Time filter

Source Type

Fang Z.-M.,Anhui University of Science and Technology | Song W.-G.,Anhui University of Science and Technology | Li Z.-J.,Yin Chuan Municipal Fire Brigade of Chinese Peoples Armed Police Force | Tian W.,Anhui University of Science and Technology | And 3 more authors.
Building and Environment | Year: 2012

One of the most important emergency accesses in a multi-layer or high-rise building is stairwell, through which people can escape from the building in case of fire. Much attention has been paid to evacuation study in the stairwell by means of experiments or modeling. In this study, an evacuation experiment was conducted in a stairwell of an 8-layer high-rise building. The evacuation process in the stairwell was recorded by video cameras. Some typical movement characteristics and parameters were extracted based on the recording data. The results demonstrate that the downward velocity is determined by three aspects: merging behavior in the entrance buffer of the stairwell, strength of participants and visibility in the stairwell. The downward velocity measured in our experiment, about 0.81 m/s, is similar to that in the previous study. The relationship between velocity and density in our study agrees well with the function indicated in SFPE handbook. © 2011 Elsevier Ltd.

Li S.,Shanghai Marine Electronic Equipment Research Institute
Proceedings - 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2011 | Year: 2011

Sparse arrays are popular in sonar, ultrasonic imaging and other acoustic equipment. For inter-element spacing larger than one wave length, there will be grate lobes for ordinary sparse array. Using genetic algorithm to optimize array with randomly spaced elements to suppress the grate lobes is introduced in this paper. Fitness function design and variable choosing method for genetic algorithms application in this kind of objects are presented. A single object fitness function instead of multi objects fitness function usually used for array design is proposed. © 2011 IEEE.

Liu Y.,Shanghai Maritime University | Yang C.,National Research Council Canada | Yang Y.,Nanjing University | Lin F.,Athabasca University | And 2 more authors.
Applied Intelligence | Year: 2012

With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records. © Springer Science+Business Media, LLC 2010.

Liu H.,Beihang University | Liu D.,Shanghai Marine Electronic Equipment Research Institute | Lu C.,Beihang University | Lu C.,Science and Technology on Reliability and Environmental Engineering Laboratory | Wang X.,Beihang University
Asian Journal of Control | Year: 2014

Fault occurrence can be embodied by the physical parameter variations of the hydraulic servo system. Faults can, therefore, be diagnosed according to the model coefficient variations of the hydraulic servo system. This paper proposes an approach for fault diagnosis based on the unscented Kalman filter (UKF) with a mathematical model of the hydraulic servo system. The mathematical model is established using the dynamic equations of the hydraulic servo system. Based on the fault mechanism analysis results, several important system model parameters that can separately represent different faults in different components of the hydraulic servo system are chosen. Discrete state space equations are derived from the dynamic equations. The UKF algorithm is used to estimate the important system model parameters of the hydraulic servo system by utilizing the discretized state space model. According to the variations of these model parameters, the fault modes and locations of the hydraulic servo system can be diagnosed and isolated. Two types of faults, namely, abrupt fault in servovalve gain and slow wear fault in hydraulic cylinder piston, which cannot be directly detected from the system output, are introduced individually to the hydraulic servo system in this work. By comparing with the extended Kalman Filter, three different experimental cases are used to validate the effectiveness of the UKF for hydraulic servo system fault diagnosis. © 2014 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd.

Teng Y.,Key Laboratory of Science and Technology on Underwater Acoustic Antagonizing | Fan W.,Shanghai Marine Electronic Equipment Research Institute
Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015 | Year: 2015

The characteristics of ship wake and a bubbles distribution model in ship wake were researched. According to the features of a vessel, a bubble distribution model in the vessel wake were simulated and then applied to predict sound propagation attenuation in ship wakes. Simultaneously, the vessel's wake was measured on a lake at range of frequency from 4kHz to 20kHz, which were analyzed to explore the performance of the presented model. The comparison results show that the void fraction of bubbles in the vessel's wake reach1.14×10-6, and the difference between the predicted attenuation and the measurements of sound propagation attenuation in the wake are less than 5dB/km, which agree well with experiment results. Thus, the bubble distribution model of ship wake can be used to research the acoustical characteristics in ship wake to overcome the data incompleteness. © 2015 IEEE.

Discover hidden collaborations