Machine learning has revolutionized many fields,including materials science.However,predicting the properties of crystalline materials using machine learning faces challenges in input encoding,output versatility,and i...Machine learning has revolutionized many fields,including materials science.However,predicting the properties of crystalline materials using machine learning faces challenges in input encoding,output versatility,and interpretability.We introduce Crystal BERT,an adaptable transformer-based framework integrating space group,elemental,and unit cell information.This novel structure can seamlessly combine diverse features and accurately predict various physical properties,including topological properties,superconducting transition temperatures,dielectric constants,and more.Crystal BERT provides insightful interpretations of features influencing target properties.Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties,underscoring their intricate nature.By incorporating these features,we achieve91%accuracy in topological classification,surpassing prior studies and identifying previously misclassified materials.This research demonstrates that integrating diverse material information enhances the prediction of complex material properties,paving the way for more accurate and interpretable machine learning models in materials science.展开更多
The inherent challenges arising from variations in user-captured viewpoints and object orientation disparities in real-world scenarios pose significant difficulties in establishing robust correspondence relationships ...The inherent challenges arising from variations in user-captured viewpoints and object orientation disparities in real-world scenarios pose significant difficulties in establishing robust correspondence relationships between image pairs.Methods based on geometric transformation estimation usually perform affine transformation of the global image for viewpoint correction,which not only increases the time complexity but also generates a large number of redundant features.To solve this problem,this paper proposes an adaptive affine transformation model(AATM)to achieve robust image matching by dividing special regions with pixel information and employing feature extraction algorithms with different granularities.First,the input image is divided into significant and non-significant regions by an adaptive algorithm.Second,for the salient region,the feature point extraction is accelerated by optimizing the longitude angle sampling algorithm and constructing the affine invariant nonlinear scale space,introducing the Hessian integral image and box filter.Then,for the non-significant region of the weak texture scene through the uniform step sampling algorithm,a dense feature description can be obtained in the weak texture scenes,so that more robust features are extracted for both significant and non-significant regions.The results of extensive experiments on two datasets show that the AATM algorithm outperforms similar algorithms in terms of the number of correctly matched pairs,elapsed time,and root mean square error(RMSE),indicating that the AATM can obtain more robust matches in scenes with large angle tilting and scale transformations.展开更多
The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic set...The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.展开更多
We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractiona...We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractional domain and improved the time-frequency concentration of the Gabor transform. The algorithm first searches for the optimal rotation factor, then performs the p-th FrFT of the signal and, finally, performs time and frequency analysis of the FrFT result. Finally, the algorithm rotates the plane in the fractional domain back to the normal time-frequency plane. This promotes the application of FrFT in the field of high-resolution reservoir prediction. Additionally, we proposed an adaptive search method for the optimal rotation factor using the Parseval principle in the fractional domain, which simplifies the algorithm. We carried out spectrum decomposition of the seismic signal, which showed that the instantaneous frequency slices obtained by the proposed algorithm are superior to the ones obtained by the traditional Gabor transform. The adaptive time frequency analysis is of great significance to seismic signal processing.展开更多
Agricultural production(especially intensive rice production)is a primary income source for over 2.0×10^(7) people in the Vietnamese Mekong River Delta.However,adverse climate change impacts,socio-economic change...Agricultural production(especially intensive rice production)is a primary income source for over 2.0×10^(7) people in the Vietnamese Mekong River Delta.However,adverse climate change impacts,socio-economic change,and high dependence on farm inputs for intensive production constrain the longer-term sustainability of rice systems.Government and agribusiness actors are encouraging more farmers to grow non-rice crops and supporting the upscaling of alternative crops to paddy rice.We used a qualitative approach to investigate the value chain characteristics,as well as constraints and opportunities of alternative crops via two case studies(baby corn and honeydew melon)in An Giang and Hau Giang provinces,Vietnam.Data collection involved focus group discussions with local farmers and interviews with farmers and industry experts.Thematic analysis was used to compile the findings,and the results were validated with local government staff.The baby corn value chain featured on-going and stable market demand(including value-addition)and better vertical coordination(e.g.,written contracts and financial support).The honeydew melon value chain featured positive relationships between farmers and traders despite less-developed vertical coordination.There are opportunities for value chain engagement through product quality certification,value-addition,and accessing high-value domestic and export markets.However,farmers require crop-specific and generic support from private and public sectors.Increased labour requirements and limited access to finance and credit limit value chain participation.Upscaling and marketing alternative crops can enhance farmer profitability and support non-farming agricultural business establishment,economic growth,and community development.Efficient value chains will be critical to ensure the adoption of alternative crops and development of crop-specific agribusiness models.These findings can inform policy-makers and change facilitators in designing targeted interventions to support the adoption of alternative crops in the study area as well as in Vietnam and globally.展开更多
The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deterior...The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.展开更多
The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Consi...The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.展开更多
Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation...Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.展开更多
The ability to accurately classify fault type within traveling wave data is crucial for real-time online fault location and protection using traveling wave technology.However,the current common practice in the power i...The ability to accurately classify fault type within traveling wave data is crucial for real-time online fault location and protection using traveling wave technology.However,the current common practice in the power industry relies on manual data screening followed by offline processing,leading to several limitations such as poor timeliness,low accuracy,and high skill requirements for operators.These drawbacks restrict the application of traveling wave acquisition devices.To address these issues,this paper proposes a fault identification method for measuring the traveling wave of transmission lines based on the CSCRFAM-Transformer.Firstly,CSCRFAM is used to encode the temporal and spatial information of the measured traveling wave data.Next,pixel-level features are further aggregated through dimensional interaction.Then,an adaptive encoding hierarchy Transformer adjustment mechanism is employed to extract multi-level differentiated traveling wave high-frequency information from the aggregated features to complete fault identification.This method combines the dimensional interaction of the EMA mechanism and the self-attention mechanism of the Transformer’s sensitivity to the spatiotemporal characteristics of traveling waves.The proposed method is trained and tested using a massive dataset of 396672 measured samples from 110 kV to 220 kV transmission lines in Yunnan Power Grid.The method is used to identify,classify,test,and compare four distinct types of traveling wave data.The obtained results show that the method reduces the number of model parameters and improves the identification accuracy.The mAUC,Accuracy,Precision,and F1 values of the algorithm reach 0.969,0.969,0.965,and 0.957,respectively,indicating better detection accuracy and identification efficiency.展开更多
Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages suc...Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.展开更多
In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency （IF） character of complex vibration signal. The improved IF estimation method is put forwar...In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency （IF） character of complex vibration signal. The improved IF estimation method is put forward aiming at the shortage of traditional Hilbert transform. It is based on Hilbert transform in wavelet domain. With the help of relationship between the real part and the imaginary part obtained from the complex coefficient of continuous wavelet transform or the analyti- cal signal reconstructed in wavelet packet decomposition, the instantaneous phase function of the subcomponent is extracted. In order to improve the precise of IF estimated out, some means such as Linear regression, adaptive filtering, resampling are applied into the instantaneous phase obtained, then, the central differencing operator is used to get desired IF. Simulation results with synthetic and gearbox fault signals are included to illustrate the proposed method.展开更多
In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation （CPPM） and linear frequency modulation （LFM）, which can be widely used in electronic count...In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation （CPPM） and linear frequency modulation （LFM）, which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval （PRI） of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform （CHT） algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio （SNR） is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.展开更多
Along with climate change and global warming, ESLEs （extreme sea level events） are seriously threatening coastal cities＇ development. In order to respond to such events, transformational adaptation strategy in urba...Along with climate change and global warming, ESLEs （extreme sea level events） are seriously threatening coastal cities＇ development. In order to respond to such events, transformational adaptation strategy in urban planning might play an important role. For instance, it has been proposed that BCR （building coverage ratio） should be minimized to a certain range in order to enhance coastal areas＇ resiliency. For the purpose of urban planning practices, the main objective of this research is to develop a method which could formulate the proper BCR range in vulnerable coastal areas. The research is conducted through simulating storm surge floods in simplified waterfront settlements with different BCRs. Data representing the impact of ESLEs collected through CFD （computational fluid dynamic） simulations has been examined. This research has proved that in dense coastal areas, ESLEs may cause serious damage to the built environment if their protective structures fail. It showed that controlling BCR is an effective way to enhance their resiliency. When the BCR is low, the pressure caused by storm surge floods and wave height can be greatly reduced. However, decreased BCR may also reduce land utilization efficiency. Simulation results indicated that controlling the BCR to around 36% might be the most effective scenario which balances resiliency and land use efficiency. They also showed that under the same storm surge flood scenario, the pressures caused by flood waves could be reduced if the length of the building is increased. This study might be considered as transformational adaptation measures that contributes some knowledge for waterfront development in vulnerable locations, and it also provides scientific and useful proof for sustainable strategies in coastal cities and reveals that particular urban design tools, such as BCR control, could play an essential role in responding to ESLEs.展开更多
Based on relevant theories of eco-translatology,this paper analyzes the translation of neologisms in the English book,The Money Is Coming from the perspective of adaptive transformation from three basic dimensions-the...Based on relevant theories of eco-translatology,this paper analyzes the translation of neologisms in the English book,The Money Is Coming from the perspective of adaptive transformation from three basic dimensions-the linguistic,cultural,and communicative dimensions.展开更多
This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using A...This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.展开更多
Subject Code:B01With the financial support from the National Natural Science Foundation of China,the research group led by Prof.Sun Qingfu(孙庆福)from the Fujian Institute of Research on the Structure of Matter,Chines...Subject Code:B01With the financial support from the National Natural Science Foundation of China,the research group led by Prof.Sun Qingfu(孙庆福)from the Fujian Institute of Research on the Structure of Matter,Chinese Academy of Sciences has recently reported a dynamic anion-adaptive self-assembly system展开更多
This paper proposes two unconstrained algorithms, the Steepest Decent (SD)algorithm and the Conjugate Gradient (CG) algorithm, based on a superexcellent cost function. At thesame time, two constrained algorithms which...This paper proposes two unconstrained algorithms, the Steepest Decent (SD)algorithm and the Conjugate Gradient (CG) algorithm, based on a superexcellent cost function. At thesame time, two constrained algorithms which include the Constrained Steepest Decent (CSD) algorithmand the Constrained Conjugate Gradient algorithm (CCG) are deduced subject to a new constraincondition. They are both implemented in unitary transform domain. The computational complexities ofthe constrained algorithms are compared to those of the unconstrained algorithms. Resultingsimulations show their performance comparisons.展开更多
基金supported by the Natural Science Foundation of China(Grant Nos.12350404 and 12174066)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302600)+2 种基金the Science and Technology Commission of Shanghai Municipality(Grant Nos.23JC1400600,24LZ1400100,and 2019SHZDZX01)sponsored by“Shuguang Program”supported by Shanghai Education Development FoundationShanghai Municipal Education Commission。
文摘Machine learning has revolutionized many fields,including materials science.However,predicting the properties of crystalline materials using machine learning faces challenges in input encoding,output versatility,and interpretability.We introduce Crystal BERT,an adaptable transformer-based framework integrating space group,elemental,and unit cell information.This novel structure can seamlessly combine diverse features and accurately predict various physical properties,including topological properties,superconducting transition temperatures,dielectric constants,and more.Crystal BERT provides insightful interpretations of features influencing target properties.Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties,underscoring their intricate nature.By incorporating these features,we achieve91%accuracy in topological classification,surpassing prior studies and identifying previously misclassified materials.This research demonstrates that integrating diverse material information enhances the prediction of complex material properties,paving the way for more accurate and interpretable machine learning models in materials science.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024 F023)+1 种基金the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050)the Open Research Fund of National Mobile Communications Research Laboratory in Southeast University(No.2023D07).
文摘The inherent challenges arising from variations in user-captured viewpoints and object orientation disparities in real-world scenarios pose significant difficulties in establishing robust correspondence relationships between image pairs.Methods based on geometric transformation estimation usually perform affine transformation of the global image for viewpoint correction,which not only increases the time complexity but also generates a large number of redundant features.To solve this problem,this paper proposes an adaptive affine transformation model(AATM)to achieve robust image matching by dividing special regions with pixel information and employing feature extraction algorithms with different granularities.First,the input image is divided into significant and non-significant regions by an adaptive algorithm.Second,for the salient region,the feature point extraction is accelerated by optimizing the longitude angle sampling algorithm and constructing the affine invariant nonlinear scale space,introducing the Hessian integral image and box filter.Then,for the non-significant region of the weak texture scene through the uniform step sampling algorithm,a dense feature description can be obtained in the weak texture scenes,so that more robust features are extracted for both significant and non-significant regions.The results of extensive experiments on two datasets show that the AATM algorithm outperforms similar algorithms in terms of the number of correctly matched pairs,elapsed time,and root mean square error(RMSE),indicating that the AATM can obtain more robust matches in scenes with large angle tilting and scale transformations.
基金supported in part by the National Science and Technol-ogy Major Project(No.2021ZD0111502)the National Nat-ural Science Foundation of China(Nos.62176147,62476163)+2 种基金the Science and Technology Planning Project of Guangdong Province of China(Nos.2022A1515110660,2021JC06X549)the STU Scientific Research Foundation for Talents(No.NTF21001)Guangdong Basic and Applied Basic Research Foundation(No.2023B1515120020)。
文摘The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.
基金supported by national natural science foundation of China(No.41274127,41301460,40874066,and 40839905)
文摘We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractional domain and improved the time-frequency concentration of the Gabor transform. The algorithm first searches for the optimal rotation factor, then performs the p-th FrFT of the signal and, finally, performs time and frequency analysis of the FrFT result. Finally, the algorithm rotates the plane in the fractional domain back to the normal time-frequency plane. This promotes the application of FrFT in the field of high-resolution reservoir prediction. Additionally, we proposed an adaptive search method for the optimal rotation factor using the Parseval principle in the fractional domain, which simplifies the algorithm. We carried out spectrum decomposition of the seismic signal, which showed that the instantaneous frequency slices obtained by the proposed algorithm are superior to the ones obtained by the traditional Gabor transform. The adaptive time frequency analysis is of great significance to seismic signal processing.
基金funded by the 2022-2023 Australian Centre for International Agricultural Research(ACIAR)Alumni Research Support Facility(ARSF)programme:“Farmers’adaptive capacity and agricultural transformation in the Vietnamese Mekong Delta:understanding and supporting value chain engagement”the ACIAR-funded project“Farmer options for crops under saline conditions(FOCUS)in the Mekong River Delta,Vietnam”(SLaM/2018/144)to this study.
文摘Agricultural production(especially intensive rice production)is a primary income source for over 2.0×10^(7) people in the Vietnamese Mekong River Delta.However,adverse climate change impacts,socio-economic change,and high dependence on farm inputs for intensive production constrain the longer-term sustainability of rice systems.Government and agribusiness actors are encouraging more farmers to grow non-rice crops and supporting the upscaling of alternative crops to paddy rice.We used a qualitative approach to investigate the value chain characteristics,as well as constraints and opportunities of alternative crops via two case studies(baby corn and honeydew melon)in An Giang and Hau Giang provinces,Vietnam.Data collection involved focus group discussions with local farmers and interviews with farmers and industry experts.Thematic analysis was used to compile the findings,and the results were validated with local government staff.The baby corn value chain featured on-going and stable market demand(including value-addition)and better vertical coordination(e.g.,written contracts and financial support).The honeydew melon value chain featured positive relationships between farmers and traders despite less-developed vertical coordination.There are opportunities for value chain engagement through product quality certification,value-addition,and accessing high-value domestic and export markets.However,farmers require crop-specific and generic support from private and public sectors.Increased labour requirements and limited access to finance and credit limit value chain participation.Upscaling and marketing alternative crops can enhance farmer profitability and support non-farming agricultural business establishment,economic growth,and community development.Efficient value chains will be critical to ensure the adoption of alternative crops and development of crop-specific agribusiness models.These findings can inform policy-makers and change facilitators in designing targeted interventions to support the adoption of alternative crops in the study area as well as in Vietnam and globally.
文摘The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.
基金This research is supported by the National Science and Technology Major Project of China(No.2011ZX05024-001-03)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2021JQ-588)Innovation Fund for graduate students of Xi’an Shiyou University(No.YCS17111017).
文摘The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.
基金Supported by the National Natural Science Foundation of China,no.69672039
文摘Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.
基金supported by the Key Program of National Science Foundation of China:Research on the Basic Theory and Method of Multiple Lightning Stroke Identification and Protection for Transmission Lines in Plateau Mountainous Areas(No.52337005).
文摘The ability to accurately classify fault type within traveling wave data is crucial for real-time online fault location and protection using traveling wave technology.However,the current common practice in the power industry relies on manual data screening followed by offline processing,leading to several limitations such as poor timeliness,low accuracy,and high skill requirements for operators.These drawbacks restrict the application of traveling wave acquisition devices.To address these issues,this paper proposes a fault identification method for measuring the traveling wave of transmission lines based on the CSCRFAM-Transformer.Firstly,CSCRFAM is used to encode the temporal and spatial information of the measured traveling wave data.Next,pixel-level features are further aggregated through dimensional interaction.Then,an adaptive encoding hierarchy Transformer adjustment mechanism is employed to extract multi-level differentiated traveling wave high-frequency information from the aggregated features to complete fault identification.This method combines the dimensional interaction of the EMA mechanism and the self-attention mechanism of the Transformer’s sensitivity to the spatiotemporal characteristics of traveling waves.The proposed method is trained and tested using a massive dataset of 396672 measured samples from 110 kV to 220 kV transmission lines in Yunnan Power Grid.The method is used to identify,classify,test,and compare four distinct types of traveling wave data.The obtained results show that the method reduces the number of model parameters and improves the identification accuracy.The mAUC,Accuracy,Precision,and F1 values of the algorithm reach 0.969,0.969,0.965,and 0.957,respectively,indicating better detection accuracy and identification efficiency.
基金supported by the NSFC (Grant Nos. 61772281,61703212, 61602254)Jiangsu Province Natural Science Foundation [grant numberBK2160968]the Priority Academic Program Development of Jiangsu Higher Edu-cationInstitutions (PAPD) and Jiangsu Collaborative Innovation Center on AtmosphericEnvironment and Equipment Technology (CICAEET).
文摘Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.
基金This project is supported by National Natural Science Foundation of China (No.50605065)Natural Science Foundation Project of CQ CSTC(No.2007BB2142).
文摘In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency （IF） character of complex vibration signal. The improved IF estimation method is put forward aiming at the shortage of traditional Hilbert transform. It is based on Hilbert transform in wavelet domain. With the help of relationship between the real part and the imaginary part obtained from the complex coefficient of continuous wavelet transform or the analyti- cal signal reconstructed in wavelet packet decomposition, the instantaneous phase function of the subcomponent is extracted. In order to improve the precise of IF estimated out, some means such as Linear regression, adaptive filtering, resampling are applied into the instantaneous phase obtained, then, the central differencing operator is used to get desired IF. Simulation results with synthetic and gearbox fault signals are included to illustrate the proposed method.
基金supported by the National Natural Science Foundation of China under Grant 61172116
文摘In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation （CPPM） and linear frequency modulation （LFM）, which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval （PRI） of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform （CHT） algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio （SNR） is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.
文摘Along with climate change and global warming, ESLEs （extreme sea level events） are seriously threatening coastal cities＇ development. In order to respond to such events, transformational adaptation strategy in urban planning might play an important role. For instance, it has been proposed that BCR （building coverage ratio） should be minimized to a certain range in order to enhance coastal areas＇ resiliency. For the purpose of urban planning practices, the main objective of this research is to develop a method which could formulate the proper BCR range in vulnerable coastal areas. The research is conducted through simulating storm surge floods in simplified waterfront settlements with different BCRs. Data representing the impact of ESLEs collected through CFD （computational fluid dynamic） simulations has been examined. This research has proved that in dense coastal areas, ESLEs may cause serious damage to the built environment if their protective structures fail. It showed that controlling BCR is an effective way to enhance their resiliency. When the BCR is low, the pressure caused by storm surge floods and wave height can be greatly reduced. However, decreased BCR may also reduce land utilization efficiency. Simulation results indicated that controlling the BCR to around 36% might be the most effective scenario which balances resiliency and land use efficiency. They also showed that under the same storm surge flood scenario, the pressures caused by flood waves could be reduced if the length of the building is increased. This study might be considered as transformational adaptation measures that contributes some knowledge for waterfront development in vulnerable locations, and it also provides scientific and useful proof for sustainable strategies in coastal cities and reveals that particular urban design tools, such as BCR control, could play an essential role in responding to ESLEs.
文摘Based on relevant theories of eco-translatology,this paper analyzes the translation of neologisms in the English book,The Money Is Coming from the perspective of adaptive transformation from three basic dimensions-the linguistic,cultural,and communicative dimensions.
基金Supported by the National Natural Science Foundation of China (No. 60872105)the Program for Science & Technology Innovative Research Team of Qing Lan Project in Higher Educational Institutions of Jiangsuthe Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.
文摘Subject Code:B01With the financial support from the National Natural Science Foundation of China,the research group led by Prof.Sun Qingfu(孙庆福)from the Fujian Institute of Research on the Structure of Matter,Chinese Academy of Sciences has recently reported a dynamic anion-adaptive self-assembly system
文摘This paper proposes two unconstrained algorithms, the Steepest Decent (SD)algorithm and the Conjugate Gradient (CG) algorithm, based on a superexcellent cost function. At thesame time, two constrained algorithms which include the Constrained Steepest Decent (CSD) algorithmand the Constrained Conjugate Gradient algorithm (CCG) are deduced subject to a new constraincondition. They are both implemented in unitary transform domain. The computational complexities ofthe constrained algorithms are compared to those of the unconstrained algorithms. Resultingsimulations show their performance comparisons.
