An indoor trajectory is the path of an object moving through corridors and stairs inside a building.There are various types of technologies that can be used to reconstruct the path of a moving object and detect its po...An indoor trajectory is the path of an object moving through corridors and stairs inside a building.There are various types of technologies that can be used to reconstruct the path of a moving object and detect its position.GPS has been used for reconstruction in outdoor environments,but for indoor environments,mobile devices with embedded sensors are used.An accelerometer sensor and a magnetometer sensor are used to detect human movement and reconstruct the trajectory on a single floor.In an indoor environment,there are many activities that will create the trajectory similar to an outdoor environment,such as passing along the corridor,going from one room to another,and other activities.We need to analyse trajectories to obtain the movement patterns,understand themost frequently visited places or paths used aswell as the least frequented ones.Furthermore,we can utilize movement patterns to obtain a better building design and layout.The latest studies focus on reconstructing the trajectory on a single floor.However,actual indoor environments are comprised of multi-floors and multibuildings.The purpose of this paper is to reconstruct a trajectory in an indoor multi-floor environment.We have conducted extensive experiments to evaluate the performance of our proposed algorithms in a campus building.The result of our experiment shows that the height of the building can be detected using a barometer sensor that gives an atmospheric pressure reading which is then transformed by setting the range value according to the number of floors,enabling the sensors to detect activity in a multi-floor building.The readings obtained from the magnetometer sensor can be used to reconstruct the trajectory similar to the real path based on the direction and degree of direction.The system accuracy in recognizing steps in a multi-floor building is about 84%.展开更多
Fire smoke movement of multi-floor and multi-room (MFMR) fire was studied at the model test building in State Key Laboratory of Fire Science (SKLFS). The ingredient, temperature, air pressure difference and air veloci...Fire smoke movement of multi-floor and multi-room (MFMR) fire was studied at the model test building in State Key Laboratory of Fire Science (SKLFS). The ingredient, temperature, air pressure difference and air velocity of smoke were measured and analyzed. Meanwhile, the hazard of smoke ingredient to exposed occupants was analyzed based on the national standard, Occupational Exposure Limit for Hazardous Agents in the Workplace (GBZ2-2002). The experimental results showed that the maximum temperature difference in MFMR fire was located along the vertical height from the fire source. With the spreading and diffusion of smoke, the temperature of smoke layer would tend to be no difference. In the fire of woodpile and kerosene, the main smoke ingredients such as SO 2 , CO and CO 2 would first exceed human’s average physiological limit, while smoke ingredients such as NO and NO 2 would come behind. Because of the higher fluctuation range and frequency of air pressure difference of smoke in multi-layer building fire, the fire smoke would spread around everywhere of the passageway and made the human evacuation more difficult.展开更多
As an important task of multi-floor localization,floor detection has elicited great attention.Wireless infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor detection.However,most floor...As an important task of multi-floor localization,floor detection has elicited great attention.Wireless infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor detection.However,most floor detection research tends to focus on data modelling but pays little attention to the data collection system,which is the basis of wireless infrastructure-based floor detection.In fact,the floor detection task can be greatly simplified with proper data collection system design.In this paper,a floor detection solution is developed in a multi-floor life science automation lab.A data collection system consisting of BLE beacons,a receiver node and an Internet of Things(IoT)cloud is provided.The features of the BLE beacon under different settings are evaluated in detail.A mean filter is designed to deal with the fluctuation of the received signal strength indicator data.A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests.The time delay and floor detection accuracy under different settings are discussed.Finally,floor detection is evaluated on the H20 multi-floor transportation robot.Two sensor nodes are installed on the robot at different heights.The floor detection performance with different installation heights is discussed.The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.展开更多
基金This research was supported by the Scientific Research Deanship,Saudi Electronic University(7732-CAI-2019-1-2-r).
文摘An indoor trajectory is the path of an object moving through corridors and stairs inside a building.There are various types of technologies that can be used to reconstruct the path of a moving object and detect its position.GPS has been used for reconstruction in outdoor environments,but for indoor environments,mobile devices with embedded sensors are used.An accelerometer sensor and a magnetometer sensor are used to detect human movement and reconstruct the trajectory on a single floor.In an indoor environment,there are many activities that will create the trajectory similar to an outdoor environment,such as passing along the corridor,going from one room to another,and other activities.We need to analyse trajectories to obtain the movement patterns,understand themost frequently visited places or paths used aswell as the least frequented ones.Furthermore,we can utilize movement patterns to obtain a better building design and layout.The latest studies focus on reconstructing the trajectory on a single floor.However,actual indoor environments are comprised of multi-floors and multibuildings.The purpose of this paper is to reconstruct a trajectory in an indoor multi-floor environment.We have conducted extensive experiments to evaluate the performance of our proposed algorithms in a campus building.The result of our experiment shows that the height of the building can be detected using a barometer sensor that gives an atmospheric pressure reading which is then transformed by setting the range value according to the number of floors,enabling the sensors to detect activity in a multi-floor building.The readings obtained from the magnetometer sensor can be used to reconstruct the trajectory similar to the real path based on the direction and degree of direction.The system accuracy in recognizing steps in a multi-floor building is about 84%.
基金This work was supported by the National Natural Science Foundation of China(Grant No.50106017)China National Key Basic Research Special Funds(Grant No.2001CB409600)the 10th Five-year Tackle Key Plan of China Science and Technology(Grant No.2001BA803B01).
文摘Fire smoke movement of multi-floor and multi-room (MFMR) fire was studied at the model test building in State Key Laboratory of Fire Science (SKLFS). The ingredient, temperature, air pressure difference and air velocity of smoke were measured and analyzed. Meanwhile, the hazard of smoke ingredient to exposed occupants was analyzed based on the national standard, Occupational Exposure Limit for Hazardous Agents in the Workplace (GBZ2-2002). The experimental results showed that the maximum temperature difference in MFMR fire was located along the vertical height from the fire source. With the spreading and diffusion of smoke, the temperature of smoke layer would tend to be no difference. In the fire of woodpile and kerosene, the main smoke ingredients such as SO 2 , CO and CO 2 would first exceed human’s average physiological limit, while smoke ingredients such as NO and NO 2 would come behind. Because of the higher fluctuation range and frequency of air pressure difference of smoke in multi-layer building fire, the fire smoke would spread around everywhere of the passageway and made the human evacuation more difficult.
基金the Synergy Project ADAM(Au-tonomous Discovery of Advanced Materials)funded by the Eu-ropean Research Council(Grant No.856405).
文摘As an important task of multi-floor localization,floor detection has elicited great attention.Wireless infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor detection.However,most floor detection research tends to focus on data modelling but pays little attention to the data collection system,which is the basis of wireless infrastructure-based floor detection.In fact,the floor detection task can be greatly simplified with proper data collection system design.In this paper,a floor detection solution is developed in a multi-floor life science automation lab.A data collection system consisting of BLE beacons,a receiver node and an Internet of Things(IoT)cloud is provided.The features of the BLE beacon under different settings are evaluated in detail.A mean filter is designed to deal with the fluctuation of the received signal strength indicator data.A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests.The time delay and floor detection accuracy under different settings are discussed.Finally,floor detection is evaluated on the H20 multi-floor transportation robot.Two sensor nodes are installed on the robot at different heights.The floor detection performance with different installation heights is discussed.The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.
