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Cheng, Xiaogang
Publications (5 of 5) Show all publications
Cheng, X., Yang, B., Tan, K., Isaksson, E., Hedman, A., Olofsson, T. & Li, H. (2019). A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning. Applied Sciences: APPS, 9(7), Article ID 1375.
Open this publication in new window or tab >>A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning
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2019 (English)In: Applied Sciences: APPS, ISSN 1454-5101, E-ISSN 1454-5101, Vol. 9, no 7, article id 1375Article in journal (Refereed) [Artistic work] Published
Abstract [en]

In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra- and inter-individual differences and skin subtleness variations, there has not been any satisfactory solution for thermal comfort measurements until now. In this paper, a contactless measuring method based on a skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named the skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, a two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.62% and 52.25% error values (NISDL method I, II) are scattered at (0 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.39%. 

Place, publisher, year, edition, pages
Switzerland: MDPI, 2019
Keywords
contactless measurements; skin sensitivity index; thermal comfort; subtleness magnification; deep learning; piecewise stationary time series
National Category
Civil Engineering
Identifiers
urn:nbn:se:umu:diva-159773 (URN)10.3390/app9071375 (DOI)
Available from: 2019-06-05 Created: 2019-06-05 Last updated: 2019-06-20Bibliographically approved
Cheng, X., Yang, B., Hedman, A., Olofsson, T., Li, H. & van Gool, L. (2019). NIDL: A pilot study of contactless measurement of skin temperature for intelligent building. Energy and Buildings, 198, 340-352
Open this publication in new window or tab >>NIDL: A pilot study of contactless measurement of skin temperature for intelligent building
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2019 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 198, p. 340-352Article in journal (Refereed) [Artistic work] Published
Abstract [en]

Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A contactless measuring method based on subtleness magnification and deep learning (NIDL) was designed to achieve a comfortable, energy efficient built environment. The method relies on skin feature data, e.g., subtle motion and texture variation, and a 315-layer deep neural network for constructing the relationship between skin features and skin temperature. A physiological experiment was conducted for collecting feature data (1.44 million) and algorithm validation. The contactless measurement algorithm based on a partly-personalized saturation temperature model (NIPST) was used for algorithm performance comparisons. The results show that the mean error and median error of the NIDL are 0.476 °C and 0.343°C which is equivalent to accuracy improvements of 39.07 % and 38.76 %, respectively.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Contactless method, Thermal comfort measurement, Vision-based subtleness magnification, Deep learning, Intelligent building
National Category
Civil Engineering
Identifiers
urn:nbn:se:umu:diva-159772 (URN)10.1016/j.enbuild.2019.06.007 (DOI)000477091800027 ()2-s2.0-85067305627 (Scopus ID)
Available from: 2019-06-05 Created: 2019-06-05 Last updated: 2019-09-06Bibliographically approved
Cheng, X., Yang, B., Liu, G., Olofsson, T. & Li, H. (2018). A total bounded variation approach to low visibility estimation on expressways. Sensors, 18(2), Article ID 392.
Open this publication in new window or tab >>A total bounded variation approach to low visibility estimation on expressways
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2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 2, article id 392Article in journal, Editorial material (Refereed) Published
Abstract [en]

Low visibility on expressways caused by heavy fog and haze is a main reason for traffic accidents. Real-time estimation of atmospheric visibility is an effective way to reduce traffic accident rates. With the development of computer technology, estimating atmospheric visibility via computer vision becomes a research focus. However, the estimation accuracy should be enhanced since fog and haze are complex and time-varying. In this paper, a total bounded variation (TBV) approach to estimate low visibility (less than 300 m) is introduced. Surveillance images of fog and haze are processed as blurred images (pseudo-blurred images), while the surveillance images at selected road points on sunny days are handled as clear images, when considering fog and haze as noise superimposed on the clear images. By combining image spectrum and TBV, the features of foggy and hazy images can be extracted. The extraction results are compared with features of images on sunny days. Firstly, the low visibility surveillance images can be filtered out according to spectrum features of foggy and hazy images. For foggy and hazy images with visibility less than 300 m, the high-frequency coefficient ratio of Fourier (discrete cosine) transform is less than 20%, while the low-frequency coefficient ratio is between 100% and 120%. Secondly, the relationship between TBV and real visibility is established based on machine learning and piecewise stationary time series analysis. The established piecewise function can be used for visibility estimation. Finally, the visibility estimation approach proposed is validated based on real surveillance video data. The validation results are compared with the results of image contrast model. Besides, the big video data are collected from the Tongqi expressway, Jiangsu, China. A total of 1,782,000 frames were used and the relative errors of the approach proposed are less than 10%.

Place, publisher, year, edition, pages
MDPI, 2018
Keywords
total bounded variation, image spectrum, low visibility estimation, piece stationary, fog and haze
National Category
Environmental Analysis and Construction Information Technology Remote Sensing
Identifiers
urn:nbn:se:umu:diva-144176 (URN)10.3390/s18020392 (DOI)000427544000075 ()29382181 (PubMedID)
Available from: 2018-01-24 Created: 2018-01-24 Last updated: 2018-06-09Bibliographically approved
Cheng, X., Yang, B., Liu, G., Olofsson, T. & Li, H. (2018). A variational approach to atmospheric visibility estimation in the weather of fog and haze. Sustainable cities and society, 39, 215-224
Open this publication in new window or tab >>A variational approach to atmospheric visibility estimation in the weather of fog and haze
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2018 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 39, p. 215-224Article in journal (Refereed) Published
Abstract [en]

Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder's formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder's formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

Keywords
Atmospheric visibility estimation, Variational approach, Piecewise stationary time series, Computer vision, Fog and haze
National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:umu:diva-144561 (URN)10.1016/j.scs.2018.02.001 (DOI)000433169800020 ()
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-09-05Bibliographically approved
Cheng, X., Yang, B., Olofsson, T., Liu, G. & Li, H. (2017). A pilot study of online non-invasive measuring technology based on video magnification to determine skin temperature. Building and Environment, 121, 1-10
Open this publication in new window or tab >>A pilot study of online non-invasive measuring technology based on video magnification to determine skin temperature
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2017 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 121, p. 1-10Article in journal, Editorial material (Refereed) Published
Abstract [en]

Much attention was paid on human centered design strategies for environmental control systems of indoor built environments. The goal is to achieve thermally comfortable, healthy and safe working or living environments in energy efficient manners. Normally building Heating, Ventilation and Air Conditioning (HVAC) systems have fixed operating settings, which can't satisfy human thermal comfort requirements under transient and non-uniform indoor thermal environments. Therefore, human thermal physiology signal such as skin temperature, which can reflect human body thermal sensation, has to be measured over time. Several trials have been performed by minimizing measuring sensors such as i-Button and mounting measuring sensors into wearable devices such as glasses. Infrared thermography technology has also been tried to achieve non-invasive measurements. However, it would be much more convenient and feasible if normal computer camera could record images, which could be used to obtain human thermal physiology signals. In this study, skin temperature of hand back, which has a high density of blood vessels and is normally not covered by clothing, was measured by i-button sensors. Images recorded by normal camera were amplified to analyzing skin temperature variation, which are impossible to see with naked eyes. The agreement between i-button sensor measuring results and image magnification results demonstrated the possibility of non-invasive measuring technology by image magnification. Partly personalized saturation-temperature model (T = 96.5 × S + bi) can be used to predict skin temperatures for young East Asia females.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Thermal sensation, Skin temperature, Video magnification, Online non-invasive, measurement
National Category
Building Technologies
Identifiers
urn:nbn:se:umu:diva-134857 (URN)10.1016/j.buildenv.2017.05.021 (DOI)000404314700001 ()
Available from: 2017-05-13 Created: 2017-05-13 Last updated: 2018-06-09Bibliographically approved
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