True Fire Research Articles

FLAME: Trusted Fire Brigade Service and Insurance Claim System Using Blockchain for Enterprises

Smart fire detection and insurance systems have gained considerable attention from researchers and industries. At the same time, automatic requests for fire brigade services to cure fire and instant claims settlement to defend insurance fraud are lacking in literatures. We propose a trusted fire brigade service and insurance claim (FLAME) framework using blockchain for enterprises to provide immediate fire brigade services and prevent insurance frauds. A system model is presented to explain architecture, and overall functionality of FLAME using blockchain. Further, a sensing network and connectivity model is proposed to detect true fire and send an emergency service request to monitoring station. Smart contracts are designed to automate fire brigade service and insurance claim processes. A prototype of the FLAME is implemented on Hyperledger Besu blockchain using Istanbul Byzantine Fault Tolerance 2.0 consensus protocol. Simulation results show that latency and throughput of the FLAME are better compared to state-of-the-art models.

Building fire alarm model based on fire source inversion according to smoke arrival time intervals

Accurate and timely triggering of a fire alarm has always been essential for building fire safety. The inversion of fire source parameters by multiple detectors to distinguish between true and false fire alarms has become a popular research topic. However, previously proposed inversion models are based on time series and are computationally intensive. Their inversion accuracy directly depends on the size of the database. Therefore, this paper proposes a fire source inversion method employing multiple smoke alarms based on Bayesian inference. A fire dynamics model along with an actual sensor response time are used to implement a fire alarm. Moreover, fire alarm authenticity is defined to distinguish false and true fire alarms, enabling early fire detection. Verification test results indicate that the proposed model can reduce the number of false alarms and realize early fire alarm detection.

“You can’t start a fire without a spark”. Enjoyment, anxiety, and the emergence of flow in foreign language classrooms

Abstract The present study adopted a mixed-methods approach using a convergent parallel design to focus on the role that positive and negative emotions have in the Foreign Language (FL) classroom on the ontogenesis of positive flow. Participants were 1,044 FL learners from around the world. They provided quantitative and qualitative data on FL enjoyment (FLE), classroom anxiety (FLCA) and experience of flow via an on-line questionnaire (Dewaele, Jean-Marc & Peter D. MacIntyre. 2014. The two faces of Janus? Anxiety and enjoyment in the foreign language classroom. Studies in Second Language Learning and Teaching 4. 237–274). FLE was a significantly stronger predictor of frequency of flow experience than FLCA. Further statistical analyses revealed that flow experiences are typically self-centred, infrequent and short-lived at the start of the FL learning journey and when the perceived social standing in the group is low. They become an increasingly shared experience, more frequent, stronger and more sustained as learners reach a more advanced level in their FL. What starts as an occasional individual spark can turn into a true fire that extends to other group members. The findings are illustrated by participants’ reports on enjoyable episodes in the FL classroom in which some reported complete involvement in an individual or collective task, merging of action and awareness, joyful bonding with classmates, intense focus and joy, loss of self-consciousness, sense of time and place.

A low-power communication scheme for wireless, 1000 channel brain–machine interfaces

Objective. Brain–machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating ‘motes’ which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption. Approach. To test PIM’s ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption. Main results. We found that PIM at 1 kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3 mW for 1000 channels. Significance. These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.

Carbon fiber oxidation in combustion environments—Effect of flame chemistry and load on bundle failure

Carbon fiber (CF)-based composites are susceptible to fire attack. In this work, we assess the effect of flame chemistry and load on the oxidation-induced failure of three polyacrylonitrile-based CFs with different tensile modulus. Traditional thermal analyses initially showed the influence of microstructure and heating rate on the oxidative behavior. Two different flame-based tests were implemented to address the CF behavior under true fire conditions. Filaments were inserted into a premixed methane/air flame. Subsequent scanning electron microscopy revealed prompt and heterogeneous fiber pitting even after short exposures (t≈ 0.5s). The flame stoichiometry, characterized by the fuel/oxidizer ratio (ϕ), was varied to assess its effect on the CF bundle failure considering time-to-failure (TTF) as an indicator of fire resistance. The trends are similar for the three CF types. The stoichiometric flame (ϕ=1.0) yielded the least aggressive conditions despite being the hottest. Slightly faster bundle failures were caused by the fuel-rich configuration (ϕ=1.2), suggesting a contribution from OH radicals. Higher tensile loads tended to reduce this difference. The enhanced aggressiveness of the fuel-lean condition (ϕ=0.7), i.e., oxygen-rich flame, was confirmed by a TTF at least 50% shorter than its stoichiometric counterpart. Moreover, we found a striking one order-of-magnitude difference in TTF between the high and standard/intermediate modulus CF bundles, regardless of ϕ. The results highlight the critical importance of flame chemistry and fiber microstructure in the fire resistance of CF-based structures.

Q-rough sets, flicker modeling and unsupervised fire threat quantification from videos

This article defines new methods for unsupervised fire region segmentation and fire threat detection from the RGB video stream. Here, an unsupervised approach has been developed to determine the threat associated with fire. With this method, the threat of fire can be quantified, and accordingly, an alarm can be generated in automated surveillance systems of indoor as well as outdoors. To overcome the requirement of any manual intervention/ labeled data, or to deal with the incomplete knowledge-base due to the absence of any prior information, rough approximations have been used here to approximate the fire region. A new concept, namely, Q- rough set, has been proposed here, where the inexactness of the rough approximation is being minimized by employing a Q-agents of reinforcement learning. Utility maximization of Q-learning has been used to minimize ambiguities in the rough approximations. The newly developed Q-rough set approximation has been applied then for fire region segmentation from video frames. Another challenge associated with fire threat measures is not getting misled by other fire-like regions, even when the true fire is not present in the scene. Here, a new method of classifying true fire flame to fire-like regions is developed by incorporating the unique fluctuating feature of fire flame and modeling it with a flicker identification model. The threat index of true fire flame over the input video stream has been defined in sync with the relative growth in the fire segments on the recent frames. All theories and indices defined here have been experimentally validated with different types of fire and non-fire videos, through demonstrations and comparisons, as superior to the state-of-the-art methods.

Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind

Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires.

«Истинный огонь» Кайи Саариахо: поэтика композиции

Finnish composer Kaija Saariaho is one of the most prominent figures in contemporary music culture. She became known to the general public after the triumphant premiere of her Love from Afar opera (L’amour de loin, 2000), held at the Salzburg festival. The article is devoted to another work of the composer — the vocal cycle True Fire (2014), which was also successfully received by critics and the public, but has not yet been the subject of a separate study. This work is the first experience of its analysis, focused on both its musical and literary components, which led to the conditional division of the article into two parts. The composer, by her own admission, sought to demonstrate in the cycle the rich palette of colors of the world-famous Canadian baritone Gerald Finley for whom the composition was written. In addition, in True Fire through the texts of R.W. Emerson, M. Darvish, S. Heaney and the lullaby of the Tewa Indians, which became the literary basis of the work, the author’s ideas of a different order are embodied. What they are and how they are reflected in music are the questions that we tried to answer in this article.

Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks

Fire is one of the most commonly occurring disasters and is the main cause of catastrophic personal injury and devastating property damage. An early detection system is necessary to prevent fires from spreading out of control. In this paper, we propose a multistage fire detection method using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. In the first stage, fire candidates are detected by using their salient features, such as their color, flickering frequency, and brightness. In the second stage, a pretrained CNN model is used to extract the 2D features of flames that are the input for the LSTM network. In the last stage, a softmax classifier is utilized to determine whether the flames represent a true fire or a nonfire moving object. The experimental results show that our proposed method can achieve competitive performance compared with other state-of-the-art methods and is suitable for real-world applications.

ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition

Forest fire is becoming one of the most significant natural disasters at the expense of ecology and economy. In this article, we develop an effective SqueezeNet based asymmetric encoder-decoder U-shape architecture, Attention U-Net and SqueezeNet (ATT Squeeze U-Net), mainly functions as an extractor and a discriminator of forest fire. This model takes attention mechanism to highlight useful features and suppress irrelevant contents by embedding Attention Gate (AG) units in the skip connection of U-shape structure. In this way, salient features are emphasized so that the proposed method could be competent at forest fire segmentation tasks with a small number of parameters. Specifically, we first replace classical convolution layer by a depthwise one and engage a Channel Shuffle operation as a feature communicator in the Fire module of classical SqueezeNet. Then, this modified SqueezeNet is employed as a substitution of the encoder of Attention U-Net and a corresponding DeFire module designed is combined into the decoder as well. Finally, to classify true fire, we take use of a fragment of the encoder in ATT Squeeze U-Net. The experimental results of modified SqueezeNet integrated Attention U-Net show that a competitive accuracy at 0.93 and an average prediction time at 0.89 second per image are achieved for reliable real-time forest fire detection.

Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets

Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO, CO2 and O2 vis-à-vis the Estimated Fire Intensity Prediction (EFIP). Results show that, using fuzzy logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws, policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places.

An Intelligent Fire Warning Application Using IoT and an Adaptive Neuro-Fuzzy Inference System.

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user’s smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output.

Using deep learning to probe the neural code for images in primary visual cortex.

Primary visual cortex (V1) is the first stage of cortical image processing, and major effort in systems neuroscience is devoted to understanding how it encodes information about visual stimuli. Within V1, many neurons respond selectively to edges of a given preferred orientation: These are known as either simple or complex cells. Other neurons respond to localized center–surround image features. Still others respond selectively to certain image stimuli, but the specific features that excite them are unknown. Moreover, even for the simple and complex cells—the best-understood V1 neurons—it is challenging to predict how they will respond to natural image stimuli. Thus, there are important gaps in our understanding of how V1 encodes images. To fill this gap, we trained deep convolutional neural networks to predict the firing rates of V1 neurons in response to natural image stimuli, and we find that the predicted firing rates are highly correlated (newcommand{bialpha}{boldsymbol{alpha}}newcommand{bibeta}{boldsymbol{beta}}newcommand{bigamma}{boldsymbol{gamma}}newcommand{bidelta}{boldsymbol{delta}}newcommand{bivarepsilon}{boldsymbol{varepsilon}}newcommand{bizeta}{boldsymbol{zeta}}newcommand{bieta}{boldsymbol{eta}}newcommand{bitheta}{boldsymbol{theta}}newcommand{biiota}{boldsymbol{iota}}newcommand{bikappa}{boldsymbol{kappa}}newcommand{bilambda}{boldsymbol{lambda}}newcommand{bimu}{boldsymbol{mu}}newcommand{binu}{boldsymbol{nu}}newcommand{bixi}{boldsymbol{xi}}newcommand{biomicron}{boldsymbol{micron}}newcommand{bipi}{boldsymbol{pi}}newcommand{birho}{boldsymbol{rho}}newcommand{bisigma}{boldsymbol{sigma}}newcommand{bitau}{boldsymbol{tau}}newcommand{biupsilon}{boldsymbol{upsilon}}newcommand{biphi}{boldsymbol{phi}}newcommand{bichi}{boldsymbol{chi}}newcommand{bipsi}{boldsymbol{psi}}newcommand{biomega}{boldsymbol{omega}}{overline {{bf{CC}}} _{{bf{norm}}}} = 0.556 ± 0.01) with the neurons' actual firing rates over a population of 355 neurons. This performance value is quoted for all neurons, with no selection filter. Performance is better for more active neurons: When evaluated only on neurons with mean firing rates above 5 Hz, our predictors achieve correlations of newcommand{bialpha}{boldsymbol{alpha}}newcommand{bibeta}{boldsymbol{beta}}newcommand{bigamma}{boldsymbol{gamma}}newcommand{bidelta}{boldsymbol{delta}}newcommand{bivarepsilon}{boldsymbol{varepsilon}}newcommand{bizeta}{boldsymbol{zeta}}newcommand{bieta}{boldsymbol{eta}}newcommand{bitheta}{boldsymbol{theta}}newcommand{biiota}{boldsymbol{iota}}newcommand{bikappa}{boldsymbol{kappa}}newcommand{bilambda}{boldsymbol{lambda}}newcommand{bimu}{boldsymbol{mu}}newcommand{binu}{boldsymbol{nu}}newcommand{bixi}{boldsymbol{xi}}newcommand{biomicron}{boldsymbol{micron}}newcommand{bipi}{boldsymbol{pi}}newcommand{birho}{boldsymbol{rho}}newcommand{bisigma}{boldsymbol{sigma}}newcommand{bitau}{boldsymbol{tau}}newcommand{biupsilon}{boldsymbol{upsilon}}newcommand{biphi}{boldsymbol{phi}}newcommand{bichi}{boldsymbol{chi}}newcommand{bipsi}{boldsymbol{psi}}newcommand{biomega}{boldsymbol{omega}}{overline {{bf{CC}}} _{{bf{norm}}}} = 0.69 ± 0.01 with the neurons' true firing rates. We find that the firing rates of both orientation-selective and non-orientation-selective neurons can be predicted with high accuracy. Additionally, we use a variety of models to benchmark performance and find that our convolutional neural-network model makes more accurate predictions.

Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system.

Fuel moisture content (FMC) is an important fuel property for assessing wildfire hazard, since it influences fuel flammability and fire behavior. The relationship between FMC and fire activity differs among land covers and seems to be a property of each ecosystem. Our objectives were to analyze pre-fire FMC among different land covers and to propose a wildfire hazard classification for the Sierras Chicas in the Chaco Serrano subregion (Argentina), by analyzing pre-fire FMC distributions observed for grasslands, shrublands and forests and using percentiles to establish thresholds. For this purpose, we used a fire database derived from Landsat imagery (30 m) and derived FMC maps every 8 days from 2002 to 2016 using MODIS reflectance products and empirical equations of FMC. Our results indicated that higher FMC constrains the extent of wildfires, whereas at lower FMC there are other factors affecting their size. Extreme and high fire hazard thresholds for grasslands were established at FMC of 55% and 67% respectively, at 72% and 105% for forests and at 106% and 121% for shrublands. Our FMC thresholds were sensitive to detect extreme fire hazard conditions during years with high fire activity in comparison to average conditions. The differences in the distributions of pre-fire FMC among land covers and between ecosystems highlighted the need to locally determine land cover-specific FMC thresholds to assess wildfire hazard. Our wildfire hazard classification applied to FMC maps in an operational framework will contribute to improving early warning systems in the Sierras Chicas. However, moisture alone is not sufficient to represent true fire hazard in Chaco forests and the combination with other variables would provide better hazard assessments. These operational wildfire hazard maps will help to better allocation of fire protective resources to minimize negative impact on people, property and ecosystems. To the best of our knowledge, this is the first study analyzing pre-fire FMC over several fire seasons in a non-Mediterranean ecosystem, aiming at assessing wildfire hazard.

Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses

Hardware processors for neuromorphic computing are gaining significant interest as they offer the possibility of real in-memory computing, thus by-passing the limitations of speed and energy consumption of the von Neumann architecture. One of the major limitations of current neuromorphic technology is the lack of bio-realistic and scalable devices to improve the current design of artificial synapses and neurons. To overcome these limitations, the emerging technology of resistive switching memory has attracted wide interest as a nano-scaled synaptic element. This paper describes the implementation of a perceptron-like neuromorphic hardware capable of spike-timing dependent plasticity (STDP), and its operation under stochastic learning conditions. The learning algorithm of a single or multiple patterns, consisting of either static or dynamic visual input data, is described. The impact of noise is studied with respect to learning efficiency (false fire, true fire) and learning time. Finally, the impact of stochastic learning rule, such as the inversion of the time dependence of potentiation and depression in STDP, is considered. Overall, the work provides a proof of concept for unsupervised learning by STDP in memristive networks, providing insight into the dynamics of stochastic learning and supporting the understanding and design of neuromorphic networks with emerging memory devices.

Simultaneous retrieval of temperature and area according to sub-pixel hotspots from nighttime Landsat 8 OLI data

The monitoring of thermal anomalies such as from wildfires, coal fires, and volcanoes has been a major application of satellite remote sensing. The Landsat 8 Operational Land Imager (OLI) has achieved sufficient enhanced radiometric performance to quantify visible and near infrared (VNIR) to shortwave infrared (SWIR) radiance emitted from thermal anomalies during nighttime. We propose a method to simultaneously retrieve the brightness temperature and corresponding area of a subpixel hotspot assuming a single isothermal heat source using the spectral radiances in VNIR and SWIR regions (bands 3–7) from nighttime Landsat 8 OLI data. We conducted a prescribed fire experiment coincident with a Landsat 8 overpass on January 24, 2015. The brightness temperature retrieved from the OLI data agreed with the peak temperature of the time series thermocouple record within 60K. The retrieved area of fire was 19% of the true fire area. The blackbody assumption (emissivity=1) might cause the underestimated retrieved area. We applied the hotspot brightness temperature and area retrieval to 17 Landsat 8 scenes that cover Kanto, Japan to validate the proposed method. More than 79% of the detected results were confirmed as true positives based on the land use for each scene. Meanwhile, the obvious false positive was only up to 1% of the detected results. The remaining ambiguous results, mostly found in areas regarding agriculture, were possibly caused by occasional burnings because these locations were never repetitively detected. Time series comparison of the retrieved results in cement plants as a typical stable heat source revealed that the brightness temperature is accurately retrieved, though the retrieval of area is somewhat sensitive to noise on OLI imagery. The proposed method had founded more than twice of the hotspots detected using Visible Infrared Imaging Radiometer Suite (VIIRS) fire products in the studied area. More than 82% among the hotspots from VIIRS were detected by OLI. However our method failed to detect spatially broad but slightly cool hotspots. Higher spatial resolution at an appropriate spectral configuration enabled enhanced sensitivity to detect small heat sources, as well as quantification of the subpixel hotspots.

Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick

Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.

A TRIAC-Dimmable LED Lamp Driver With Wide Dimming Range

LED light bulbs are becoming increasingly popular as they consume much less power than traditional incandescent lamps. To enable full compatibility with incandescent lamps, apart from delivering the same light output and quality, they are expected to be operable with standard TRIAC-based light dimmers. However, the input current of the LED light bulbs at dimming could fall below the holding current of the TRIAC, resulting in limited dimming range and lamp flickering. This paper presents a TRIAC-dimmable LED lamp driver allowing wide dimming range. The concept is based on controlling the input reactive power, so that the input current is increased to a level higher than the holding current of the TRIAC, while the LED array power is regulated. The driver consists of two power conversion stages, including a four-quadrant ac-dc converter for shaping the input current and a resonant converter for regulating the output power to the LED array. The two converters share the same switching network. Modeling, analysis, and design of the driver will be presented. An LED prototype has been built and evaluated. Experimental results reveal that the true firing angle of the TRIAC can be adjusted down to 172°, and the lamp power can be dimmed from 7.2 to 0.3 W linearly.

주단계(朱丹溪)의 상화론(相火論)이 나오기 이전까지의 화(火)에 대한 고찰

Objective : The writer has come to see that a study of the Theory of Ministerial Fire of JuDanGae presupposes a study of earlier theories of fire. Therefore, I have analyzed and studied the theories of fire presented by medical doctors since "Nae Gyeong(黃帝內經)" Method : To begin with, I have studied the concepts of grand fire(壯火) and small fire(少火), imperial fire(君火) and ministerial fire(相火) in "Nae Gyeong", and have construed the meaning of human fire(人火) and dragon fire(龍火) by Wang Bing(王冰), and fatty fire(膏火) by Sohn Sa-mak(孫思邈). Next, I have studied true fire(眞火) by Huh Sook-mi(許叔微) and true yang(眞陽) and pit fire(坎火) by Eom Yong-hwa(嚴用和), Theory on Right Kidney as the Gate of Life and Ministeiral Fire(右腎命門相火說) by Yu Wan-so(劉完素), and the reason why Jang Wan-so(張元素) asserted Theory of Three Burners as Ministerial Fire(三焦相火說). Result : I have understood the influence that the concepts of grand fire and small fire, imperial fire and ministerial fire in "Nae Gyeong"have exercised on the later medical doctors, and have also understood the physiological and pathological Fire that influences the human body. Moreover, I have seen that Fire, as discussed by scholars from Huh Sook-mi and Eom Yong-hwa to Yu Wan-so and Jang Wan-so, made great contribution to the later development of Theory of the Gate of Life, getting its entire picture in Eum Hwa. Conclusion : Seeing that the theories of Fire presented by medical doctors since "Nae Gyeong" and prior to JuDan-Gae had great influence on the later development of Theory of the Gate of Life, I have come to understand that along with Eum Hwa by Lee Dong-wan, they must precede a study of the Theory of Ministerial Fire of JuDan-Gae.

Low false reject rate and false accept rate multi-step fire detection method

This paper proposes an efficient fire detection method for intelligent monitoring of self-service banks. This method includes the following steps: motion region detection, color-based fire detection and fire region refinement. These steps are as follows. First, the method uses four Gaussian distributions to construct an adaptive background model of the scene, and exploits this model to obtain the moving regions of the image. Second, the method uses RGB color and HSI color to determine whether the moving regions are fire candidates or not. These two steps are able to detect almost all of the true fire regions but it also takes a portion of non-fire regions as the fire. Third, the proposed frame difference procedure and the distance of two fire regions of two adjacent frames to eliminate the false fire regions obtained using the first two steps. The experimental results show that the proposed method performs very well in small-scale fire detection. The false reject rate (FRR) and false accept rate (FAR) of our method are 10.4%, and 6.02%, respectively.