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Building thermal energy efficiency modeling based on light image inspection and super resolution algorithm in interior landscape design

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 54卷

作者： Yang, Zhao Anhui Sanlian Coll Coll Digital Creat Modern Ind Hefei 230601 Anhui Peoples R China

With the intensification of global climate change and energy crisis, the thermal efficiency of buildings has become increasingly important. Interior landscape design not only affects living comfort, but also plays a key role in building energy efficiency. Therefore, it is of great practical significance to explore the modeling method of building thermal energy efficiency based on optical image inspection and super resolution algorithm. The light image inspection technique is used to obtain the data of light and temperature distribution inside the building. The super-resolution algorithm of deep learning is used to process the acquired low-resolution image to improve the clarity and detail of the image. Combined with the physical characteristics of the building, the thermal efficiency model of the building is constructed and multi-dimensional analysis is carried out. The experimental results show that the combination of optical image inspection and super resolution algorithm can effectively improve the accuracy of building thermal efficiency modeling. Compared with traditional methods, the prediction error of the model is reduced, and the recommended optimization scheme performs better in terms of energy consumption in different interior landscape design schemes. Therefore, the building thermal efficiency modeling method based on optical image inspection and super resolution algorithm provides a new idea for interior landscape design. Through accurate thermal efficiency analysis, it can provide more scientific decisionmaking basis for architectural designers, so as to realize the sustainable development of buildings and maximize the energy efficiency.

关键词： Optical image inspection super resolution algorithm Building thermal efficiency modeling Interior landscape design

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An Effective and Comprehensive Image super resolution algorithm Combined With a Novel Convolutional Neural Network and Wavelet Transform

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IEEE ACCESS 2021年 9卷 98790-98799页

作者： Yang, Hui Wang, Yibo Hunan Food & Drug Vocat Coll Changsha 410208 Peoples R China Hunan Inst Informat Technol Changsha 410100 Peoples R China

In order to further improve the reconstruction effect of the image super resolution algorithm, this paper proposes an image super resolution algorithm combining deep learning and wavelet transform (ISRDW). In terms of network design, it is not only simple in structure, but also more effective in capturing image details compared with other neural network structures. At the same time, cross-connection and residual learning methods are used to reduce the difficulty of the training model. In terms of loss function, this paper uses the loss generated in the original image space domain and the wavelet domain to strengthen the constraint of network training. Experimental results show that the algorithm proposed in this paper achieves better results under different data sets and different evaluation indexes.

关键词： Image reconstruction Image resolution Wavelet transforms Training Wavelet domain superresolution Convolutional neural networks Deep learning space domain loss function super resolution algorithm wavelet domain

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Detail Enhancement and Segmentation Extraction of Cladding Image Based on super resolution

Detail Enhancement and Segmentation Extraction of Cladding I...

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Conference on AOPC - Advanced Laser Technology and Applications

作者： Tang Mengyu Xu Zhenying Wu Ziqian Li Qiling Ling Jun Yan JinJin Jiangsu Univ Sch Mech & Engn Zhenjiang 212013 Jiangsu Peoples R China

ISBN: (数字)9781510649965

ISBN: (纸本)9781510649965;9781510649958

Aiming at the images of relevant monitored objects in the process of laser cladding, a super resolution algorithm technology was proposed to optimize and enhance the key details of the images, and the enhanced image content was segmented, extracted and counted. First, construct a training of a sub-resolution convolutional neural network (SRCNN) model;the original low resolution is predicted by the weight after training, the image quality evaluation results: peak signal-to-noise ratio (PSNR) is 30.198212, structural similarity (SSIM) is 0.969966;the most based on the maximum entropy dual threshold split algorithm combined with image processing, extracting and statistics on the powder object in the segmentation result image, the number of effective powders and proportion of the original wandering map and the predicted output delay image is [112, 33.6%] and [240, 40.6%]. The research results show that the cladding image output from the original image after the super resolution model has been significantly improved in terns of clarity and quality as well as the optimization and enhancement of the details of the monitored object.

关键词： super resolution algorithm Cladding Image Image Quality Evaluation Segmentation and Extraction Detail Enhancement

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SUBPIXEL RESOLVING OPTOFLUIDIC MICROSCOPE BASED ON super resolution algorithm

SUBPIXEL RESOLVING OPTOFLUIDIC MICROSCOPE BASED ON SUPER RES...

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8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Zheng, Guoan Lee, Seung Ah Yang, Samuel Yang, Changhuei CALTECH Dept Elect Engn Pasadena CA 91125 USA

ISBN: (纸本)9781424441280

We report the implementation of a fully on-chip, lensless, sub-pixel resolving optofluidic microscope (SROFM) based on the super resolution algorithm. The device utilizes microfluidic flow to deliver specimens directly across a complementary metal oxide semiconductor (CMOS) sensor to generate a sequence of low-resolution (LR) projection images, where resolution is limited by the sensor's pixel size. This image sequence is then processed with a pixel super-resolution algorithm to reconstruct a single high resolution (HR) image, where features beyond the Nyquist rate of the LR images are resolved. We demonstrate the device's capabilities by imaging red blood cell, microspheres, protist Euglena gracilis, and Entamoeba invadens cysts with sub-cellular resolution. We also demonstrate the capability of SROFM for malaria infected red blood cell diagnostics.

关键词： super resolution algorithm optofluidic microscopy imaging lab on a chip

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Accurate Position Location in TDS-OFDM Based Digital Television Broadcasting Networks

Accurate Position Location in TDS-OFDM Based Digital Televis...

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21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Dai, Linglong Wang, Zhaocheng Wang, Jun Wang, Jintao Zhang, Yu Tsinghua Univ State Key Lab Microwave & Digital Commun Tsinghua Natl Lab Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781424480166

Compared with the global positioning system (GPS), the digital television (DTV) broadcasting signal is a promising candidate for position location due to low implementation cost and strong signal reception. Without changing the current infrastructure of the Chinese DTV broadcasting network, this paper proposes a novel positioning scheme using the multi-carrier pseudo-noise (PN-MC) training sequence in the guard interval of the time domain synchronous OFDM (TDS-OFDM) signal frame. Different from the existing positioning methods based on timing synchronization or super resolution algorithms, the joint time-frequency estimation utilizing the properties of the received PN-MC sequence both in the time and frequency domain with respect to transmission delay, results in the accurate time of arrival (TOA) estimation. Performance of the proposed scheme is evaluated by Monte Carlo simulations in comparison with other the-state-of-art methods. The positioning accuracy of less than 0.1 m when the signal-to-noise ratio (SNR) is greater than 15 dB is achieved, under both the additive white Gaussian noise (AWGN) and the simulated multi-path channels.

关键词： AWGN channels DTV broadcasting Global Positioning System Monte Carlo methods Monte Carlo simulations OFDM OFDM modulation PN-MC sequence TDS additive white Gaussian noise channel digital television broadcasting digital video broadcasting multi-carrier pseudo-noise multipath channels multipath channels position location signal frame signal resolution signal-to-noise ratio super resolution algorithm synchronisation time domain synchronous time of arrival estimation time-frequency analysis time-frequency estimation time-of-arrival estimation timing synchronization training sequence

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Approach to reach high range resolution by using neural networks

Approach to reach high range resolution by using neural netw...

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IEEE 1996 National Aerospace and Electronics Conference (NAECON 1996)

作者： Zhang, WF He, SH Guo, GR NATL DEF UNIV ATR LABCHANGSHA 410073HUNANPEOPLES R CHINA

ISBN: (纸本)0780333071

For automatic target recognition, especially on high resolution radars, range profiles are very important, because they can be used to describe the accurate geometric shape and structural features of targets, and can be obtained in a few periods of pulse, and then, the real-time data processing realized easily. However, the traditional algorithms can not give us satisfactory resolution. In this paper, neural networks are used to reach high range resolution, and recognize these range profiles.

关键词： radar target recognition range profiles artificial neural networks super resolution algorithm

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