In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study pr...In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.展开更多
In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in faci...In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.展开更多
Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places.However,existing methods of the guardrail layout mainly rely on manual design or mathem...Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places.However,existing methods of the guardrail layout mainly rely on manual design or mathematical models,which are not flexible or effective enough for crowd control in large public places.To address this limitation,this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm.The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout.In addition to optimizing pedestrian passage efficiency and safety,the paper also considers the situation of pedestnan counterflow,making the guardrai layout more practical.Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion,as well as its significant improvements in passage effciency and prevention of pedestrian counte rflow.展开更多
Internal vibrations underlie transient structure formation,spectroscopy,and dynamics.However,at least two challenges exist when aiming to elucidate the contributions of vibrational motions on the potential energy surf...Internal vibrations underlie transient structure formation,spectroscopy,and dynamics.However,at least two challenges exist when aiming to elucidate the contributions of vibrational motions on the potential energy surfaces.One is the acquisition of well-resolved experimental infrared spectra,and the other is the development of efficient theoretical methodologies that reliably predict band positions,relative intensities,and substructures.Here,we report size-specific infrared spectra of ammonia clusters to address these two challenges.Unprecedented agreement between experiment and state-of-the-art quantum simulations reveals that the vibrational spectra are mainly contributed by proton-donor ammonia.A striking Fermi resonance observed at approximately 3210 and 3250 cm^(−1)originates from the coupling of NH symmetric stretch fundamentals with overtones of free and hydrogen-bonded NH bending,respectively.These novel,intriguing findings contribute to a better understanding of vibrational motions in a large variety of hydrogen-bonded complexes with orders of magnitude improvements in spectral resolution,efficiency,and specificity.展开更多
基金National Natural Science Foundation Major Project of China,Grant/Award Number:42192580Guangdong Province Key Construction Discipline Scientific Research Ability Promotion Project,Grant/Award Number:2022ZDJS015。
文摘In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.
基金supported in part by the Guangzhou Science and Technology Plan Project under Grants 2024B03J1361,2023B03J1327,and 2023A04J0361in part by the Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control under Grant OHIC2023Y10+3 种基金in part by the Guangdong Province Ordinary Colleges and Universities Young Innovative Talents Project under Grant 2023KQNCX036in part by the Special Fund for Science and Technology Innovation Strategy of Guangdong Province(Climbing Plan)under Grant pdjh2024a226in part by the Key Discipline Improvement Project of Guangdong Province under Grant 2022ZDJS015in part by theResearch Fund of Guangdong Polytechnic Normal University under Grants 22GPNUZDJS17 and 2022SDKYA015.
文摘In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.
基金supported in part by the National Natural Science Foundation of China under Grant 62076098in part by the Guangdong Basic and Applied Basic Research Foundation under Grants 2021A1515110072 and 2023A1515012291+2 种基金in part by the Key Research and Development Program of JiangXi Province under Grant 20212BBE53002in part by the Key Research and Development Program of YiChun City under Grant 20211YFG4270in part by the Special Projects in Key Fields of Ordinary Universities of Guangdong Province under Grant 2021ZDZX1087.Besides,the authors express sincere gratitude to the Transportation Operations Coordination Center(TOCC)in Chengdu for their invaluable support in conducting this research。
文摘Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places.However,existing methods of the guardrail layout mainly rely on manual design or mathematical models,which are not flexible or effective enough for crowd control in large public places.To address this limitation,this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm.The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout.In addition to optimizing pedestrian passage efficiency and safety,the paper also considers the situation of pedestnan counterflow,making the guardrai layout more practical.Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion,as well as its significant improvements in passage effciency and prevention of pedestrian counte rflow.
基金supported by the National Natural Science Foundation of China(grant nos.21673231 and 21688102)the Strategic Priority Research Program of the Chinese Academy of Sciences(CAS)(grant no.XDB17000000)+3 种基金the Dalian Institute of Chemical Physics(DICP DCLS201702)K.C.Wong Education Foundation(GJTD-2018-06)supported by the Ministry of Science and Technology of Taiwan(MOST-106-2811-M-001-051 and MOST-107-2628-M-001-002-MY4)the Academia Sinica.
文摘Internal vibrations underlie transient structure formation,spectroscopy,and dynamics.However,at least two challenges exist when aiming to elucidate the contributions of vibrational motions on the potential energy surfaces.One is the acquisition of well-resolved experimental infrared spectra,and the other is the development of efficient theoretical methodologies that reliably predict band positions,relative intensities,and substructures.Here,we report size-specific infrared spectra of ammonia clusters to address these two challenges.Unprecedented agreement between experiment and state-of-the-art quantum simulations reveals that the vibrational spectra are mainly contributed by proton-donor ammonia.A striking Fermi resonance observed at approximately 3210 and 3250 cm^(−1)originates from the coupling of NH symmetric stretch fundamentals with overtones of free and hydrogen-bonded NH bending,respectively.These novel,intriguing findings contribute to a better understanding of vibrational motions in a large variety of hydrogen-bonded complexes with orders of magnitude improvements in spectral resolution,efficiency,and specificity.
