| 1. |
- Kong, Depeng, et al.
(författare)
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A method for linking safety factor to the target probability of failure in fire safety engineering
- 2013
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Ingår i: Journal of Civil Engineering and Management. - : Vilnius Gediminas Technical University. - 1392-3730 .- 1822-3605. ; 19, s. 212-221
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Tidskriftsartikel (refereegranskat)abstract
- Ensuring occupants' safety in building fires is one of the most important aspects for fire safety engineering. Many uncertainties are inevitably introduced when estimating the occupant safety level, due to the high complexity of fire dynamics and the human behaviour in fires. Safety factor methods are traditionally employed to deal with such uncertainties. This kind of methods is easy to apply but leaves fire safety engineers unsure of the margin by which the design has failed. A method of linking safety factor and probability of failure in fire safety engineering is proposed in this study. An event tree is constructed to analyse potential fire scenarios that arise from the failure of fire protection systems. Considering uncertainties related to fire dynamics and evacuation, the traditional deterministic safety factor is considered as a random variable. Because there is no target probability of failure accepted by the whole fire safety engineering community, the concept of expected risk to life (ERL) is integrated to determine the target probability of failure. This method employs a Monte Carlo Simulation using Latin Hypercube Sampling (LHS) to calculate the required safety factor. A practical case study is conducted using the method proposed in this study.
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| 2. |
- Kong, Depeng, et al.
(författare)
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A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time
- 2013
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Ingår i: Journal of Fire Protection Engineering. - : SAGE Publications. - 1042-3915 .- 1532-172X. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Available safe egress time is an important criterion to determine occupant safety in performance-based fire protection design of buildings. There are many factors affecting the calculation of available safe egress time, such as heat release rate, smoke toxicity and the geometry of the building. Heat release rate is the most critical factor. Due to the variation of fuel layout, initial ignition location and many other factors, significant uncertainties are associated with heat release rate. Traditionally, fire safety engineers prefer to ignore these uncertainties, and a fixed value of heat release rate is assigned based on experience. This makes the available safe egress time results subjective. To quantify the effect of uncertainties in heat release rate on available safe egress time, a Monte Carlo simulation approach is implemented for a case study of a single hypothetical fire compartment in a commercial building. First, the effect of deterministic peak heat release rate and fire growth rate on the predicted available safe egress time is studied. Then, the effect of uncertainties in peak heat release rate and fire growth rate are analyzed separately. Normal and log-normal distributions are employed to characterize peak heat release rate and fire growth rate, respectively. Finally, the effect of uncertainties in both peak heat release rate and fire growth rate on available safe egress time are analyzed. Illustrations are also provided on how to utilize probabilistic functions, such as the cumulative density function and complementary cumulative distribution function, to help fire safety engineers develop proper design fires.
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| 3. |
- Kong, Depeng, et al.
(författare)
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Bioinspired Co-Design of Tactile Sensor and Deep Learning Algorithm for Human-Robot Interaction
- 2022
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : Wiley. - 2640-4567. ; 4:6
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Tidskriftsartikel (refereegranskat)abstract
- Robots equipped with bionic skins for enhancing the robot perception capability are increasingly deployed in wide applications ranging from healthcare to industry. Artificial intelligence algorithms that can provide bionic skins with efficient signal processing functions further accelerate the development of this trend. Inspired by the somatosensory processing hierarchy of humans, the bioinspired co-design of a tactile sensor and a deep learning-based algorithm is proposed herein, simplifying the sensor structure while providing computation-enhanced tactile sensing performance. The soft piezoresistive sensor, based on the carbon black-coated polyurethane sponge, offers a continuous sensing area. By utilizing a customized deep neural network (DNN), it can detect external tactile stimulus spatially continuously. Besides, a novel data augmentation method is developed based on the sensor's hexagonal structure that has a sixfold rotation symmetry. It can significantly enhance the generalization ability of the DNN model by enriching the collected training data with generated pseudo-data. The functionality of the sensor and the robustness of the proposed data augmentation strategy are verified by precisely recognizing five touch modalities, illustrating a well-generalized performance, and providing a promising application prospect in human-robot interaction.
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