An improved typhoon monitoring model based on precipitable water vapor and pressure

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.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

THE RIEMANN PROBLEM WITH DELTA INITIAL DATA FOR THE NON-ISENTROPIC IMPROVED AW-RASCLE-ZHANG MODEL

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.

Detection of Citrus Psyllid Based on Improved YOLOX Model

[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.

Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support

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.

Improved AHP–TOPSIS model for the comprehensive risk evaluation of oil and gas pipelines

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.

Improved algorithm of atmospheric refraction error in Longley-Rice channel model

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.

Application of the Improved Generalized Autoregressive Conditional Heteroskedast Model Based on the Autoregressive Integrated Moving Average Model in Data Analysis

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.

Research on Plant Species Identification Based on Improved Convolutional Neural Network

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.

Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

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.

Assessment of South Pacific Albacore Stock (Thunnus alalunga) by Improved Schaefer Model

基于钩子和金枪鱼多钩长线渔业在南方太平洋操作的努力数据，南方太平洋青花鱼科的海产鱼股票被一个改进 Schaefer 模型估计。结果表明内在的生长率是大约 1.28374 并且从 73734 ～ 266732 公吨在范围带 capacitiesvareied。这种的生长能力是显著的。股票动力学主要取决于环境条件。股票仍然处于好状况。然而，在最近的年里的生物资源的连续减少应该被注意。

Improved frequency modeling and solution for parallel liquid-filled pipes considering both fluid-structure interaction and structural coupling

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.

Research on AGV task path planning based on improved A^(*) algorithm

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.

Topography Image Segmentation Based on Improved Chan-Vese Model

Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good per）brmances of maintaining topological properties of the traditional level set method and avoiding the numerical so- lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation tbr rapid segmenta- tion-based on image reconstruction identification is established.

An improved Mesri creep model for unsaturated weak intercalated soils

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.

Comprehensive Assessment of Seawater Quality Based on an Improved Attribute Recognition Model

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.

Improved Genetic Optimization Algorithm with Subdomain Model for Multi-objective Optimal Design of SPMSM

For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.

Improved stress release model: Application to the study of earthquake prediction in Taiwan Region

Stress release model used to be applied to seismicity study of large historical earthquakes in a space of large scale. In this paper, we improve the stress release model, and discuss whether the stress release model is still applicable or not in the case of smaller spatio-temporal scale and weaker earthquakes. As an example of testing the model, we have analyzed the M greater than or equal 6 earthquakes in recent about 100 years. The result shows that the stress release model is still applicable. The earthquake conditional probability intensity in Taiwan Region is calculated with the improved stress release model. We see that accuracy of earthquake occurrence time predicted by the improved stress release model is higher than that by Poisson model in the test of retrospect earthquake prediction.

Stability analysis of wireless network with improved fluid model

Wireless communication is easily disturbed by unfortunate factors which drive the wireless environment unstable and complicated. Therefore, it is essential to consider these factors in stability analysis of the wireless network. However, wireless channel characteristics and packets collisions are neglected in the classical fluid model. A wireless TCP fluid model （WTFM） for stability analysis of wireless network is proposed based on cross layers, which not only makes the congestion control based on random early detection （RED） available for wireless network, but also provides a more accurate model to analyze the stability of wireless system theoretically. In the proposed model, active queue management, abnormality of wireless channels and packets collisions are taken into consideration. The comparisons between evaluating results from the WTFM and the practical performance from NS2 simulations validate the accuracy of the proposed WTFM in the perspectives of delay, dropping probability, throughput, sliding window size and queue length. A set of comparisons among the proposed WTFM, the classical fluid model and the convex optimization model are conducted. The results demonstrate that the proposed WTFM model performs better than other schemes in comprehensive aspects on capturing the characteristic of the wireless network and computing complexity.

An extended improved global structure model for influential node identification in complex networks

Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses.Many classical approaches have been proposed by researchers regarding different aspects.To explore the impact of location information in depth,this paper proposes an improved global structure model to characterize the influence of nodes.The method considers both the node’s self-information and the role of the location information of neighboring nodes.First,degree centrality of each node is calculated,and then degree value of each node is used to represent self-influence,and degree values of the neighbor layer nodes are divided by the power of the path length,which is path attenuation used to represent global influence.Finally,an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes.In this paper,the propagation process of a real network is obtained by simulation with the SIR model,and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy.The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.