• The KIPS Transactions:PartB
• /
• v.18B no.4
• /
• pp.213-220
• /
• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.12A
• /
• pp.1171-1180
• /
• 2010

In wireless sensor networks, reliable event detection is one of the most important research issues. For the reliable event detection, previous works usually assume the events are only individual objects such as tanks and soldiers. Recently, many researches focus on detection of continuous objects such as wild fire and bio-chemical material, but they merely aim at methods to reduce communication costs. Hence, we propose a reliable continuous object detection scheme. However, it might not be trivial. Unlike individual objects that could be referred as a point, a continuous object is shown in a dynamic two-dimensional diagram since it may cover a wide area and it could dynamically alter its own shape according to physical environments, e.g. geographical conditions, wind, and so on. Hence, the continuous object detection reliability can not be estimated by the indicator for individual objects. This paper newly defines the reliability indicator for continuous object detection and proposes an error recovery mechanism relying on the estimation result from the new indicator.

• 재활복지
• /
• v.21 no.3
• /
• pp.129-146
• /
• 2017

In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.

• Journal of the Korean Society of Safety
• /
• v.37 no.4
• /
• pp.20-27
• /
• 2022

This article aims to develop an algorithm that detects fire pump defects by analyzing the current signals of an induction motor, which are triggered by changes in the flow rate and pressure of multistage volute pumps that are used for fire services. The operational status of the pumps was categorized into three: first, normal operation; second, a defect that is caused by a change in the current value; and third, a defect occasioned by a change in current, pressure, and flow rate. When a fire pump was in normal operation, the motor's operating current was measured between 5.06 A and 6.9 A, the flow rate was estimated at 0-0.27 m³/min, and the pressure ranged from 0 to 0.47 MPa. In the event that a defect was caused by an abnormal current value in the motor, it was attributed to the pump's adherence. Furthermore, if there was no source of water, the defect was considered to have been induced by phase-loss operation, no-load operation, or run-stop operation, with the current value of each scenario being measured at > 52.8 A, < 4.13 A, > 45.15 A, and < 3.8 A, respectively, placing its overall range between 0 and 50 A. The sources of defects were detected based on an analysis of the flow rate, pressure, and current, which represent the following causes: air inflow into the casing, inadequate suction of water, and reverse-phase operation, respectively. Each cause entailed the following values: when air seeped into the casing, the pressure was measured at 0.24 MPa irrespective of changes in the flow rate; when there was inadequate suction of water, the pressure was recorded between 0 and 0.05 MPa despite changes in the flow rate; and when the power line's reverse-phase loss was the cause of the defect, the pressure was measured at 0.33 MPa for a flow rate of 0 L/min, and a higher flow rate decreased the pressure to nearly 0 MPa. The results of this study will enable engineers to develop a pump defect detection algorithm that is based on an analysis of current, and this algorithm will facilitate the execution of a program that will control a fire pump defect detection system.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.9
• /
• pp.1120-1131
• /
• 2016

In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker's activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.882-885
• /
• 2003

In this paper, we describe a haptic jacket and wheel as a haptic interface to enhance VR game realism. Building upon the VR game system using this devices, our haptic interface technique allows the user to intuitive interact on game contents, and then to sense the game event properties such as walking, attacking, driving and fire in a natural way. In addition, we extended the initial haptic model to support haptic decoration and dynamic interactions due to the added game event in a real time display. An application example presented here is a VR Dino-Attack game. This game supports interactions among dynamic and our intuitive haptic interface. Modeling physic interactions involves precise collision detection, real-time force computation, and high control-loop bandwidth.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.11 no.2
• /
• pp.148-156
• /
• 2003

Many car manufacturers have frequently adopted an aggressive inflator and a lower threshold speed for airbag deployment in order to meet an injury requirement for unbolted occupant at high speed crash test. Consequently, today's occupant safety restraint system has a weakness due to an airbag induced injury at low speed crash event. This paper proposes a new crash algorithm to improve the weakness by suppressing airbag deployment at low speed crash event in case of belted condition. The proposed algorithm consists of two major blocks-crash severity algorithm and deployment logic block. The first block decides crash severity with two levels by means of velocity and crash energy calculation from acceleration signal. The second block implemented by simple AND/OR logic combines the crash severity level and seat belt status information to generate firing commands for airbag and belt pretensioner. Furthermore, it can be extended to adopt additional sensor information from passenger presence detection sensor and safing sensor. A simulation using real crash data for a 1,800cc passenger vehicle has been conducted to verify the performance of proposed algorithm.

• Journal of Service Research and Studies
• /
• v.12 no.2
• /
• pp.23-37
• /
• 2022

Based on the service scenario proposed by the existing Kim Tae-wan (2018) who can safely evacuate inmates with the help of a mobile application linked to a fire detection system in the event of a fire, the final purpose of this study is to develop the scenario by incorporating more realistic scenarios with mobile stimuli that can help them escape or act through the Delph In addition, to make the scenarios produced more realistic considering the structure and copper lines of a typical building, expert scenario verification and Delphi technique were applied to exclude unnecessary or impractical aspects of the existing scenarios. The results of the second Delphi survey showed that the primary psychology that could be seen at the time of the fire alarm were doubts, safety concerns and alarm, and the results of the second Delphi survey were analyzed, and the satisfaction of the content adequacy (CVR), convergence, and consensus was derived. Finally, this was applied to create a scenario in which a mobile application was assisted to evacuate the fire response phase. This study will allow the use of methods to increase the evacuation rate of those who are in the event of a fire.

• Journal of Advanced Marine Engineering and Technology
• /
• v.40 no.9
• /
• pp.852-858
• /
• 2016

To safeguard the accommodation spaces on cargo ships from fire, structural fire protection provisions introduced by SOLAS and these measures retard the propagation of flames and smoke. SOLAS also specifies provisions for fire fighting drills. These provisions are a combination of regulations regarding structure and equipment and those dealing with the human element for the fire protection and effective responses in the event of fire. Requirements related to the human element play a supporting role to the requirements for structure and equipment because the present accommodation structure and equipment are insufficient for extinguishing a fire, therefore, fire-extinguishing activity performed by crew members is essential. To reduce human error and ensure effective fire fighting, it is necessary to install a fire-fighting system and improve the fire fighting process. The fundamental concept of fire fighting exercises is to commence fire fighting before the fire grows too big to extinguish. It is essential to relocate the storage place of fire fighting equipment to expedite the fire-fighting exercise. This study was carried out to reduce human risk for this purpose, the fire control station was relocated to a site that could be accessed from the open deck. Further, two sets of a fire fighter's outfit were stored at the same site. This relocation eliminated the risk of the crew reentering to operate the fire fighting system in the fire control station and allowed the crew to pick up the fire fighters' outfits quickly in the event of a fire. In addition, it was proposed that the IIC method be made mandatory. This method is combination of automatic fire detection system and sprinkler system which can reduce the risk of the fire fighting exercises for the crew and to suppress fire in the initial stage. This study was carried out to provide a foundation to the possible amendment of the relevant SOLAS regulations and national legislation.

• Journal of the Society of Disaster Information
• /
• v.16 no.1
• /
• pp.96-110
• /
• 2020

Recently, due to global warming, it is easily exposed to various disasters such as fire, flood, and earthquake. In particular, large-scale disasters have continuously been occurring in crowded areas such as traditional markets, facilities for the elderly and children, and public facilities where various people stay. Purpose: This study aims to detect a fire occurred in crowded facilities early in the event to analyze and provide an optimal evacuation route using big data and advanced technology. Method: The researchers propose a new algorithm through context-aware 3D object model technology and A* algorithm optimization and propose a scenario-based optimal evacuation route selection technique. Result: Using the HPA* E algorithm, the evacuation simulation in the event of a fire was reproduced as a 3D model and the optimal evacuation route and evacuation time were calculated for each scenario. Conclusion: It is expected to reduce fatalities and injuries through the evacuation induction technique that enables evacuation of the building in the shortest path by analyzing in real-time via fire detection sensors that detects the temperature, flame, and smoke.