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Multi-hazard could exacerbate in coastal Bangladesh in the context of climate change

JOURNAL OF CLEANER PRODUCTION 2024年 457卷

作者： Rahman, Mahfuzur Tian, Shufeng Tumon, Md Sakib Hasan Hossain, Md Anuwer Kim, Hyeong-Joo Islam, Md Monirul Alam, Mehtab Sadiq, Shamsher Chen, Ningsheng Ullah, Kashif Abu Zafor, Md Raju, Matiur Rahman Int Univ Business Agr & Technol IUBAT Dept Civil Engn Dhaka 1230 Bangladesh Kunsan Natl Univ Renewable Energy Res Inst 558 Daehakro Gunsan 54150 Jeollabugdo South Korea Kunsan Natl Univ Dept Civil Engn 558 Daehakro Gunsan 54150 Jeollabugdo South Korea Int Univ Business Agr & Technol IUBAT Geomat & Spatial Analyt Res Lab GSAR Dhaka 1230 Bangladesh Chinese Acad Sci Key Lab Mt Hazards & Surface Proc Inst Mt Hazards & Environm Chengdu 610041 Peoples R China Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Swabi 23640 Pakistan Tongji Univ Coll Civil Engn Dept Geotech Engn Shanghai 200092 Peoples R China China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China Bangladesh Army Int Univ Sci & Technol BAIUST Dept Civil Engn Cumilla 3501 Bangladesh

Floods and landslides have cascading effects on coastal areas of Bangladesh. This study aims to develop multihazard maps (e.g., floods and landslides) for the coastal region, by integrating a genetic algorithm optimizer with Long Short -Term Memory deep learning algorithm (GA-LSTM-DLA), global climate model, and geospatial data. It assesses vulnerabilities to multiple hazards and projects future risks considering the Australian Community Climate and Earth System Simulator Earth System Model 1.5 (ACCESS-ESM1-5) under three climate scenarios, i.e., Shared Socioeconomic Pathways1-2.6, 2 -4.5, and 5 -8.5 (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Receiver Operating Characteristic (ROC) curve is used to assess the performance of GA-LSTM-DLA model. According to important feature obtained by random forest (RF) algorithm, most important features for flash, riverine, tidal floods, and landslides are drainage density, rainfall, and geology, respectively. The baseline susceptibility maps for flash, riverine, tidal floods, and landslides are initially recorded as 0.11%, 7.99%, 3.46%, and 0.03%. Projections for the year 2100, under different SSPs, show significant increase, i.e., SSP1-2.6 -flash 1.31%, riverine 23.74%, tidal 26.97%, landslides 2.05%;SSP2-4.5 -flash 0.12%, riverine 30.97%, tidal 10.23%, landslides 0.18%;SSP5-8.5 -flash 0.02%, riverine 17.42%, tidal 6.19%, landslides 0.14%. These projections highlight urgent need for mitigation and adaptation measures against hazards susceptibility, particularly under more extreme socioeconomic scenarios. Overall, the findings of this work are critical for policymakers to develop informed strategies for climate resilience, sustainable development, and disaster risk reduction in the coastal region of Bangladesh.

关键词： Multi-hazard deep learning algorithm Global climate model Sustainable development Coastal region

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Error Analysis of Air-Core Coil Current Transformer Based on Stacking Model Fusion

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ENERGIES 2021年第7期14卷 1912-1912页

作者： Li, Zhenhua Chen, Xingxin Wu, Lin Ahmed, Abu-Siada Wang, Tao Zhang, Yujie Li, Hongbin Li, Zhenxing Xu, Yanchun Tong, Yue China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China State Grid Hubei Power Co Technol Training Ctr Wuhan Elect Power Tech Coll Wuhan 430014 Peoples R China Curtin Univ Discipline Elect & Comp Engn Perth WA 6000 Australia Huazhong Univ Sch & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Chinese Acad Sci Wuhan Branch Wuhan 430071 Peoples R China

Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of the air-core coil current transformer, this paper investigates the influence of these factors using the maximum information coefficient. The interference mechanism of influencing factors on the transformer error is also analyzed. Finally, the Stacking model fusion algorithm is used to predict transformer errors. The developed base model consists of deep learning, integrated learning and traditional learning algorithms. Compared with gated recurrent units and extreme gradient boosting algorithms, the prediction model based on stacking model fusion algorithm proposed in this paper features higher accuracy and reliability which helps improve the performance and safety of future digital substations.

关键词： digital substations air-core coil current transformer stacking model fusion deep learning algorithm

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Facial image inpainting for big data using an effective attention mechanism and a convolutional neural network

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FRONTIERS IN NEUROROBOTICS 2023年 16卷 1111621页

作者： Lu, Xiaoman Lu, Ran Zhao, Wenhao Ma, Erbin Northeastern Univ Coll Sci Dept Math Shenyang Liaoning Peoples R China

Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output image artifacts or fuzzy textures. Therefore, it is of practical significance to study how to effectively restore an incomplete facial image. In this study, we proposed a facial image inpainting method using a multistage generative adversarial network (GAN) and the global attention mechanism (GAM). For the overall network structure, we used the GAN as the main body, then we established skip connections to optimize the network structure, and used the encoder-decoder structure to better capture the semantic information of the missing part of a facial image. A local refinement network has been proposed to enhance the local restoration effect and to weaken the influence of unsatisfactory results. Moreover, GAM is added to the network to magnify the interactive features of the global dimension while reducing information dispersion, which is more suitable for restoring human facial information. Comparative experiments on CelebA and CelebA-HQ big datasets show that the proposed method generates realistic inpainting results in both regular and irregular masks and achieves peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as well as other evaluation indicators that illustrate the performance and efficiency of the proposed model.

关键词： big data artificial intelligence (AI) deep learning algorithm deep learning-based facial image inpainting generative adversarial network convolutional neural networks

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Data-driven state estimation for light-emitting diode underwater optical communication ?

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IFAC-PapersOnLine 2023年第2期56卷 9862-9868页

作者： Yingquan Li Zhenwen Liang Ibrahima N'Doye Xiangliang Zhang Mohamed-Slim Alouini Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia University of Notre Dame Notre Dame IN 46556 USA INRIA Paris-Saclay France

Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for underwater robots. However, maintaining a controlled line of sight link between transmitter and receiver is challenging due to the constant movement of the underlying optical platform caused by the dynamic uncertainties of the LED model and vibration effects. Additionally, the alignment angle required for tracking is not directly measured and has to be estimated. Besides, the light scattering propagates beam pulse in water temporally, resulting in nonlinearities and time-varying underwater optical links with interference and introducing challenges in the estimation problem. In this paper, we address the state estimation problem by designing a Luenberger observer for the LED communication system that provides the angular position and velocity state information involved in the challenges of maintaining a controlled LOS optical wireless communication. In this line, we leverage the power of deep learning-based observer design to estimate the state of the LED communication model online. Simulation results are presented to illustrate the performance of the data-driven LED state estimation.

关键词： Light-emitting diode underwater optical wireless communication online estimation observer design nonlinear systems deep learning algorithm neural networks

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High relative amount of nodular calcification in femoral plaques is associated with milder lower extremity arterial disease

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BMC CARDIOVASCULAR DISORDERS 2022年第1期22卷 563-563页

作者： Azeez, Mae Laivuori, Mirjami Tolva, Johanna Linder, Nina Lundin, Johan Albaeck, Anders Venermo, Maarit Maeyraenpaeae, Mikko I. Lokki, Marja-Liisa Lokki, A. Inkeri Sinisalo, Juha Univ Helsinki Dept Pathol Transplantat Lab Haartmaninkatu 3 FIN-00014 Helsinki Finland Helsinki Univ Hosp Univ Helsinki Dept Vasc Surg Haartmaninkatu 4 PL 340 FIN-00029 Helsinki Finland Univ Helsinki Inst Mol Med Finland HILIFE Helsinki 00014 Finland Karolinska Inst Dept Publ Hlth Sci Global Hlth IHCAR SE-17177 Stockholm Sweden Univ Helsinki Helsinki Univ Hosp Dept Pathol Haartmaninkatu 3 FIN-00014 Helsinki Finland Univ Helsinki Translat Immunol Res Program Res Programs Unit Haartmaninkatu 8 FIN-00014 Helsinki Finland Helsinki Univ Hosp Heart & Lung Ctr Dept Cardiol PL 340 Haartmaninkatu 4 FIN-00029 Helsinki Finland Univ Helsinki Haartmaninkatu 4 PL 340 FIN-00029 Helsinki Finland

Background: Clinical implications of different types of vascular calcification are poorly understood. The two most abundant forms of calcification, nodular and sheet calcification, have not been quantitatively analyzed in relation to the clinical presentation of lower extremity arterial disease (LEAD).Methods: The study analyzed 51 femoral artery plaques collected during femoral endarterectomy, characterized by the presence of > 90% stenosis. Comprehensive clinical data was obtained from patient records, including magnetic resonance angiography (MRA) images, toe pressure and ankle brachial index measurements and laboratory values. The plaques were longitudinally sectioned, stained with Hematoxylin and Eosin and digitized in a deep learning platform for quantification of the relative area of nodular and sheet calcification to the plaque section area. A deep learning artificial intelligence algorithm was designed and independently validated to reliably quantify nodular calcification and sheet calcification. Vessel measurements and quantity of each calcification category was compared to the risk factors and clinical ***: On average, > 90% stenosed vessels contained 22.4 +/- 12.3% of nodular and 14.5 +/- 11.8% of sheet calcification. Nodular calcification area proportion in lesions with > 90% stenosis is associated with reduced risk of critically low toe pressure (< 30 mmHg) (OR = 0.910, 95% CI = 0.835-0.992, p < 0.05), severely lowered ankle brachial index (< 0.4) (OR = 0.912, 95% CI = 0.84-0.986, p < 0.05), and semi-urgent operation (OR = 0.882, 95% CI = 0.797-0.976, p < 0.05). Sheet calcification did not show any significant ***: Large amount of nodular calcification is associated with less severe LEAD. Patients with nodular calcification may have better flow reserves despite local obstruction.

关键词： Lower extremity arterial disease deep learning algorithm Vascular calcification Sheet calcification Nodular calcification Atherosclerosis

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A Competent and Novel Approach of Designing an Intelligent Image Retrieval System

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2020年第24期7卷 1-14页

作者： Dhingra, Shefali Bansal, Poonam Guru Gobind Singh Indraprastha Univ New Delhi India Kurukshetra Univ Univ Inst Engn & Technol Kurukshetra India Maharaja Surajmal Inst Technol New Delhi India

A prominent technique used to search imperceptible images from any vast repository is denoted by Content based image retrieval (CBIR) system. The most eminent features of CBIR. system are Texture, Color and Shape and in this paper these features are extracted using Local binary pattern, Color moment and Automatic segmentation process respectively. The features of these descriptors are combined for the formation of a hybrid feature vector by utilizing the process of normalization. Then, for enhancing the retrieval accuracy of the proposed system, Extreme learning machine (ELM) has been utilized as a classifier. The proposed hybrid CBIR system with ELM leads to an evolution of an intelligent system. Various evaluation metrics like Precision, Recall, Accuracy, etc. have been calculated on the proposed system on standard datasets. Average precision of 90%, 78%. 72% and 88.70% has been obtained respectively on Core1-1K, Core1-5K, Core1-10K and GHIM datasets which is significantly higher than the related techniques.

关键词： Content based Image retrieval (CBIR) Local Binary Pattern (LBP) Precision deep learning algorithm Extreme learning Machine Neural Networks

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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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Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 86-92页

作者： Kalaivani, M. Padmavathi, G. Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore India Avinashilingam Inst Home Sci & Higher Educ Women Sch Phys Sci & Computat Sci Coimbatore India Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore India

the recent years, number of threats to network security increases exponentially as the Internet users which poses serious threat in cloud storage application. Detection and defending against the multiple threats are currently a hot topic in industry and considered as one of the challenging research in academia. Many methodologies and algorithms devised to predict the different attacks. Still, most of the methods cannot simultaneously achieve high performance of prediction with a small number of false alarm rates. In this scenario, deep learning (DL) algorithms are appropriate and intelligent to categorize the multiple attacks. Still, most of the existing DL techniques are computationally inefficient that may degrade the performance in predicting the both normal and attack information. To overcome this aforementioned problem, this paper proposes the hybrid combination of attention maps with deep recurrent networks to mitigate the multiple attacks with low computational overhead. Initially, the pre-processing step is proposed to the inputs in a specified range. Later on, input data are fed into the Attention Enabled Gated Recurrent Networks (AEGRN) which is used to remove the redundant features and select the optimal features that aids for the better classification. Further to enhance the faster response, deep feed forward layers are proposed to replace the traditional deep neural networks. Numerous metrics for performance, including accuracy, precision, recall, specificity, and F1-score, are examined and analyzed as part of the thorough experimentation utilizing multiple datasets, including NSL-KDD-99, UNSW -2019, and CIDC-001. Comparisons of performance between the method that is suggested and existing models developed with DL are used to demonstrate the proposed algorithm's supremacy. The suggested framework surpasses the other DL models and has the best accuracy in predicting with little computational overhead, according to an investigation.

关键词： Multiple threats deep learning algorithm attention enabled gated recurrent networks NSL-KDD UNSW CIDC-001

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Predicting Ca²⁺ and Mg²⁺ ligand binding sites by deep neural network algorithm

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BMC BIOINFORMATICS 2022年第SUPPL 12期22卷 324-324页

作者： Sun, Kai Hu, Xiuzhen Feng, Zhenxing Wang, Hongbin Lv, Haotian Wang, Ziyang Zhang, Gaimei Xu, Shuang You, Xiaoxiao Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China Inner Mongolia Key Lab Stat Anal Theory Life Data Hohhot Peoples R China Inner Mongolia Univ Technol Coll Data Sci & Applicat Hohhot 010051 Peoples R China Hohhot First Hosp Hohhot 010051 Peoples R China

Background Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues. Results In this paper, Mg2+ and Ca2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm. Conclusions An efficient method for predicting Mg2+ and Ca2+ ligand binding sites was presented.

关键词： deep learning algorithm Protein Metal ion ligand Binding residue

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A Trainable Open-Source Machine learning AccelerometerActivity Recognition Toolbox: deep learning Approach

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JMIR AI 2023年 2卷 e42337页

作者： Wieland, Fluri Nigg, Claudio Univ Bern Inst Sport Sci Dept Hlth Sci Bremgartenstr 145 CH-3012 Bern Switzerland

Background: The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source. Objective: To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors. Methods: We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data. Results: Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural Conclusions: HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.

关键词： activity classification deep learning accelerometry open source activity recognition machinelearning activity recorder digital health application smartphone app deep learning algorithm sensor device

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