Sarwar Barera, Bajwa Imran Sarwar, Jamil Noreen, Ramzan Shabana, Sarwar Nadeem
Department of Computer Science and IT, The Islamia University Bahawalpur, Bahawalpur 63100, Pakistan.
Department of Computer Science, FAST - National University, Islamabad 44000, Pakistan.
Sensors (Basel). 2019 Jul 17;19(14):3150. doi: 10.3390/s19143150.
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.
最近，已经出现了一些基于烟雾传感器和报警装置组合设计的火灾预警和报警系统，以构建生命安全系统。然而，这类火灾报警系统有时容易出错，可能会对被归类为误报的非实际火灾迹象做出反应。因此，需要高质量的智能火灾报警系统，利用多个传感器值（如火焰探测器、湿度、热量和烟雾传感器等的信号）来检测真正的火灾事件。本文使用自适应神经模糊推理系统（ANFIS）来计算火灾真正发生的最大可能性并生成火灾警报。本文提出的新颖想法是通过利用火灾发生时烟雾的变化率、温度变化率和湿度，使用ANFIS来识别真正的火灾事件。该模型由传感器组成，用于从传感器节点收集重要数据，其中模糊逻辑将原始数据转换为语言变量，该语言变量在ANFIS中进行训练以获得火灾发生的概率。所提出的想法还会生成警报，并直接向用户的智能手机发送消息。我们的系统使用小尺寸、经济高效的传感器，并确保该解决方案具有可重复性。基于MATLAB的仿真用于实验，结果显示输出令人满意。
