The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focus...The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.展开更多
The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant par...The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.展开更多
Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern clas...Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.展开更多
This paper develops a deep learning tool based on neural processes(NPs)called the Peri-Net-Pro,to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified u...This paper develops a deep learning tool based on neural processes(NPs)called the Peri-Net-Pro,to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified uncertainties.In particular,image classification and regression studies are conducted by means of convolutional neural networks(CNNs)and NPs.First,the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method(FEM)in generating crack pattern images.Second,case studies are conducted with the prototype microelastic brittle(PMB),linear peridynamic solid(LPS),and viscoelastic solid(VES)models obtained by using the peridynamic theory.The case studies are performed to classify the images by using CNNs and determine the suitability of the PMB,LBS,and VES models.Finally,a regression analysis is performed on the crack pattern images with NPs to predict the crack patterns.The regression analysis results confirm that the variance decreases when the number of epochs increases by using the NPs.The training results gradually improve,and the variance ranges decrease to less than 0.035.The main finding of this study is that the NPs enable accurate predictions,even with missing or insufficient training data.The results demonstrate that if the context points are set to the 10th,100th,300th,and 784th,the training information is deliberately omitted for the context points of the 10th,100th,and 300th,and the predictions are different when the context points are significantly lower.However,the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the NPs.Therefore,if the NPs are employed for training,the missing information of the training data can be supplemented to predict the results.展开更多
Chip designers employ computer-aided design,circuit simulation,and design rule check systems.Lithography engineers employ model-based OPC(Optical Proximity Correction)and model-based print-simulation systems.Reticle i...Chip designers employ computer-aided design,circuit simulation,and design rule check systems.Lithography engineers employ model-based OPC(Optical Proximity Correction)and model-based print-simulation systems.Reticle inspection teams employ Aerial Image Measurement Systems®and Virtual Stepper®Systems.These teams are accustomed to evaluating and deploying state-of-the-art computational systems.When real-silicon fabrication begins,however,the teams responsible for line monitoring,wafer inspection,and yield attainment operate without the benefit of similarly advanced computational systems.In this paper we describe such a system and explore its applications and benefits.The system has received three U.S.patents[1-3]and brings together the significant potential of CAD(Computer Aided Design)layout(GDS,OASIS),Die-to-Database,and Machine Learning to build a dynamic,self-improving computational system.Featuring care area generation,advanced machine learning-based SEM(Scanning Electron Microscope)sampling that optimizes both DOI(Defect of Interest)capture rate and discovery of new defect types,comprehensive extraction of all Information of Interest(IOI)from all SEM images,detection of defect types not possible before,massive pattern fidelity analysis,full chip pattern decomposition and risk scoring via machine learning,innovative PWQ(Process Window Qualification)analysis and process window determination,risk assessment of new tape-outs,large scale in-wafer OPC verification and more,the system delivers a comprehensive pattern centric platform for process technology development and manufacturing.展开更多
Algorithm of STA/LTA is frequently used in automatic signal detection, in which the range of detection threshold is （0, ∞）, the optimal threshold should be determined by experiment to make a balance between false d...Algorithm of STA/LTA is frequently used in automatic signal detection, in which the range of detection threshold is （0, ∞）, the optimal threshold should be determined by experiment to make a balance between false detection and missing detection. By using the theory of pattern recognition, a new algorithm for automatic signal detection based on support vector machine was proposed and the method of preprocess and pattern feature extraction were dis- cussed as well as the selection of kernel function for support vector machine. The detection performance of the new algorithm was analyzed by means of real seismic data. The experiments showed that the new method could simplify the selection of threshold and detect signal accurately. In addition to the better performance of anti-noise, the ratio of false detection could decrease 85% in comparison with that of STA/LTA.展开更多
受气候变化和城市规模不断扩张等因素影响,城市地区降雨过程的时空动态性愈发明显,通常采用的点、面雨量等表达方式难以体现这种时空动态性。随着降雨观测技术的进步,大多城市积累了长序列降雨观测数据,其中蕴含了丰富的降雨时空过程信...受气候变化和城市规模不断扩张等因素影响,城市地区降雨过程的时空动态性愈发明显,通常采用的点、面雨量等表达方式难以体现这种时空动态性。随着降雨观测技术的进步,大多城市积累了长序列降雨观测数据,其中蕴含了丰富的降雨时空过程信息,为预报降雨或设计降雨的时空展布提供了可能。设计了一套降雨时空展布方法,包括数据收集、标准化处理、降雨场次划分、时空模式提取、标准网格插值、时空展布等环节,并提出每个环节的处理步骤和关键问题。以北京市为例,对该技术方法进行了验证,选择了12 h、24 h、72 h 3个历时的场次降雨,提取的时空模式和展布结果表达出了场次降雨的时空动态性,并与历史的降雨过程表现出很好的匹配性。表明该方法可用于城市地区降雨典型模式提取,以及降雨过程的时空展布。该方法利用历史降雨数据提取降雨展布模板,输入场次降雨(或预报降雨)的总雨量,得到该场次降雨的时空分布过程,展布结果可为洪水预报提供更为准确的降雨输入条件。展开更多
文摘The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.
文摘The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A5A8033165)the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and was granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(No.20214000000200).
文摘Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.
基金Project supported by the National Science Foundation of U.S.A.(Nos.DMS-1555072,DMS-2053746DMS-2134209)+1 种基金the Brookhaven National Laboratory of U.S.A.(No.382247)U.S.Department of Energy(DOE)Office of Science Advanced Scientific Computing Research Program(Nos.DESC0021142 and DE-SC0023161)。
文摘This paper develops a deep learning tool based on neural processes(NPs)called the Peri-Net-Pro,to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified uncertainties.In particular,image classification and regression studies are conducted by means of convolutional neural networks(CNNs)and NPs.First,the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method(FEM)in generating crack pattern images.Second,case studies are conducted with the prototype microelastic brittle(PMB),linear peridynamic solid(LPS),and viscoelastic solid(VES)models obtained by using the peridynamic theory.The case studies are performed to classify the images by using CNNs and determine the suitability of the PMB,LBS,and VES models.Finally,a regression analysis is performed on the crack pattern images with NPs to predict the crack patterns.The regression analysis results confirm that the variance decreases when the number of epochs increases by using the NPs.The training results gradually improve,and the variance ranges decrease to less than 0.035.The main finding of this study is that the NPs enable accurate predictions,even with missing or insufficient training data.The results demonstrate that if the context points are set to the 10th,100th,300th,and 784th,the training information is deliberately omitted for the context points of the 10th,100th,and 300th,and the predictions are different when the context points are significantly lower.However,the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the NPs.Therefore,if the NPs are employed for training,the missing information of the training data can be supplemented to predict the results.
文摘Chip designers employ computer-aided design,circuit simulation,and design rule check systems.Lithography engineers employ model-based OPC(Optical Proximity Correction)and model-based print-simulation systems.Reticle inspection teams employ Aerial Image Measurement Systems®and Virtual Stepper®Systems.These teams are accustomed to evaluating and deploying state-of-the-art computational systems.When real-silicon fabrication begins,however,the teams responsible for line monitoring,wafer inspection,and yield attainment operate without the benefit of similarly advanced computational systems.In this paper we describe such a system and explore its applications and benefits.The system has received three U.S.patents[1-3]and brings together the significant potential of CAD(Computer Aided Design)layout(GDS,OASIS),Die-to-Database,and Machine Learning to build a dynamic,self-improving computational system.Featuring care area generation,advanced machine learning-based SEM(Scanning Electron Microscope)sampling that optimizes both DOI(Defect of Interest)capture rate and discovery of new defect types,comprehensive extraction of all Information of Interest(IOI)from all SEM images,detection of defect types not possible before,massive pattern fidelity analysis,full chip pattern decomposition and risk scoring via machine learning,innovative PWQ(Process Window Qualification)analysis and process window determination,risk assessment of new tape-outs,large scale in-wafer OPC verification and more,the system delivers a comprehensive pattern centric platform for process technology development and manufacturing.
文摘Algorithm of STA/LTA is frequently used in automatic signal detection, in which the range of detection threshold is （0, ∞）, the optimal threshold should be determined by experiment to make a balance between false detection and missing detection. By using the theory of pattern recognition, a new algorithm for automatic signal detection based on support vector machine was proposed and the method of preprocess and pattern feature extraction were dis- cussed as well as the selection of kernel function for support vector machine. The detection performance of the new algorithm was analyzed by means of real seismic data. The experiments showed that the new method could simplify the selection of threshold and detect signal accurately. In addition to the better performance of anti-noise, the ratio of false detection could decrease 85% in comparison with that of STA/LTA.
文摘受气候变化和城市规模不断扩张等因素影响,城市地区降雨过程的时空动态性愈发明显,通常采用的点、面雨量等表达方式难以体现这种时空动态性。随着降雨观测技术的进步,大多城市积累了长序列降雨观测数据,其中蕴含了丰富的降雨时空过程信息,为预报降雨或设计降雨的时空展布提供了可能。设计了一套降雨时空展布方法,包括数据收集、标准化处理、降雨场次划分、时空模式提取、标准网格插值、时空展布等环节,并提出每个环节的处理步骤和关键问题。以北京市为例,对该技术方法进行了验证,选择了12 h、24 h、72 h 3个历时的场次降雨,提取的时空模式和展布结果表达出了场次降雨的时空动态性,并与历史的降雨过程表现出很好的匹配性。表明该方法可用于城市地区降雨典型模式提取,以及降雨过程的时空展布。该方法利用历史降雨数据提取降雨展布模板,输入场次降雨(或预报降雨)的总雨量,得到该场次降雨的时空分布过程,展布结果可为洪水预报提供更为准确的降雨输入条件。
