试验结果表明:依据测站IFCB观测值提取IFCB功率谱密度可行且有效。相较于忽略IFCB方法,采用PSD约束的随机模型估计IFCB,PPP在收敛时间提升最大,提升46.51%,采用iGMAS产品和CNES产品平均提升43.54%、34.50%,三维定位精度分别提升41.68%、32.24%、24.64%。并且,将IFCB采用时变特性约束的随机模型参数优化方案能真实地反映IFCB变化。因此,在多GNSS多频PPP处理中,将IFCB参数采用时变特性约束的随机模型参数优化方案能够加快位置收敛速度,提升定位精度,优于产品改正方法,更有利于实时多频PPP的应用。展开更多
Grain-oriented silicon steels were prepared at different heating rates during high temperature annealing,in which the evolution of magnetic properties,grain orientations and precipitates were studied.To illustrate the...Grain-oriented silicon steels were prepared at different heating rates during high temperature annealing,in which the evolution of magnetic properties,grain orientations and precipitates were studied.To illustrate the Zener factor,the diameter and number density of precipitates of interrupted testing samples were statistically calculated.The effect of precipitate ripening on the Goss texture and magnetic property was investigated.Data indicated that the trend of Zener factor was similar under different heating rates,first increasing and then decreasing,and that the precipitate maturing was greatly inhibited as the heating rate increased.Secondary recrystallization was developed at the temperature of 1010℃when a heating rate of 5℃/h was used,resulting in Goss,Brass and{110}<227>oriented grains growing abnormally and a magnetic induction intensity of 1.90T.Furthermore,increasing the heating rate to 20℃/h would inhibit the development of undesirable oriented grains and obtain a sharp Goss texture.However,when the heating rate was extremely fast,such as 40℃/h,poor secondary recrystallization was developed with many island grains,corresponding to a decrease in magnetic induction intensity to 1.87 T.At a suitable heating rate of 20℃/h,the sharpest Goss texture and the highest magnetic induction of 1.94 T with an onset secondary recrystallization temperature of 1020℃were found among the experimental variables in this study.The heating rate affected the initial temperature of secondary recrystallization by controlling the maturation of precipitates,leading to the deviation and dispersion of Goss texture,thereby reducing the magnetic properties.展开更多
Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performa...Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.展开更多
Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies.Traditiona...Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies.Traditional core-based interpretation introduces subjectivity,while conventional deep learning models often fail to capture stratigraphic sequences effectively.To address these limitations,we propose a hybrid CNN–GRU framework that integrates spatial feature extraction and sequential modeling.Heat Kernel Imputation is applied to reconstruct missing log data,and Borderline SMOTE(BSMOTE)improves class balance by augmenting boundary-case minority samples.The CNN component extracts localized petrophysical features,and the GRU component captures depth-wise lithological transitions,to enable spatial-sequential feature fusion.Experiments on real-well datasets from tight sandstone reservoirs show that the proposed model achieves an average accuracy of 93.3%and a Macro F1-score of 0.934.It outperforms baseline models,including RF(87.8%),GBDT(81.8%),CNN-only(87.5%),and GRU-only(86.1%).Leave-one-well-out validation further confirms strong generalization ability.These results demonstrate that the proposed approach effectively addresses data imbalance and enhances classification robustness,offering a scalable and automated solution for lithofacies interpretation under complex geological conditions.展开更多
Time Division Multiplexing-Passive Optical Networks(TDM-PONs)play a vital role in Fiberto-the-Home(FTTH)deployments.To improve the service quality of home networks,FTTH is expanding to the Fiber-to-the-Room(FTTR)scena...Time Division Multiplexing-Passive Optical Networks(TDM-PONs)play a vital role in Fiberto-the-Home(FTTH)deployments.To improve the service quality of home networks,FTTH is expanding to the Fiber-to-the-Room(FTTR)scenario,where fibers are deployed to connect individual rooms(i.e.,Fiber In-premises Network(FIN)in the ITU-T G.9940 standard).In this scenario,a point-to-multipoint(P2MP)fiber network is deployed as FTTR FIN to offer gigabit access to each room,which forms a two-tier cascaded network together with the FTTH segment.To optimize the capacity utilization of the cascaded network and reduce the overall system cost,a centralized architecture,known as Centralized Fixed Access Network(C-FAN),has been introduced.C-FAN centralizes the medium access control(MAC)modules of both the FTTH and FTTR networks at the FTTH’s Optical Line Terminal(OLT)for unified control and management of the cascaded network.We develop a unified bandwidth scheduling protocol by extending the ITU-T PON standard for both the upstream and downstream directions of C-FAN.We also propose a unified dynamic bandwidth allocation(UDBA)algorithm for efficient bandwidth allocation for multiple traffic flows in the two-tier cascaded network.Simulations are conducted to evaluate the performance of the proposed control protocol and the UDBA algorithm.The results show that,in comparison to the conventional DBA algorithm,the UDBA algorithm can utilize upstream bandwidth more efficiently to reduce packet delay and loss,without adversely impacting downstream transmission performance.展开更多
Friction stir additive manufacturing(FSAM)is an innovative additive manufacturing(AM)method.The various heat treatment conditions of aluminum-lithium alloys using this method have not been widely discussed.In this stu...Friction stir additive manufacturing(FSAM)is an innovative additive manufacturing(AM)method.The various heat treatment conditions of aluminum-lithium alloys using this method have not been widely discussed.In this study,the microstructure evolution and mechanical properties of FSAM 2195 aluminum-lithium alloy in different heat treatment conditions(T3 and T8)were investigated.The results demonstrated that the heat treatment state of 2195 Al-Li alloys was minimally influenced by FSAM as the FSAM temperature exceeded the solid solution temperature.After conducting a single-pass FSAM experiment,a notable grain refinement was observed in the nugget zone(NZ)region compared to the base material(BM).The average grain size of the 2195-T3 alloy decreased from 6.1 to 2.9µm,while the proportion of high-angle grain boundaries increased from 16.5%to 43.9%.Similarly,the average grain size of the 2195-T8 alloy decreased from 8.9 to 2.8µm,with an increase in high-angle grain boundary from 37.6%to 59.2%.The tensile strength of the 2195-T3 Al-Li alloy reached 466 and 478 MPa in the NZ of single-pass and lap experiments,respectively.In comparison,the tensile strength of the 2195-T8 Al-Li alloy in the NZ could reach 452 and 481 MPa in single-pass and lap experiments,respectively.These results demonstrate the significant improvements in microstructure and mechanical properties were achieved through the FSAM process.展开更多
For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compare...For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.展开更多
Based on 5G and IoT technology,this study addresses the issues of manual control dependency,poor reliability,and high labor costs in traditional ceramic kiln exhaust cover operations.An intelligent control system for ...Based on 5G and IoT technology,this study addresses the issues of manual control dependency,poor reliability,and high labor costs in traditional ceramic kiln exhaust cover operations.An intelligent control system for kiln exhaust covers was designed in this paper.The system employs temperature measurement devices to monitor the internal temperature of the kiln in real time.A controller automatically operates the actuator to open or close the kiln exhaust cover based on temperature measurements.Additionally,the system integrates data transmission units and cloud services,enabling remote monitoring of kiln temperature and historical data storage.Experimental results demonstrate that the system effectively reduces labor costs and significantly enhances the IoT capabilities of kiln operations.展开更多
The simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access(NOMA)netw...The simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access(NOMA)networks.However,the transmission of STAR-RIS enhanced NOMA networks performance is severely limited due to the inter-user interference(IUI)on multi-user detections.To mitigate this drawback,we propose a generalized quadrature spatial modulation(GQSM)aided STAR-RIS in conjunction with the NOMA scheme,termed STARRIS-NOMA-GQSM,to improve the performance of the corresponding NGMA network.By STAR-RISNOMA-GQSM,the information bits for all users in transmission and reflection zones are transmitted via orthogonal signal domains to eliminate the IUI so as to greatly improve the system performance.The lowcomplexity detection and upper-bounded bit error rate(BER)of STAR-RIS-NOMA-GQSM are both studied to evaluate its feasibility and performance.Moreover,by further utilizing index modulation(IM),we propose an enhanced STAR-RIS-NOMA-GQSM scheme,termed E-STAR-RIS-NOMA-GQSM,to enhance the transmission rate by dynamically adjusting reflection patterns in both transmission and reflection zones.Simulation results show that the proposed original and enhanced scheme significantly outperform the conventional STAR-RIS-NOMA and also confirm the precision of the theoretical analysis of the upper-bounded BER.展开更多
Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora...Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora images to predict local geomagnetic station component,breaking the spatial limitations of geomagnetic stations.Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data.Essentially,the model comprises a visual geometry group(VGG)image feature extraction network,a ViT-based encoder network,and a regression prediction network.Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features.Specifically,the hybrid model achieves a 39.1%reduction in root mean square error compared to the VGG model,a 29.5%reduction compared to the ViT model and a 35.3%reduction relative to the residual network(ResNet)model.Moreover,the fitting accuracy of the model surpasses that of the VGG,ViT,and ResNet models by 2.14%1.58%,and 4.1%,respectively.展开更多
文摘The fasting evolving information technology has driven the global industry into a new period of development where integration of informatization and industrialization became the focus of all countries around the world,and a key trend for thousands of enterprises to follow.This now plays a decisive role on the evolution of world’s industrial development as well as the international competition.
文摘In the evening of October 17, 2012, the vessel Hangjun 12 of Changhang Wuhan Engineering Bureau was struck by lightning when working in Malaysia red ridge channel. In the lightning accident, the weather instrument of the ship was destroyed by the lightning to the ground, and the electronic information equipment in the vessel was influenced by induction lightning, which had damaged the electronic information system and elec- trical equipment. According to the Code for Design Protection of Structures against Lightning （ GB 50057-2010）, this lightning accident suffered by Hangjun 12 was analyzed theoretically from aspects of the protection scope of the two lightning reds, main measures against lightning electromag- netic impulse, and specific reasons for the lightning accident. Finally, some measures to prevent the ship from being struck by lightning were put forward, such as improving countermeasures against direct lightning flash, enhancing equipotential bonding and shielding measures and so forth.
文摘Grain-oriented silicon steels were prepared at different heating rates during high temperature annealing,in which the evolution of magnetic properties,grain orientations and precipitates were studied.To illustrate the Zener factor,the diameter and number density of precipitates of interrupted testing samples were statistically calculated.The effect of precipitate ripening on the Goss texture and magnetic property was investigated.Data indicated that the trend of Zener factor was similar under different heating rates,first increasing and then decreasing,and that the precipitate maturing was greatly inhibited as the heating rate increased.Secondary recrystallization was developed at the temperature of 1010℃when a heating rate of 5℃/h was used,resulting in Goss,Brass and{110}<227>oriented grains growing abnormally and a magnetic induction intensity of 1.90T.Furthermore,increasing the heating rate to 20℃/h would inhibit the development of undesirable oriented grains and obtain a sharp Goss texture.However,when the heating rate was extremely fast,such as 40℃/h,poor secondary recrystallization was developed with many island grains,corresponding to a decrease in magnetic induction intensity to 1.87 T.At a suitable heating rate of 20℃/h,the sharpest Goss texture and the highest magnetic induction of 1.94 T with an onset secondary recrystallization temperature of 1020℃were found among the experimental variables in this study.The heating rate affected the initial temperature of secondary recrystallization by controlling the maturation of precipitates,leading to the deviation and dispersion of Goss texture,thereby reducing the magnetic properties.
基金supported in part by the Key Technologies R&D Program of Jiangsu under Grants BE2023022 and BE2023022-2National Natural Science Foundation of China under Grants 62471204, 62531015+2 种基金Major Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under Grant 24KJA510003Shanghai Kewei 24DP1500500the Fundamental Research Funds for the Central Universities under Grant 2242025K30025
文摘Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings.
基金supported by the Langfang Science and Technology Program with self-raised funds under the project“Application of Deep Learning-Based Joint Well-Seismic Analysis in Lithology Prediction”(Project No.2024011013)the Science and Technology Innovation Program for Postgraduate students in IDP subsidized by Fundamental Research Funds for the Central Universities,under the project“Research on CNN Algorithm Enhanced by Physical Information for Lithofacies Prediction in Tight Sandstone Reservoirs”(Project No.ZY20250328).
文摘Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies.Traditional core-based interpretation introduces subjectivity,while conventional deep learning models often fail to capture stratigraphic sequences effectively.To address these limitations,we propose a hybrid CNN–GRU framework that integrates spatial feature extraction and sequential modeling.Heat Kernel Imputation is applied to reconstruct missing log data,and Borderline SMOTE(BSMOTE)improves class balance by augmenting boundary-case minority samples.The CNN component extracts localized petrophysical features,and the GRU component captures depth-wise lithological transitions,to enable spatial-sequential feature fusion.Experiments on real-well datasets from tight sandstone reservoirs show that the proposed model achieves an average accuracy of 93.3%and a Macro F1-score of 0.934.It outperforms baseline models,including RF(87.8%),GBDT(81.8%),CNN-only(87.5%),and GRU-only(86.1%).Leave-one-well-out validation further confirms strong generalization ability.These results demonstrate that the proposed approach effectively addresses data imbalance and enhances classification robustness,offering a scalable and automated solution for lithofacies interpretation under complex geological conditions.
基金supported by National Nature Science Founding of China(62101372)Open Fund of IPOC(BUPT,IPOC2022A07)+1 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks(2023GZKF11)Leading Youth Talents of Innovation and Entrepreneurship of Gusu(ZXL2023162).
文摘Time Division Multiplexing-Passive Optical Networks(TDM-PONs)play a vital role in Fiberto-the-Home(FTTH)deployments.To improve the service quality of home networks,FTTH is expanding to the Fiber-to-the-Room(FTTR)scenario,where fibers are deployed to connect individual rooms(i.e.,Fiber In-premises Network(FIN)in the ITU-T G.9940 standard).In this scenario,a point-to-multipoint(P2MP)fiber network is deployed as FTTR FIN to offer gigabit access to each room,which forms a two-tier cascaded network together with the FTTH segment.To optimize the capacity utilization of the cascaded network and reduce the overall system cost,a centralized architecture,known as Centralized Fixed Access Network(C-FAN),has been introduced.C-FAN centralizes the medium access control(MAC)modules of both the FTTH and FTTR networks at the FTTH’s Optical Line Terminal(OLT)for unified control and management of the cascaded network.We develop a unified bandwidth scheduling protocol by extending the ITU-T PON standard for both the upstream and downstream directions of C-FAN.We also propose a unified dynamic bandwidth allocation(UDBA)algorithm for efficient bandwidth allocation for multiple traffic flows in the two-tier cascaded network.Simulations are conducted to evaluate the performance of the proposed control protocol and the UDBA algorithm.The results show that,in comparison to the conventional DBA algorithm,the UDBA algorithm can utilize upstream bandwidth more efficiently to reduce packet delay and loss,without adversely impacting downstream transmission performance.
基金Project(U22A20190)supported by International Science and Technology Cooperation under the National Natural Science Foundation of ChinaProjects(U2241248,52205379)supported by the National Natural Science Foundation of ChinaProject(BE2023026)supported by Jiangsu Provincial Key Research and Development Program and Nanjing Science and Technology Innovation Project for Overseas Scholars,China。
文摘Friction stir additive manufacturing(FSAM)is an innovative additive manufacturing(AM)method.The various heat treatment conditions of aluminum-lithium alloys using this method have not been widely discussed.In this study,the microstructure evolution and mechanical properties of FSAM 2195 aluminum-lithium alloy in different heat treatment conditions(T3 and T8)were investigated.The results demonstrated that the heat treatment state of 2195 Al-Li alloys was minimally influenced by FSAM as the FSAM temperature exceeded the solid solution temperature.After conducting a single-pass FSAM experiment,a notable grain refinement was observed in the nugget zone(NZ)region compared to the base material(BM).The average grain size of the 2195-T3 alloy decreased from 6.1 to 2.9µm,while the proportion of high-angle grain boundaries increased from 16.5%to 43.9%.Similarly,the average grain size of the 2195-T8 alloy decreased from 8.9 to 2.8µm,with an increase in high-angle grain boundary from 37.6%to 59.2%.The tensile strength of the 2195-T3 Al-Li alloy reached 466 and 478 MPa in the NZ of single-pass and lap experiments,respectively.In comparison,the tensile strength of the 2195-T8 Al-Li alloy in the NZ could reach 452 and 481 MPa in single-pass and lap experiments,respectively.These results demonstrate the significant improvements in microstructure and mechanical properties were achieved through the FSAM process.
基金supported by National Natural Science Foundation of China(No.12172157)Key Project of Natural Science Foundation of Gansu Province(No.25JRRA150)Key Research and Development Planning Project of Gansu Province(No.23YFWA0007).
文摘For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.
基金supported by Jiangxi 03 Special and 5G Project(20232ABC03A33)Ganpo Talent Support Program(20232BCJ23106)。
文摘Based on 5G and IoT technology,this study addresses the issues of manual control dependency,poor reliability,and high labor costs in traditional ceramic kiln exhaust cover operations.An intelligent control system for kiln exhaust covers was designed in this paper.The system employs temperature measurement devices to monitor the internal temperature of the kiln in real time.A controller automatically operates the actuator to open or close the kiln exhaust cover based on temperature measurements.Additionally,the system integrates data transmission units and cloud services,enabling remote monitoring of kiln temperature and historical data storage.Experimental results demonstrate that the system effectively reduces labor costs and significantly enhances the IoT capabilities of kiln operations.
基金supported in part by Guangdong Basic and Applied Basic Research Foundation under Grants 2023A1515030118 and 2024A1515010012in part by the Guangzhou Science and Technology Project under Grant 2023A03J0110+3 种基金in part by Guangzhou Basic Research Program Municipal School(College)Joint Funding Project under Grant 2025A03J3119in part by National Natural Science Foundation of China under Grant 62173101in part by the Key Discipline Project of Guangzhou Education Bureau under Grant 202255467in part by the Key Laboratory of on-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes under Grant 2023KSYS002。
文摘The simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access(NOMA)networks.However,the transmission of STAR-RIS enhanced NOMA networks performance is severely limited due to the inter-user interference(IUI)on multi-user detections.To mitigate this drawback,we propose a generalized quadrature spatial modulation(GQSM)aided STAR-RIS in conjunction with the NOMA scheme,termed STARRIS-NOMA-GQSM,to improve the performance of the corresponding NGMA network.By STAR-RISNOMA-GQSM,the information bits for all users in transmission and reflection zones are transmitted via orthogonal signal domains to eliminate the IUI so as to greatly improve the system performance.The lowcomplexity detection and upper-bounded bit error rate(BER)of STAR-RIS-NOMA-GQSM are both studied to evaluate its feasibility and performance.Moreover,by further utilizing index modulation(IM),we propose an enhanced STAR-RIS-NOMA-GQSM scheme,termed E-STAR-RIS-NOMA-GQSM,to enhance the transmission rate by dynamically adjusting reflection patterns in both transmission and reflection zones.Simulation results show that the proposed original and enhanced scheme significantly outperform the conventional STAR-RIS-NOMA and also confirm the precision of the theoretical analysis of the upper-bounded BER.
基金supported by the National Natural Science Foundation of China(No.41471381)the General Project of Jiangsu Natural Science Foundation(No.BK20171410)the Major Scientific and Technological Achievements Cultivation Fund of Nanjing University of Aeronautics and Astronautics(No.1011-XBD23002)。
文摘Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora images to predict local geomagnetic station component,breaking the spatial limitations of geomagnetic stations.Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data.Essentially,the model comprises a visual geometry group(VGG)image feature extraction network,a ViT-based encoder network,and a regression prediction network.Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features.Specifically,the hybrid model achieves a 39.1%reduction in root mean square error compared to the VGG model,a 29.5%reduction compared to the ViT model and a 35.3%reduction relative to the residual network(ResNet)model.Moreover,the fitting accuracy of the model surpasses that of the VGG,ViT,and ResNet models by 2.14%1.58%,and 4.1%,respectively.
