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The obscuration thresholds for various smoke detectors and combustibles, required as an input parameter in fire simulation, were measured to predict the accurate activation time of detectors. One ionization detector and nine photoelectric detectors were selected. A fire detector evaluator, which can uniformly control the velocity and smoke concentration, was utilized. Filter paper, liquid fuels, and polymer pellets were employed as smoke-generation combustibles. The nominal obscuration thresholds of the considered detectors were 15 %/m, but the ionization detectors activated at approximately 40 %/m and 16 %/m, respectively, on applying filter paper and kerosene. In contrast, the reverse obscuration thresholds were found quantitatively according to the combustibles in the photoelectric detector. This phenomenon was caused by differences in the color of the smoke particles according to the combustibles, which is explained by single-scattering albedo (ratio of light scattering to light extinction). The obscuration thresholds for liquid fuels (kerosene, heptane and toluene) as well as fire types of polymer plastic pellets were also measured for several photoelectric detectors. A database of obscuration thresholds was thereby established according to the detector and combustible types, and it is expected to provide useful information for predicting more accurate detector activation time and required safe egress time (REST).

With the growing popularity of leisure activities such as camping and glamping, the incidence of fires at camping sites has increased. This study focuses on improving the effectiveness of photoelectric fire alarm devices by incorporating temperature and humidity data for early fire detection in confined spaces, such as campsites. This study proposes a novel multi-sensor fire alarm system that dynamically adjusts fire detection threshold values based on temperature and humidity data collected by unmanned automatic weather observation systems. The prototype, which was implemented using Raspberry Pi and multiple sensors, demonstrated approximately 20% faster fire detection speed than existing photoelectric fire alarm systems, as verified through experiments in a simulated camping environment. The proposed approach is expected to advance fire alarm systems, enabling faster and more accurate fire detection in diverse environments, particularly at campsites.

Photoelectric smoke detectors are used for early detection of building fires, and sensitivity adjustment is generally performed using white smoke generated by the burning of filter paper. Therefore, when black smoke of the same concentration is introduced, the detector is often not activated. To address this problem, differences in the distribution of light scattered by smoke of various colors were analyzed. A light-scattering chamber with a light-receiving unit for various scattering angles was constructed to measure the scattered light generated inside the chamber of the smoke detector. The light scattering distribution was measured for smoke generated from three combustibles—filter paper (white smoke), kerosene (black smoke), and polyurethane (gray-black smoke)—and three analysis criteria were applied. By identifying a section where the measured values were concentrated for a specific analysis criterion and scattering angle, it was confirmed that some combustibles can be distinguished. Specifically, criterion III, a probabilistic section, was presented to determine which combustible smoke particles were close by applying the proposed section in a complex manner. A preliminary study was conducted to evaluate a methodology for the color classification of smoke particles flowing into a smoke detector chamber; this can be utilized as a foundation for determining optical properties.

Due to the complex structure of high-rise space buildings, traditional point fire detectors are not effective in terms of detection range and installation difficulty. Although linear beam smoke detectors are widely adopted, they still face problems such as low accuracy and false alarms caused by interference. To address these limitations, we constructed a 120 m experimental platform for analyzing smoke–light interactions. Through systematic investigation of spectral scattering phenomena, optimal operational wavelengths were identified for beam-type detection. By improving the gated recurrent unit (GRU) neural network, an algorithm combining dual-wavelength information fusion and an attention mechanism was designed. The algorithm integrates dual-wavelength information and introduces the cross-attention mechanism into the GRU network to achieve collaborative modeling of microscale scattering characteristics and macroscale concentration changes of smoke particles. The alarm strategy based on time series accumulation effectively reduces false alarms caused by instantaneous interference. The experiment shows that our method is significantly better than traditional algorithms in terms of accuracy (96.8%), false positive rate (2.1%), and response time (6.7 s).

A small ember, created by a chemical reaction between a substance and oxygen, can grow into a large fire as temperature, wind, and weather conditions change. A growing fire incident can have devastating consequences, including property loss, environmental damage, and loss of life, which is why early fire detection is so important. There are various detection devices such as smoke detectors, heat detectors, fire detectors, and gas detectors that can be used in the early stages of a fire. While early fire detection system developments incorporating IoT technology are emerging in various industries, Smoke alarms, the most common type of smoke detector in homes and offices, accounted for 96.6% of all malfunctions from 2021 to July of the previous year, totaling 249,445 incidents. The analysis of detector malfunctions showed that non-fire alarm factors such as food, cooking, and dust accounted for the largest share of 40.6%. This paper proposes an algorithm for early fire detection by incorporating a deep learning-based model to compensate for the problem of non-fire warning malfunctions, which is a shortcoming of existing detectors. Finally, for fire detection, a bounding box for the fire is specified using a smoke detector, a thermal imaging camera, and a webcam camera trained with the Yolov7 model. Then, we propose an algorithm to remove the bounding box of non-fire reports and malfunctions from the heating map using smoke detectors and thermal imaging cameras. After applying the algorithm proposed in this paper, only fires with heat sources are recognized, and all bounding boxes for non-fire reports are removed.

Photoelectric smoke detectors, commonly used in aircraft cargo compartments, have high false alarm rates, which may result in huge economic losses and threaten flight safety. Dust aerosols are conventionally considered the main reason for this. Distinguishing dust aerosols from fire smoke by considering the difference between their scattering characteristics using a relatively simple combination of optical elements is crucial for reducing the false alarm rate. According to the relevant provisions for fire smoke and dust specified in the aerospace standard (AS 8036A) of the Society of Automotive Engineers (SAE) and considering the common cargo types, we studied the scattering characteristics of three types of aerosols, including six non-flaming fire smokes, four flaming fire smokes, and two dust aerosols, through numerical calculation and experimental methods. The results showed that these aerosols have different light scattering characteristics for forward and backward scattering due to particle size, morphology, etc. Considering these differences, we distinguished fire smoke from dust aerosols and reduced the false alarm rate in photoelectric smoke detectors in aircraft cargo compartments using an optimised optical structure design with dual wavelengths of 405 and 850 nm and dual scattering angles of 45° and 135°.

In recent years, there has been an increasing demand for small‐scale hostels, such as vacation rentals, known as “minpaku” in Japanese, which were originally single‐family houses. To convert these buildings for use as accommodation facilities, improvements must be made to fire evacuation safety measures. An effective and reasonable measure for this improvement is the installation of wireless, interconnected alarm smoke detectors. We conducted experiments to examine how sleeping guests recognize these alarms when they are activated. Eleven participants experienced the alarm sounding while sleeping at night and while awake during the daytime at a wooden cottage. They were exposed to three different sounding patterns. In the sleeping experiment, 9 of 11 participants were woken up by alarms sounding in other rooms, while the remaining two were woken up by alarms in their own rooms. However, those two participants had difficulty understanding the meaning of the voice alarm clearly. The voice stated, “Other detector was activated,” indicating a fire in another room and urging evacuation, even though there was no smoke in their room. Comprehension of the alarm system is essential for guests to understand its intentions accurately and make proper decisions regarding evacuation. Wireless, interconnected alarm smoke detectors are increasingly utilized in vacation rentals, converted from single‐family houses known in Japanese as “minpaku.” The participants in the present study did not have prior knowledge of interconnected alarm smoke detectors, and some of them were not able to understand the meaning of the alarm voice's message clearly. In order for the guests to understand the content correctly, it is necessary for the guests to be informed and understand the mechanism of the alarm smoke detector.

Optical smoke detectors (OSDs) are fire-fighting equipment used to detect fire by detecting smoke with scattering phenomenon. From the principle of OSDs we can see that they are vulnerable to false alarms caused by dust or water steam. To reduce false alarms and to make OSDs more reliable, we present a deep learning approach to train a classifier to distinguish fire event from non-fire ones based on time series data. The classifier is modelled with a 1-Dimension convolutional neural network, and generative adversarial network is used to augment and balance training data. Experiment shows that our classifier can reduce more than 50% false alarms caused by water steam while maintaining sensitivity for fire events.

A fire in the cargo compartment has a major impact on civil aviation flight safety, and according to the airworthiness clause of the CCAR-25, the detector must sound an alarm within 1 min of a fire in the cargo compartment. As for the cargo compartment of large transport aircrafts, the internal space is high and open, and the smoke movement speed becomes slower with significant cooling in the process of diffusion. Hysteresis can occur in smoke detectors because of their internal labyrinth structure, which causes the detector’s internal and external response signals to be out of sync. This research employs a numerical simulation to examine the detector response parameters under an ambient wind speed of 0.1–0.2 m/s and fits a Cleary two-stage hysteresis model, where τ1= 0.09u−1.43 and τ2= 0.67u−1.59. Finally, multiple full-scale cargo cabin experiments were conducted to validate the prediction model. The results show that the model’s predicted alarm range is 43.1 s to 49.0 s, and the actual alarm time obtained by the experiment falls within this interval, confirming the model’s accuracy and providing theoretical support for the structural design and layout of the aircraft cargo cabin smoke detector.

The activation status of a smoke alarm in a fire becomes a central question in forensic fire investigations when there are injuries or deaths. Forensic techniques have been developed to answer this question through the evaluation of the presence of enhanced soot deposition on specific locations of a smoke alarm. Testing was conducted to evaluate the thresholds where these observational indicators may be obscured or destroyed. Fifty-four smoke alarms were exposed to simulated fire conditions to create enhanced soot deposition observational indicators. A series of post exposure tests were conducted to determine the threshold(s) of thermal damage to determine when those indicators begin to be obscured. All powered smoke alarms presented observational indicators in locations like those identified in previous research, as well as four additional locations for this smoke alarm geometry. Enhanced soot deposition indicators were found to persist in most cases when the alarm was exposed to high temperatures (up to 325°C to 450°C), when the alarms were exposed to high temperatures and dropped to a floor surface, and when the alarm was exposed to carpet contamination.