Low Power Wireless Smoke Alarm System in Home Fires

Luis, Juan; Gómez-Galán, Juan Antonio; Espigado, Javier Alcina

doi:10.3390/s150820717

Cited by 30 publications

(11 citation statements)

References 17 publications

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“…Oleh karena itu kebakaran dapat terjadi di mana saja, seperti di hutan, di tempat kerja, di perumahan, dan di tempat lainnya. Kebakaran seharusnya dapat ditanggulangi secara dini, yaitu dengan mendeteksi munculnya asap [12] dan titik api [7]. Lebih jauh, pendeteksian dini untuk asap sangat penting bahkan hal ini menjadi standar pembangunan gedung-gedung [3].…”

Section: Pengertian Kebakaran Dan Penanganannyaunclassified

Pencegahan Kesalahan Alarm dalam Sistem Pendeteksi Dini Kebakan dan Pemadaman Berbasis Internet of Things

Muharam

Latif²,

Baharuddin³

et al. 2020

j.inf.tech.comp.eng

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False alarm in fire detection can cause a huge loss. False alarm is generated by unwanted signal of smoke detector such as outdoor smoke or smoking. Therefore, it is designed a system that can reduce false alarm. The purposed system is built based on three components, those are sensors, actuators and data communication. Sensors are smoke, flame and camera sensor. Smoke sensor is used as the first thing to sense a signal from the system that warns the system there is a fire. Flame sensor and camera are used to confirm that a signal of fire whether false alarm or not. Internet of Things (IoT) is applied to control the system. The result show that the system is applicable.

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Section: Pengertian Kebakaran Dan Penanganannyaunclassified

Pencegahan Kesalahan Alarm dalam Sistem Pendeteksi Dini Kebakan dan Pemadaman Berbasis Internet of Things

Muharam

Latif²,

Baharuddin³

et al. 2020

j.inf.tech.comp.eng

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“…This system provides detection results with a false negative error rate of 4% and a false positive rate of 2%. Aponte et al [8] introduced a hardware solution using different sensors such as CO, smoke and temperature sensors. Xuehui et al [9] used a camera and different characteristics such as texture, wavelet, and color edge orientation and proposed an AdaBoost-like classifier.…”

Section: Introductionmentioning

confidence: 99%

Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

et al. 2019

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This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.

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“…Andrew et al [ 31 ] utilized the principal component analysis (PCA)-PNN scheme to reduce and classify the features extracted from the measurements of the gas sensors, dust particles, temperature sensors, and humidity sensors for classifying incipient stage fires in buildings. Luis et al [ 32 ] integrated a CO sensor, a smoke sensor, temperature sensors, a microcontroller, a short-range radio transceiver, a battery, a capacitive touch button, a LED, and a buzzer into a novel sensing device and developed a fire detection algorithm for home fire detection in indoor environments.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

Hsu

Chou

Chang

et al. 2017

Sensors

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This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

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Low Power Wireless Smoke Alarm System in Home Fires

Cited by 30 publications

References 17 publications

Pencegahan Kesalahan Alarm dalam Sistem Pendeteksi Dini Kebakan dan Pemadaman Berbasis Internet of Things

Pencegahan Kesalahan Alarm dalam Sistem Pendeteksi Dini Kebakan dan Pemadaman Berbasis Internet of Things

Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

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