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The firewater deluge system (FDS) can provide water automatically through a deluge valve when a fire breaks out. However, there are many fire hazards caused by the abnormal operating state of the FDS. To monitor and predict the working state of the FDS, this paper aims to propose a firewater deluge monitoring and forecasting system using the Internet of Things (IoT) technology.

The firewater deluge monitoring and forecasting system consists of three layers: the sensing layer, network layer and application layer. The firewater pressure obtained by the monitoring nodes was transmitted to the local gateway and then to the remote monitoring center. In the application layer, an autoregressive moving average (ARMA) model was put forward to forecast the firewater pressure. Furthermore, a genetic algorithm (GA) was proposed to perfect the order determination method of the ARMA model. Finally, a Web application was developed to display the real time and predicted working status of the FDS.

The predicted results show that the ARMA model improved by the GA (GA-ARMA) is significantly better than traditional ARMA models in terms of mean relative error, mean absolute error and mean square error. Moreover, the proposed system is demonstrated to be effective, and an early warning can be alerted to remind users of repairing abnormal FDS equipment ahead of fire dangers.

The proposed system cannot only be applied to the FDS of all buildings to avoid fire hazards by monitoring and predicting the working state of the FDS, but can also be widely used in other fields, such as environmental monitoring, intelligent logistics and intelligent transportation.

Hoping to uncover the physical principles of the vibration of the functionally graded material (FGM) microplate, by which the authors can make contributions to the design and manufacturing process in factories like micro-electro-mechanical system (MEMS) and other industries.

The authors design a method by establishing a reasonable mathematical model of the physical microplate composed of a porous FGM.

The authors discover that the porosity, the distributions of porosity, the power law of the FGM and the length-to-thickness ratio all affect the natural frequency of the vibration of the microplate, but in different ways.

Originally proposed a model of the micro FGM plate considering the different distributions of the porosity and scale effect and analyzed the vibration frequency of it.

The purpose of this paper is to find a theoretical reference to adjust the unsymmetrical arc shape and plasma flow of overlapping deposition in wire arc additive manufacturing (WAAM) and ensure the effect of the gas shielding and stable heat and mass transfer in deposition process. The multiphysical numerical simulation and physical experiment are used for validation.

In this study, welding torch tilt deposition and external parallel magnetic field–assisted deposition are presented to adjust the unsymmetrical arc shape and plasma flow of overlapping deposition, and a three-dimensional numerical model is developed to simulate the arc of torch tilt overlapping deposition and external parallel magnetic field–assisted overlapping deposition.

The comparison of simulated results indicate that the angle of welding torch tilt equal to 20° and the magnetic flux density of external transverse magnetic field equal to 0.001 Tesla are capable of balancing the electric arc and shielding gas effectively, respectively. The arc profiles captured by a high-speed camera match well with simulated results.

These studies of this paper can provide a theoretical basis and reference for the calibration and optimization of WAAM process parameters.

The aim of this article is to present a PIN (pedestrian inertial navigation) solution that incorporates altitude error correction, which eliminates the altitude error accurately without using external sensors. The main problem of PIN is the accumulation of positioning errors due to the drift caused by the noise in the sensors. Experiment results show that the altitude errors are significant when navigating in multilayer buildings, which always lead to localization to incorrect floors.

The PIN proposed is implemented over an inertial navigation systems (INS) framework and a foot-mounted IMU. The altitude error correction idea is identifying the most probable floor of each horizontal walking motion. To recognize gait types, the walking motion is described with angular rate measured by IMU, and the dynamic time warping algorithm is used to cope with the different dimension samples due to the randomness of walking motion. After gait recognition, the altitude estimated with INS of each horizontal walking is checked for association with one of the existing in a database.

Experiment results show that high accuracy altitude is achieved with altitude errors below 5 centimeters for upstairs and downstairs routes in a five floors building.

The main limitations of the study is the assumption that accuracy floor altitude information is available.

Our PIN system eliminates altitude errors accurately and intelligently, which benefits from the new idea of combination of gait recognition and map-matching. In addition, only one IMU is used which is different from other approach that use external sensors.