随着物联网技术的迅猛发展,各传感器及其应用所产生的数据量呈海量增长。用于在网络设备上进行传递、加速、展示、计算、存储数据信息的数据中心机房建设迎来了蓬勃发展。鉴于机房的重要地位,一旦发生火灾将会对整个数据中心造成毁灭性...随着物联网技术的迅猛发展,各传感器及其应用所产生的数据量呈海量增长。用于在网络设备上进行传递、加速、展示、计算、存储数据信息的数据中心机房建设迎来了蓬勃发展。鉴于机房的重要地位,一旦发生火灾将会对整个数据中心造成毁灭性影响。为了识别当前火灾所处状态并发出分级火灾报警信息,文章提出了基于长短时记忆网络(Long Short Term Memory,LSTM)算法与模糊推理的多传感器信息融合机房火灾报警模型。该模型分为数据层、特征层和决策层三层。其中数据层对多传感器的数据进行采集与预处理;特征层使用LSTM算法对传感器数据进行火灾状态识别;决策层将识别结果与数据中心机房保护等级和火灾持续时间进行模糊推理融合得出最终的火灾报警决策。通过燃烧实验进行验证,结果表明:文章所提模型对机房火灾状态识别效果优于反向传播(Back Propagation,BP)算法与循环神经网络(Gate Recurrent Unit,GRU)算法的同时可以发出符合当前火情的分级报警决策。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
We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment ...We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. We introduce several improvements over previous approaches based on color and spatial reasoning exploiting Manhattan world priors. In particular, we introduce a new method for geometric context extraction based on a 3D facet representation,which combines color distribution analysis of individual images with sparse multi-view clues. We also introduce an efficient method to combine the facets from different viewpoints in a single consistent model, taking into the reliability of the facet information. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments in cases where most previous approaches fail, e.g., in the presence of hidden corners and large clutter, without the need for additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes. Our test data is available to allow further studies and comparisons.展开更多
文摘随着物联网技术的迅猛发展,各传感器及其应用所产生的数据量呈海量增长。用于在网络设备上进行传递、加速、展示、计算、存储数据信息的数据中心机房建设迎来了蓬勃发展。鉴于机房的重要地位,一旦发生火灾将会对整个数据中心造成毁灭性影响。为了识别当前火灾所处状态并发出分级火灾报警信息,文章提出了基于长短时记忆网络(Long Short Term Memory,LSTM)算法与模糊推理的多传感器信息融合机房火灾报警模型。该模型分为数据层、特征层和决策层三层。其中数据层对多传感器的数据进行采集与预处理;特征层使用LSTM算法对传感器数据进行火灾状态识别;决策层将识别结果与数据中心机房保护等级和火灾持续时间进行模糊推理融合得出最终的火灾报警决策。通过燃烧实验进行验证,结果表明:文章所提模型对机房火灾状态识别效果优于反向传播(Back Propagation,BP)算法与循环神经网络(Gate Recurrent Unit,GRU)算法的同时可以发出符合当前火情的分级报警决策。
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partially supported by projects VIGEC and 3DCLOUDPRO
文摘We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. We introduce several improvements over previous approaches based on color and spatial reasoning exploiting Manhattan world priors. In particular, we introduce a new method for geometric context extraction based on a 3D facet representation,which combines color distribution analysis of individual images with sparse multi-view clues. We also introduce an efficient method to combine the facets from different viewpoints in a single consistent model, taking into the reliability of the facet information. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments in cases where most previous approaches fail, e.g., in the presence of hidden corners and large clutter, without the need for additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes. Our test data is available to allow further studies and comparisons.
