Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagon...Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagonal grid track images obtained using gas pixel detectors(GPDs)for better anisotropy do not match the classical rectangle-based CNN,and converting the track images from hexagonal to square results in a loss of information.We developed a new hexagonal CNN algorithm for track reconstruction and polarization estimation in X-ray polarimeters,which was used to extract the emission angles and absorption points from photoelectron track images and predict the uncer-tainty of the predicted emission angles.The simulated data from the PolarLight test were used to train and test the hexagonal CNN models.For individual energies,the hexagonal CNN algorithm produced 15%-30%improvements in the modulation factor compared to the moment analysis method for 100%polarized data,and its performance was comparable to that of the rectangle-based CNN algorithm that was recently developed by the Imaging X-ray Polarimetry Explorer team,but at a lower computational and storage cost for preprocessing.展开更多
The increasing elderly population has heightened the need for accurate and reliable fall detection systems,as falls can lead to severe health complications.Existing systems often suffer from high false positive and fa...The increasing elderly population has heightened the need for accurate and reliable fall detection systems,as falls can lead to severe health complications.Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques.This study introduces an advanced fall detection model integrating YOLOv8,Faster R-CNN,and Generative Adversarial Networks(GANs)to enhance accuracy and robustness.A modified YOLOv8 architecture serves as the core,utilizing spatial attention mechanisms to improve critical image regions’detection.Faster R-CNN is employed for fine-grained human posture analysis,while GANs generate synthetic fall scenarios to expand and diversify the training dataset.Experimental evaluations on the DiverseFALL10500 and CAUCAFall datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods.The model achieves a mean Average Precision(mAP)of 0.9507 on DiverseFALL10500 and 0.996 on CAUCAFall,surpassing conventional YOLO and R-CNN-based models.Precision and recall metrics also indicate superior detection performance,with a recall of 0.929 on DiverseFALL10500 and 0.9993 on CAUCAFall,ensuring minimal false negatives.Real-time deployment tests on the Xilinx Kria™K26 System-on-Module confirm an average inference time of 43ms per frame,making it suitable for real-time monitoring applications.These results establish the proposed R-CNN_GAN_YOLOv8 model as a benchmark in fall detection,offering a reliable and efficient solution for healthcare applications.By integrating attention mechanisms and GAN-based data augmentation,this approach significantly enhances detection accuracy while reducing false alarms,improving safety for elderly individuals and high-risk environments.展开更多
The analysis of WDM (Wavelength-Division Multiplexing) optical network is essential to have the routed wavelength blocking probability with the conversion of wavelength using techniques. In this paper, an enhanced ana...The analysis of WDM (Wavelength-Division Multiplexing) optical network is essential to have the routed wavelength blocking probability with the conversion of wavelength using techniques. In this paper, an enhanced analytical model is proposed to evaluate the blocking performances in topology network and to improve the performances of reduction of blocking probability. The variation of probability is based on the wavelength and load used in the network. The conversion is carried out with the support of optical backbone of the inherent flexibility of the network using the proposed IMCA in Sparse-Partial Wavelength Conversion (SPWC) architecture. It reduces the number of converters significantly with efficient process and provides placement scheme of wavelength converters in the network. The proposed model utilizes the network with the assignment and routing of wavelength using dynamic process of assignment algorithm. The proposed model provides dynamic and static routing process with the range limit to have a minimum conversion for the same probabilities of blocking. The proposed system analysis and the simulation results show the better performances in faster coverage, minimum number of conversions, blocking probability improvement for high load.展开更多
Communication is a basic need of every human being to exchange thoughts and interact with the society.Acute peoples usually confab through different spoken languages,whereas deaf people cannot do so.Therefore,the Sign...Communication is a basic need of every human being to exchange thoughts and interact with the society.Acute peoples usually confab through different spoken languages,whereas deaf people cannot do so.Therefore,the Sign Language(SL)is the communication medium of such people for their conversation and interaction with the society.The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs.The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively.The signs for singular words such as I,eat,drink,home are unalike the plural words as school,cars,players.A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately.Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration.The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment.Hence,there is still a demand for specific environment to automate such a communication solution to interact with such type of special people.This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words.Finally,these are converted into the respective words/sentences within a reasonable time.This provide a real time solution for the deaf people to communicate and interact with the society.展开更多
The novel coronavirus disease 2019(COVID-19)is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people.The first step to mitigate and control its spread is t...The novel coronavirus disease 2019(COVID-19)is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people.The first step to mitigate and control its spread is to identify and isolate the infected people.But,because of the lack of reverse transcription polymerase chain reaction(RT-CPR)tests,it is important to discover suspected COVID-19 cases as early as possible,such as by scan analysis and chest X-ray by radiologists.However,chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case.In this paper,an automated novel detection model of COVID-19 cases is proposed to perform real-time detection of COVID-19 cases.The proposed model consists of three main stages:image segmentation using Harris Hawks optimizer,synthetic image augmentation using an enhanced Wasserstein And Auxiliary Classifier Generative Adversarial Network,and image classification using Conventional Neural Network.Raw chest X-ray images datasets are used to train and test the proposed model.Experiments demonstrate that the proposed model is very efficient in the automatic detection of COVID-19 positive cases.It achieved 99.4%accuracy,99.15%precision,99.35%recall,99.25%F-measure,and 98.5%specificity.展开更多
Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical world.Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The...Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical world.Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The Machine Learning(ML)and the Deep Learning(DL)algorithms are the predomi-nant techniques to project and explore the images of DR.Even though some solu-tions were adapted to challenge the cause of DR disease,still there should be an efficient and accurate DR prediction to be adapted to refine its performance.In this work,a hybrid technique was proposed for classification and prediction of DR.The proposed hybrid technique consists of Ensemble Learning(EL),2 Dimensional-Conventional Neural Network(2D-CNN),Transfer Learning(TL)and Correlation method.Initially,the Stochastic Gradient Boosting(SGB)EL method was used to predict the DR.Secondly,the boosting based EL method was used to predict the DR of images.Thirdly 2D-CNN was applied to categorize the various stages of DR images.Finally,the TL was adopted to transfer the clas-sification prediction to training datasets.When this TL was applied,a new predic-tion feature was increased.From the experiment,the proposed technique has achieved 97.8%of accuracy in prophecies of DR images and 98%accuracy in grading of images.The experiment was also extended to measure the sensitivity(99.6%)and specificity(97.3%)metrics.The predicted accuracy rate was com-pared with existing methods.展开更多
The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power fore...The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.展开更多
Air environmental information plays an important role during plant growth and reproduction, prompt and accurate prediction of atmospheric environmental data is helpful for agricultural robots to make a timely decision...Air environmental information plays an important role during plant growth and reproduction, prompt and accurate prediction of atmospheric environmental data is helpful for agricultural robots to make a timely decision. For efficiency, an online learning method for predicting air environmental information was presented in this work. This method combines the advantages of convolutional neural network (CNN) and experience replay technique: CNN is used to extract features from raw data and predict atmospheric environmental information, experience replay technique can store environmental data over some time and update the hyperparameters of CNN. To validate the effects of this method, this online method was compared with three different predictive methods (including random forest, multi-layer perceptron, and support vector regression) using a public dataset (Jena). According to results, a suitable sample sequence size (e.g., 16) has a smaller number of training sessions and stable results, a larger replay memory size (e.g., 200) can provide enough samples to capture useful features, and 6 d of historical information is the best setting for training predictor. Compared with traditional methods, the method proposed in this study is the only method applied for various conditions.展开更多
基金supported by the National Natural Science Foundation of China(No.12025301)the Tsinghua University Initiative Scientific Research Program.
文摘Track reconstruction algorithms are critical for polarization measurements.Convolutional neural networks(CNNs)are a promising alternative to traditional moment-based track reconstruction approaches.However,the hexagonal grid track images obtained using gas pixel detectors(GPDs)for better anisotropy do not match the classical rectangle-based CNN,and converting the track images from hexagonal to square results in a loss of information.We developed a new hexagonal CNN algorithm for track reconstruction and polarization estimation in X-ray polarimeters,which was used to extract the emission angles and absorption points from photoelectron track images and predict the uncer-tainty of the predicted emission angles.The simulated data from the PolarLight test were used to train and test the hexagonal CNN models.For individual energies,the hexagonal CNN algorithm produced 15%-30%improvements in the modulation factor compared to the moment analysis method for 100%polarized data,and its performance was comparable to that of the rectangle-based CNN algorithm that was recently developed by the Imaging X-ray Polarimetry Explorer team,but at a lower computational and storage cost for preprocessing.
文摘The increasing elderly population has heightened the need for accurate and reliable fall detection systems,as falls can lead to severe health complications.Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques.This study introduces an advanced fall detection model integrating YOLOv8,Faster R-CNN,and Generative Adversarial Networks(GANs)to enhance accuracy and robustness.A modified YOLOv8 architecture serves as the core,utilizing spatial attention mechanisms to improve critical image regions’detection.Faster R-CNN is employed for fine-grained human posture analysis,while GANs generate synthetic fall scenarios to expand and diversify the training dataset.Experimental evaluations on the DiverseFALL10500 and CAUCAFall datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods.The model achieves a mean Average Precision(mAP)of 0.9507 on DiverseFALL10500 and 0.996 on CAUCAFall,surpassing conventional YOLO and R-CNN-based models.Precision and recall metrics also indicate superior detection performance,with a recall of 0.929 on DiverseFALL10500 and 0.9993 on CAUCAFall,ensuring minimal false negatives.Real-time deployment tests on the Xilinx Kria™K26 System-on-Module confirm an average inference time of 43ms per frame,making it suitable for real-time monitoring applications.These results establish the proposed R-CNN_GAN_YOLOv8 model as a benchmark in fall detection,offering a reliable and efficient solution for healthcare applications.By integrating attention mechanisms and GAN-based data augmentation,this approach significantly enhances detection accuracy while reducing false alarms,improving safety for elderly individuals and high-risk environments.
文摘The analysis of WDM (Wavelength-Division Multiplexing) optical network is essential to have the routed wavelength blocking probability with the conversion of wavelength using techniques. In this paper, an enhanced analytical model is proposed to evaluate the blocking performances in topology network and to improve the performances of reduction of blocking probability. The variation of probability is based on the wavelength and load used in the network. The conversion is carried out with the support of optical backbone of the inherent flexibility of the network using the proposed IMCA in Sparse-Partial Wavelength Conversion (SPWC) architecture. It reduces the number of converters significantly with efficient process and provides placement scheme of wavelength converters in the network. The proposed model utilizes the network with the assignment and routing of wavelength using dynamic process of assignment algorithm. The proposed model provides dynamic and static routing process with the range limit to have a minimum conversion for the same probabilities of blocking. The proposed system analysis and the simulation results show the better performances in faster coverage, minimum number of conversions, blocking probability improvement for high load.
基金The work presented in this paper is part of an ongoing research funded by Yayasan Universiti Teknologi PETRONAS Grant(015LC0-311 and 015LC0-029).
文摘Communication is a basic need of every human being to exchange thoughts and interact with the society.Acute peoples usually confab through different spoken languages,whereas deaf people cannot do so.Therefore,the Sign Language(SL)is the communication medium of such people for their conversation and interaction with the society.The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs.The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively.The signs for singular words such as I,eat,drink,home are unalike the plural words as school,cars,players.A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately.Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration.The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment.Hence,there is still a demand for specific environment to automate such a communication solution to interact with such type of special people.This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words.Finally,these are converted into the respective words/sentences within a reasonable time.This provide a real time solution for the deaf people to communicate and interact with the society.
文摘The novel coronavirus disease 2019(COVID-19)is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people.The first step to mitigate and control its spread is to identify and isolate the infected people.But,because of the lack of reverse transcription polymerase chain reaction(RT-CPR)tests,it is important to discover suspected COVID-19 cases as early as possible,such as by scan analysis and chest X-ray by radiologists.However,chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case.In this paper,an automated novel detection model of COVID-19 cases is proposed to perform real-time detection of COVID-19 cases.The proposed model consists of three main stages:image segmentation using Harris Hawks optimizer,synthetic image augmentation using an enhanced Wasserstein And Auxiliary Classifier Generative Adversarial Network,and image classification using Conventional Neural Network.Raw chest X-ray images datasets are used to train and test the proposed model.Experiments demonstrate that the proposed model is very efficient in the automatic detection of COVID-19 positive cases.It achieved 99.4%accuracy,99.15%precision,99.35%recall,99.25%F-measure,and 98.5%specificity.
文摘Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical world.Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The Machine Learning(ML)and the Deep Learning(DL)algorithms are the predomi-nant techniques to project and explore the images of DR.Even though some solu-tions were adapted to challenge the cause of DR disease,still there should be an efficient and accurate DR prediction to be adapted to refine its performance.In this work,a hybrid technique was proposed for classification and prediction of DR.The proposed hybrid technique consists of Ensemble Learning(EL),2 Dimensional-Conventional Neural Network(2D-CNN),Transfer Learning(TL)and Correlation method.Initially,the Stochastic Gradient Boosting(SGB)EL method was used to predict the DR.Secondly,the boosting based EL method was used to predict the DR of images.Thirdly 2D-CNN was applied to categorize the various stages of DR images.Finally,the TL was adopted to transfer the clas-sification prediction to training datasets.When this TL was applied,a new predic-tion feature was increased.From the experiment,the proposed technique has achieved 97.8%of accuracy in prophecies of DR images and 98%accuracy in grading of images.The experiment was also extended to measure the sensitivity(99.6%)and specificity(97.3%)metrics.The predicted accuracy rate was com-pared with existing methods.
基金National Key Research and Development Program(No.2020YFB0905900)National Natural Science Foundation of China(No.51777065).
文摘The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.
基金funded by the National Key Research and Development Program(Grant No.2022YFD2002202)Beijing Innovation Consortium of Agriculture Research System(BAIC08-2024-FQ04)+2 种基金Key Laboratory of Agricultural Sensors,Ministry of Agriculture and Rural Affairs(Grant No.PT2024-46)China Postdoctoral Science Foundation(Grant No.BX20230048)Postdoctoral fund of Beijing Academy of Agriculture and Forestry Sciences(Grant No.2023-ZZ-025).
文摘Air environmental information plays an important role during plant growth and reproduction, prompt and accurate prediction of atmospheric environmental data is helpful for agricultural robots to make a timely decision. For efficiency, an online learning method for predicting air environmental information was presented in this work. This method combines the advantages of convolutional neural network (CNN) and experience replay technique: CNN is used to extract features from raw data and predict atmospheric environmental information, experience replay technique can store environmental data over some time and update the hyperparameters of CNN. To validate the effects of this method, this online method was compared with three different predictive methods (including random forest, multi-layer perceptron, and support vector regression) using a public dataset (Jena). According to results, a suitable sample sequence size (e.g., 16) has a smaller number of training sessions and stable results, a larger replay memory size (e.g., 200) can provide enough samples to capture useful features, and 6 d of historical information is the best setting for training predictor. Compared with traditional methods, the method proposed in this study is the only method applied for various conditions.
