Data Processing for Multiple Electronic Noses Using Sensor Response Visualization

Liu, Yingjie; Meng, Qinghao; Zhang, Xiao-Nei

doi:10.1109/jsen.2018.2871599

Cited by 23 publications

(6 citation statements)

References 37 publications

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“…Liu et al mentioned in the article "Opportunities, Challenges and Changes in the Development of Journalism in the Era of 'Big Data,'" that the information and technological progress in the era of big data has brought massive amounts of data to the news media industry. Faced with such a huge amount of data, the media industry has transformed the way of news analysis and mining, and transformed the traditional way of news dissemination into a way of data news dissemination [12]. Golelli and Rizzis proposed that through certain information mining technology, we can find the subtle relationships and valuable contents behind these seemingly meaningless things [13].…”

Section: Related Workmentioning

confidence: 99%

Visual Management of Sports Based on Intelligent Analysis of Big Data

Dan-ya

Wang

Liu

2022

Mobile Information Systems

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After years of development, big data technology has become more mature and reliable in its application in various fields. With the explosive growth of data and the rise of data complexity, the processing and application of data have actually changed our daily life. The development of data in sports is very active in recent years, and the natural dependence of sports on data determines that the visualization of sports has a great creative space in data sports. Therefore, it is necessary to deeply study the visual management of sports under the background of big data technology. As we all know, in sports, competitive games occupy the vast majority, and the data generated by this is not limited to the score of the game. People are paying more and more attention to information related to sports, but these information are generally very complicated and huge. This is also an area with the widest audience, and the core of competitive sports is data. The final results and progress of most sports competitions are reflected through changes and comparisons of data. With the continuous development of big data information collection technology, the available sports data are more detailed, which also makes sports develop toward visualization. Therefore, with the help of the excellent ability of big data in screening data, this article proposes algorithms such as DTW and recurrent neural networks to reasonably and reliably analyze and process a large number of data generated in the process of sports and embeds an error analysis module in the designed model to ensure the accuracy requirements in data processing to a greater extent. The model designed in this article has been able to improve the accuracy by more than 86%. This will greatly facilitate our management and have a far-reaching impact on sports visualization.

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Section: Related Workmentioning

confidence: 99%

Visual Management of Sports Based on Intelligent Analysis of Big Data

Dan-ya

Wang

Liu

2022

Mobile Information Systems

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“…The conventional approach for data processing in the Electronic Nose implies using the entire response curves (including rising, steady-state, recovery phases, and other) of the gas sensors array. Besides, this approach includes steps such as signal preprocessing and feature generation/extraction before performing the classification tasks, which requires the selection of a suitable method for each stage, increasing the necessary time to find the appropriate classification and forecasting models [1,2]. Nowadays, some researches have focused their efforts on reducing the steps and the essential know-how for model generation, such as the works presented by [3] and [4].…”

Section: Introductionmentioning

confidence: 99%

Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines

Gamboa

Silva

Araujo

et al. 2021

Sensors and Actuators B: Chemical

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“…Some recent works encoded E-Nose data into an image before adopting a CNN for classification. Liu et al [86] encoded the time-series signal of each sensor to an image of three channels to preserve the temporal dependencies. Two out of three total channels in the encoded images were built from a polar transition field, and the other channel was built from a Markov transition matrix.…”

Section: Qualitative Aroma Analysismentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

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Data Processing for Multiple Electronic Noses Using Sensor Response Visualization

Cited by 23 publications

References 37 publications

Visual Management of Sports Based on Intelligent Analysis of Big Data

Visual Management of Sports Based on Intelligent Analysis of Big Data

Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

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