False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
Adaptive optics(AO)has significantly advanced high-resolution solar observations by mitigating atmospheric turbulence.However,traditional post-focal AO systems suffer from external configurations that introduce excess...Adaptive optics(AO)has significantly advanced high-resolution solar observations by mitigating atmospheric turbulence.However,traditional post-focal AO systems suffer from external configurations that introduce excessive optical surfaces,reduced light throughput,and instrumental polarization.To address these limitations,we propose an embedded solar adaptive optics telescope(ESAOT)that intrinsically incorporates the solar AO(SAO)subsystem within the telescope's optical train,featuring a co-designed correction chain with a single Hartmann-Shack full-wavefront sensor(HS f-WFS)and a deformable secondary mirror(DSM).The HS f-WFS uses temporal-spatial hybrid sampling technique to simultane-ously resolve tip-tilt and high-order aberrations,while the DSM performs real-time compensation through adaptive modal optimization.This unified architecture achieves symmetrical polarization suppression and high system throughput by min-imizing optical surfaces.A 600 mm ESAOT prototype incorporating a 12×12 micro-lens array HS f-WFS and 61-actuator piezoelectric DSM has been developed and successfully conducted on-sky photospheric observations.Validations in-cluding turbulence simulations,optical bench testing,and practical observations at the Lijiang observatory collectively confirm the system's capability to maintain aboutλ/10 wavefront error during active region tracking.This architectural breakthrough of the ESAOT addresses long-standing SAO integration challenges in solar astronomy and provides scala-bility analyses confirming direct applicability to the existing and future large solar observation facilities.展开更多
Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabe...Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well.Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.展开更多
Based on the adaptive analysis paradigm,this paper constructs an evaluation index system and an evaluation model of the level of industrial ecology of a restricted development zone from the perspective of the industri...Based on the adaptive analysis paradigm,this paper constructs an evaluation index system and an evaluation model of the level of industrial ecology of a restricted development zone from the perspective of the industrial system and of the environmental system,and studies the spatial-temporal differentiation characteristics and the driving factors of the level of industrial ecology of the restricted development zone of the Shandong Province,China,by using a variety of measurement methods.The results show that:1)In the temporal dimension,the level of industrial ecology of the research area increased from 2005 to 2017,while in the regional dimension,it was higher in the eastern coastal areas,followed by the northwestern area and the southwestern area;2)In the spatial dimension,from 2005 to 2017 the level of industrial ecology of the research area had a clear spatial dependence,and the regional spatial agglomeration of the restricted development zones with similar industrial ecology levels become increasingly evident;3)On the whole,the industrial ecology level in the study area had a clear spatial differentiation pattern,as it was higher in the north and in the east and lower in the south and in the west.Moreover,its evolution model changed from a‘three-core driven model’to a‘spatial scattered mosaic distribution model’,and then to a‘single-core driven model’;4)Industrial ecology was positively correlated with economic development,foreign investment,science and technology,and negatively correlated with the government role,while industrial structure and environmental regulation failed to pass the statistical significance test.展开更多
Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning mode...Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.展开更多
Knowledge graph embedding aims at embedding entities and relations in a knowledge graph into a continuous, dense, low-dimensional and realvalued vector space. Among various embedding models appeared in recent years, t...Knowledge graph embedding aims at embedding entities and relations in a knowledge graph into a continuous, dense, low-dimensional and realvalued vector space. Among various embedding models appeared in recent years, translation-based models such as TransE, TransH and TransR achieve state-of-the-art performance. However, in these models, negative triples used for training phase are generated by replacing each positive entity in positive triples with negative entities from the entity set with the same probability;as a result, a large number of invalid negative triples will be generated and used in the training process. In this paper, a method named adaptive negative sampling (ANS) is proposed to generate valid negative triples. In this method, it first divided all the entities into a number of groups which consist of similar entities by some clustering algorithms such as K-Means. Then, corresponding to each positive triple, the head entity was replaced by a negative entity from the cluster in which the head entity was located and the tail entity was replaced in a similar approach. As a result, it generated a set of high-quality negative triples which benefit for improving the effectiveness of embedding models. The ANS method was combined with the TransE model and the resulted model was named as TransE-ANS. Experimental results show that TransE-ANS achieves significant improvement in the link prediction task.展开更多
The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical propertie...The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical properties of deposited metals directly according to the components of coating on the electrodes. In this paper an electrode intelligent design system is developed by means of fuzzy neural network technology and genetic algorithm,, dynamic link library, object linking and embedding and multithreading. The front-end application and customer interface of the system is realized by using visual C ＋＋ program language and taking SQL Server 2000 as background database. It realizes series functions including automatic design of electrode formula, intelligent prediction of electrode properties, inquiry of electrode information, output of process report based on normalized template and electronic storage and search of relative files.展开更多
A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an ada...A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an adaptive optimal control algorithm is proposed. The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed. The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.展开更多
Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction dur...Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.展开更多
With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adapt...With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adaptive Gating Graph Convolutional Network(DBAG-GCN)model for spatio-temporal traffic forecasting.The proposed model leverages the power of graph convolutional networks to capture the spatial dependencies in the road network topology and incorporates bi-directional gating mechanisms to control the information flow adaptively.Furthermore,we introduce a multi-scale temporal convolution module to capture multi-scale temporal dynamics and a contextual attention mechanism to integrate external factors such as weather conditions and event information.Extensive experiments on real-world traffic datasets demonstrate the superior performance of DBAG-GCN compared to state-of-the-art baselines,achieving significant improvements in prediction accuracy and computational efficiency.The DBAG-GCN model provides a powerful and flexible framework for spatio-temporal traffic forecasting,paving the way for intelligent transportation management and urban planning.展开更多
An embedded protective device for 35kV power line is worked out based on Philips’ LPC2292 ARM MCU. Several aspects such as embedded design technique adopted in the system framework, application of adaptive theory in ...An embedded protective device for 35kV power line is worked out based on Philips’ LPC2292 ARM MCU. Several aspects such as embedded design technique adopted in the system framework, application of adaptive theory in data acquisition, Board Support Packet (BSP) developing and task dispatching related to operating system are discussed. Both hardware and software framework of the system are given. Advanced hardware platform and software development environment is applied in design of the system, with the advanced co-design technology.展开更多
In the field of reversible data hiding(RDH),designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding informa...In the field of reversible data hiding(RDH),designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects.In this paper,we propose a new RDH method,including a predictor based on a transformer and a novel embedding strategy with multiple embedding rules.In the predictor part,we first design a transformer-based predictor.Then,we propose an image division method to divide the image into four parts,which can use more pixels as context.Compared with other predictors,the transformer-based predictor can extend the range of pixels for prediction from neighboring pixels to global ones,making it more accurate in reducing the embedding distortion.In the embedding strategy part,we first propose a complexity measurement with pixels in the target blocks.Then,we develop an improved prediction error ordering rule.Finally,we provide an embedding strategy including multiple embedding rules for the first time.The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images,and experimental results show that the performance of our RDH method is leading the field.展开更多
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
基金support from the National Science Foundation of China(NSFC)(Grants No.12293031 and No.61905252)the National Science Foundation for Distinguished Young Scholars(Grant No.12022308)the National Key R&D Program of China(Grants No.2021YFC2202200 and No.2021YFC2202204).
文摘Adaptive optics(AO)has significantly advanced high-resolution solar observations by mitigating atmospheric turbulence.However,traditional post-focal AO systems suffer from external configurations that introduce excessive optical surfaces,reduced light throughput,and instrumental polarization.To address these limitations,we propose an embedded solar adaptive optics telescope(ESAOT)that intrinsically incorporates the solar AO(SAO)subsystem within the telescope's optical train,featuring a co-designed correction chain with a single Hartmann-Shack full-wavefront sensor(HS f-WFS)and a deformable secondary mirror(DSM).The HS f-WFS uses temporal-spatial hybrid sampling technique to simultane-ously resolve tip-tilt and high-order aberrations,while the DSM performs real-time compensation through adaptive modal optimization.This unified architecture achieves symmetrical polarization suppression and high system throughput by min-imizing optical surfaces.A 600 mm ESAOT prototype incorporating a 12×12 micro-lens array HS f-WFS and 61-actuator piezoelectric DSM has been developed and successfully conducted on-sky photospheric observations.Validations in-cluding turbulence simulations,optical bench testing,and practical observations at the Lijiang observatory collectively confirm the system's capability to maintain aboutλ/10 wavefront error during active region tracking.This architectural breakthrough of the ESAOT addresses long-standing SAO integration challenges in solar astronomy and provides scala-bility analyses confirming direct applicability to the existing and future large solar observation facilities.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006)the National Natural Science Foundation of China (61972102)+2 种基金the Guangzhou Science and Technology Plan Project (023A04J1729)the Science and Technology development fund (FDCT)Macao SAR (015/2020/AMJ)。
文摘Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well.Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.
基金Under the auspices of National Natural Science Foundation of China(No.41801105,41771138)National Natural Science Foundation of Shandong(No.ZR2018BD002)Social Science Planning Research Project of Shandong(No.18DJJJ14)。
文摘Based on the adaptive analysis paradigm,this paper constructs an evaluation index system and an evaluation model of the level of industrial ecology of a restricted development zone from the perspective of the industrial system and of the environmental system,and studies the spatial-temporal differentiation characteristics and the driving factors of the level of industrial ecology of the restricted development zone of the Shandong Province,China,by using a variety of measurement methods.The results show that:1)In the temporal dimension,the level of industrial ecology of the research area increased from 2005 to 2017,while in the regional dimension,it was higher in the eastern coastal areas,followed by the northwestern area and the southwestern area;2)In the spatial dimension,from 2005 to 2017 the level of industrial ecology of the research area had a clear spatial dependence,and the regional spatial agglomeration of the restricted development zones with similar industrial ecology levels become increasingly evident;3)On the whole,the industrial ecology level in the study area had a clear spatial differentiation pattern,as it was higher in the north and in the east and lower in the south and in the west.Moreover,its evolution model changed from a‘three-core driven model’to a‘spatial scattered mosaic distribution model’,and then to a‘single-core driven model’;4)Industrial ecology was positively correlated with economic development,foreign investment,science and technology,and negatively correlated with the government role,while industrial structure and environmental regulation failed to pass the statistical significance test.
基金Youth Innovation Promotion Association CAS,Grant/Award Number:2021103Strategic Priority Research Program of Chinese Academy of Sciences,Grant/Award Number:XDC02060500。
文摘Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.
基金the National Natural Science Foundation of China (Nos. U1501252, 61572146 and U1711263)the Natural Science Foundation of Guangxi Province (No. 2016GXNSFDA380006)+1 种基金the Guangxi Innovation-Driven Development Project (No. AA17202024)the Guangxi Universities Young and Middle-aged Teacher Basic Ability Enhancement Project (No. 2018KY0203).
文摘Knowledge graph embedding aims at embedding entities and relations in a knowledge graph into a continuous, dense, low-dimensional and realvalued vector space. Among various embedding models appeared in recent years, translation-based models such as TransE, TransH and TransR achieve state-of-the-art performance. However, in these models, negative triples used for training phase are generated by replacing each positive entity in positive triples with negative entities from the entity set with the same probability;as a result, a large number of invalid negative triples will be generated and used in the training process. In this paper, a method named adaptive negative sampling (ANS) is proposed to generate valid negative triples. In this method, it first divided all the entities into a number of groups which consist of similar entities by some clustering algorithms such as K-Means. Then, corresponding to each positive triple, the head entity was replaced by a negative entity from the cluster in which the head entity was located and the tail entity was replaced in a similar approach. As a result, it generated a set of high-quality negative triples which benefit for improving the effectiveness of embedding models. The ANS method was combined with the TransE model and the resulted model was named as TransE-ANS. Experimental results show that TransE-ANS achieves significant improvement in the link prediction task.
文摘The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical properties of deposited metals directly according to the components of coating on the electrodes. In this paper an electrode intelligent design system is developed by means of fuzzy neural network technology and genetic algorithm,, dynamic link library, object linking and embedding and multithreading. The front-end application and customer interface of the system is realized by using visual C ＋＋ program language and taking SQL Server 2000 as background database. It realizes series functions including automatic design of electrode formula, intelligent prediction of electrode properties, inquiry of electrode information, output of process report based on normalized template and electronic storage and search of relative files.
基金Supported by the National Natural Science Foundation of China （60774080） and BJNOVA 2005B 15.
文摘A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an adaptive optimal control algorithm is proposed. The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed. The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.
基金This work was supported in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant(No.ZJWKT202204),author J.Q,http://zfsg.gd.gov.cn/xxfb/ywsd/index.html.
文摘Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.
基金supported by the National Natural Science Foundation of China(Nos.62202247 and 62306073)the National Key Research and Development Program of China(No.2022ZD0115303).
文摘With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adaptive Gating Graph Convolutional Network(DBAG-GCN)model for spatio-temporal traffic forecasting.The proposed model leverages the power of graph convolutional networks to capture the spatial dependencies in the road network topology and incorporates bi-directional gating mechanisms to control the information flow adaptively.Furthermore,we introduce a multi-scale temporal convolution module to capture multi-scale temporal dynamics and a contextual attention mechanism to integrate external factors such as weather conditions and event information.Extensive experiments on real-world traffic datasets demonstrate the superior performance of DBAG-GCN compared to state-of-the-art baselines,achieving significant improvements in prediction accuracy and computational efficiency.The DBAG-GCN model provides a powerful and flexible framework for spatio-temporal traffic forecasting,paving the way for intelligent transportation management and urban planning.
文摘An embedded protective device for 35kV power line is worked out based on Philips’ LPC2292 ARM MCU. Several aspects such as embedded design technique adopted in the system framework, application of adaptive theory in data acquisition, Board Support Packet (BSP) developing and task dispatching related to operating system are discussed. Both hardware and software framework of the system are given. Advanced hardware platform and software development environment is applied in design of the system, with the advanced co-design technology.
基金Project supported by the National Natural Science Foundation of China(No.62172053)the National Key Research and Development Program of China(Nos.2021YFC3340701 and 2021YFC3340602)。
文摘In the field of reversible data hiding(RDH),designing a high-precision predictor to reduce the embedding distortion and developing an effective embedding strategy to minimize the distortion caused by embedding information are the two most critical aspects.In this paper,we propose a new RDH method,including a predictor based on a transformer and a novel embedding strategy with multiple embedding rules.In the predictor part,we first design a transformer-based predictor.Then,we propose an image division method to divide the image into four parts,which can use more pixels as context.Compared with other predictors,the transformer-based predictor can extend the range of pixels for prediction from neighboring pixels to global ones,making it more accurate in reducing the embedding distortion.In the embedding strategy part,we first propose a complexity measurement with pixels in the target blocks.Then,we develop an improved prediction error ordering rule.Finally,we provide an embedding strategy including multiple embedding rules for the first time.The proposed RDH method can effectively reduce the distortion and provide satisfactory results in improving the visual quality of data-hidden images,and experimental results show that the performance of our RDH method is leading the field.
