UAV imagery based potential safety hazard evaluation for high-speed railroad using Real-time instance segmentation

Potential safety hazards (PSHs) along the track needs to be inspected and evaluated regularly to ensure a safe environment for high-speed railroad operations. Other than track inspection, evaluating potential safety hazards in the nearby areas often requires inspectors to patrol along the track and visually identify potential threads to the train operation. The current visual inspection approach is very time-consuming and may raise safety concerns for the inspectors, especially in remote areas. Using the unmanned aerial vehicle (UAV) has great potential to complement the visual inspection by providing a better view from the top and ease the safety concerns in many cases. This study develops an automatic PSH detection framework named YOLARC (You Only Look at Railroad Coefficients) using UAV imagery for high-speed railroad monitoring. First, YOLARC is equipped with a new backbone having multiple available receptive fields to strengthen the multi-scale representation capability at a granular level and enrich the semantic information in the feature space. Then, the system integrates the abundant semantic features at different high-level layers by a light weighted feature pyramid network (FPN) with multi-scale pyramidal architecture and a Protonet with residual structure to precisely predict the track areas and PSHs. A hazard level evaluation (HLE) method, which calculates the distance between identified PSH and the track, is also developed and integrated for quantifying the hazard level. Experiments conducted on the UAV imagery of high-speed railroad dataset show the proposed system can quickly and effectively turn UAV images into useful information with a high detection rate and processing speed.     

Enabling automated facility maintenance from articulated robot Collision-Free designs

Key challenges found in some facilities maintenance (FM) operations include the dependency on placing sophisticated workers in undesirable working conditions that could make them susceptible to accidents. Robot-assisted in-situ facilities maintenance represents new opportunities by assisting human operators when exposed to harsh environments. However, given the complexities and potential varieties of advanced robotic machinery available for use in facility maintenance workplaces in the future, there is still a legitimate risk of collisions between future robotic systems and the facility structure during the robot-assisted processes. This paper proposes and tests a simulation-based collision-free design method for future facilities that may be benefited from a robot-assisted FM process. At the most fundamental level, we consider interactions between the articulated manipulator (robot) and the surrounding facility structure. Inverse Kinematics (IK) and game engines are used to simulate the possible collisions between a given robot and the parameterized facility layout. To account for the uncertainty about the kinematic specifications of the future robotic machinery, a Monte Carlo method is used to model unique priority circumstances for each joint on the articulated manipulator. The simulation result, presented as the robot work zone envelope, is then used to estimate the collision probability given a certain design parameter, and the corresponding optimal design that balances the initial construction cost and the FM costs. The method is assessed for identifying a 3D spatial collision probability cloud within reach of a 7 DOF articulated manipulator that is marginally positioned within a pressure valve piping facility. These insights can provide future facility designers, managers, and equipment operators with a quick and flexible 3D spatial collision probability indicator to better design the facility and proximity limitations of any articulated manipulator positioned throughout a confined space for future robot-assisted FM processes.     

Stakeholder requirement evaluation of smart industrial service ecosystem under Pythagorean fuzzy environment for complex industrial contexts: A case study of renewable energy park

This study focuses on ways to systematically evaluate stakeholder requirements when developing a smart industrial service ecosystem (SISE) in a complex industrial context. The SISE development requires considering the service requirement from both the complex industrial context and service ecosystem manners. This study proposes a systematic framework for stakeholder requirement evaluation in SISE. The first part of the framework is the industrial context-viable system model with ecological thinking (IC-VESM) to elicit the service requirements for the SISE, which facilitates a systematic analysis of the service value proposition and service requirement elicitation in the operational lifecycle of an entire industrial context. This second part of the framework proposes a method for evaluating service requirements that is both feasible and systematic. This is achieved by combining the Fuzzy Kano and AHP methods in a Pythagorean fuzzy (PF) environment. The PF Kano computes the categories and determines the weights of service requirements from a consumer perspective, while the PF AHP hierarchically analyzes the service requirements and provides pairwise comparison paths for design experts. Finally, an illustrative case study in a renewable energy context was used to demonstrate the feasibility and effectiveness of the methodology. The proposed theoretical model provides more reliable and systematic outcomes than traditional methods when eliciting service requirements and evaluating complex smart industrial service solutions. The study has practical implications by providing useful insights for companies to recognize key smart service requirements in complex industrial contexts and to improve sustainable development.     

Adaptive multi-mode switching strategy for the spherical underwater robot with hybrid thrusters

The conventional autonomous cruise and tracking system cannot transition and change its motion mode according to the underwater environment during operation, so it is of great significance to study the multi-mode switching of cruise control for Autonomous Underwater Vehicles (AUVs). In this paper, a Multi-Mode Adaptive Switching (MMAS) strategy for the Spherical Underwater Robot with Hybrid Thruster (HPSUR) was proposed, which provided the possibility for the robot to choose optimal control mode according to the unpredictable operating environment. Then, the dynamic and force of the hybrid thruster are analyzed to improve the accuracy of multi-mode switching, and the MMAS strategy is developed linking the attitude adjustment and switching problems. Furthermore, a series of multi-mode switching experiments were conducted using water-jet mode, propeller mode and hybrid mode. Finally, the experimental result was discussed, which verified the effectiveness and accuracy of the proposed MMAS strategy. The proposed control strategy has a certain reference value for the multi-mode switching of propulsion devices.     

Railcar reallocation optimization on water-rail network under uncertain busyness

Water-rail intermodal transportation can reduce cargo losses and transportation transferring costs. However, the imbalance between the capacity of the scheduled railway network and the large container freight demand greatly reduces operational efficiency. To minimize the total transportation cost and relocation cost, a railcar reallocation stochastic optimization model is formulated to deal with uncertain congestion in the railway network. To capture the uncertain busyness and queuing pattern, a hypercube spatial queue model is embedded in the optimization model by estimating the expected queue length and waiting time. To solve the proposed nonlinear nonconcave stochastic model, an approximate hypercube based iterative algorithm is proposed. A real-world case study is presented to show the effectiveness and efficiency of the proposed method. The proposed model outperforms the comparable deterministic model in the objective value. Sensitivity analyses on the ratio of the unit waiting cost and the unit travel cost for empty cars, and the total number of freight cars show the robustness of the proposed method.     

Participants matter: Effectiveness of VR-based training on the knowledge,trust in the robot,and self-efficacy of construction workers and university students

Virtual Reality (VR)-based training has gained attention from the scientific community in the Architecture, Engineering, and Construction (AEC) industry as a cost-effective and safe method that eliminates the safety risks that may impose on workers during the training compared to traditional training methods (e.g., in-person hands-on training, apprenticeship). Although researchers have developed VR-based training for construction workers, some have recruited students rather than workers to understand the effect of their VR-based training. However, students are different from construction workers in many ways, which can threaten the validity of such studies. Hence, research is needed to investigate the extent to which the findings of a VR-based training study are contingent on whether students or construction workers were used as the study sample. This paper strives to compare the effectiveness of VR-based training on university students’ and construction workers’ knowledge acquisition, trust in the robot, and robot operation self-efficacy in remote operation of a construction robot. Twenty-five construction workers and twenty-five graduate construction engineering students were recruited to complete a VR-based training for remote operating a demolition robot. We used quantitative analyses to answer our research questions. Our study shows that the results are dependent on the target sample in that students gained more knowledge, whereas construction workers gained more trust in the robot and more self-efficacy in robot operation. These findings suggest that the effectiveness of VR-based training on students may not necessarily associate with its effectiveness on construction workers.     

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.     

基于水下连续波体制的捕获跟踪技术研究

针对水下高速潜器回收导引过程中实现实时测量位置与传输指控信息的问题,该文提出基于水下连续波体制的捕获跟踪技术。利用连续波体制实现测距与通信的同步解析,通过并行处理结构压缩数据捕获时长,并基于锁相环原理设计出适应水声环境与高速背景的最佳环路跟踪策略。从理论仿真与松花湖试验结果来看,算法的捕获时间从传统匹配算法的83.87 s缩短至0.66 s,计算量缩小为时域算法的2.36%。信号跟踪技术在匀速模型和加速度模型下都具有良好的性能,从通信角度讲跟踪算法能够准确无误地传输数据,从参数估计角度讲,基于跟踪结果输出的参数估计精度高且随速度变化缓慢,但传统检测精度随速度增大而变差。该方法实现了对多普勒频偏的精确估计与动态调整,保证了声学测量与指控信息传输的连续性与稳定性,对水下高速潜器的实时回收导引具有重要意义。     

水声网络全双工定向碰撞避免媒体接入控制协议

近年来水声网络(UAN)技术飞速发展,但仍然面临诸多严峻挑战,能量效率成为水声网络的首要考虑因素。此外,水声信道传播时延大且可用带宽受限,严重制约了水声通信技术的可靠性和有效性,进而限制了水声网络的整体性能。定向通信技术可以有效改善上述情况,通过波束聚焦能力将声波能量聚集在一定角度范围内,获得比全向通信更高的通信范围和信噪比,提升整个网络的能耗效率和空间复用率。但该技术需要对目的节点的位置具备先验知识,且会面临“聋节点”问题,因此该文提出一个水声网络全双工定向碰撞避免(FDDCA)媒体接入控制(MAC)协议,通过装备两个分别处于不同工作频带的全向换能器和定向换能器解决“聋节点”问题,并通过降低节点的冲突域解决了“暴露终端”问题。仿真结果表明,与水下Aloha(UW-Aloha)和时隙地面多址接入(S-FAMA)协议相比,FDDCA在多汇聚节点的网络拓扑下吞吐量分别提升了140%和400%,网络能效上节省了90%和94%。     

基于云模型的基本概率赋值生成方法及应用

针对证据理论应用中基本概率赋值(BPA)生成模型难以确定问题,该文提出一种基于云模型的BPA生成方法。首先基于样本属性的正态云模型构建单子集命题的BPA模型函数,并将复合子集的模型函数表示为高斯函数乘积融合。其次提出一种根据测试样本动态度量属性权重的方法来兼顾信息源的可靠性。最后,用属性权重修正模型函数输出的结果得到BPA。鸢尾花等数据集分类识别实验表明,该方法识别准确性高,且适用于样本较少的情况。