The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movem...The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.展开更多
In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible t...In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
Domestic violence is a serious threat to the basic human rights of family members,especially victimized women.In order to effectively prevent domestic violence,the criminal law needs to respond in an appropriate and t...Domestic violence is a serious threat to the basic human rights of family members,especially victimized women.In order to effectively prevent domestic violence,the criminal law needs to respond in an appropriate and timely manner.However,the traditional criminal governance model suffers from the problem of the lagging involvement of public power.At the same time,the public-private partnership governance model,based on the personal safety protection order system stipulated in the Anti-domestic Violence Law of the People's Republic of China,is also flawed in its institutional design and specific implementation.The criminal governance model for domestic violence should be guided by the view of positive criminal law,and the governance path should be furtheroptimized from two aspects:strengthening the foundation of the public-private cooperation governance model and deepeningthe criminal-civil interface.This will help to fully protect the legitimate rights and interests of victims of domestic violence.展开更多
In this paper,we study the Radon measure initial value problem for the nonisentropic improved Aw-Rascle-Zhang model.For arbitrary convex F(u)in this model we construct the Riemann solutions by elementary waves andδ-s...In this paper,we study the Radon measure initial value problem for the nonisentropic improved Aw-Rascle-Zhang model.For arbitrary convex F(u)in this model we construct the Riemann solutions by elementary waves andδ-shock waves using the method of generalized characteristic analysis.We obtain the solutions constructively for initial data containing the Dirac measure by taking the limit of the solutions for that with three piecewise constants.Moreover,we analyze different kinds of wave interactions,including the interactions of theδ-shock waves with elementary waves.展开更多
[Objectives]The paper was to explore a faster and more accurate detection method for citrus psyllid to prevent and control yellow-shoot disease and inhibit its transmission.[Methods]We used an improved YOLOX based edg...[Objectives]The paper was to explore a faster and more accurate detection method for citrus psyllid to prevent and control yellow-shoot disease and inhibit its transmission.[Methods]We used an improved YOLOX based edge detection method for psyllid,added Convolutional Block Attention Module(CBAM)to the backbone network,and further extracted important features in the channel and space dimensions.The Cross Entropy Loss in the object loss was changed to Focal Loss to further reduce the missed detection rate.[Results]The algorithm described in the study fitted in with the detection platform of psyllid.The data set of psyllid was taken in Lianjiang Orange Garden,Zhanjiang City,Guangdong Province,deeply adapted to the actual needs of agricultural and rural development.Based on YOLOX model,the backbone network and loss function were improved to achieve a more excellent detection method of citrus psyllid.The AP value of 85.66%was obtained on the data set of citrus psyllid,which was 2.70%higher than that of the original model,and the detection accuracies were 8.61%,4.32%and 3.62%higher than that of YOLOv3,YOLOv4-Tiny and YOLOv5-s,respectively,which had been greatly improved.[Conclusions]The improved YOLOX model can better identify citrus psyllid,and the accuracy rate has been improved,laying a foundation for the subsequent real-time detection platform.展开更多
Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for...Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.展开更多
Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in thes...Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.展开更多
A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is establis...A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.展开更多
Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth...Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.展开更多
Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use o...Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.展开更多
The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model for the issue that financial product sales data have singular information when applying this model, and the improved outlier d...This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model for the issue that financial product sales data have singular information when applying this model, and the improved outlier detection method was used to detect the location of outliers, which were processed by the iterative method. Secondly, in order to describe the peak and fat tail of the financial time series, as well as the leverage effect, this work used the skewed-t Asymmetric Power Autoregressive Conditional Heteroskedasticity model based on the Autoregressive Integrated Moving Average Model to analyze the sales data. Empirical analysis showed that the model considering the skewed distribution is effective.展开更多
The weak intercalated soils in redbed soft rocks of Badong formation have obvious creep characters. In order to predict the unsaturated creep behaviors of weak intercalated soils, an unsaturated creep model was establ...The weak intercalated soils in redbed soft rocks of Badong formation have obvious creep characters. In order to predict the unsaturated creep behaviors of weak intercalated soils, an unsaturated creep model was established based on the unsaturated creep tests of weak intercalated soils by using GDS triaxial apparatus. The results show that the creep behaviors of intercalated soils are apparent and significantly affected by matric suction. Based on this, an empirical Mesri creep model for intercalated soils under varying matric suctions was built. The fitting results show that the parameters Ed and m of this model are in good power relations with matric suction s and stress level Dr, respectively. An improved Mesri creep model was established involving stress-matric suction-strain-time, which is more precise than the Mesri creep model in predicting the unsaturated creep behaviors of weak intercalated soils.展开更多
Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requiremen...Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy.Therefore,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this study.Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning.The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum.In this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University.Finally,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational effort.The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original model.In addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species.展开更多
According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Ma...According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.展开更多
The dynamic characteristics of a single liquid-filled pipe have been broadly studied in the previous literature.The parallel liquid-filled pipe(PLFP)system is also widely used in engineering,and its structure is more ...The dynamic characteristics of a single liquid-filled pipe have been broadly studied in the previous literature.The parallel liquid-filled pipe(PLFP)system is also widely used in engineering,and its structure is more complex than that of a single pipe.However,there are few reports about the dynamic characteristics of the PLFPs.Therefore,this paper proposes improved frequency modeling and solution for the PLFPs,involving the logical alignment principle and coupled matrix processing.The established model incorporates both the fluid-structure interaction(FSI)and the structural coupling of the PLFPs.The validity of the established model is verified by modal experiments.The effects of some unique parameters on the dynamic characteristics of the PLFPs are discussed.This work provides a feasible method for solving the FSI of multiple pipes in parallel and potential theoretical guidance for the dynamic analysis of the PLFPs in engineering.展开更多
In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,etc.Hence,the sensor nodes of W...In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,etc.Hence,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to attacks.The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats.In traditional models,the solution has been made for defending the networks,only to specific attacks.However,in real-time applications,the kind of attack that is launched by the adversary is not known.Additionally,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be considered.With that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various attacks.Moreover,for efficient model design,the work includes the part of feature evaluation of some general attacks of WSNs.The IDRM also includes determination of optimal secure paths and Node security for secure routing operations.The performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications.It is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.展开更多
Modeling experiences of traditional grey-Markov show that the prediction results are not accurate when analyzed data are rare and fluctuated.So it is necessary to revise or improve the original modeling procedure of t...Modeling experiences of traditional grey-Markov show that the prediction results are not accurate when analyzed data are rare and fluctuated.So it is necessary to revise or improve the original modeling procedure of the grey-Markov(GM)model.Therefore,a new idea is brought forward that the Markov theory is used twice,where the first time is to extend the original data and the second to calculate and estimate the residual errors.Then by comparing the original data sequence from a fault prediction case with the simulation sequence produced by the use of GM(1,1) and the new GM method,results are conforming to the original data.Finally,an assumption of GM model is put forward as the future work.展开更多
In this paper,under the assumption that the labor force function increases strictly and is bounded and the labor force growth rate function decreases monotonically from a positive value to zero,we obtain an improved S...In this paper,under the assumption that the labor force function increases strictly and is bounded and the labor force growth rate function decreases monotonically from a positive value to zero,we obtain an improved Solow Swan model. We prove that the per capita capital trends stabilitily to the steady state of the classical Solow Swan model with zero the labor force growth rate. Two comparison theorems,a limited theorem and a stability theorem are given. At the end of this paper,we give an example and discuss the economic meaning of this model and the theorems.展开更多
基金supported by the Guangxi Natural Science Foundation of China (2020GXNSFBA297145,Guike AD23026177)the Foundation of Guilin University of Technology(GUTQDJJ6616032)+3 种基金Guangxi Key Laboratory of Spatial Information and Geomatics (21-238-21-05)the National Natural Science Foundation of China (42064002,42004025,42074035,42204006)the Innovative Training Program Foundation (202210596015,202210596402)the Open Fund of Hubei Luojia Laboratory(gran 230100020,230100019)。
文摘The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.
基金supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02.
文摘In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
基金a phased achievement of the Tianjin Philosophy and Social Science Planning Project“Systematic Study on the Justified Exoneration”(Project Number TJFXQN20-001)supported by the Fundamental Research Funds for the Central Universities“Research on Criminal Law Regulation of Family Offenses”(Project Number 63222047)。
文摘Domestic violence is a serious threat to the basic human rights of family members,especially victimized women.In order to effectively prevent domestic violence,the criminal law needs to respond in an appropriate and timely manner.However,the traditional criminal governance model suffers from the problem of the lagging involvement of public power.At the same time,the public-private partnership governance model,based on the personal safety protection order system stipulated in the Anti-domestic Violence Law of the People's Republic of China,is also flawed in its institutional design and specific implementation.The criminal governance model for domestic violence should be guided by the view of positive criminal law,and the governance path should be furtheroptimized from two aspects:strengthening the foundation of the public-private cooperation governance model and deepeningthe criminal-civil interface.This will help to fully protect the legitimate rights and interests of victims of domestic violence.
基金supported by the Natural Science Foundation of Zhejiang(LQ18A010004)Matematical Analysis,The First class courses in Zhejiang Province(210052)+1 种基金the Fundamental Research Funds for the Provincial Universities of Zhejiang(210039)supported by the National Natural Science Foundation of China(11771442)。
文摘In this paper,we study the Radon measure initial value problem for the nonisentropic improved Aw-Rascle-Zhang model.For arbitrary convex F(u)in this model we construct the Riemann solutions by elementary waves andδ-shock waves using the method of generalized characteristic analysis.We obtain the solutions constructively for initial data containing the Dirac measure by taking the limit of the solutions for that with three piecewise constants.Moreover,we analyze different kinds of wave interactions,including the interactions of theδ-shock waves with elementary waves.
基金Supported by Research and Development Program in Key Areas of Guangdong Province(2020B0202090005)Lianjiang Think Tank Enterprise Project"Demonstration of Intelligent Monitoring and Ecological Prevention and Control Technology of Red Orange Yellow-shoot Disease and Psyllid in Lianjiang"。
文摘[Objectives]The paper was to explore a faster and more accurate detection method for citrus psyllid to prevent and control yellow-shoot disease and inhibit its transmission.[Methods]We used an improved YOLOX based edge detection method for psyllid,added Convolutional Block Attention Module(CBAM)to the backbone network,and further extracted important features in the channel and space dimensions.The Cross Entropy Loss in the object loss was changed to Focal Loss to further reduce the missed detection rate.[Results]The algorithm described in the study fitted in with the detection platform of psyllid.The data set of psyllid was taken in Lianjiang Orange Garden,Zhanjiang City,Guangdong Province,deeply adapted to the actual needs of agricultural and rural development.Based on YOLOX model,the backbone network and loss function were improved to achieve a more excellent detection method of citrus psyllid.The AP value of 85.66%was obtained on the data set of citrus psyllid,which was 2.70%higher than that of the original model,and the detection accuracies were 8.61%,4.32%and 3.62%higher than that of YOLOv3,YOLOv4-Tiny and YOLOv5-s,respectively,which had been greatly improved.[Conclusions]The improved YOLOX model can better identify citrus psyllid,and the accuracy rate has been improved,laying a foundation for the subsequent real-time detection platform.
基金the Science and Technology Cooperation Research and Development Project of Sichuan Provincial Academy and University(Grant No.2019YFSY0024)the Key Research and Development Program in Sichuan Province of China(Grant No.2019YFG0050)the Natural Science Foundation of Guangxi Province of China(Grant No.AD19245021).
文摘Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.
基金Supported by the Natural Science Foundation of Jiangsu Province (BK20211037)the Science and Technology Development Fund of Wuxi (N20201011)the Nanjing University of Information Science and Technology Wuxi Campus District graduate innovation Project。
文摘Background Automatic guided vehicles(AGVs)have developed rapidly in recent years and have been used in several fields,including intelligent transportation,cargo assembly,military testing,and others.A key issue in these applications is path planning.Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination.Using the global planning method,the ideal path should meet the requirements of as few turns as possible,a short planning time,and continuous path curvature.Methods We propose a global path-planning method based on an improved A^(*)algorithm.The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios.To improve the efficiency of the path-finding time,we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process.Subsequently,the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method.Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition,we adopt a curve smoothing scheme and select the optimal result that meets the constraints.Conclusions Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness.Additionally,this scheme provides a new solution for global path planning of unmanned vehicles.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFC0805804,2017YFC0805801)
文摘A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.
文摘Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.
文摘Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China (50579009, 70471090) the National 10 th Five Year Scientific Project of China for Tackling the Key Problems (2004BA608B-02 - 02) and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China (Department of Education and Personnel [2002] 350).
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
文摘This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model for the issue that financial product sales data have singular information when applying this model, and the improved outlier detection method was used to detect the location of outliers, which were processed by the iterative method. Secondly, in order to describe the peak and fat tail of the financial time series, as well as the leverage effect, this work used the skewed-t Asymmetric Power Autoregressive Conditional Heteroskedasticity model based on the Autoregressive Integrated Moving Average Model to analyze the sales data. Empirical analysis showed that the model considering the skewed distribution is effective.
基金Project supported by Science&Technology Program of Hubei Traffic and Transport Office,ChinaProject(41272377)supported by the National Natural Science Foundation of China
文摘The weak intercalated soils in redbed soft rocks of Badong formation have obvious creep characters. In order to predict the unsaturated creep behaviors of weak intercalated soils, an unsaturated creep model was established based on the unsaturated creep tests of weak intercalated soils by using GDS triaxial apparatus. The results show that the creep behaviors of intercalated soils are apparent and significantly affected by matric suction. Based on this, an empirical Mesri creep model for intercalated soils under varying matric suctions was built. The fitting results show that the parameters Ed and m of this model are in good power relations with matric suction s and stress level Dr, respectively. An improved Mesri creep model was established involving stress-matric suction-strain-time, which is more precise than the Mesri creep model in predicting the unsaturated creep behaviors of weak intercalated soils.
基金supported by the Key Project Supported by Science and Technology of Tianjin Key Research and Development Plan[Grant No.20YFZCSN00220]Tianjin Science and Technology Plan Project[Grant No.21YFSNSN00040]+1 种基金Central Government Guides Local Science and Technology Development Project[Grant No.21ZYCGSN00590]Inner Mongolia Autonomous Region Department of Science and Technology Project[Grant No.2020GG0068].
文摘Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy.Therefore,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this study.Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning.The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum.In this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University.Finally,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational effort.The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original model.In addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species.
基金Under the auspices of Major Special Technological Program of Water Pollution Control and Management (No.2009ZX07106-001)National Natural Science Foundation of China (No. 51079037, 50909063)
文摘According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.
基金Project supported by the National Natural Science Foundation of China(No.11972112)the Fundamental Research Funds for the Central Universities of China(Nos.N2103024 and N2103002)the Major Projects of Aero-Engines and Gasturbines(No.J2019-I-0008-0008)。
文摘The dynamic characteristics of a single liquid-filled pipe have been broadly studied in the previous literature.The parallel liquid-filled pipe(PLFP)system is also widely used in engineering,and its structure is more complex than that of a single pipe.However,there are few reports about the dynamic characteristics of the PLFPs.Therefore,this paper proposes improved frequency modeling and solution for the PLFPs,involving the logical alignment principle and coupled matrix processing.The established model incorporates both the fluid-structure interaction(FSI)and the structural coupling of the PLFPs.The validity of the established model is verified by modal experiments.The effects of some unique parameters on the dynamic characteristics of the PLFPs are discussed.This work provides a feasible method for solving the FSI of multiple pipes in parallel and potential theoretical guidance for the dynamic analysis of the PLFPs in engineering.
文摘In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,etc.Hence,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to attacks.The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats.In traditional models,the solution has been made for defending the networks,only to specific attacks.However,in real-time applications,the kind of attack that is launched by the adversary is not known.Additionally,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be considered.With that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various attacks.Moreover,for efficient model design,the work includes the part of feature evaluation of some general attacks of WSNs.The IDRM also includes determination of optimal secure paths and Node security for secure routing operations.The performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications.It is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.
基金supported by the National Natural Science Foundation of China(No.61303098)
文摘Modeling experiences of traditional grey-Markov show that the prediction results are not accurate when analyzed data are rare and fluctuated.So it is necessary to revise or improve the original modeling procedure of the grey-Markov(GM)model.Therefore,a new idea is brought forward that the Markov theory is used twice,where the first time is to extend the original data and the second to calculate and estimate the residual errors.Then by comparing the original data sequence from a fault prediction case with the simulation sequence produced by the use of GM(1,1) and the new GM method,results are conforming to the original data.Finally,an assumption of GM model is put forward as the future work.
文摘In this paper,under the assumption that the labor force function increases strictly and is bounded and the labor force growth rate function decreases monotonically from a positive value to zero,we obtain an improved Solow Swan model. We prove that the per capita capital trends stabilitily to the steady state of the classical Solow Swan model with zero the labor force growth rate. Two comparison theorems,a limited theorem and a stability theorem are given. At the end of this paper,we give an example and discuss the economic meaning of this model and the theorems.