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A novel combined model for wind speed prediction - Combination of linear model, shallow neural networks, and deep learning approaches

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ENERGY 2021年 234卷 121275-121275页

作者： Wang, Shuai Wang, Jianzhou Lu, Haiyan Zhao, Weigang Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sch Comp Sci Sydney NSW Australia Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Beijing Inst Technol Ctr Energy & Environm Policy Res Beijing 100081 Peoples R China

Accurate wind speed forecasting is increasingly essential for improving the operating efficiency of electric power systems. Numerous models have been proposed to obtain the accurate and stable wind speed forecasting results. However, previous proposed models are limited by single predictive model or cannot deal with complex nonlinear data characteristic, which resulted in poor and unstable prediction results. In this paper, a novel forecasting model that combines noise processing, statistical approaches, deep learning frameworks and multi-objective optimization algorithm is proposed. Multi-objective optimization algorithms can take advantage of the merits of benchmark prediction models to address nonlinear characteristics of wind speed series. The 10-min real wind speed data from three Sites in China are adopted for verifying the effectiveness of this proposed model. The experimental results of multi-step prediction show that the model achieves MAPE(1-step) = 2.2109%, MAPE(2-step) = 3.0309%, and MAPE(3-step) = 4.2536% at Site 1;MAPE(1-step) = 2.4586%, MAPE(2-step) = 3.2034%, and MAPE(3-step) = 4.6843% at Site 2;MAPE(1-step) = 2.3180%, MAPE(2-step) = 3.0846%, and MAPE(3-step) = 4.4193% at Site 3. Therefore, the forecasting performance of this model is excellent, and it is beneficial to the dispatching and planning of power grid. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind speed prediction deep learning algorithm Data preprocessing Combination forecasting strategy

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Air Pollution Prediction by deep learning Model

Air Pollution Prediction by Deep Learning Model

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International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Jeya, S. Sankari, L. Sri Ramakrishna Coll Arts & Sci Women Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9781728148762

The impact of harmful pollutants in the air on human health is a vast area of research, preventing or controlling, and also monitoring the pollutant is the huge responsibility of any governing body. Several computing models starting from statistical and machine learning to deep learning have compared and contrasted to prove the accuracy of forecasting air quality standards until date. The level of pollutants is still not in control in several parts of the world due to various sources and reasons. This paper attempts to forecast PM2.5 pollutant which is one of the detrimental diseases triggering pollutants throughout the globe by using bidirectional long short term memory model. The proposed model accuracy is comparatively greater than the existing model by evaluating the following error estimation metrics Root mean square error = 9.86, mean absolute error = 7.53, and symmetric mean absolute percentage error = 0.1664.

关键词： Forecasting PM2.5 deep learning algorithm error evaluation metrics bidirectional long short term memory

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A deep neural network algorithm-based approach for predicting recovery period of accidents according to construction scale

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HELIYON 2024年第11期10卷 e32215页

作者： Kim, Ji-Myong Das Adhikari, Manik Bae, Junseo Yum, Sang-Guk Mokpo Natl Univ Dept Architectural Engn Mokpo 58554 South Korea Gangneung Wonju Natl Univ Dept Civil & Environm Engn Kangnung 25457 South Korea Korea Univ Div Smart Cities Sejong 30019 South Korea

Despite ongoing safety efforts, construction sites experience a concerningly high accident rate. Notwithstanding that policies and research to reduce the risk of accidents in the construction industry have been active for a long time, the accident rate in the construction industry is considerably higher than in other industries. This trend may likely be further exacerbated by the rapid growth of large-scale construction projects driven by urban population expansion. Consequently, accurately predicting recovery periods of accidents at construction sites in advance and proactively investing in measures to mitigate them is critical for efficiently managing construction projects. Therefore, the purpose of this study is to propose a framework for developing accident prediction models based on the deep Neural Network (DNN) algorithm according to the scale of the construction site. This study suggests DNN models and applies the DNN for each construction site scale to predict accident recovery periods. The model performance and accuracy were evaluated using mean absolute error (MAE) and root-mean-square error (RMSE) and compared with the widely used multiple regression analysis model. As a result of model comparison, the DNN models showed a lower prediction error rate than the regression analysis models for both small-to-medium and large construction sites. The findings and framework of this study can be applied as the opening stage of accident risk assessment using deep learning techniques, and the introduction of deep learning technology to safety management according to the scale of the construction site is provided as a guideline.

关键词： Construction accident Accident recovery period Construction scale deep learning algorithm Small-to-medium sized construction site Large construction site

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Developing the network social media in graphic design based on artificial neural network

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第4期12卷 640-653页

作者： Liu, Yaxuan Luxun Acad Fine Arts Dalian 116650 Peoples R China

The purposes are to effectively solve the graphic design problems, develop an easy-to-use supporting design program, and make graphic design more reliable and accurate. Based on the analysis of the current graphic design framework, graphic design data are obtained from the network social media. A system of surrounding rock classification and support optimization design is developed by a deep neural network structure. The model's effectiveness is verified by more than 3000 road conditions data. The results show that the three-layer network's errors are 0.0062 with a training time of 12,455, and the five-layer network's errors are 0.00019 with a training time of 69,895. With the input layer, hidden layer, and the output layer of 8, 15, and 5 respectively, the model performs best. In the deep learning algorithm, the deep backpropagation neural network (deep BPNN) can obtain the best training effects with less training time. Therefore, the roadway drawing system's application based on the deep learning algorithm to the roadway support design can improve design efficiency and scientificity.

关键词： deep learning algorithm Weight coefficient Bias matrix Accuracy requirement Roadway support design

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Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model

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COMPUTERS & GEOSCIENCES 2021年 150卷 104737-104737页

作者： Ghosh, S. M. Behera, M. D. IIT Kharagpur CORAL SAM Lab Kharagpur W Bengal India

The availability of advanced Machine learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and deep learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the deep learning model. However, the negligible variations in deep learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a deep learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like deep learning rather than semi-empirical models.

关键词： Interferometric Water Cloud Model SAR remote sensing Machine learning Temporal decorrelation deep learning algorithm

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Classification and Regression of Pinhole Corrosions on Pipelines Based on Magnetic Flux Leakage Signals Using Convolutional Neural Networks

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algorithmS 2024年第8期17卷 347页

作者： Shen, Yufei Zhou, Wenxing Univ Western Ontario Dept Civil & Environm Engn London ON N6A 5B9 Canada

Pinhole corrosions on oil and gas pipelines are difficult to detect and size and, therefore, pose a significant challenge to the pipeline integrity management practice. This study develops two convolutional neural network (CNN) models to identify pinholes and predict the sizes and location of the pinhole corrosions according to the magnetic flux leakage signals generated using the magneto-static finite element analysis. Extensive three-dimensional parametric finite element analysis cases are generated to train and validate the two CNN models. Additionally, comprehensive algorithm analysis evaluates the model performance, providing insights into the practical application of CNN models in pipeline integrity management. The proposed classification CNN model is shown to be highly accurate in classifying pinholes and pinhole-in-general corrosion defects. The proposed regression CNN model is shown to be highly accurate in predicting the location of the pinhole and obtain a reasonably high accuracy in estimating the depth and diameter of the pinhole, even in the presence of measurement noises. This study indicates the effectiveness of employing deep learning algorithms to enhance the integrity management practice of corroded pipelines.

关键词： deep learning algorithm convolutional neural network pipeline corrosion pinhole finite element analysis magnetic flux leakage signal

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deep learning based remote sensing technique for environmental parameter retrieval and data fusion from physical models

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第13期14卷 1230-1230页

作者： Vemuri, Ratna Kumari Reddy, Pundru Chandra Shaker Kumar, B. S. Puneeth Ravi, Jayavadivel Sharma, Sudhir Ponnusamy, Sivakumar PVP Siddhartha Inst Technol Dept Elect & Commun Engn Vijayawada 520007 Andhra Pradesh India CMR Coll Engn & Technol Dept Comp Sci & Engn Hyderabad Telangana India St Josephs Coll Dept Comp Sci Lalbagh Rd Bengaluru India Lovely Profess Univ Dept Comp Sci & Engn GT Rd Phagwara 144411 Punjab India Birla Inst Appl Sci Dept CSE Naini Tal 263136 Uttarakhand India SRM Inst Sci & Technol Dept Comp Sci & Engn Ghaziabad 201204 Uttar Pradesh India

Signal and data processing has established a new standard by using deep learning (DL) and deep neural network (DNN). This is done by obtaining advanced performance in terms of audio, image, and understanding of the language naturally. The major research in remote sensing has been given to the DL (deep learning) applications. The main application of deep learning in remote sensing is the classification of the data. The quality of the data received from the remote sensing sensors is insufficient. Thus, equal importance has to be given to the challenges associated with the magnification of these low-quality images. Addressing such challenges becomes predominant as it involves different environmental conditions, the tradeoff in the imaging system, and varying altitude images. This becomes the reason for the low quality of observations, thus making classification and identification a difficult task. Another huge challenge faced in the process of classification and identification is the heterogeneous nature of the remote sensing sensors. This also affects the efficiency and effectiveness of the data from remote sensing. The approaches for processing the remote sensing data can be improved by using the multi-modal datasets from the increasing sensing and additional secondary datasets when handy applications primary consideration. This makes researchers around the world get more interest in the fusion of data from multi-source for application diversity. The temporal data integration with spectral or spatial information is made possible using the spaceborne sensor's revisit capabilities. It helps in the representation of data structures with fresh data that is time variable, as well as data extraction techniques. Thus, this paper involves the development of the deep learning technique which is based on the remote sensing data for the extraction of features from the environmental parameters.

关键词： deep learning algorithm Remote sensing technique Temporal data fusion deep neural network

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Accelerating deep convolutional neural network on FPGA for ECG signal classification

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MICROPROCESSORS AND MICROSYSTEMS 2023年 103卷

作者： Aruna, V. B. K. L. Chitra, E. Padmaja, M. SRM Inst Sci & Technol ECE Dept Chennai 603203 India VR Siddhartha Engn Coll ECE Dept Vijayawada 520007 Andhra Pradesh India

Abnormal activity of the heart is known as cardiac arrhythmia which must be recognized in earlier stage to prevent sudden death and premature death. The occurrence of arrhythmia increases with age, and it is detected using an electrocardiogram (ECG) signal. Conversely, it is very complex to manually achieve the quick and exact classification due to the complexity, non-linearity and low amplitude of the ECG signal. As a result, the healthcare field requires an automatic system to recognize abnormal heartbeats from a huge amount of ECG records. So, the deep learning algorithm named as deep Convolutional Neural Network (DCNN) is proposed in this research to analyze the ECG signal on a field-programmable gate array (FPGA). Before performing a clas-sification process, two different processes called signal pre-processing as well as feature extraction are required. For the process of signal pre-processing, the Error Normalised Least Mean Square (ENLMS) algorithm is utilized in our work, and Discrete Wavelet Transform (DWT) technique is performed to take out the relevant features from the ECG signal. Finally, FPGA based one-dimensional DCNN with 3 convolutional layers, 3 pooling layers, and 3 fully connected layers is proposed to classify the signals with proper complex features. The publicly available MIT-BIH arrhythmia and PTB databases are exploited in this research to process ECG signals on the multi-input structure. In addition, different performance parameters like classification accuracy, specificity, sensitivity, and precision are engaged to evaluate the proposed methodology;also, it is compared with different FPGA based existing classifiers. The analysis shows that the proposed design accomplishes 98.6 % classification accuracy on the MIT-BIH arrhythmia database and 99.67 % accuracy on the PTB database, which is 0.304 % higher than a multilayer perception (MLP) and 0.47 % higher than decision-based classifier. Moreover, the proposed FPGA based DCNN accelerator cons

关键词： ECG signal classification deep learning algorithm Signal de-noising process Feature extraction DWT method Error Normalised Least Mean Square (ENLMS) algorithm

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Combined framework based on data preprocessing and multi-objective optimizer for electricity load forecasting

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期119卷

作者： Xia, Yurui Wang, Jianzhou Wei, Danxiang Zhang, Ziyuan Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

In recent years, accurate electricity load forecasting has become increasingly essential for improving the management efficiency of power-generation systems. However, previously proposed hybrid models directly apply signal-processing technology in data preprocessing, resulting in poor efficiency in matching the electricity sequence characteristics. Moreover, most studies exhibit non-optimal performance in practical applications because they focus on forecasting accuracy and ignore forecasting stability. In this study, a combined framework that includes amodified noise processing strategy, multi-objective optimization algorithm, and deep neural network is proposed to solve the low prediction-accuracy problem in electricity load forecasting. The 30-minute real time data of electricity load from Queensland, Australia, are employed to verify the reliability of the proposed framework. The mean absolute percentage error values of the proposed framework in a multi-step prediction approached the values of MAPE1-step = 0.79%, MAPE2-step = 1.13%, and MAPE3-step = 1.50% in Series 1, which significantly outperformed the existing contrast models.

关键词： Electricity load forecasting Combined framework Signal processing deep learning algorithm

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Multi-Stream Fusion Network for Skeleton-Based Construction Worker Action Recognition

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SENSORS 2023年第23期23卷 9350-9350页

作者： Tian, Yuanyuan Liang, Yan Yang, Haibin Chen, Jiayu City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong 999077 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Key Lab Resilient Infrastruct Coastal Cities MOE Shenzhen 518060 Peoples R China Tsinghua Univ Sch Civil Engn Beijing 100084 Peoples R China

The global concern regarding the monitoring of construction workers' activities necessitates an efficient means of continuous monitoring for timely action recognition at construction sites. This paper introduces a novel approach-the multi-scale graph strategy-to enhance feature extraction in complex networks. At the core of this strategy lies the multi-feature fusion network (MF-Net), which employs multiple scale graphs in distinct network streams to capture both local and global features of crucial joints. This approach extends beyond local relationships to encompass broader connections, including those between the head and foot, as well as interactions like those involving the head and neck. By integrating diverse scale graphs into distinct network streams, we effectively incorporate physically unrelated information, aiding in the extraction of vital local joint contour features. Furthermore, we introduce velocity and acceleration as temporal features, fusing them with spatial features to enhance informational efficacy and the model's performance. Finally, efficiency-enhancing measures, such as a bottleneck structure and a branch-wise attention block, are implemented to optimize computational resources while enhancing feature discriminability. The significance of this paper lies in improving the management model of the construction industry, ultimately aiming to enhance the health and work efficiency of workers.

关键词： construction worker action recognition deep learning algorithm 3D skeleton data multi-stream network

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