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
A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the cr...A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images, from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the 'best' channel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.展开更多
Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear,global and local algorithms are proposed. The key step of manifold learning algorith...Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear,global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However,so far for the references we know,few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper,we propose an adaptive neighboring selection algorithm,which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry,Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm,the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K,making the residual variance relatively small and better visualization of the results. By quantitative analysis,the embedding quality measured by residual variance is increased 45. 45% after using the proposed algorithm in LLE.展开更多
Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to requi...Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks;hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.展开更多
Considering that perfect channel state information(CSI) is difficult to obtain in practice,energy efficiency(EE) for distributed antenna systems(DAS) based on imperfect CSI and antennas selection is investigated in Ra...Considering that perfect channel state information(CSI) is difficult to obtain in practice,energy efficiency(EE) for distributed antenna systems(DAS) based on imperfect CSI and antennas selection is investigated in Rayleigh fading channel.A novel EE that is defined as the average transmission rate divided by the total consumed power is introduced.In accordance with this definition,an adaptive power allocation(PA) scheme for DAS is proposed to maximize the EE under the maximum transmit power constraint.The solution of PA in the constrained EE optimization does exist and is unique.A practical iterative algorithm with Newton method is presented to obtain the solution of PA.The proposed scheme includes the one under perfect CSI as a special case,and it only needs large scale and statistical information.As a result,the scheme has low overhead and good robustness.The theoretical EE is also derived for performance evaluation,and simulation result shows the validity of the theoretical analysis.Moreover,EE can be enhanced by decreasing the estimation error and/or path loss exponents.展开更多
The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The tr...The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.展开更多
Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from nume...Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.展开更多
This article conducts an analysis and exploration of the English subtitles translation of the domestic animated film Chang’an from the perspective of eco-translatology.Taking three-dimensional transformation as the e...This article conducts an analysis and exploration of the English subtitles translation of the domestic animated film Chang’an from the perspective of eco-translatology.Taking three-dimensional transformation as the entry point,it analyzes the adaptation and selection of subtitle translators in the translation process from the dimensions of language,culture,and communication in Chang’an,aiming to enrich the practical analysis of eco-translatology and provide valuable insights and references for the theoretical construction and practical application of eco-translatology.This article enriches the practical analysis of eco-translatology and also validates to a certain extent the applicability of“three-dimensional”transformation.展开更多
Finding a highly interpretable nonlinear model has been an important yet challenging problem, and related research is relatively scarce in the current literature. To tackle this issue, we propose a new algorithm calle...Finding a highly interpretable nonlinear model has been an important yet challenging problem, and related research is relatively scarce in the current literature. To tackle this issue, we propose a new algorithm called Feat-ABESS based on a framework that utilizes feature transformation and selection for re-interpreting many machine learning algorithms. The core idea behind Feat-ABESS is to parameterize interpretable feature transformation within this framework and construct an objective function based on these parameters. This approach enables us to identify a proper interpretable feature transformation from the optimization perspective. By leveraging a recently advanced optimization technique, Feat-ABESS can obtain a concise and interpretable model. Moreover, Feat-ABESS can perform nonlinear variable selection. Our extensive experiments on 205 benchmark datasets and case studies on two datasets have demonstrated that Feat-ABESS can achieve powerful prediction accuracy while maintaining a high level of interpretability. The comparison with existing nonlinear variable selection methods exhibits Feat-ABESS has a higher true positive rate and a lower false discovery rate.展开更多
This paper presents an algorithm that can adaptively select the intermediate frequency（IF） and compensate the IQ mismatch according to the power ratio of the adjacent channel interference to the desired signal in a ...This paper presents an algorithm that can adaptively select the intermediate frequency（IF） and compensate the IQ mismatch according to the power ratio of the adjacent channel interference to the desired signal in a low-IF GSM receiver.The IF can be adaptively selected between 100 and 130 kHz.Test result shows an improvement of phase error from 6.78°to 3.23°.Also a least mean squares（LMS） based IQ mismatch compensation algorithm is applied to improve image rejection ratio（IRR） for the desired signal along with strong adjacent channel interference.The IRR is improved from 29.1 to 44.3 dB in measurement.The design is verified in a low-IF GSM receiver fabricated in SMIC 0.13μm RF CMOS process with a working voltage of 1.2 V.展开更多
The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with dive...The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.展开更多
Error Estimating Code (EEC) is a new channel coding method to estimate the Bit Error Rate (BER) information of the transmitted sequence. However, the estimated BER is not precise enough if the practical value of BER i...Error Estimating Code (EEC) is a new channel coding method to estimate the Bit Error Rate (BER) information of the transmitted sequence. However, the estimated BER is not precise enough if the practical value of BER is high. A weighted EEC estimation method is proposed to improve the accuracy performance of BER estimation by classifying the raw estimation results into intervals and multiplying them by different coefficients separately. The applications of weighted EEC in modulation selection scheme and distributed video coding are discussed. Simulation results show that the EEC-based modulation selection method can achieve better performance at a cost of little redundancy and computation, and the EEC-based rate estimation method in distributed video coding can save the decoding time.展开更多
In this article, we consider a class of kernel quantile estimators which is the linear combi- nation of order statistics. This class of kernel quantile estimators can be regarded as an extension of some existing estim...In this article, we consider a class of kernel quantile estimators which is the linear combi- nation of order statistics. This class of kernel quantile estimators can be regarded as an extension of some existing estimators. The exact mean square error expression for this class of estimators will be provided when data are uniformly distributed. The implementation of these estimators depends mostly on the bandwidth selection. We then develop an adaptive method for bandwidth selection based on the intersection confidence intervals （ICI） principle. Monte Carlo studies demonstrate that our proposed approach is comparatively remarkable. We illustrate our method with a real data set.展开更多
Mark Bender and Aku Wuwu translate Hnewo Teyy,a crucial epic for Nuosu people from Yi to English directly and entirely for the first time.Their translation and introduction of the epic are involved in The Nuosu Book o...Mark Bender and Aku Wuwu translate Hnewo Teyy,a crucial epic for Nuosu people from Yi to English directly and entirely for the first time.Their translation and introduction of the epic are involved in The Nuosu Book of Origins,in which Bender concerns plants and animals.This paper mainly analyses Bender’s adaptation and selection concerning his investigation and translation of plants and animals under the guidance of Eco-translatology.Significantly,Eco-translatology and Bender’s translation and introduction provide enlightenment to the transmission of Chinese culture.展开更多
HBO TV series–Game of Thrones has gone viral ever since its first release in 2011 and its soaring popularity among Chineseaudience has no doubt benefited from the"appropriate"E-C subtitles attached to it. T...HBO TV series–Game of Thrones has gone viral ever since its first release in 2011 and its soaring popularity among Chineseaudience has no doubt benefited from the"appropriate"E-C subtitles attached to it. This paper, through the analysis and discussion ofthe E-C subtitles of Game of Thrones from the perspective of Eco-translatology, demonstrates that the approach of Translation as Adap-tation and Selection(TAS) proves to be operational in subtitle translation, and gives further recommendations in the communicativetransformation of subtitle translation.展开更多
On the framework of eco- translatology, this thesis presents an introduction of the novel Fortress Besieged, the author and the translation work, and then gives an analysis of the translator's adaptation and selec...On the framework of eco- translatology, this thesis presents an introduction of the novel Fortress Besieged, the author and the translation work, and then gives an analysis of the translator's adaptation and selection of the translational eco-environment.展开更多
The importance of an interdisciplinary investigation into the translation of publicity texts cannot be overestimated at a time when the role of international communication grows rapidly,which in turn pushes the develo...The importance of an interdisciplinary investigation into the translation of publicity texts cannot be overestimated at a time when the role of international communication grows rapidly,which in turn pushes the development of globalization.Ecotranslatology takes the concept of"adaptation/selection"of Darwin’s Evolution Theory as its theoretical foundation,focusing on the integrity of the translation ecosystem,making a novel description and interpretation to the nature,process,standard,principles,methods of translation and translation phenomena from the perspective of Eco-translatology.Though the construct of the theory keeps expanding,there is still a lack of systematic discussion on translation activities with focus on the integrity and harmony in Eco-translatology.This study therefore aims to promote the need of researching publicity text translation from an Ecotranslatological perspective through the review of the existing literature in this field.展开更多
Chinese sprangletop (Leptochloa chinensis), belonging to the grass subfamily Chloridoideae, is one of the most notorious weeds in rice ecosystems. Here, we report a chromosome-scale reference genome assembly and a gen...Chinese sprangletop (Leptochloa chinensis), belonging to the grass subfamily Chloridoideae, is one of the most notorious weeds in rice ecosystems. Here, we report a chromosome-scale reference genome assembly and a genomic variation map of the tetraploid L. chinensis. The L. chinensis genome is derived from two diploid progenitors that diverged ∼10.9 million years ago, and its two subgenomes display neither fractionation bias nor overall gene expression dominance. Comparative genomic analyses reveal substantial genome rearrangements in L. chinensis after its divergence from the common ancestor of Chloridoideae and, together with transcriptome profiling, demonstrate the important contribution of tetraploidization to the gene sources for the herbicide resistance of L. chinensis. Population genomic analyses of 89 accessions from China reveal that L. chinensis accessions collected from southern/southwestern provinces have substantially higher nucleotide diversity than those from the middle and lower reaches of the Yangtze River, suggesting that L. chinensis spread in China from the southern/southwestern provinces to the middle and lower reaches of the Yangtze River. During this spread, L. chinensis developed significantly increased herbicide resistance, accompanied by the selection of numerous genes involved in herbicide resistance. Taken together, our study generated valuable genomic resources for future fundamental research and agricultural management of L. chinensis, and provides significant new insights into the herbicide resistance as well as the origin and adaptive evolution of L. chinensis.展开更多
With the increasing data volume of train on-board system,real-time performance has become the most critical factor to ensure the safety of train operation.Considering that standard Ethernet cannot meet the real-time r...With the increasing data volume of train on-board system,real-time performance has become the most critical factor to ensure the safety of train operation.Considering that standard Ethernet cannot meet the real-time requirement of existing train communication network(TCN),the time-sensitive network(TSN)technology for TCN is introduced.To solve the time-delay problem,an adaptive switch queue selection mechanism for traffic scheduling is proposed.Firstly,the topology model of TCN based on TSN and the traffic model are described.Then,the K shortest path routing algorithm based on load balancing provides the optimal routing for the scheduling process.Finally,the adaptive switch queue selection mechanism is introduced to solve the aggregation flow conflict problem effectively,queue resources are properly allocated,and the gate control list(GCL)of each frame in the queue is obtained.Experimental results show that compared with the traditional constraint model,the schedulability of the model with an adaptive switch queue selection mechanism increases by 33.0%,and the maximum end-to-end delay and network jitter decrease by 19.1%and 18.6%on average respectively.It can provide theoretical support and application reference for the real-time performance optimization of TCN based on TSN.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The National Basic Research Program (973) of China (No. 2003CB716103) The Key Lab of Image Processing & Intelligent control of National Education Ministry (No. TKLJ0306)
文摘A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images, from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the 'best' channel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 61101122 and 61071105)Fundamental Research Funds for the Central Universities (Grant No. HIT. NSRIF. 2010090)+1 种基金Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory (Grant No. ITD-U12004)Postdoctoral Science Research Development Foundation of Heilongjiang Province (Grant No. LBH-Q12080)
文摘Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear,global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However,so far for the references we know,few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper,we propose an adaptive neighboring selection algorithm,which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry,Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm,the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K,making the residual variance relatively small and better visualization of the results. By quantitative analysis,the embedding quality measured by residual variance is increased 45. 45% after using the proposed algorithm in LLE.
文摘Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks;hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.
基金partially supported by the National Natural Science Foundation of China(61571225,61271255,61232016,U1405254)the Open Foundation of Jiangsu Engineering Center of Network Monitoring(Nanjing University of Information Science and Technology)(Grant No.KJR1509)+2 种基金the PAPD fundthe CICAEET fundShenzhen Strategic Emerging Industry Development Funds(JSGG20150331160845693)
文摘Considering that perfect channel state information(CSI) is difficult to obtain in practice,energy efficiency(EE) for distributed antenna systems(DAS) based on imperfect CSI and antennas selection is investigated in Rayleigh fading channel.A novel EE that is defined as the average transmission rate divided by the total consumed power is introduced.In accordance with this definition,an adaptive power allocation(PA) scheme for DAS is proposed to maximize the EE under the maximum transmit power constraint.The solution of PA in the constrained EE optimization does exist and is unique.A practical iterative algorithm with Newton method is presented to obtain the solution of PA.The proposed scheme includes the one under perfect CSI as a special case,and it only needs large scale and statistical information.As a result,the scheme has low overhead and good robustness.The theoretical EE is also derived for performance evaluation,and simulation result shows the validity of the theoretical analysis.Moreover,EE can be enhanced by decreasing the estimation error and/or path loss exponents.
基金Project(61171133) supported by the National Natural Science Foundation of ChinaProject(11JJ1010) supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,China
文摘The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.
基金supported by Fundamental Research Program of Shanxi Province(Nos.202203021211088,202403021212254,202403021221109)Graduate Research Innovation Project in Shanxi Province(No.2024KY616).
文摘Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.
文摘This article conducts an analysis and exploration of the English subtitles translation of the domestic animated film Chang’an from the perspective of eco-translatology.Taking three-dimensional transformation as the entry point,it analyzes the adaptation and selection of subtitle translators in the translation process from the dimensions of language,culture,and communication in Chang’an,aiming to enrich the practical analysis of eco-translatology and provide valuable insights and references for the theoretical construction and practical application of eco-translatology.This article enriches the practical analysis of eco-translatology and also validates to a certain extent the applicability of“three-dimensional”transformation.
基金Supported by NSFC (Grant Nos. 72171216, 12231017, 71921001 and 71991474)the National Key R&D Program of China (Grant No. 2022YFA1003803)。
文摘Finding a highly interpretable nonlinear model has been an important yet challenging problem, and related research is relatively scarce in the current literature. To tackle this issue, we propose a new algorithm called Feat-ABESS based on a framework that utilizes feature transformation and selection for re-interpreting many machine learning algorithms. The core idea behind Feat-ABESS is to parameterize interpretable feature transformation within this framework and construct an objective function based on these parameters. This approach enables us to identify a proper interpretable feature transformation from the optimization perspective. By leveraging a recently advanced optimization technique, Feat-ABESS can obtain a concise and interpretable model. Moreover, Feat-ABESS can perform nonlinear variable selection. Our extensive experiments on 205 benchmark datasets and case studies on two datasets have demonstrated that Feat-ABESS can achieve powerful prediction accuracy while maintaining a high level of interpretability. The comparison with existing nonlinear variable selection methods exhibits Feat-ABESS has a higher true positive rate and a lower false discovery rate.
基金supported by the Important National Science and Technology Specific Projects of China(No.2009ZX01031-003-002)the National High Technology Research and Development Program of China(No.2009AA011605)the National Natural Science Foundation of China(No.61076028)
文摘This paper presents an algorithm that can adaptively select the intermediate frequency（IF） and compensate the IQ mismatch according to the power ratio of the adjacent channel interference to the desired signal in a low-IF GSM receiver.The IF can be adaptively selected between 100 and 130 kHz.Test result shows an improvement of phase error from 6.78°to 3.23°.Also a least mean squares（LMS） based IQ mismatch compensation algorithm is applied to improve image rejection ratio（IRR） for the desired signal along with strong adjacent channel interference.The IRR is improved from 29.1 to 44.3 dB in measurement.The design is verified in a low-IF GSM receiver fabricated in SMIC 0.13μm RF CMOS process with a working voltage of 1.2 V.
基金supported by the National Key R&D Program of China(2018AAA0101203)the National Natural Science Foundation of China(61673403,71601191)the JSPS KAKENHI(JP17K12751)。
文摘The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.
基金supported bythe 111 Project under Grant No. B08004the major project of Ministry of Industry and Information Technology of the People's Republic of China under Grant No. 2010ZX03002-006China Fundamental Research Funds for the Central Universities
文摘Error Estimating Code (EEC) is a new channel coding method to estimate the Bit Error Rate (BER) information of the transmitted sequence. However, the estimated BER is not precise enough if the practical value of BER is high. A weighted EEC estimation method is proposed to improve the accuracy performance of BER estimation by classifying the raw estimation results into intervals and multiplying them by different coefficients separately. The applications of weighted EEC in modulation selection scheme and distributed video coding are discussed. Simulation results show that the EEC-based modulation selection method can achieve better performance at a cost of little redundancy and computation, and the EEC-based rate estimation method in distributed video coding can save the decoding time.
基金Supported by Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China(Grant Nos.10XNL018,10XNK025)National Natural Science Foundation of China(Grant No.11271368)+3 种基金Beijing Planning Office of Philosophy and Social Science(Grant No.12JGB051)China Statistical Research Project(Grant No.2011LZ031)Project of Ministry of Education supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20130004110007)the Key Program of National Philosophy and Social Science Foundation Grant(No.13AZD064)
文摘In this article, we consider a class of kernel quantile estimators which is the linear combi- nation of order statistics. This class of kernel quantile estimators can be regarded as an extension of some existing estimators. The exact mean square error expression for this class of estimators will be provided when data are uniformly distributed. The implementation of these estimators depends mostly on the bandwidth selection. We then develop an adaptive method for bandwidth selection based on the intersection confidence intervals （ICI） principle. Monte Carlo studies demonstrate that our proposed approach is comparatively remarkable. We illustrate our method with a real data set.
文摘Mark Bender and Aku Wuwu translate Hnewo Teyy,a crucial epic for Nuosu people from Yi to English directly and entirely for the first time.Their translation and introduction of the epic are involved in The Nuosu Book of Origins,in which Bender concerns plants and animals.This paper mainly analyses Bender’s adaptation and selection concerning his investigation and translation of plants and animals under the guidance of Eco-translatology.Significantly,Eco-translatology and Bender’s translation and introduction provide enlightenment to the transmission of Chinese culture.
文摘HBO TV series–Game of Thrones has gone viral ever since its first release in 2011 and its soaring popularity among Chineseaudience has no doubt benefited from the"appropriate"E-C subtitles attached to it. This paper, through the analysis and discussion ofthe E-C subtitles of Game of Thrones from the perspective of Eco-translatology, demonstrates that the approach of Translation as Adap-tation and Selection(TAS) proves to be operational in subtitle translation, and gives further recommendations in the communicativetransformation of subtitle translation.
文摘On the framework of eco- translatology, this thesis presents an introduction of the novel Fortress Besieged, the author and the translation work, and then gives an analysis of the translator's adaptation and selection of the translational eco-environment.
文摘The importance of an interdisciplinary investigation into the translation of publicity texts cannot be overestimated at a time when the role of international communication grows rapidly,which in turn pushes the development of globalization.Ecotranslatology takes the concept of"adaptation/selection"of Darwin’s Evolution Theory as its theoretical foundation,focusing on the integrity of the translation ecosystem,making a novel description and interpretation to the nature,process,standard,principles,methods of translation and translation phenomena from the perspective of Eco-translatology.Though the construct of the theory keeps expanding,there is still a lack of systematic discussion on translation activities with focus on the integrity and harmony in Eco-translatology.This study therefore aims to promote the need of researching publicity text translation from an Ecotranslatological perspective through the review of the existing literature in this field.
基金supported by grants from the National Key R&D Program of China(No.2021YFD1700101)the National Natural Science Foundation of China(No.32130091 and No.32001923)+2 种基金the science And and Technology Innovation Program of Hunan Province (No.2020WK2014 and No.2020WK2023)the Training Program for Excellent Young Innovators of Changsha(kg2106079)the China Agriculture Research System of MOF and MARA(CARS-16-E19)。
文摘Chinese sprangletop (Leptochloa chinensis), belonging to the grass subfamily Chloridoideae, is one of the most notorious weeds in rice ecosystems. Here, we report a chromosome-scale reference genome assembly and a genomic variation map of the tetraploid L. chinensis. The L. chinensis genome is derived from two diploid progenitors that diverged ∼10.9 million years ago, and its two subgenomes display neither fractionation bias nor overall gene expression dominance. Comparative genomic analyses reveal substantial genome rearrangements in L. chinensis after its divergence from the common ancestor of Chloridoideae and, together with transcriptome profiling, demonstrate the important contribution of tetraploidization to the gene sources for the herbicide resistance of L. chinensis. Population genomic analyses of 89 accessions from China reveal that L. chinensis accessions collected from southern/southwestern provinces have substantially higher nucleotide diversity than those from the middle and lower reaches of the Yangtze River, suggesting that L. chinensis spread in China from the southern/southwestern provinces to the middle and lower reaches of the Yangtze River. During this spread, L. chinensis developed significantly increased herbicide resistance, accompanied by the selection of numerous genes involved in herbicide resistance. Taken together, our study generated valuable genomic resources for future fundamental research and agricultural management of L. chinensis, and provides significant new insights into the herbicide resistance as well as the origin and adaptive evolution of L. chinensis.
基金supported by the National Natural Science Foundation of China(52072081)Major Project of Science and Technology of Guangxi Province of China(Guike AB23075209)+2 种基金Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund(24050-44-S015)Innovation Project of Guangxi Graduate Education(YCSW2024135)Major Talent Project in Guangxi Zhuang Autonomous Region。
文摘With the increasing data volume of train on-board system,real-time performance has become the most critical factor to ensure the safety of train operation.Considering that standard Ethernet cannot meet the real-time requirement of existing train communication network(TCN),the time-sensitive network(TSN)technology for TCN is introduced.To solve the time-delay problem,an adaptive switch queue selection mechanism for traffic scheduling is proposed.Firstly,the topology model of TCN based on TSN and the traffic model are described.Then,the K shortest path routing algorithm based on load balancing provides the optimal routing for the scheduling process.Finally,the adaptive switch queue selection mechanism is introduced to solve the aggregation flow conflict problem effectively,queue resources are properly allocated,and the gate control list(GCL)of each frame in the queue is obtained.Experimental results show that compared with the traditional constraint model,the schedulability of the model with an adaptive switch queue selection mechanism increases by 33.0%,and the maximum end-to-end delay and network jitter decrease by 19.1%and 18.6%on average respectively.It can provide theoretical support and application reference for the real-time performance optimization of TCN based on TSN.
