From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized t...From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized the achievements and experience of China’s neighborhood work in the new era,scientifically analyzed the situation,clarified the goals,tasks,ideas and measures for neighborhood work in the coming period,and emphasized the need to focus on building a community with a shared future with neighboring countries,striving to break new ground in neighborhood work.Facing the ever-changing international landscape,especially the surrounding environment,China needs to carry out integrated diplomacy,reshape the neighborhood environment,translate the concepts and policies of the CPC neighborhood diplomacy into diplomatic practices and achieve greater results.展开更多
The neighborhood is where China survives and thrives as well as the foundation of its development and prosperity.Chinese President Xi Jinping stressed during the Central Conference on Work Relating to Neighborhood Dip...The neighborhood is where China survives and thrives as well as the foundation of its development and prosperity.Chinese President Xi Jinping stressed during the Central Conference on Work Relating to Neighborhood Diplomacy convened in October 2013 that“China’s neighborhood is of critical strategic significance in terms of geographical location,natural environment as well as mutual relations.”展开更多
Today city planners are confronted with two global trends:on one hand,living space is getting less due to urbanization;on the other hand,demands on living space are constantly rising as for example through stricter cl...Today city planners are confronted with two global trends:on one hand,living space is getting less due to urbanization;on the other hand,demands on living space are constantly rising as for example through stricter climate and energy political objectives based on the Paris Agreement.Therefore,it will be necessary to take into account—near urban planning and social aspects—also the climate compatibility as one central aspect in the construction of buildings,settlements,districts or neighborhoods.To identify and to push successful concepts,Austria has developed a planning tool that allows planning,assessing and ensuring high quality standards of neighborhoods.As the tool has been highly successful,additional planning tools are being developed for specific topics such as“PED—Positive Energy Districts”,“NEB—New European Bauhaus”and“CND—Climate Neutral Districts”.Central quantitative and qualitative criteria—which have been elaborated in the recent years—will be presented in this paper.展开更多
The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,exist...The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,existing literature predominantly adopts the utilitarian approach,understanding well-being as people’s feelings about their lives and viewing the neighborhood environment as resources that benefit well-being.The Capability Approach,a novel approach that conceptualize well-being as the freedoms to do or to be and regard environment as conversion factors that influence well-being,can offer new lens by incorporating human development in-to these topics.This paper proposes an alternative theoretical framework:well-being is conceptualized and measured by capability;neighborhood environment affects well-being by providing spatial services,functioning as environmental conversion factors,and serving as social conversion factors.We conducted a case study of Changshu City located in eastern China,utilizing multiple resource data,applying explainable artificial intelligence(XAI),namely eXtreme Gradient Boosting(XGBoost)and SHapley Additive exPlana-tions(SHAP).Our findings highlight the significance of viewing the neighborhood environment as a set of conversion factors,as it provides more explanatory power than providing spatial services.Compared to conventional research based on linear relationship as-sumption,our results demonstrate that the effects of neighborhood environment on well-being are non-linear,characterized by threshold effects and interaction effects.These insights are crucial for informing urban planning and public policy.This research enriches our un-derstanding of well-being,neighborhood environment,and their relationship as well as provides empirical evidence for the core concept of conversion factors in the capability approach.展开更多
The year 2025 marks the 75th anniversary of the establishment of diplomatic relations between China and Vietnam.It represents a crucial opportunity for the two parties and two countries to consolidate bilateral relati...The year 2025 marks the 75th anniversary of the establishment of diplomatic relations between China and Vietnam.It represents a crucial opportunity for the two parties and two countries to consolidate bilateral relations and jointly forge ahead into the future.展开更多
Owing to process conditions such as uneven clearance of base metal assembly and welding deformation,it is difficult to obtain well-formed structural welds with robot constant specification parameters welding.Determini...Owing to process conditions such as uneven clearance of base metal assembly and welding deformation,it is difficult to obtain well-formed structural welds with robot constant specification parameters welding.Determining how to extract a structured,anti-interference,concise,and dynamic knowledge model from measurable data,and then adjust the welding parameters with corresponding control methods in real time is a central problem to be solved in welding formation control.Hence,this paper proposes a welding penetration control method based on a Neighborhood Rough Set-Adaptive Neuro-Fuzzy Inference System(NRS-ANFIS)to achieve effective penetration control for the GMAW welding process.In orthogonal experiments,the NRS algorithm,which is based on visual sensing to obtain the properties of the weld pool and gap changes,is used to reduce the established frontal weld pool feature information decision table,and the minimum feature set of the weld pool tail width WTand the tail area coefficient CTSis obtained.The minimum feature set of the effective frontal weld pool,real-time line laser distance change,and real-time current information are used as the input for the ANFIS control system.The experimental results for the two groups of time-varying gaps demonstrate that under the condition of no preheating of the base metal,the complete welding penetration rate of the adjusted welding process parameters output by the trained ANFIS model reaches 87%,and the backside melting width is uniform and consistent,which meets the welding specification requirements.展开更多
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
Neighborhood diplomacy will be pursued as necessary conditions to achieve the“Two Centenary Goals”and realize the Chinese Dream of the national rejuvenation.Since the 18th National Congress of the Communist Party of...Neighborhood diplomacy will be pursued as necessary conditions to achieve the“Two Centenary Goals”and realize the Chinese Dream of the national rejuvenation.Since the 18th National Congress of the Communist Party of China(CPC),China’s neighborhood diplomacy has adhered to theoretical innovation and put forward many original and contemporary concepts,reflecting the characteristics of the Marxist theories on international relations and enriching the substance of the Xi Jinping Thought on Diplomacy.展开更多
Semi-supervised new intent discovery is a significant research focus in natural language understanding.To address the limitations of current semi-supervised training data and the underutilization of implicit informati...Semi-supervised new intent discovery is a significant research focus in natural language understanding.To address the limitations of current semi-supervised training data and the underutilization of implicit information,a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model(SNID-ENSEF)is proposed.Syntactic elimination contrast learning leverages verb-dominant syntactic features,systematically replacing specific words to enhance data diversity.The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency.A neighborhood sample fusion strategy,based on sample distribution patterns,dynamically adjusts neighborhood size and fuses sample vectors to reduce noise and improve implicit information utilization and discovery accuracy.Experimental results show that SNID-ENSEF achieves average improvements of 0.88%,1.27%,and 1.30%in Normalized Mutual Information(NMI),Accuracy(ACC),and Adjusted Rand Index(ARI),respectively,outperforming PTJN,DPN,MTP-CLNN,and DWG models on the Banking77,StackOverflow,and Clinc150 datasets.The code is available at https://github.com/qsdesz/SNID-ENSEF,accessed on 16 January 2025.展开更多
The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a...The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.展开更多
Let G be a graph, the square graph G 2 of G is a graph satisfying V(G 2)=V(G) and E(G 2)=E(G)∪｛uv: dist G(u, v)=2｝ . In this paper, we use the technique of vertex insertion on l -connected ( l=k or k...Let G be a graph, the square graph G 2 of G is a graph satisfying V(G 2)=V(G) and E(G 2)=E(G)∪｛uv: dist G(u, v)=2｝ . In this paper, we use the technique of vertex insertion on l -connected ( l=k or k+1, k≥2 ) claw-free graphs to provide a unified proof for G to be Hamiltonian, 1 -Hamiltonian or Hamiltonian-connected. The sufficient conditions are expressed by the inequality concerning ∑ k i=0N(Y i) and n(Y) in G for each independent set Y=｛y 0, y 1, …, y k｝ of the square graph of G , where b ( 0<b<k+1 ) is an integer, Y i=｛y i, y i-1, …, y i-(b-1)｝Y for i∈｛0, 1, …, k｝ , where subscriptions of y j s will be taken modulo k+1 , and n(Y)=｛v∈ V(G): dist (v, Y)≤ 2｝ .展开更多
Let G be a graph, an independent set Y in G is called an essential independent set (or essential set for simplicity), if there is {y 1,y 2} Y such that dist (y 1,y 2)=2. In this paper, we wi...Let G be a graph, an independent set Y in G is called an essential independent set (or essential set for simplicity), if there is {y 1,y 2} Y such that dist (y 1,y 2)=2. In this paper, we will use the technique of the vertex insertion on l connected ( l=k or k+1,k≥2 ) claw free graphs to provide a unified proof for G to be hamiltonian or 1 hamiltonian, the sufficient conditions are expressed by the inequality concerning ∑ki=0N(Y i) and n(Y) for each essential set Y={y 0,y 1,...,y k} of G , where Y i={y i,y i-1 ,...,y i-(b-1) }Y for i∈{0,1,...,k} (the subscriptions of y j ’s will be taken modulo k+1 ), b ( 0【b【k+1 ) is an integer, and n(Y)={v∈V(G): dist (v,Y)≤2 }.展开更多
In this paper we prove that a space X with point countable sequential neighborhood network if and only if it is α 4 space with point countable cs network.
Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencie...Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.展开更多
Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique f...Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.展开更多
Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode che...Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.展开更多
Local spatial interaction between neighborhood land-use categories （i.e. neighborhood interaction） is an important factor which affects urban land-use change patterns. Therefore,it is a key component in cellular aut...Local spatial interaction between neighborhood land-use categories （i.e. neighborhood interaction） is an important factor which affects urban land-use change patterns. Therefore,it is a key component in cellular automata （CA）-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan,i.e. Tokyo,Osaka,and Nagoya,were compared using such aids as the neighborhood interaction model and similarity measure function. As a result,urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories,meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas,which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.展开更多
A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely no...A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.展开更多
Seismic anisotropy provides important constraints on deformation patterns of Earth＇s material. Rayleigh wave dispersion data with azimuthal anisotropy can be used to invert for depth-dependent shear wavespeed azimuth...Seismic anisotropy provides important constraints on deformation patterns of Earth＇s material. Rayleigh wave dispersion data with azimuthal anisotropy can be used to invert for depth-dependent shear wavespeed azimuthal anisotropy, therefore reflecting depth-varying deformation patterns in the crust and upper mantle. In this study, we propose a two-step method that uses the Neighborhood Algorithm （NA） for the point-wise inversion of depth-dependent shear wavespeeds and azimuthal anisotropy from Rayleigh wave azimuthally anisotropic dispersion data. The first step employs the NA to estimate depth- dependent Vsv （or the elastic parameter L） as well as their uncertainties from the isotropic part Rayleigh wave dispersion data. In the second step, we first adopt a difference scheme to compute approximate Rayleigh-wave phase velocity sensitivity kernels to azimuthally anisotropic parameters with respect to the velocity model obtained in the first step. Then we perform the NA to estimate the azimuthally anisotropic parameters Gc/L and Gs/L at depths separately from the corresponding cosine and sine terms of the azimuthally anisotropic dispersion data. Finally, we compute the depth-dependent magnitude and fast polariza- tion azimuth of shear wavespeed azimuthal anisotropy. The use of the global search NA and Bayesian analysis allows for more reliable estimates of depth-dependent shear wavespeeds and azimuthal anisotropy as well as their uncertainties.We illustrate the inversion method using the azimuthally anisotropic dispersion data in SE Tibet, where we find apparent changes of fast axes of shear wavespeed azimuthal anisotropy between the crust and uppermost mantle.展开更多
Interlaid rock is an important component in the construction of neighborhood tunnels that supports and reinforces the area between two tunnels.However,the blasting load during excavation can sometimes damage the inter...Interlaid rock is an important component in the construction of neighborhood tunnels that supports and reinforces the area between two tunnels.However,the blasting load during excavation can sometimes damage the interlaid rock and threaten the stability of a tunnel’s structure.This paper presents a case study of the small clearance section of the Liantang highway tunnel project in Shenzhen,China,where the minimum distance between the two tunnels involved is only 0.5 m.To reduce the damage to the interlaid rock caused by blasting loads,we proposed a four-part excavation method with a vibrationcushioning rock layer in the following tunnel of neighborhood tunnels.Numerical simulation was used to model the damage prevention mechanism of the vibration-cushioning rock layer and to better understand the propagation of cracks in the interlaid rock.Furthermore,based on the simulation results,combined microseismic controlled-blasting technology was implemented,using innovative blasting patterns combined with different charge structures and blasting equipment designed according to the varying thickness of the interlaid rock.Finally,this implementation succeeded in protecting interlaid rock during blasting operations.展开更多
文摘From April 8 to 9,2025,the Central Conference on Work Related to Neighboring Countries was held in Beijing.General Secretary of the Communist Party of China(CPC)Central Committee Xi Jinping systematically summarized the achievements and experience of China’s neighborhood work in the new era,scientifically analyzed the situation,clarified the goals,tasks,ideas and measures for neighborhood work in the coming period,and emphasized the need to focus on building a community with a shared future with neighboring countries,striving to break new ground in neighborhood work.Facing the ever-changing international landscape,especially the surrounding environment,China needs to carry out integrated diplomacy,reshape the neighborhood environment,translate the concepts and policies of the CPC neighborhood diplomacy into diplomatic practices and achieve greater results.
文摘The neighborhood is where China survives and thrives as well as the foundation of its development and prosperity.Chinese President Xi Jinping stressed during the Central Conference on Work Relating to Neighborhood Diplomacy convened in October 2013 that“China’s neighborhood is of critical strategic significance in terms of geographical location,natural environment as well as mutual relations.”
文摘Today city planners are confronted with two global trends:on one hand,living space is getting less due to urbanization;on the other hand,demands on living space are constantly rising as for example through stricter climate and energy political objectives based on the Paris Agreement.Therefore,it will be necessary to take into account—near urban planning and social aspects—also the climate compatibility as one central aspect in the construction of buildings,settlements,districts or neighborhoods.To identify and to push successful concepts,Austria has developed a planning tool that allows planning,assessing and ensuring high quality standards of neighborhoods.As the tool has been highly successful,additional planning tools are being developed for specific topics such as“PED—Positive Energy Districts”,“NEB—New European Bauhaus”and“CND—Climate Neutral Districts”.Central quantitative and qualitative criteria—which have been elaborated in the recent years—will be presented in this paper.
基金Under the auspices of National Natural Science Foundation of China(No.42271230,42330510)。
文摘The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,existing literature predominantly adopts the utilitarian approach,understanding well-being as people’s feelings about their lives and viewing the neighborhood environment as resources that benefit well-being.The Capability Approach,a novel approach that conceptualize well-being as the freedoms to do or to be and regard environment as conversion factors that influence well-being,can offer new lens by incorporating human development in-to these topics.This paper proposes an alternative theoretical framework:well-being is conceptualized and measured by capability;neighborhood environment affects well-being by providing spatial services,functioning as environmental conversion factors,and serving as social conversion factors.We conducted a case study of Changshu City located in eastern China,utilizing multiple resource data,applying explainable artificial intelligence(XAI),namely eXtreme Gradient Boosting(XGBoost)and SHapley Additive exPlana-tions(SHAP).Our findings highlight the significance of viewing the neighborhood environment as a set of conversion factors,as it provides more explanatory power than providing spatial services.Compared to conventional research based on linear relationship as-sumption,our results demonstrate that the effects of neighborhood environment on well-being are non-linear,characterized by threshold effects and interaction effects.These insights are crucial for informing urban planning and public policy.This research enriches our un-derstanding of well-being,neighborhood environment,and their relationship as well as provides empirical evidence for the core concept of conversion factors in the capability approach.
文摘The year 2025 marks the 75th anniversary of the establishment of diplomatic relations between China and Vietnam.It represents a crucial opportunity for the two parties and two countries to consolidate bilateral relations and jointly forge ahead into the future.
基金Supported by National Natural Science Foundation of China(Grant Nos.52261044,51969001)the Guangxi Provincial Science and Technology Major Project(Grant No.Guike AA23062037)Research Foundation Ability Enhancement Project for Young and Middle Aged Teachers in Guangxi Universities of China(Grant No.2024KY0441)。
文摘Owing to process conditions such as uneven clearance of base metal assembly and welding deformation,it is difficult to obtain well-formed structural welds with robot constant specification parameters welding.Determining how to extract a structured,anti-interference,concise,and dynamic knowledge model from measurable data,and then adjust the welding parameters with corresponding control methods in real time is a central problem to be solved in welding formation control.Hence,this paper proposes a welding penetration control method based on a Neighborhood Rough Set-Adaptive Neuro-Fuzzy Inference System(NRS-ANFIS)to achieve effective penetration control for the GMAW welding process.In orthogonal experiments,the NRS algorithm,which is based on visual sensing to obtain the properties of the weld pool and gap changes,is used to reduce the established frontal weld pool feature information decision table,and the minimum feature set of the weld pool tail width WTand the tail area coefficient CTSis obtained.The minimum feature set of the effective frontal weld pool,real-time line laser distance change,and real-time current information are used as the input for the ANFIS control system.The experimental results for the two groups of time-varying gaps demonstrate that under the condition of no preheating of the base metal,the complete welding penetration rate of the adjusted welding process parameters output by the trained ANFIS model reaches 87%,and the backside melting width is uniform and consistent,which meets the welding specification requirements.
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.
文摘Neighborhood diplomacy will be pursued as necessary conditions to achieve the“Two Centenary Goals”and realize the Chinese Dream of the national rejuvenation.Since the 18th National Congress of the Communist Party of China(CPC),China’s neighborhood diplomacy has adhered to theoretical innovation and put forward many original and contemporary concepts,reflecting the characteristics of the Marxist theories on international relations and enriching the substance of the Xi Jinping Thought on Diplomacy.
基金supported by Research Projects of the Nature Science Foundation of Hebei Province(F2021402005).
文摘Semi-supervised new intent discovery is a significant research focus in natural language understanding.To address the limitations of current semi-supervised training data and the underutilization of implicit information,a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model(SNID-ENSEF)is proposed.Syntactic elimination contrast learning leverages verb-dominant syntactic features,systematically replacing specific words to enhance data diversity.The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency.A neighborhood sample fusion strategy,based on sample distribution patterns,dynamically adjusts neighborhood size and fuses sample vectors to reduce noise and improve implicit information utilization and discovery accuracy.Experimental results show that SNID-ENSEF achieves average improvements of 0.88%,1.27%,and 1.30%in Normalized Mutual Information(NMI),Accuracy(ACC),and Adjusted Rand Index(ARI),respectively,outperforming PTJN,DPN,MTP-CLNN,and DWG models on the Banking77,StackOverflow,and Clinc150 datasets.The code is available at https://github.com/qsdesz/SNID-ENSEF,accessed on 16 January 2025.
文摘The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.
文摘Let G be a graph, the square graph G 2 of G is a graph satisfying V(G 2)=V(G) and E(G 2)=E(G)∪｛uv: dist G(u, v)=2｝ . In this paper, we use the technique of vertex insertion on l -connected ( l=k or k+1, k≥2 ) claw-free graphs to provide a unified proof for G to be Hamiltonian, 1 -Hamiltonian or Hamiltonian-connected. The sufficient conditions are expressed by the inequality concerning ∑ k i=0N(Y i) and n(Y) in G for each independent set Y=｛y 0, y 1, …, y k｝ of the square graph of G , where b ( 0<b<k+1 ) is an integer, Y i=｛y i, y i-1, …, y i-(b-1)｝Y for i∈｛0, 1, …, k｝ , where subscriptions of y j s will be taken modulo k+1 , and n(Y)=｛v∈ V(G): dist (v, Y)≤ 2｝ .
文摘Let G be a graph, an independent set Y in G is called an essential independent set (or essential set for simplicity), if there is {y 1,y 2} Y such that dist (y 1,y 2)=2. In this paper, we will use the technique of the vertex insertion on l connected ( l=k or k+1,k≥2 ) claw free graphs to provide a unified proof for G to be hamiltonian or 1 hamiltonian, the sufficient conditions are expressed by the inequality concerning ∑ki=0N(Y i) and n(Y) for each essential set Y={y 0,y 1,...,y k} of G , where Y i={y i,y i-1 ,...,y i-(b-1) }Y for i∈{0,1,...,k} (the subscriptions of y j ’s will be taken modulo k+1 ), b ( 0【b【k+1 ) is an integer, and n(Y)={v∈V(G): dist (v,Y)≤2 }.
文摘In this paper we prove that a space X with point countable sequential neighborhood network if and only if it is α 4 space with point countable cs network.
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.
基金supported in part by the National Natural Science Foundation of China(61379049,61772120)
文摘Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.
基金Supported by the National Natural Science Foundation of China(61273160,61403418)the Natural Science Foundation of Shandong Province(ZR2011FM014)+1 种基金the Fundamental Research Funds for the Central Universities(10CX04046A)the Doctoral Fund of Shandong Province(BS2012ZZ011)
文摘Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.
基金Foundation： National Natural Science Foundation of China, No.40901090 No.70863014+1 种基金 Foundation of Japan Society for the Promotion of Science （JSPS）, No.1907003 Talents Introduced into Universities Foundation of Guangdong Province of China, No.2009-26.
文摘Local spatial interaction between neighborhood land-use categories （i.e. neighborhood interaction） is an important factor which affects urban land-use change patterns. Therefore,it is a key component in cellular automata （CA）-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan,i.e. Tokyo,Osaka,and Nagoya,were compared using such aids as the neighborhood interaction model and similarity measure function. As a result,urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories,meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas,which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.
基金supported by the National Natural Science Foundation of China (7060103570801062)
文摘A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.
基金supported by National Natural Science Foundation of China (Grant number 41222028)China National Special Fund for Earthquake Scientific Research in Public Interest (Grant number 201508008)the Fundamental Research Funds for the Central Universities (WK2080000053)
文摘Seismic anisotropy provides important constraints on deformation patterns of Earth＇s material. Rayleigh wave dispersion data with azimuthal anisotropy can be used to invert for depth-dependent shear wavespeed azimuthal anisotropy, therefore reflecting depth-varying deformation patterns in the crust and upper mantle. In this study, we propose a two-step method that uses the Neighborhood Algorithm （NA） for the point-wise inversion of depth-dependent shear wavespeeds and azimuthal anisotropy from Rayleigh wave azimuthally anisotropic dispersion data. The first step employs the NA to estimate depth- dependent Vsv （or the elastic parameter L） as well as their uncertainties from the isotropic part Rayleigh wave dispersion data. In the second step, we first adopt a difference scheme to compute approximate Rayleigh-wave phase velocity sensitivity kernels to azimuthally anisotropic parameters with respect to the velocity model obtained in the first step. Then we perform the NA to estimate the azimuthally anisotropic parameters Gc/L and Gs/L at depths separately from the corresponding cosine and sine terms of the azimuthally anisotropic dispersion data. Finally, we compute the depth-dependent magnitude and fast polariza- tion azimuth of shear wavespeed azimuthal anisotropy. The use of the global search NA and Bayesian analysis allows for more reliable estimates of depth-dependent shear wavespeeds and azimuthal anisotropy as well as their uncertainties.We illustrate the inversion method using the azimuthally anisotropic dispersion data in SE Tibet, where we find apparent changes of fast axes of shear wavespeed azimuthal anisotropy between the crust and uppermost mantle.
基金the National Natural Science Foundation of China(No.51934001).
文摘Interlaid rock is an important component in the construction of neighborhood tunnels that supports and reinforces the area between two tunnels.However,the blasting load during excavation can sometimes damage the interlaid rock and threaten the stability of a tunnel’s structure.This paper presents a case study of the small clearance section of the Liantang highway tunnel project in Shenzhen,China,where the minimum distance between the two tunnels involved is only 0.5 m.To reduce the damage to the interlaid rock caused by blasting loads,we proposed a four-part excavation method with a vibrationcushioning rock layer in the following tunnel of neighborhood tunnels.Numerical simulation was used to model the damage prevention mechanism of the vibration-cushioning rock layer and to better understand the propagation of cracks in the interlaid rock.Furthermore,based on the simulation results,combined microseismic controlled-blasting technology was implemented,using innovative blasting patterns combined with different charge structures and blasting equipment designed according to the varying thickness of the interlaid rock.Finally,this implementation succeeded in protecting interlaid rock during blasting operations.
