The Estimation of Hazardous Gas Release Rate Using Optical Sensor and Neural Network

So, Won; Koo, Joonhoi; Shin, Dongil; Yoon, En Sup

doi:10.1016/s1570-7946(10)28034-3

Cited by 20 publications

(7 citation statements)

References 6 publications

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“…Other research works combine sensor data and artificial intelligence to improve the detection of the gas release rate. To that end, So et al collected data using optical sensors. In addition, there are some fire detectors that combine information from different sources and analyze data using artificial intelligence techniques to improve the accuracy of the system …”

Section: Related Workmentioning

confidence: 99%

Real‐time force doors detection system using distributed sensors and neural networks

et al. 2019

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Intelligent security systems have evolved enormously in the last few years. Most of these security systems use a group of physics sensors and algorithms for data analysis and communication systems to notify security alarms. Many security systems that are included in doors can detect intruders when they have already opened the door, but not while intruders are forcing upon the door. However, some security systems include preventive systems, which can detect intruders before they open the door. These preventive systems are usually based on video cameras (image processing) or in‐presence sensors, which can generate many false positives, for instance, when a person is next to the door for a few seconds, even if this person is not manipulating the door. This research work proposes a novel force door detection system. The system includes a specific device for monitoring door small vibrations and movements; it analyzes these data using neural networks to detect accurately if someone is forcing upon the door. Artificial intelligence must be able to categorize data records without confusing when someone is forcing upon the door with other actions, like knocking on the door.

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Section: Related Workmentioning

confidence: 99%

Real‐time force doors detection system using distributed sensors and neural networks

et al. 2019

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“…Zhang et al [20] developed a knowledge-based genetic algorithm for detecting mixed gas in mines. Won et al [21] proposed a system for estimation of hazardous gas release rate using optical sensor and NN-based technique. The following salient points came out of the above mentioned articles:…”

Section: Background Studymentioning

confidence: 99%

Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

Ojha

Dutta

Chaudhuri

2016

Neural Comput & Applic

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In this work, we formulated a real-world problem related to sewerpipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer-pipeline to offer safe and non-hazardous access to sewer-pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature-sources were organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer-pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based-learning algorithm performed better than many other algorithms such as multi-layer perceptron, radial basis function network, support vector machine, reduced pruning tree, etc. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors.

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“…Zhang et al [20] developed a knowledge based genetic algorithms for mine mixed gas detection. Shin et al [21] proposed a system for estimation of hazardous gas release rate using optical sensor and NN based technique. A comprehensive studied of the above mentioned articles results the following conclusion; (i) Mostly the BP and NN based technique are used for gas detection problem for respective application areas.…”

Section: A Brief Literature Surveymentioning

confidence: 99%

Convergence Analysis of Backpropagation Algorithm for Designing an Intelligent System for Sensing Manhole Gases

Ojha

Dutta

Chaudhuri

et al. 2015

Hybrid Soft Computing Approaches

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Human fatalities are reported due to the excessive proportional presence of hazardous gas components in manhole, such as Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, Carbon Monoxide, etc. Hence, predetermination of these gases is imperative. A neural network (NN) based intelligent sensory system is proposed for the avoidance of such fatalities. Backpropagation (BP) was applied for the supervised training of the neural network. A Gas sensor array consists of many sensor elements was employed for the sensing manhole gases. Sensors in the sensor array are responsible for sensing their target gas components only. Therefore, the presence of multiple gases results in cross sensitivity. The cross sensitivity is a crucial issue to this problem and it is viewed as pattern recognition and noise reduction problem. Various performance parameters and complexity of the problem influences NN training. In present chapter the performance of BP algorithm on such a real life application problem was comprehensively studied, compared and contrasted with the several other hybrid intelligent approaches both, in theoretical and in statistical sense.

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The Estimation of Hazardous Gas Release Rate Using Optical Sensor and Neural Network

Cited by 20 publications

References 6 publications

Real‐time force doors detection system using distributed sensors and neural networks

Real‐time force doors detection system using distributed sensors and neural networks

Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

Convergence Analysis of Backpropagation Algorithm for Designing an Intelligent System for Sensing Manhole Gases

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