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Pervasive computing techniques provides application guidance and support for intelligent transportation systems. On the basis of connected autonomous vehicles, a extended model has been proposed. This paper focuses on studying the effects of the electronic throttle and driver’s memory. In order to show the influence of these two factors on the model, the extended model is studied by theoretical analysis and numerical methods. Stability conditions are obtained by adding perturbation to the linear analysis, the TDGL equation and mKdV equation are derived by nonlinear analysis, which shows that the phase change behavior of traffic jams can be described by both of them. The numerical simulation results show the correctness of the analysis results. Numerical results show that the effects of driver’s memory and electronic throttle play an key role in the stability of traffic flow, which is consistent with the theoretical analysis results.

Each system of the coal mine has independent management and control terminal platform. When an emergency occurs, the dispatcher needs to switch back and forth between different platforms. There are some problems such as low dispatching and commanding efficiency, incomplete information acquisition, incomplete data display on one screen, lack of unified visual management platform, etc. In view of the above problems, the mine integrated dispatching management and control platform is designed. The platform takes the dispatching host as the core, and realizes the interconnection and intercommunication of various heterogeneous communication systems and integrated voice dispatching through the voice gateway. Based on the wired/wireless IP transmission network, with the video access server as the core, the differences between video terminals are shielded through protocol docking. The unified video access is realized. At the same time, the video conference function is realized through the video exchange server. The GIS technology is used to introduce the plan of mine excavation engineering, create a 'one map' mode to present different resources of multiple systems, and realize map scheduling. The data visualization interface is developed based on the B/S architecture. The data of different systems are gathered and displayed in the way of 'one machine and three screens' , so as to improve the data utilization efficiency. The test results show that the platform integrates voice scheduling, video scheduling, map scheduling and data visualization. The platform can further strengthen the information communication and management and control capability of production scheduling command. The platform can improve the emergency rescue capability of coal mines. The platform can assist in the informatization construction of coal mine emergency management.

[Purpose/Significance] At present, most of the training models used in the research of news classification are non-active learning. There are common problems about these models, including data cannot be labeled immediately and the labeling cost is too high, which also hinders the analysis of agricultural news. Especially because of the explosive growth of news data in the network era, it is more difficult to label data, train supervised text classification models, and screen relevant news in the field of agriculture from diversified online news sources. In order to solve this problem, the most commonly used pool based active learning or deep active learning technique is used to select more valuable and representative data from unlabeled data for manual labeling, and construct labeled data sets to improve the efficiency and effect of news classification and agricultural news mining. [Method/Process] The commonly used machine learning models for text classification, such as random forest classifier, polynomial naive Bayes classifier and logistic regression classifier, were combined with the active learning method with the lowest confidence to analyze the effect, and the BERT model was combined with the three sampling strategies of discriminative active learning, deep Bayes active learning and lowest confidence for deep active learning training. On the news corpus of 19 847 samples crawled and cleaned by crawler technology from Sina and other news websites, aiming at screening agricultural related news from diversified news samples of various topics, the iterative experiment of adding 30 samples per round was tested to check the improvement effect of F1 score under various method combinations with the increase of the number of annotation. In addition, the representativeness and diversity of the samples selected by the sampling function of each method in the deep active learning method of the BERT model were compared, so as to understand the characteristics of each strategy and provide inspiration for the selection and improvement of Al strategy in the future. In addition, this paper also analyzed how much labeling cost can be saved by using the proposed method. [Results/Conclusions] When comparing a variety of machine learning models, it is found that although the gradient boosting tree and support vector machine classifier have high accuracy, they are not suitable for active learning because of their low efficiency in text data processing of large-scale high-dimensional data. After combining other machine learning models and the BERT model and training text models with the corresponding active learning or deep active learning methods, it is found that the application of active learning method can significantly improve the training process of each model. Among them, the BERT model, combined with discriminative active learning sampling function, has the best news text classification effect and the lowest annotation data requirements. The representativeness and diversity of the samples selected by discriminative active learning sampling function are also the highest, which explains the source of the advantages of this method. It can also be found that for the same task model, the higher the accuracy of classification is required, and the active learning method can save more annotation cost than non-active learning.

Monitoring rice planting areas and their phenological phases is crucial for yield estimation and informed decision-making. This study proposed a unified method for mapping rice field and rice phenology with a dynamic time wrapping (DTW) distance-based classifier and its variant sub-DTW algorithm using Sentinel-1’s synthetic aperture radar (SAR) VH band. Field samplings were conducted for broad landcover types in one of the areas of interest (AOIs). We implemented a pixel-wise k-nearest neighbor classification model with DTW distance to identify paddy rice pixels. Standard rice phenological profiles of the SAR VH band were defined by ground monitoring of a sample rice field. Based on rice planting maps and the standard phenological profiles, rice phenological phases were estimated by pattern matching strategy with the sub-DTW algorithm. Experiments on six counties in Northeast China presented promising results. The overall producer and user accuracy reached 92.9 and 91.9% for rice mapping, respectively. The mean root mean square error (RMSE) for phenology estimation was 3.5 days. Rice planting and rice phenology maps were generated for the six AOIs. The phenological variances of the AOIs implied the effects of climate and rice cultivars on phenological development.

Objective: The driver's inability to fully absorb and react to operational cues while driving is like boiling a frog in warm water. With intermittent, low-volume information, drivers can underreact by ignoring these minor but continuous changes. This paper aims to provide an opportunity to test the effects of intermittently occurring low-volume information on drivers. Methods: A real vehicle test with naturalistic driving was used to collect driving speed data from 40 drivers on a highway tunnel section in Chongqing, China, where nine tunnels are located. Drivers were classified into three categories according to the degree of compliance of their driving speed with the speed limit required by traffic signs, and drivers were analyzed in terms of their sensitivity to traffic signs and their reaction to driving maneuvers. Results: Conservative drivers are the most absorbent of low-volume intermittent information, and the cumulative effect of the frog effect does not exceed 2.00 km; eager drivers tend to ignore this information, and the cumulative effect of the frog effect reaches 2.91 km; and the general type of driver is in the middle of these two types of drivers, and the frog effect gradually penetrates the driving speed in a weakly increasing manner, up to a maximum of 9.8 km. Conclusion: At the beginning of a journey, drivers are most sensitive to traffic signs, and low-volume intermittent information can also play a role in guiding driving operations effectively at this time. However, as the driving distance increases, the effect of the frog effect on different types of drivers gradually increases, even exceeding the effect caused by the black-and-white hole effect, especially when driving in tunnel groups. Considering the driving characteristics of different types of drivers to improve the deployment of low-volume intermittent information and reduce the distance of the frog effect can effectively improve driving safety. [ABSTRACT FROM AUTHOR]

BACKGROUND: The effects of traffic sign information volume (TSIV) on the visual characteristics and workload of novice and experienced drivers were investigated in this study. TSIV plays a crucial role in road traffic safety, and understanding its impact on drivers is essential for designing effective traffic sign systems. OBJECTIVE: This research aimed to compare the visual characteristics and workload of novice and experienced drivers under varying TSIV doses through simulated driving tests. The objective was to provide insights for optimizing the design of road TSIV. METHODS: Six TSIV levels were considered: S0, S1, S2, S3, S4, and S5, representing different workload levels. Participants, including both novice and experienced drivers, were involved in simulated driving scenarios with varying TSIV levels. Eye movement data was collected using an eye tracker device. The study was conducted in China, and appropriate driving simulators and equipment were utilized. RESULTS: The findings revealed several valuable results. Experienced drivers exhibited a higher proportion of saccade behavior in the 30–90 ms time period and did not show rapid saccade behavior during the 0–30 ms period, indicating superior visual search strategies. Both novice and experienced drivers demonstrated improved visual cognitive abilities at the S3 level of TSIV, which corresponds to normal and safe driving conditions. Furthermore, a majority of both groups had saccade amplitudes in the range of 0°–4°, with experienced drivers showing a slightly higher proportion. About 82% of experienced drivers had saccade behavior within the range of 0°–2°, compared to 75% of novice drivers. CONCLUSIONS: The study concludes that the S3 level of TSIV, corresponding to 30 bits/km, is optimal for both novice and experienced drivers. This level promotes better visual performance and reduces visual workload, indicating that drivers' information acquisition capacity and visual search strategies are maximized while keeping the workload associated with driving at a minimum. These findings have significant implications for enhancing driving safety. [ABSTRACT FROM AUTHOR]

Objective: The visual guiding system, as a tunnel traffic safety improvement method by using visual guiding facilities to actively guide driving safely, has been widely used in countries with many tunnels, in recent years. This paper aims to quantitatively study the comprehensive evaluation of traffic safety of the visual guiding system in tunnels, which has certain engineering application value and can provide support for the quantitative evaluation and optimal design of tunnel traffic safety. Methods: Based on the analysis of the relevant factors of urban tunnel traffic safety, a multi-factor comprehensive evaluation system with 5 upper-level indicators and 12 basic-level indicators was proposed. Considering the independent and incompatible indicators, a comprehensive evaluation method of traffic safety of the visual guiding system in urban tunnels was constructed by using the extension matter-element model. Taking the scene of 4 types of tunnel curves, such as no facilities, horizontal stripe, chevron alignment sign, and LED arch, as examples, the comprehensive evaluation of various schemes were carried out by using simulation tests. Results: The traffic safety comprehensive evaluation system of visual guiding system in urban tunnels can be analyzed from five aspects: perception reaction, guidance ability, driver factor, driving task, and facility appearance. The results demonstrated significant the comprehensive evaluation result of the target level of scene 1 was L4, scene 2 was L3, scene 3 was L2, and scene 4 was L1. That is, the final results of the comprehensive evaluation of the four scenes were poor, medium, good, and very good, respectively. Conclusions: In the scheme of visual guiding system for urban tunnel curves, the effectiveness of the three types of designs, from high to low, was the LED arch, chevron alignment sign, and horizontal stripe, and the safety of the scene without facilities was the lowest. Hence, setting the LED arch in the urban tunnel curve has a good effect in the aspects of guidance ability, sight distance, and sight zone, and is conducive to the driver's perception reaction and driving task. [ABSTRACT FROM AUTHOR]

Objectives: This research aims to: (i) compare the effects of different sidewall entrainment facilities on drivers' visual behavior; (ii) compare the effects of the same sight entrainment facilities on drivers in different lanes; (iii) give recommendations for engineering applications based on the results of the study; Methods: The study designed four different scenes, each with symmetrically designed visual facilities on the both sidewalls of the tunnel, scene a represents a typical urban tunnel in China (horizontal stripes on sidewalls), scene b includes vertical stripes on sidewalls in addition to scene a, scene c introduces an LED-arch based on scene b, and scene d features a rhythmic pattern (Wave pattern on sidewalls). 30 participants, 21 men and 9 women, aged 21–54, drove the four scenes. Eye movement data of participants in each lane for different scenes were collected using an eye-tracking device. Visual performance indicators including fixation duration, number of fixations, saccade duration, and saccade amplitude were utilized to comprehensively evaluate drivers' visual behavior. Factor analysis was employed to analyze the impact of different visual guiding facilities on drivers' visual searching abilities. Results: There is a significant effect of sidewall guiding facilities and lane location on drivers' visual behavior and loading. Across scenes, drivers' visual load is ranked as follows, from highest to lowest: scene a (baseline) > scene b (horizontal stripes added to scene a) > scene c (LED-arch added to scene b) > scene d (Wave pattern). Furthermore, under the same scene, drivers' visual load in each lane is ranked in descending order: Middle lane > Right lane > Left lane. Conclusion: Due to the effect of the tunnel structure on the drivers' visual field, drivers in the left lane have the highest visual load in any scenario compared to the other two lanes, which can be ameliorated but not eliminated. Traditional guiding facilities and decorated pattern both improve the visual behavior and reduce drivers' visual load in urban tunnels, especially in scene c and scene d, but scene d should not be used for the entire length of the tunnel in order to prevent driver distraction. In engineering practice, scene c (LED-arch added to scene b) can be set up in general sections of urban tunnels, and decorated pattern can be added to fatigue reminder regions to alleviate driving fatigue. [ABSTRACT FROM AUTHOR]

With the rapid development of information construction, the wisdom mine platform has higher requirements for the capabilities of data interaction and integration in multi-source heterogeneous systems including personnel positioning system. The current personnel positioning system usually used closed architecture, which has many problems such as cumbersome development process, difficult data integration and inelastic expansion. In order to solve the problems, a framework of personnel positioning system adopting microservice architecture was proposed. This method introduced a storage technique of data categorization based on a time series database, designed a system architecture of soft bus, and realized a lightweight communication mechanism between different business and external heterogeneous systems. On the hosting platform, business application created by microservice and deployed by Docker image, which greatly improves the scalability and portability of the system.

Currently, there are two main clustering perforation methods: coiled tubing-conveyed perforation (TCP) and cable-conveyed perforation (CCP). To improve the efficiency of CCP strings in wellbore pass-through capacity problems, considering wellbore geometry constraints, pipe string variable cross-section, flexible tool deformation, and other factors, the pass-through capacity analysis model of the perforating tool string was established and solved by geometric analysis and beam-column theory. Aiming at the running depth problem of a TCP string, considering the influence of the buckling effect, a model of coiled tubing running depth was established by the infinitesimal method. The field test data were used to verify the effectiveness of the two models. The effects of well trajectory, perforating string size, casing inner diameter, and coiled tubing size on the pass-through capacity of the CCP string and running depth of the TCP string were explored. The results indicate that with the increase of perforation string size or the reduction of casing inner diameter, the pass-through capacity of CCP string decreases; with the increase of coiled tubing specification, the running capacity of TCP string is improved. The work presented in this paper provides an effective analysis method and theoretical guidance for clustering perforation jobs in the field. [ABSTRACT FROM AUTHOR]