Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper. In this method, the original image sequence data is first separated from the foreground and background. Then, the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction. Finally, human posture nodes are extracted from each frame of the image sequence, which are then used to identify the abnormal behavior of the human. Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.     

Numerical study of strain development in high-density polyethylene geomembrane liner system in landfills using a new constitutive model for municipal solid waste

Tensile strain development in high-density polyethylene (HDPE) geomembrane (GMB) liner systems in landfills was numerically investigated. A new constitutive model for municipal solid waste (MSW) that incorporates both mechanical creep and biodegradation was employed in the analyses. The MSW constitutive model is a Cam-Clay type of plasticity model and was implemented in the finite difference computer program FLAC?. The influence of the friction angle of the liner system interfaces, the biodegradation of MSW, and the MSW filling rate on tensile strains were investigated. Several design alternatives to reduce the maximum tensile strain under both short- and long-term waste settlement were evaluated. Results of the analyses indicate that landfill geometry, interface friction angles, and short- and long-term waste settlement are key factors in the development of tensile strains. The results show that long-term waste settlement can induce additional tensile strains after waste placement is complete. Using a HDPE GMB with a friction angle on its upper interface that is lower than the friction angle on the underlying interface, increasing the number of benches, and reducing the slope inclination are shown to mitigate the maximum tensile strain caused by waste placement and waste settlement.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Shared human–robot path following control of an unmanned ground vehicle

This paper considers the shared path following control of an unmanned ground vehicle by a single person. A passive measure of human intent is used to blend the human and machine inputs in a mixed initiative approach. The blending law is combined with saturated super-twisting sliding mode speed and heading controllers, so that exogenous disturbances can be counteracted via equivalent control. It is proven that when the proposed blending law is used, the combined control signals from both the human and automatic controller respect the actuator magnitude constraints of the machine. To demonstrate the approach, shared control experiments are performed using an unmanned ground vehicle, which follows a lawn mower pattern shaped path.     

Coastal trapped waves in the southern Caspian Sea

Previous studies reported coastal trapped waves (CTWs) in the Caspian Sea (CS). This study deals with the generation mechanisms, the temporal and spatial variability of CTWs in this area, and their transformations during propagation from the origin to the destination using recent measurements and high-resolution numerical simulations. CTWs are observed at all stations with periods of 2–6 days after northerly storms. The Absheron Peninsula, old Sefidrud delta, and Nur coasts were identified as the CTWs prone regions. The generation of CTWs in these locations was confirmed using numerical experiments. The propagation away from the generating location of CTWs was analyzed using a representative real wind storm. In the west part of the CS, the generation mechanism of CTWs is mainly similar to the barotropic Kelvin waves; in contrast, it is similar to the continental shelf waves in the southern shelves. The results can be used to study the contribution of generated CTWs to the transport of sediment and biological materials in all large lakes.     

松科2井抗超高温钻井液技术

松科2井是1口部署在松辽盆地的高温科学钻探深井，目的为钻穿白垩纪地层，获取基底陆相沉积记录，预测井底温度超过220℃；四、五开钻沙河子组的泥岩、砂岩混层，火石岭组破碎的凝灰岩、泥岩、煤线的混层，容易发生坍塌现象，防塌措施尤其重要。四、五开属于连续提钻取心钻进，多次起下钻及长时间的裸眼对井壁稳定提出较大挑战；不同钻进工艺具有不同的特点，对钻井液也提出挑战。针对以上难点，研制出了抗高温聚合物钻井液:1.0%钠膨润土+2%凹凸棒土+0.2% KOH+（0.5%~1%）高分子量降滤失剂+1%中分子量降滤失剂+2.5%成膜剂+（2%~4%） SMC+2% FT+3% KCl+2% NaCOOH+3%白油。四开初期对三开的井浆进行评价，结合大量的室内小型转换实验，以原井浆为基础逐步进行转换，以满足钻进要求；五开因处理事故对井浆性能调整较大，事故结束后简化钻井液处理剂种类，逐渐将性能调至稳定状态。在松科2井现场应用时，根据实际情况及时调整，该阶段钻井液高温下流变性好、高温稳定性强，室内评价其抗温能力达到240℃，热滚72 h后性能良好，为顺利完成该阶段钻探工作提供了技术支撑，对提高钻探质量、降低勘探成本起到指导作用。      

Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.     

Biodegradability Study of the Biosurfactant Contained in a Crude Extract from Corn Steep Water

Over the last few years, the global biosurfactant market has raised due to the increasing awareness among consumers, for the use of biological or bio-based products. Because of their composition, it can be speculated that these are more biocompatible and more biodegradable than their chemical homologous. However, at the moment, no studies exist in the literature about the biodegradability of biosurfactants. In this work, a biosurfactant contained in a crude extract, obtained from a corn wet-milling industry stream that ferments spontaneously in the presence of lactic acid bacteria, was subjected to a biodegradation study, without addition of external microbial biomass, under different conditions of temperature (5–45 °C), biodegradation time (15–55 days), and pH (5–7). For that, a Box–Behnken factorial design was applied, which allowed to predict the percentage of biodegradation for the biosurfactant contained in the crude extract, between the range of the independent variables selected in the study, obtaining biodegradation values between 3 and 80%. The percentage of biodegradation for the biosurfactant was calculated based on the increase in the surface tension of samples of the crude extract. Furthermore, it was also possible to predict the variation in t_1/2 for the biosurfactant (time to achieve the 50% of biodegradation) under different conditions.     

基于现场监测与数值模拟的浅层采空区稳定性分析

隐患采空区是目前影响露天开采矿山安全生产的主要危害源之一。随着台阶开采的不断剥离,露天开采境界内各台阶与地下空区群的隔离层厚度越来越薄,随时有可能发生采空区顶板坍塌事故。考虑到露天矿采空区地质赋存条件和围岩稳固性等特征,以弓长岭露天铁矿浅层采空区为工程背景,运用现场监测和数值模拟相结合的手段综合分析了浅层采空区的稳定性。将液体静力水准地表沉降监测系统的监测数据与FLAC数值模拟结果对比,调整蠕变参数使得数值模拟的蠕变速率与现场监测结果一致,而后据此进行未来结果的预测。最终根据地表沉降数据确定的蠕变参数取值为A=1.0×10^-12、m=1.75、n=0.35。研究表明:静力水准测点地表最大沉降位移为-9.8 mm,蠕变计算结果顶板最大垂直位移约20.4 mm,应力最大值约25 MPa,综合分析显示该采空区较稳定。上述研究提供了一种基于采空区现场监测数据的数值模拟蠕变分析方法,可为类似矿山采空区稳定性分析提供借鉴。     

Modeling task completion time of in-vehicle information systems while driving with keystroke level modeling

The use of touchscreen-based in-vehicle information systems (IVIS) is increasing. To ensure safe driving, it is important to evaluate IVIS task performance during driving situations. Therefore, we proposed a model to assess the task completion time (TCT) of IVIS tasks while driving using a keystroke-level modeling (KLM) technique. The basic assumptions and heuristic rules of driver behaviors were considered. In addition, based on the characteristics of visual and manual IVIS interactions, we determined the basic unit operators (i.e., visual, manual, and mental operators). User experiments were conducted to determine the individual execution times of unit tasks and to measure the TCT of IVIS tasks while driving. Based on the heuristic rules for model development and individual task execution times, we derive a predictive model for the TCT of IVIS tasks. We used a regression analysis to validate the modeling procedure, showing that the observed TCT was found to have a strong positive correlation with the predicted time from the modeling process. The findings showed that the task completion time needed to perform a secondary task in a driving context can be predicted by KLM. This study provides meaningful insights into the design of touchscreen-based IVIS to enhance driving safety.