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A sophisticated solution to numerical and engineering optimization problems using Chaotic Beluga Whale Optimizer

Soft Computing 2024年第17-18期28卷 9803-9843页

作者： Bhardwaj, Shrikant Saxena, Sobhit Kamboj, Vikram Kumar Malik, O.P. School of Electronics and Electrical Engineering Lovely Professional University Punjab India Department of Electrical and Software Engineering Schulich School of Engineering University of Calgary Calgary Canada

Beluga Whale Optimization (BWO) metaheuristic search algorithm has recently emerged as a promising approach to address benchmark optimization problems. However, the local search phase of the fundamental BWO algorithm has been observed to suffer from low rate of convergence, stemming from its inadequate exploitation capabilities. The aim of this study is to present a hybrid algorithm, called Chaotic Beluga Whale Optimization (CBWO), to bolster the potential of this technique. CBWO combines chaotic behavior to reach a balance between exploration and exploitation, aiming for improved performance. To assess the effectiveness of CBWO, comprehensive evaluation is conducted on 23 common benchmark functions, and a comparative comparison is performed with several existing algorithms to showcase the advantages of the proposed approach. Furthermore, to ascertain its practical utility, CBWO is applied to 11 traditional engineering challenges and the results are compared with other state-of-the-art algorithms. The findings of these studies show that CBWO demonstrates greater efficiency in optimization, demonstrating quicker and more accurate convergence rates. Specifically, CBWO achieves an average convergence rate improvement of 23% over BWO and outperforms other algorithms by up to 14.8% in terms of solution accuracy. Pseudocode for the CBWO algorithm, enabling easy implementation and understanding, is also presented. Results of this study emphasize the potential of CBWO as a promising optimization tool for addressing complex real-world problems effectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Optimization

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Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data

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Yearbook of Medical Informatics 2025年第1期33卷 266-276页

作者： Dehghani, Farzaneh Derafshi, Reihaneh Lin, Joanna Bayat, Sayeh Bento, Mariana Biomedical Engineering Department University of Calgary Canada Computer Science Department University of Calgary Canada Geomatics Engineering Department University of Calgary Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Canada Electrical and Software Engineering Department University of Calgary Canada

OBJECTIVES: Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical records (EMR). METHODS: We explored the literature on automated AD detection from 2022-2023. Specifically, we focused on various data resources and reviewed several preprocessing and learning methodologies applied to each data type, as well as evaluation metrics for model performance evaluation. Further, we focused on challenges, future perspectives, and recommendations regarding automated AD diagnosis. RESULTS: Compared to other modalities, medical imaging was the most common data type. The prominent modality was Magnetic Resonance Imaging (MRI). In contrast, studies based on EMR data type were marginal because EMR is mostly used for AD prediction rather than detection. Several challenges were identified: data scarcity and bias, imbalanced datasets, missing information, anonymization, lack of standardization, and explainability. CONCLUSION: Despite recent developments in automated AD detection, improving the trustworthiness and performance of these models, and combining different data types will improve usability and reliability of CAD tools for early AD detection in the clinical practice. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： Alzheimer’s Disease computer-aided Diagnosis Electronic Medical Records Medical Imaging Signals and Sensors

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Performance Evaluation of Downlink IRS and Uplink Cluster NOMA-Aided WPCN 35

Performance Evaluation of Downlink IRS and Uplink Cluster NO...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Jafari, Reza Fapojuwo, Abraham O. University of Calgary Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350362244

This paper studies the performance of integrated intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA)-aided wireless powered communication networks (WPCNs). The problem of maximizing the sum-throughput of the system under realistic operating conditions is formulated as a non-convex optimization problem. Given the complexity of jointly optimizing the decision variables for sum-throughput maximization, we propose a two-stage algorithm to achieve a solution. The algorithm first establishes an active DL energy beamforming matrix and IRS phase shift vector to maximize the sensors' aggregated received power, followed by optimizing DL and UL time slots and UL energy resources. The numerical results demonstrate that the proposed algorithm increases the average sum-throughput by 45 % in the perfect channel state information (CSI) scenarios. However, its performance declines with an increase in IRS elements under imperfect CSI, due to increased channel estimation errors. © 2024 IEEE.

关键词： Convex optimization

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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An Equivalent Dynamic Phasor Model for a Single-Phase Boost Power-Factor-Correction Converter

IEEE Open Journal of Power Electronics

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IEEE Open Journal of Power Electronics 2025年 6卷 1002-1021页

作者： Nwaneto, Udoka C. Kashani, Seyed Ali Seif Knight, Andrew M. University of Calgary Electrical and Software Engineering Department CalgaryABT2N 1N4 Canada University of Nigeria Department of Electrical Engineering Nsukka Nigeria

To mitigate harmonic current flow in distribution systems, single-phase diode-bridge rectifiers (DBRs) are commonly equipped with active power factor correction (PFC) controllers. Achieving high power quality and dynamic performance in PFC controller design demands a precise understanding of PFC converter behavior. While detailed electromagnetic transient (EMT) simulations provide accurate insights, they are time-consuming. To address this, the dynamic phasor (DP) method offers a more efficient modeling approach for power converters. This paper introduces and explores the DP model of a single-phase boost PFC converter, along with guidelines to integrate it with existing simulation platforms. To overcome challenges arising from differing driving frequencies (line frequency for the DBR and switching frequency for the DC-DC converter) in the DP modeling of boost PFC converters, we employ the sign function to transform the dynamic model of the single-phase DBR and boost DC-DC converter into an equivalent single-phase active rectifier model. Subsequently, we convert this equivalent model into a DP model for the single-phase boost PFC converter. Utilizing small-signal analysis, we establish a systematic design procedure for using the proposed DP model to tune and optimize PFC controller gains. Simulations conducted in MATLAB/Simulink, along with error calculations, demonstrate a strong correlation between the proposed DP model and the detailed EMT model results, while also highlighting significant numerical simulation advantages of the DP model over the detailed EMT model. Experimental results further validate the practical utility of the proposed DP model in tuning control systems for single-phase boost PFC converters. © 2020 IEEE.

关键词： MATLAB

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Optimizing Satellite Imagery Object Detection in Challenging Weather Conditions using IoT-Driven Fusion Strategies 10

Optimizing Satellite Imagery Object Detection in Challenging...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Ahmed, Mahmoud El-Sheimy, Naser Leung, Henry University of Calgary Department of Electrical and Software Engineering Calgary Canada University of Calgary Department of Geomatics Engineering Calgary Canada

ISBN: (纸本)9798350373011

IoT devices collect data from remote areas for applications like environmental monitoring and disaster management. Satellite imagery complements this data by providing largescale coverage and context, enhancing decision-making in various domains. Aligning sensor data with satellite imagery resolution to integrate IoT and satellite data for advanced analytics poses a challenge, requiring a balance between local and global detection capabilities in the context of diverse spatiotemporal characteristics. Diverse weather conditions further complicate detection accuracy within IoT frameworks, emphasizing the need for comprehensive data capture and accurate analytics. To address this, we employ weighted fusion techniques that combine multiple detection models like You Only Look Once version (YOLOv7) and Detection TRansformer (DETR) algorithms to achieve objected detection with enhanced accuracy and robustness across different weather conditions and spatial scales. Specifically, this method incorporates the Weighted Fusion with Intersection over Union Matching (WF-IoU), Machine Learning (ML)-based fusion using Random Forest Regression (RFR), AdaBoost Regression (ABR), and XGBoost Regression (XGBR), as well as Deep Learning (DL)based fusion. These techniques integrate various algorithms to optimize object detection performance within IoT environments. Nevertheless, determining optimal Fusion Weights (FWs) and ensuring robustness across scenarios remains a challenge. Furthermore, addressing computational complexities in training and optimizing fusion models is imperative for seamless integration into IoT system. To validate our proposals, we conducted detection tasks across eight weather conditions, simulating them with a Satellite Cloud Generator (SCG). From that, we found that fusion method and its sub-approaches outperform the individual algorithms, enhancing object detection in integration with IoT applications, and thereby improving decision-making and resource managemen

关键词： Satellite imagery

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Federated Learning for Complex AI Models in multi-UAV Systems Assisted by Mobile Edge Servers 10

Federated Learning for Complex AI Models in multi-UAV System...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Lu, Junyu Ma, King Leung, Henry University of Calgary Electrical and Software Engineering Calgary Canada

ISBN: (纸本)9798350373011

This paper proposes a federated learning (FL)-assisted multi-UAV system incorporated with an intermediate mobile edge server (MES) to enhance the computational capabilities of UAVs. This system architecture allows UAVs to utilize complex AI models, such as YOLO, for object detection, improving decision-making and adaptability in dynamic environments. The proposed system includes an asynchronous cooperative network for continuous model updates, facilitated by MESs positioned near UAVs to reduce energy consumption and improve efficiency. Experimental results demonstrate the feasibility and effectiveness of this approach, as well as the improvements in training and inference speeds when leveraging MESs, suggesting that the proposed method can provide UAVs with improved accuracy, enhanced decision-making, and better adaptability to dynamic environments. © 2024 IEEE.

关键词： Federated learning

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The Human Behind the Data: Reflections from an Ongoing Co-Design and Deployment of a Data-Navigation Interface for Front-Line Emergency Housing Shelter Staff

The Human Behind the Data: Reflections from an Ongoing Co-De...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Masrani, Teale W. He, Helen Ai Messier, Geoffrey Department of Computer Science University of Calgary Calgary Canada Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (纸本)9781450394222

On any night in Canada, at least 35,000 individuals experience homelessness. These individuals use emergency shelters to transition out of homelessness and into permanent housing. We designed and deployed a technology to support front-line staff at the largest emergency housing shelter in calgary, Canada. Over a period of five months in 2022, we worked closely with front-line staff to co-design an interface for supporting a holistic understanding of client context and facilitating decision-making. The tool is currently in-use and our collaboration is ongoing. In this paper, we reflect on preliminary findings regarding the second iteration of the tool. We find that supporting shelter staff in understanding the human behind the data was a critical component of design. This work contributes to literature on how data tools may be integrated into homeless shelters in a way that aligns with shelters' values. © 2023 Owner/Author.

关键词： Housing

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Extended Reality and Digital Twin in the Oil and Gas Pipeline Industry: A Systematic Review on Applications, Trends, and Future Directions 23

Extended Reality and Digital Twin in the Oil and Gas Pipelin...

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23rd IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2024

作者： Sarvesh, Muskan Kang, Ryan Nam, Hyeongil Park, Simon Hugo, Ron Maurer, Frank Kim, Kangsoo University of Calgary Department of Electrical and Software Engineering Canada University of Calgary Department of Mechanical and Manufacturing Engineering Canada University of Calgary Department of Computer Science Canada

ISBN: (纸本)9798331516475

Immersive and simulation technologies, such as Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), and Digital Twins (DTs), have proven effective in enhancing decision-making, streamlining operations, and improving safety through intuitive visualizations. Unsurprisingly, the oil and gas (O&G) pipeline industry is increasingly turning to these technologies to tackle complex construction and resource management challenges. This paper reviews the applications, benefits, and future trends of these technologies, focusing on integration and emerging trends in monitoring, training, maintenance, and testing. It identifies potential gaps, offers guidance for future research, and emphasizes adapting to technological evolution, providing insights to improve system reliability and sustainability in O&G pipeline operations using immersion and simulation methods. © 2024 IEEE.

关键词： Gas pipelines

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FTASD - A Fine Tuning Approach for Stable Diffusion Models 15

FTASD - A Fine Tuning Approach for Stable Diffusion Models

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Roy, Sutirtha Nath, Sayan Chowdhury, Moshfiq-Us-Saleheen Pratishtha University of Calgary Department of Electrical and Software Engineering Calgary Canada

ISBN: (纸本)9798350370249

Image generation is one of the critical tasks performed in today's world for the expansion of research domains like computer vision and Generative Artificial Intelligence (Generative AI). Therefore, there is an ultimate need for 2-D image synthesis models, which are already introduced by the researchers in the form of stable diffusion models. Recently, various companies like OpenAI and Google AI introduced such models in the computer vision industry. The fundamental approach is to generate target images through a diffusion process. The various applications of stable diffusion include text-to-image generation, image restoration, image-to-image generation, video generation, and facial restoration. In recent years, multiple development plans have been incorporated for improving the image generation models. The improvements have been done in the form of improving the loss function, architecture designs and optimization method. In this paper, we propose a fine-tuning method for improving the performance of various image generation models using Stable Diffusion (SD). Our major focus is on various stable diffusion models which involve text (prompt) to image generation methodology for generating various synthetic images. In our fine tuning process, we leveraged the KerasCV and trained the pre-trained Stable Diffusion Model on a diversified POKEMON (BLIP caption generated) dataset fetched from the Hugging Face database. Our model outperformed the existing KerasCV stable diffusion model which is responsible for text to image generation. We also fine-tuned the model using our self collected Keji National Forest dataset and it produced outstanding application specific results. © 2024 IEEE.

关键词： computer Vision Fine-Tuning Generative-AI SD

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