Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on th...Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.展开更多
The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasti...The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.展开更多
In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring meth...In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.展开更多
Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribu...Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.展开更多
The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist...The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.展开更多
Satellite communication systems(SCS) operating on frequency bands above 10 GHz are sensitive to atmosphere physical phenomena, especially rain attenuation. To evaluate impairments in satellite performance, stochastic ...Satellite communication systems(SCS) operating on frequency bands above 10 GHz are sensitive to atmosphere physical phenomena, especially rain attenuation. To evaluate impairments in satellite performance, stochastic dynamic modeling(SDM) is considered as an effective way to predict real-time satellite channel fading caused by rain. This article carries out a survey of SDM using stochastic differential equations(SDEs) currently in the literature. Special attention is given to the different input characteristics of each model to satisfy specific local conditions. Future research directions in SDM are also suggested in this paper.展开更多
Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produce...Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.展开更多
We present a new method for estimating missing values or correcting unreliable observed values of time dependent physical fields. This method, is based on Hidden Markov Models and Self-Organizing Maps, and is named PR...We present a new method for estimating missing values or correcting unreliable observed values of time dependent physical fields. This method, is based on Hidden Markov Models and Self-Organizing Maps, and is named PROFHMM_UNC. PROFHMM_UNC combines the knowledge of the physical process under study provided by an already known dynamic model and the truncated time series of observations of the phenomenon. In order to generate the states of the Hidden Markov Model, Self-Organizing Maps are used to discretize the available data. We make a modification to the Viterbi algorithm that forces the algorithm to take into account a priori information on the quality of the observed data when selecting the optimum reconstruction. The validity of PROFHMM_UNC was endorsed by performing a twin experiment with the outputs of the ocean biogeochemical NEMO-PISCES model.展开更多
Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of c...Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.展开更多
Reinforcement learning(RL)has emerged as a promising approach for building energy management(BEM).However,most existing research focuses on model-free reinforcement learning(MFRL)approaches,which can encounter the lea...Reinforcement learning(RL)has emerged as a promising approach for building energy management(BEM).However,most existing research focuses on model-free reinforcement learning(MFRL)approaches,which can encounter the learning challenge for heating,ventilation and air conditioning(HVAC)control due to extensive trial-and-error explorations and lengthy training times.To address this challenge,we propose a model-based reinforcement learning(MBRL)framework that incorporates a virtual environment to augment the agent’s exploration.By leveraging the branched rollout strategy to generate short rollout predictions branched from the experience trajectory,the MBRL method mitigates compounding errors introduced by the time-series prediction model,enabling robust and efficient policy updates.Evaluated in an EnergyPlus testbed with real-world data verification,the proposed method demonstrates significant advantages:(1)RL-based controllers outperform the rule-based control(RBC)baseline after one training episode,(2)MBRL reduces training time by over 50%compared to MFRL while maintaining comparable control performance,and(3)an equal mix of real and synthetic data for MBRL training achieves an optimal trade-off between efficiency and control outcomes.This study contributes an efficient model-based training method for RL development in HVAC control,offering insights into advanced control strategies for BEM applications.展开更多
BACKGROUND The coronavirus disease 2019(COVID-19)pandemic affects all aspects of our lives,including children.With the advancement of the pandemic,children under five years old are at increased risk of hospitalization...BACKGROUND The coronavirus disease 2019(COVID-19)pandemic affects all aspects of our lives,including children.With the advancement of the pandemic,children under five years old are at increased risk of hospitalization relative to other age groups.This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-COVID among this age group.AIM To demonstrate the role of children in the COVID-19 spread in Bulgaria and to test the hypothesis that there are no secondary transmissions in schools and from children to adults.METHODS Our modeling and data show with high probability that in Bulgaria,with our current measures,vaccination strategy and contact structure,the pandemic is driven by the children and their contacts in school.RESULTS This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-Covid among this age group.CONCLUSION Our modeling rejects that hypothesis,and the epidemiological data supports that.We used epidemiological data to support the validity of our modeling.The first summer wave in 2020 from the listed here school proms endorse the idea of transmissions from students to teachers.展开更多
The Paris Agreement calls for maintaining a global temperature less than 2℃ above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5℃. To realize this objective and p...The Paris Agreement calls for maintaining a global temperature less than 2℃ above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5℃. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas （GHG） emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system （SEMS） and its deployment in house- holds and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time- series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy- prediction models can be used for low-carbon planning.展开更多
Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble th...Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.展开更多
Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autore...Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.展开更多
Background: Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident r...Background: Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. Methods: A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. Results: A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants’accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0)12, and SARIMA (1, 1, 1) (0, 0, 1)12, respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Conclusion: Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.展开更多
Since April 2002,the Gravity Recovery and Climate Experiment Satellite(GRACE)has provided monthly total water storage anomalies(TWSAs)on a global scale.However,these TWSAs are discontinuous because some GRACE observat...Since April 2002,the Gravity Recovery and Climate Experiment Satellite(GRACE)has provided monthly total water storage anomalies(TWSAs)on a global scale.However,these TWSAs are discontinuous because some GRACE observation data are missing.This study presents a combined machine learning-based modeling algorithm without hydrological model data.The TWSA time-series data for 11 large regions worldwide were divided into training and test sets.Autoregressive integrated moving average(ARIMA),long short-term memory(LSTM),and an ARIMA-LSTM combined model were used.The model predictions were compared with GRACE observations,and the model accuracy was evaluated using fi ve metrics:the Nash-Sutcliff e effi ciency coeffi cient(NSE),Pearson correlation coeffi cient(CC),root mean square error(RMSE),normalized RMSE(NRMSE),and mean absolute percentage error.The results show that at the basin scale,the mean CC,NSE,and NRMSE for the ARIMA-LSTM model were 0.93,0.83,and 0.12,respectively.At the grid scale,this study compared the spatial distribution and cumulative distribution function curves of the metrics in the Amazon and Volga River basins.The ARIMA-LSTM model had mean CC and NSE values of 0.89 and 0.61 and 0.92 and 0.61 in the Amazon and Volga River basins,respectively,which are superior to those of the ARIMA model(0.86 and 0.48 and 0.88 and 0.46,respectively)and the LSTM model(0.80 and 0.41 and 0.89 and 0.31,respectively).In the ARIMA-LSTM model,the proportions of grid cells with NSE>0.50 for the two basins were 63.3%and 80.8%,while they were 54.3%and 51.3%in the ARIMA model and 53.7%and 43.2%in the LSTM model.The ARIMA-LSTM model significantly improved the NSE values of the predictions while guaranteeing high CC values in the GRACE data reconstruction at both scales,which can aid in fi lling in discontinuous data in temporal gravity fi eld models..展开更多
With the development of the Industrial Internet of Things(IIoT)and cloud computing technologies,intelligent sensors in the field that can generate large volumes of time-series data continuously have emerged.Due to the...With the development of the Industrial Internet of Things(IIoT)and cloud computing technologies,intelligent sensors in the field that can generate large volumes of time-series data continuously have emerged.Due to the lack of equipment and network impacts,highly distributed industrial applications cannot capture and transfer all production data to a distant cloud server in real time.Consequently,a portion of critical production data is lost,which poses the significant challenge of the timely replenishment of missing data.Employing deep learning in the cloud center for data trend prediction based on relevant data can solve this problem.The objective of this study was to develop a time-series prediction model that combines a Transformer model with a sparse Mixture of Experts(MoE).The model is designed specifically for an IIoT system that is used in oil-well operations.The proposed TransMoE prediction model combines the advantages of the MoE and the Transformer model.The MoE can effectively handle multiple subtasks while the Transformer algorithm can reflect the long-range dependency of the input data series.The proposed model was used to predict oil-well yields,and the predicted outcomes were compared with those obtained using a CNN-GRU and CNNLSTM models,as well as the actual recorded data.The experimental results indicated that the proposed TransMoE model can significantly increase the efficiency and accuracy of oil well production sequence data prediction,with an average relative error of 6.26%,which can satisfy the requirements of enterprise data usage.展开更多
Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast ele...Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.展开更多
Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial int...Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial intelligence),particularly ML(machine learning)and DL(deep learning),offers transformative solutions.This article explores how AI enhances forecasting accuracy,enables real-time adaptability,and supports strategic energy management.It examines the synergy between AI,IoT(Internet of Things)devices,and smart grids in generating predictive and prescriptive insights.Through case studies,we analyze the benefits and challenges of deploying AI in this domain,including data quality,model explainability,and infrastructure needs.Ultimately,AI emerges as a key enabler for the resilient,data-driven energy systems required to meet modern society’s evolving demands and achieve a sustainable future.展开更多
In order to catch more process details in chemical processes, adynamic model for prediction of process trends is proposed bymodifying traditional time-series ANN (artificial neural networks)model with impulse response...In order to catch more process details in chemical processes, adynamic model for prediction of process trends is proposed bymodifying traditional time-series ANN (artificial neural networks)model with impulse response identification means. The applicationresults of the model is briefly discussed.展开更多
基金supported by the National Natural Science Foundation of China (51909228)the Postdoctoral Science Foundation of China (2020M671623)the ‘‘Blue Project” of Yangzhou University。
文摘Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.
文摘The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.
基金provided by the Program for New Century Excellent Talents in University (No. NCET-06-0477)the Independent Research Project of the State Key Laboratory of Coal Resources and Mine Safety of China University of Mining and Technology (No. SKLCRSM09X01)the Fundamental Research Funds for the Central Universities
文摘In order to compensate for the deficiency of present methods of monitoring plane displacement in similarity model tests,such as inadequate real-time monitoring and more manual intervention,an effective monitoring method was proposed in this study,and the major steps of the monitoring method include:firstly,time-series images of the similarity model in the test were obtained by a camera,and secondly,measuring points marked as artificial targets were automatically tracked and recognized from time-series images.Finally,the real-time plane displacement field was calculated by the fixed magnification between objects and images under the specific conditions.And then the application device of the method was designed and tested.At the same time,a sub-pixel location method and a distortion error model were used to improve the measuring accuracy.The results indicate that this method may record the entire test,especially the detailed non-uniform deformation and sudden deformation.Compared with traditional methods this method has a number of advantages,such as greater measurement accuracy and reliability,less manual intervention,higher automation,strong practical properties,much more measurement information and so on.
基金Under the auspices of National Natural Science Foundation of China(No.42171407,42077242)Key Program of National Natural Science Foundation of China(No.42330607)。
文摘Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.
基金This work was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0016977,The Establishment Project of Industry-University Fusion District).
文摘The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.
基金supported by the National Natural Science Foundation of China (Grant No.91338201)
文摘Satellite communication systems(SCS) operating on frequency bands above 10 GHz are sensitive to atmosphere physical phenomena, especially rain attenuation. To evaluate impairments in satellite performance, stochastic dynamic modeling(SDM) is considered as an effective way to predict real-time satellite channel fading caused by rain. This article carries out a survey of SDM using stochastic differential equations(SDEs) currently in the literature. Special attention is given to the different input characteristics of each model to satisfy specific local conditions. Future research directions in SDM are also suggested in this paper.
基金The Science and Technology Research and Development Program Project of China Railway Group Ltd provided funding for this study(Project Nos.2020-Special-02 and 2021Special-08)。
文摘Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.
文摘We present a new method for estimating missing values or correcting unreliable observed values of time dependent physical fields. This method, is based on Hidden Markov Models and Self-Organizing Maps, and is named PROFHMM_UNC. PROFHMM_UNC combines the knowledge of the physical process under study provided by an already known dynamic model and the truncated time series of observations of the phenomenon. In order to generate the states of the Hidden Markov Model, Self-Organizing Maps are used to discretize the available data. We make a modification to the Viterbi algorithm that forces the algorithm to take into account a priori information on the quality of the observed data when selecting the optimum reconstruction. The validity of PROFHMM_UNC was endorsed by performing a twin experiment with the outputs of the ocean biogeochemical NEMO-PISCES model.
文摘Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.
基金supported by the China Scholarship Council(No.202306090264)granted to Kaichen Qu for his study at Politecnico di Milano.
文摘Reinforcement learning(RL)has emerged as a promising approach for building energy management(BEM).However,most existing research focuses on model-free reinforcement learning(MFRL)approaches,which can encounter the learning challenge for heating,ventilation and air conditioning(HVAC)control due to extensive trial-and-error explorations and lengthy training times.To address this challenge,we propose a model-based reinforcement learning(MBRL)framework that incorporates a virtual environment to augment the agent’s exploration.By leveraging the branched rollout strategy to generate short rollout predictions branched from the experience trajectory,the MBRL method mitigates compounding errors introduced by the time-series prediction model,enabling robust and efficient policy updates.Evaluated in an EnergyPlus testbed with real-world data verification,the proposed method demonstrates significant advantages:(1)RL-based controllers outperform the rule-based control(RBC)baseline after one training episode,(2)MBRL reduces training time by over 50%compared to MFRL while maintaining comparable control performance,and(3)an equal mix of real and synthetic data for MBRL training achieves an optimal trade-off between efficiency and control outcomes.This study contributes an efficient model-based training method for RL development in HVAC control,offering insights into advanced control strategies for BEM applications.
文摘BACKGROUND The coronavirus disease 2019(COVID-19)pandemic affects all aspects of our lives,including children.With the advancement of the pandemic,children under five years old are at increased risk of hospitalization relative to other age groups.This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-COVID among this age group.AIM To demonstrate the role of children in the COVID-19 spread in Bulgaria and to test the hypothesis that there are no secondary transmissions in schools and from children to adults.METHODS Our modeling and data show with high probability that in Bulgaria,with our current measures,vaccination strategy and contact structure,the pandemic is driven by the children and their contacts in school.RESULTS This makes it paramount that we develop tools to address the two critical aspects of preserving children's health–new treatment protocols and new predictive models.For those purposes,we need to understand better the effects of COVID-19 on children,and we need to be able to predict the number of affected children as a proportion of the number of infected children.This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID,as a part of the general picture of post-Covid among this age group.CONCLUSION Our modeling rejects that hypothesis,and the epidemiological data supports that.We used epidemiological data to support the validity of our modeling.The first summer wave in 2020 from the listed here school proms endorse the idea of transmissions from students to teachers.
文摘The Paris Agreement calls for maintaining a global temperature less than 2℃ above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5℃. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas （GHG） emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system （SEMS） and its deployment in house- holds and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time- series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy- prediction models can be used for low-carbon planning.
文摘Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.
文摘Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.
文摘Background: Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. Methods: A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. Results: A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants’accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0)12, and SARIMA (1, 1, 1) (0, 0, 1)12, respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Conclusion: Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.
基金financially supported by The National Natural Science Foundation of China (42374004)the Open Fund of Hubei Luojia Laboratory (220100045)the Natural Science Foundation of Sichuan Province (2022NSFSC1047)。
文摘Since April 2002,the Gravity Recovery and Climate Experiment Satellite(GRACE)has provided monthly total water storage anomalies(TWSAs)on a global scale.However,these TWSAs are discontinuous because some GRACE observation data are missing.This study presents a combined machine learning-based modeling algorithm without hydrological model data.The TWSA time-series data for 11 large regions worldwide were divided into training and test sets.Autoregressive integrated moving average(ARIMA),long short-term memory(LSTM),and an ARIMA-LSTM combined model were used.The model predictions were compared with GRACE observations,and the model accuracy was evaluated using fi ve metrics:the Nash-Sutcliff e effi ciency coeffi cient(NSE),Pearson correlation coeffi cient(CC),root mean square error(RMSE),normalized RMSE(NRMSE),and mean absolute percentage error.The results show that at the basin scale,the mean CC,NSE,and NRMSE for the ARIMA-LSTM model were 0.93,0.83,and 0.12,respectively.At the grid scale,this study compared the spatial distribution and cumulative distribution function curves of the metrics in the Amazon and Volga River basins.The ARIMA-LSTM model had mean CC and NSE values of 0.89 and 0.61 and 0.92 and 0.61 in the Amazon and Volga River basins,respectively,which are superior to those of the ARIMA model(0.86 and 0.48 and 0.88 and 0.46,respectively)and the LSTM model(0.80 and 0.41 and 0.89 and 0.31,respectively).In the ARIMA-LSTM model,the proportions of grid cells with NSE>0.50 for the two basins were 63.3%and 80.8%,while they were 54.3%and 51.3%in the ARIMA model and 53.7%and 43.2%in the LSTM model.The ARIMA-LSTM model significantly improved the NSE values of the predictions while guaranteeing high CC values in the GRACE data reconstruction at both scales,which can aid in fi lling in discontinuous data in temporal gravity fi eld models..
文摘With the development of the Industrial Internet of Things(IIoT)and cloud computing technologies,intelligent sensors in the field that can generate large volumes of time-series data continuously have emerged.Due to the lack of equipment and network impacts,highly distributed industrial applications cannot capture and transfer all production data to a distant cloud server in real time.Consequently,a portion of critical production data is lost,which poses the significant challenge of the timely replenishment of missing data.Employing deep learning in the cloud center for data trend prediction based on relevant data can solve this problem.The objective of this study was to develop a time-series prediction model that combines a Transformer model with a sparse Mixture of Experts(MoE).The model is designed specifically for an IIoT system that is used in oil-well operations.The proposed TransMoE prediction model combines the advantages of the MoE and the Transformer model.The MoE can effectively handle multiple subtasks while the Transformer algorithm can reflect the long-range dependency of the input data series.The proposed model was used to predict oil-well yields,and the predicted outcomes were compared with those obtained using a CNN-GRU and CNNLSTM models,as well as the actual recorded data.The experimental results indicated that the proposed TransMoE model can significantly increase the efficiency and accuracy of oil well production sequence data prediction,with an average relative error of 6.26%,which can satisfy the requirements of enterprise data usage.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.
文摘Accurate energy demand forecasting is crucial in today’s rapidly electrifying world with decentralized systems and integrated renewables.Traditional models struggle with the dynamic complexities,but AI(artificial intelligence),particularly ML(machine learning)and DL(deep learning),offers transformative solutions.This article explores how AI enhances forecasting accuracy,enables real-time adaptability,and supports strategic energy management.It examines the synergy between AI,IoT(Internet of Things)devices,and smart grids in generating predictive and prescriptive insights.Through case studies,we analyze the benefits and challenges of deploying AI in this domain,including data quality,model explainability,and infrastructure needs.Ultimately,AI emerges as a key enabler for the resilient,data-driven energy systems required to meet modern society’s evolving demands and achieve a sustainable future.
文摘In order to catch more process details in chemical processes, adynamic model for prediction of process trends is proposed bymodifying traditional time-series ANN (artificial neural networks)model with impulse response identification means. The applicationresults of the model is briefly discussed.
