This paper proposes a video-based SlowFast model that combines the SlowFast deep learning model with a new boundary box annotation algorithm. The new algorithm, namely the MTB (i.e., the ratio of the number of Moving object pixels To the number of Bounding box pixels) algorithm, is devised to automatically annotate the bounding box that includes the smoke with fuzzy boundaries. The model parameters of the MTB algorithm are examined by multifactor analysis of variance. To demonstrate the validity of the proposed approach, a case study is provided that examines real video clips of incipient forest fires with small amounts of smoke. The performance of the proposed approach is compared with those of existing deep learning models, including convolutional neural network (CNN), faster region-based CNN (faster R-CNN), and SlowFast. It is demonstrated that the proposed approach achieves enhanced detection accuracy, while reducing false negative rates.

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

No abstract