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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest X-rays

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Multimedia Tools and Applications 2025年第15期84卷 14885-14913页

作者： Asham, Mohammed A. Al-Shargabi, Asma A. Al-Sabri, Raeed Meftah, Ibrahim School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Information Technology Collage of Computer Qassim University Buraydah51452 Saudi Arabia College of Mechanical and Electrical Engineering Central South University Changsha410017 China

Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often require extensive computing resources and complex procedures, rendering them impractical. This study focuses on the development of a lightweight deep-learning model for the detection of pulmonary diseases. Leveraging the benefits of knowledge distillation (KD) and the integration of the ConvMixer block, we propose a novel lightweight student model based on the MobileNet architecture. The methodology begins with training multiple teacher model candidates to identify the most suitable teacher model. Subsequently, KD is employed, utilizing the insights of this robust teacher model to enhance the performance of the student model. The objective is to reduce the student model's parameter size and computational complexity while preserving its diagnostic accuracy. We perform an in-depth analysis of our proposed model's performance compared to various well-established pre-trained student models, including MobileNetV2, ResNet50, InceptionV3, Xception, and NasNetMobile. Through extensive experimentation and evaluation across diverse datasets, including chest X-rays of different pulmonary diseases such as pneumonia, COVID-19, tuberculosis, and pneumothorax, we demonstrate the robustness and effectiveness of our proposed model in diagnosing various chest infections. Our model showcases superior performance, achieving an impressive classification accuracy of 97.92%. We emphasize the significant reduction in model complexity, with 0.63 million parameters, allowing for efficient inference and rapid prediction times, rendering it ideal for resource-constrained environments. Outperforming various pre-trained student models in terms of overall performance and computation cost, our findings underscore the effectiveness of the proposed KD strategy and the integration of the Conv

关键词： Diagnosis

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data Science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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Transforming Electric Vehicle Navigation Strategies: An AI-Enhanced Framework for Achieving Superior Route Efficiency in Varied Conditions

Transforming Electric Vehicle Navigation Strategies: An AI-E...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Prakash, P. Anandha Radha, R. School of Electrical Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350387230

Electric vehicles (EVs) are essential for environmentally friendly transportation, there are significant challenges in maximizing their routing in many scenarios. This paper explores advanced predictive analytics strategies aimed at enhancing electric vehicle routing in India. Electric vehicles (EVs) are essential to the progress of sustainable transportation;yet, there are significant challenges in optimizing EV routing in a range of challenging scenarios. This paper focuses on enhancing EV routing in the Indian setting, where varied road conditions, dynamic traffic patterns, and infrastructural constraints are common. We provide a sophisticated routing algorithm that accounts for these problems by including a traffic coefficient, which denotes a 50% increase in effective distance due to high traffic. This method makes use of this traffic coefficient to modify routing computations, guaranteeing more precise and useful route planning in practical situations. By dynamically adjusting to real-time traffic data, the system maximizes route efficiency and lowers EV operating costs. We describe the algorithm's implementation in detail and illustrate its efficacy with simulated results and case studies showing significant gains in routing accuracy. The suggested methodology bridges the gap between theoretical models and real-world implementations, providing insightful information to stakeholders in the Indian EV ecosystem. This work intends to promote EV infrastructure and routing efficiency in India by offering a strong foundation for EV route optimization, which will inform future research and policy-making in this crucial field. © 2024 IEEE.

关键词： charging optimization charging prediction Dijkistra Routing and comparative analysis electric vehicle routing Machine learning traffic prediction

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Numerical simulation of highly efficient double perovskite solar cell using SCAPS-1D

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Materials Today: Proceedings 2023年 73卷 584-589页

作者： Rana, Ashish D. Pharne, Indrajit D. Bhargava, Kshitij Department of Electrical and Computer Science Engineering Institute of Infrastructure Technology Research And Management Gujarat Ahmedabad 380026 India Department of Electrical Engineering Sanjay Ghodawat University Maharashtra Kolhapur 41610 India

This study focuses on designing of lead-free double perovskite solar cells (DPSCs). Lead-free organic–inorganic DPSCs have achieved very good efficiency within a short period of active research. Formamidinium based double perovskite solar cells ((FA)2BiCuI6) have emerged as a promising replacement of lead-based organic–inorganic PSCs. The electron and hole transport layers (ETLs and HTLs) play important role towards the performance of DPSCs. Herein, we investigate the effect of various combinations of ETLs and HTLs for achieving an optimized highly efficient (FA)2BiCuI6 based DPSCs using SCAPS-1D. The selected ETLs were SnO2, TiO2, PCBM and HTLs were SpiroOMeTAD, NiO, PEDOT: PSS. Total 09 cells were analyzed to achieve the maximum power conversion efficiency (PCE). The best efficiency of 15.49% was yielded by a cell having SnO2 as ETL and NiO as HTL. © 2022

关键词： Perovskite solar cells

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Fabrication and characterization of magnesium-based nanocomposites reinforced with Baghdadite and carbon nanotubes for orthopaedical applications

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Journal of Magnesium and Alloys 2024年第12期12卷 5144-5163页

作者： Mojtaba Ansari Shiva Mahdavikia Hossein Eslami Mozhdeh Saghalaini Hamid Taghipour Fatemeh Zare Shahin Shirani Mohammad Hossein Alizadeh Roknabadi Department of Biomedical Engineering Meybod UniversityMeybodIran Department of Biomedical Engineering Amirkabir University of TechnologyTehranIran Center for Education and Research on Macromolecules(CERM) CESAM Research UnitDepartment of ChemistryUniversity of LiègeLiègeBelgium Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Aerospace Engineering Amirkabir University of TechnologyTehranIran

This study explores the potential of Mg/Carbon Nanotubes/Baghdadite composites as biomaterials for bone regeneration and repair while addressing the obstacles to their clinical *** powder was synthesized using the sol-gel method to ensure a fine distribution within the Mg/CNTs ***/1.5 wt.%CNT composites were reinforced with BAG at weight fractions of 0.5,1.0,and 1.5 wt.%using spark plasma sintering at 450℃and 50 MPa after homogenization via ball *** cellular bioactivity of these nanocomposites was evaluated using human osteoblast-like cells and adipose-derived mesenchymal stromal *** proliferation and attachment of MG-63cells were assessed and visualized using the methylthiazol tetrazolium(MTT)assay and SEM,while AD-MSC differentiation was measured using alkaline phosphatase activity *** were also generated to visualize the diameter distributions of particles in SEM images using image processing *** Mg/CNTs/0.5 wt.%BAG composite demonstrated optimal mechanical properties,with compressive strength,yield strength,and fracture strain of 259.75 MPa,180.25 MPa,and 31.65%,*** learning models,including CNN,LSTM,and GRU,were employed to predict stress-strain relationships across varying BAG amounts,aiming to accurately model these curves without requiring extensive physical *** shown by contact angle measurements,enhanced hydrophilicity promoted better cell adhesion and ***,corrosion resistance improved with a higher BAG *** study concludes that Mg/CNTs composites reinforced with BAG concentrations below 1.0 wt.%offer promising biodegradable implant materials for orthopedic applications,featuring adequate load-bearing capacity and improved corrosion resistance.

关键词： Biodegradable material Magnesium-based composite Carbon nanotube Baghdadite Spark plasma sintering

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of Electronics and Information Engineers.

关键词： Demand response

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LIME-RAY: What Does a Neural Network See from X-Rays? 2

LIME-RAY: What Does a Neural Network See from X-Rays?

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Datta, Ayon Al Wazed Wasi, Wasif Muntasir, Fahim Nafis, Mahdee Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9798331508227

In the field of medical imaging, neural networks have significantly enhanced the analysis and interpretation of X-ray images, providing advanced capabilities in detecting patterns and anomalies. Despite their potential, challenges remain, particularly concerning the interpretability and reliability of these models. The LIME-RAY approach addresses these challenges by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. This hybrid technique leverages CNNs for spatial feature extraction and LSTMs for temporal pattern recognition, improving the accuracy and interpretability of the model’s predictions. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) are used to provide transparent, interpretable results, crucial for gaining the trust of healthcare professionals. Our approach is validated using a diverse dataset of chest X-rays, including cases of Covid-19, Pneumonia, and Tuberculosis. The proposed method demonstrates superior performance in multi-class disease classification and offers valuable insights into the decision-making process, bridging the gap between complex neural network models and practical healthcare applications. ©2024 IEEE.

关键词： Convolutional neural networks

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Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

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Energy engineering 2023年第1期120卷 69-86页

作者： Saeed Akbari Seyed Saeed Fazel Hamed Hashemi-Dezaki School of Railway Engineering Iran University of Science and TechnologyTehran13114-16846Iran Department of Electrical and Computer Engineering University of KashanKashan***Iran Research and Innovational Center for Electrical Engineering(RICE) Faculty of Electrical EngineeringUniversity ofWest Bohemia(UWB)Pilsen30100Czech Republic

The networking of microgrids has received significant attention in the form of a smart *** this paper,a set of smart railway stations,which is assumed as microgrids,is connected *** has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility ***,the operational costs of stations under various conditions decrease by applying the proposed *** smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking *** regenerative braking is one of the essential ***,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based *** networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are *** proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB ***,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

关键词： Energy management system(EMS) smart railway stations coordinated operation photovoltaic generation regenerative braking uncertainty scenario-based model mixed-integer linear programming(MILP)

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