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Prediction of the COVID-19 infectivity and the sustainable impact on public health under deep learning algorithm

SOFT COMPUTING 2023年第5期27卷 2695-2704页

作者： Wang, Weiwei Cai, Jinghui Xu, Jiali Wang, Yuxiang Zou, Yulin Zhejiang Univ Coll Media & Int Culture Hangzhou 310058 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510000 Peoples R China Shanghai Univ Finance & Econ Sch Math Shanghai 200433 Peoples R China Johns Hopkins Univ Whiting Sch Engn Baltimore MD 21218 USA China Three Gorges Univ Gezhouba Cent Hosp Sinopharm Clin Med Coll 3 Yichang 443002 Peoples R China

The aim is to explore the development trend of COVID-19 (Corona Virus Disease 2019) and predict the infectivity of 2019-nCoV (2019 Novel Coronavirus), as well as its impact on public health. First, the existing data are analyzed through data pre-processing to extract useful feature factors. Then, the LSTM (Long-Short Term Memory) prediction model in the deep learning algorithm is used to predict the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country, respectively. Meanwhile, the impact of intervention time changes on the epidemic situation is compared. The results show that the prediction results are almost consistent with the actual values. Specifically, Hubei Province abolishes quarantine restrictions after the Spring Festival holiday, and the first COVID-19 peak is reached in late February, while the second COVID-19 peak has been reached in early March. Finally, the cumulative number of diagnoses reaches 85,000 cases, with an increase of 15,000 cases compared with the nationwide cases outside Hubei under the continuous implementation of prevention and control measures. Under the prediction of the proposed LSTM model, if the nationwide implementation of prevention and control interventions is postponed by 5 days, the epidemic will peak in early March, and the cumulative number of diagnoses will be about 200,000;and if the intervention measures are implemented five days earlier, the epidemic will peak in mid-February, with a cumulative number of diagnoses of approximately 40,000. Meanwhile, the proposed LSTM model predicts the RMSE values of the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country as 34.63, 75.42, and 50.27, respectively. Under model comparison analysis, the prediction error of the proposed LSTM model is small and has better applicability over similar algorithms. The results show that the LSTM model is effective and has high performance in infectious disease prediction, and the res

关键词： deep learning algorithm 2019-nCoV Infectivity Prediction model Public health

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Vibration States Detection of HTS Pinning Maglev System Based on deep learning algorithm

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2022年第6期32卷 1页

作者： Ke, Zhihao Deng, Zigang Chen, Yining Yi, Huiyang Liu, Xiaoning Wang, Li Zhang, Penghui Ren, Tianci Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Peoples R China Southwest Jiaotong Univ Leeds Joint Sch Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China

Due to self-stable levitation, high-temperature-superconducting (HTS) pinning maglev system has a great potential to become a high-speed transportation mode. As its most significant factor, operation stability and safety reflected by monitoring of vehicle vibration states during its running procedure should get more attention. However, both of the inadequacy of previous researches and continuous growth of vibration conditions increase the detection difficulty of HTS pinning system vibration. Therefore, this paper adapts deep learning algorithm in this detection, by virtue of its plasticity and universality. Initially, horizontal, and vertical acceleration data of HTS pinning system under 108 vibration conditions is collected. Subsequently, this aggregated data is divided into four types of datasets performed with or without Gaussian filter and fast Fourier transform (FFT). Then, back propagation neural network (BPNN) is adapted to distinguish maglev vehicle vibration conditions. Eventually, the detection accuracy of this model in four types of datasets is discussed. The results verify the effectiveness of deep learning algorithm, and the BPNN model on dataset considering Gaussian filter shows the best performance. This paper firstly combines deep learning algorithm with vibration detection of HTS pinning system. Additionally, it also provides an efficient vibration detection approach for HTS pinning system, which could lay a simple basement for its future research fields such as fault identification, operation states classification and so on.

关键词： Vibrations High-temperature superconductors deep learning Resonant frequency Time-frequency analysis Machine learning algorithms Cooling deep learning algorithm HTS pining Maglev system vibration signal analysis vibration states detection

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Printed, Wireless, Soft Bioelectronics and deep learning algorithm for Smart Human-Machine Interfaces

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ACS APPLIED MATERIALS & INTERFACES 2020年第44期12卷 49398-49406页

作者： Kwon, Young-Tae Kim, Hojoong Mahmood, Musa Kim, Yun-Soung Demolder, Carl Yeo, Woon-Hong Georgia Inst Technol Inst Elect & Nanotechnol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Georgia Inst Technol Parker H Petit Inst Bioengn & Biosci Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA Georgia Inst Technol Neural Engn Ctr Inst Mat Inst Robot & Intelligent MachinesCtr Human Centr Atlanta GA 30332 USA

Recent advances in flexible materials and wearable electronics offer a noninvasive, high-fidelity recording of biopotentials for portable healthcare, disease diagnosis, and machine interfaces. Current device-manufacturing methods, however, still heavily rely on the conventional cleanroom microfabrication that requires expensive, time-consuming, and complicated processes. Here, we introduce an additive nanomanufacturing technology that explores a contactless direct printing of aerosol nanomaterials and polymers to fabricate stretchable sensors and multilayered wearable electronics. Computational and experimental studies prove the mechanical flexibility and reliability of soft electronics, considering direct mounting to the deformable human skin with a curvilinear surface. The dry, skin-conformal graphene biosensor, without the use of conductive gels and aggressive tapes, offers an enhanced biopotential recording on the skin and multiple uses (over ten times) with consistent measurement of electromyograms. The combination of soft bioelectronics and deep learning algorithm allows classifying six classes of muscle activities with an accuracy of over 97%, which enables wireless, real-time, continuous control of external machines such as a robotic hand and a robotic arm. Collectively, the comprehensive study of nanomaterials, flexible mechanics, system integration, and machine learning shows the potential of the printed bioelectronics for portable, smart, and persistent human-machine interfaces.

关键词： additive nanomanufacturing printed bioelectronics deep learning algorithm human-machine interface electromyograms (EMGs)

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Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings

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ACTA RADIOLOGICA 2023年第4期64卷 1422-1430页

作者： Zuo, Zhichao Wang, Peng Zeng, Weihua Qi, Wanyin Zhang, Wei Xiangtan Cent Hosp Dept Radiol Xiangtan Peoples R China WuHan 1 Hosp Dept Radiol Wuhan Peoples R China Southwest Med Univ Dept Radiol Affiliated Hosp Luzhou Peoples R China Guangxi Med Univ Dept Radiol Liuzhou Peoples Hosp 8 Wenchang Rd Liuzhou 545006 Peoples R China

Background deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. Purpose To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). Material and Methods In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland-Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. Results The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 (P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group (P < 0.001). Conclusion Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

关键词： Pure ground-glass nodules computed tomography deep learning algorithm radiologist measurement agreement

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Ultra-short-term irradiance forecasting model based on ground-based cloud image and deep learning algorithm

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IET RENEWABLE POWER GENERATION 2022年第12期16卷 2604-2616页

作者： Zhen, Zhao Zhang, Xuemin Mei, Shengwei Chang, Xiqiang Chai, Hua Yin, Rui Wang, Fei Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China North China Elect Power Univ Dept Elect Engn Baoding Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China State Grid Xinjiang Elect Power Co Ltd Urumqi Peoples R China State Grid Hebei Elect Power Co Ltd Shijiazhuang Hebei Peoples R China

Solar irradiance is the main factor affecting the output of a photovoltaic (PV) power station, which is chiefly determined by the cloud distribution over the power station. For ultra-short-term, especially the intro-hour time scale irradiance forecasting, ground-based cloud image is considered as a very necessary data as Global Horizontal Irradiance (GHI). However, the information content in the image is much higher than that of GHI record, and there is even a difference in magnitude between them. Therefore, how to effectively extract the key features in the cloud images and fuse them with GHI record data is the decisive factor affecting the performance of the forecasting model. Here, a novel convolutional auto-encoder based cloud distribution feature (CDF) extraction method is first proposed. Then for feature fusion part, an LSTM-FUSION irradiance forecasting model is established based on long short-term memory (LSTM) neural network and feature fusion by time steps considering the one-to-one correlation between CDFs and GHI. Finally, a novel determination method of input time step length based on attention distribution analysis is also proposed. Simulation results show that the proposed LSTM-FUSION model is overall superior to the benchmark models.

关键词： photovoltaic power systems intro-hour time scale irradiance forecasting clouds power engineering computing solar power convolutional autoencoder based cloud distribution feature extraction method deep learning (artificial intelligence) deep learning algorithm long short-term memory neural network global horizontal irradiance input time step length convolutional neural nets object detection recurrent neural nets solar irradiance cloud distribution feature extraction LSTM-FUSION irradiance forecasting model PV power station attention distribution analysis photovoltaic power station image fusion GHI record data feature fusion CDF ground-based cloud image ultrashort-term irradiance forecasting model

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Highly Precise and Broadband Full-Stokes Polarimeter Based on a deep learning algorithm

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ACS PHOTONICS 2023年第7期10卷 2432-2439页

作者： Xian, Shilin Huang, Shanshan Qiu, Kehai Wang, Yifan Chen, Feiliang Wang, Ke Gao, Fuhua Hou, Yidong Sichuan Univ Coll Phys Chengdu 610065 Sichuan Peoples R China Univ Cambridge Dept Comp Sci & Technol Cambridge CB30FD England Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 610054 Peoples R China Sichuan Univ Key Lab High Energy Dens Phys & Technol Minist Educ Chengdu 610064 Peoples R China

Recent developments in nanophotonics enable the practical realization of ultracompact full-Stokes polarimeters and imaging devices based on the well-defined polarization-sensitive structures of subwavelength size. However, the resonant feature of building blocks and the limited ability of the current processing technology greatly limit their detection accuracy and working waveband. Herein, we experimentally demonstrate a highly precise and broadband full-Stokes polarimeter based on the high-performance dielectric chiral shells (DCSs) and the deep learning (DL) algorithm. The large-area dielectric chiral metasurfaces (DCMs) fabricated by depositing Si on the self-assembled microsphere monolayer own a large transmittance, strong chirality, and anisotropy in the visible waveband, which enable the effective perception of the polarization state of light. The Stokes parameters are successfully detected by measuring several transmittances from DCSs with different rotation angles, i.e., I, and then analyzing with the pre-established mapping relationship between the Stokes parameters S and the recorded transmittance I based on the DL algorithm, i.e., S = f(I). In contrast to the traditional polarimeters, the detection accuracy based on the DL approach seems insensitive to the fabrication errors. A mean square error (MSE) of smaller than 0.5% at 760.06 nm was recorded, and an averaged MSE of less than 4% for Stokes parameters over a broad waveband from 400 to 840 nm was obtained, although the optical anisotropy and chirality of DCMs own a strong dispersion feature. The small MSE in a broad waveband, together with the easily accessing DCMs, make our polarimeter as the most potential candidate for future applications, such as the color polarization imaging devices and the ultraviolent polarimeter.

关键词： deep learning algorithm Stokes parameters dielectric chiral shells polarimeter glancing angle deposition

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Computer-assisted demand-side energy management in residential smart grid employing novel pooling deep learning algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第5期45卷 7961-7973页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi Tamil Nadu India

Demand-side energy management increases the unpredictability and ambiguity of forecasting the load profiles of residential energy management. The energy management accuracy seems to be low by employing a traditional residential energy forecasting algorithm. This research work emphasizes on design and development of computer-assisted residential energy management by forecasting employing a deep learning algorithm. Hankel matrix is formed using copula function to process the collected automatic metering infrastructure (AMI) load data in the smart grid. From this data processing, model optimization was obtained by the proposed novel pooling-based deep neural network (PDNN). Moreover, this proposed PDNN avoid overfitting problem in training and testing by increasing AMI data variety and data size. The proposed PDNN is implemented in the TensorFlow platform. Based on real-time AMI southern grid data onto Tamil Nadu Electricity Board, India testing case studies was carried. Compared to traditional residential energy management techniques the proposed deep learning model outperforms support vector machine by 9.5% and 12.7%, deep belief network by 6.5% and 9.5%, and neural network auto aggressive integral moving average by 20.5% and 8.5% in terms of accuracy of energy forecasting and mean absolute error, respectively. Overall, the obtained results proved the effectiveness of the proposed deep learning algorithm for residential short-term load forecasting and management over other traditional methods. Highlights In this research work: Novel pooling-based deep neural network is applied for residential energy management in a smart grid. The copula fusion theory is adopted to improve the accuracy of load management in a smart grid. Day-ahead and week-ahead prediction load on Tamil Nadu Electricity Board dataset in summer and winter season was used to validate the performance of the proposed method with other data-driven methods.

关键词： deep learning algorithm energy management residential power system planning smart grid

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Smart grid security enhancement by detection and classification ofnon-technicallosses employing deep learning algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第9期30卷 1-12页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Kathiresan, Aravind C. Asokan, Siva Prakash Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India

Non-technical loss (NTL) is detrimental to the smart grid. Intelligent application of advanced metering infrastructure (AMI) helps to solve NTL detection and classification. By using advanced learning algorithms, data analysis on the massive data generated by AMI is helpful in the detection and classification of electricity theft. Conventional data analysis algorithm, like Support Vector Machine (SVM), Random Forest algorithm (RFA), and 1D- Conventional Neural Network (1D-CNN), has low detection and classification accuracy of electricity theft. Because these methods failed to predict and classify multidimensional electricity consumption data by various consumers in AMI in the smart grid. In this research work, a multidimensional deep learning algorithm is proposed to learn and classify the non-periodicity of electricity. This helps to detect electricity theft by a consumer from the periodic load curve. Both weekly load patterns and daily load patterns are processed as 2D electricity data samples. From the proposed multidimensional deep learning model, an average classification accuracy of 97.5% and a precision-recall of 0.97 were obtained. This validates that the proposed deep learning model outperforms other conventional data analysis classification algorithm.

关键词： data analysis deep learning algorithm non-technical loss detection smart grid security

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A self-adaptive deep learning algorithm for accelerating multi-component flash calculation

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020年 369卷 113207-113207页

作者： Zhang, Tao Li, Yu Li, Yiteng Sun, Shuyu Gao, Xin King Abdullah Univ Sci & Technol Div Phys Sci & Engn Computat Transport Phenomena Lab Thuwal 239556900 Saudi Arabia King Abdullah Univ Sci & Technol KAUST Computat Biosci Res Ctr CBRC Comp Elect & Math Sci & Engn CEMSE Div Thuwal Saudi Arabia

In this paper, the first self-adaptive deep learning algorithm is proposed in details to accelerate flash calculations, which can quantitatively predict the total number of phases in the mixture and related thermodynamic properties at equilibrium for realistic reservoir fluids with a large number of components under various environmental conditions. A thermodynamically consistent scheme for phase equilibrium calculation is adopted and implemented at specified moles, volume and temperature, and the flash results are used as the ground truth for training and testing the deep neural network. The critical properties of each component are considered as the input features of the neural network and the final output is the total number of phases at equilibrium and the molar compositions in each phase. Two network structures are well designed, one of which transforms the input of various numbers of components in the training and the objective fluid mixture into a unified space before entering the productive neural network. "Ghost components" are defined and introduced to process the data padding work in order to modify the dimension of input flash calculation data to meet the training and testing requirements of the target fluid mixture. Hyperparameters on both two neural networks are carefully tuned in order to ensure the physical correlations underneath the input parameters are preserved properly through the learning process. This combined structure can make our deep learning algorithm to be self-adaptive to the change of input components and dimensions. Furthermore, two Softmax functions are used in the last layer to enforce the constraint that the summation of mole fractions in each phase is equal to 1. An example is presented that the flash calculation results of a 8-component Eagle Ford oil is used as input to estimate the phase equilibrium state of a 14-component Eagle Ford oil. The results are satisfactory with very small estimation errors. The capability of the prop

关键词： deep learning algorithm Flash calculation Self-adaptive neural network Multi-component reservoir fluid

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APPLE FRUIT RECOGNITION BASED ON A deep learning algorithm USING AN IMPROVED LIGHTWEIGHT NETWORK

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APPLIED ENGINEERING IN AGRICULTURE 2021年第1期37卷 123-134页

作者： Ji, J. Zhu, X. Ma, H. Wang, H. Jin, X. Zhao, K. Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang Henan Peoples R China

Accurate fruit identification is the basis for automating the operation of orchard production. To better apply the identification model in mobile devices so that venue becomes a less restrictive factor for application, this study proposes an apple fruit identification method based on an improved lightweight network named "MobileNetV3-Small." The whale optimization algorithm was introduced to improve the model by obtaining an optimal compound-scaling coefficient for the MobileNetV3-Small network. A multiscale pooling approach was used for fruit recognition, comprising operations such as lossless scaling and feature exfraction on sample images. The obtained images were then inputted into the model for recognition and classification. The experimental process was conducted on an apple data set. The test results show that the multiclass average precision of apple recognition using this model was 94.43% and the running time of recognition was 0.051 s per image. Both indicators outperformed the control network models of "MobileNetV3-Small," ResNet-50, and VGG-19. This model is 14.63% more accurate and 1.95 times quicker on average in identification than the next best model. These findings indicate that the method can realize high-efficiency and high precision recognition of apples with high stability and portability, which lays a good foundation for the mechanization of repetitive operations such as orchard yield estimation, fruit labeling, and fruit picking.

关键词： Apple recognition Compound scaling deep learning algorithm Lightweight network Yield estimation

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