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Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

computers, Materials & Continua 2021年第12期69卷 2863-2882页

作者： Jagannath Paramguru Subrat Kumar Barik Ajit Kumar Barisal Gaurav Dhiman Rutvij HJhaveri Mohammed Alkahtani Mustufa Haider Abidi KIIT University BhubaneswarOdishaIndia Department of Electrical Engineering CETBhubaneswarOdishaIndia Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Computer Science&Engineering Pandit Deendayal Petroleum UniversityGandhinagarIndia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in *** non-linearity are added to make the fossil fuel-based power systems more *** order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic *** this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum *** conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in *** adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

关键词： Economic load dispatch valve point loading industry 4.0 prohibited operating zones ramp rate limit oscillatory particle swarm optimization

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Enhanced Diagnosis of Schizophrenia Using Integrated Node Embedding and Causal Discovery on Imbalanced fMRI Data

Enhanced Diagnosis of Schizophrenia Using Integrated Node Em...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Sina Sabzevar Saeed Masoudnia Babak N Araabi MohammadReza NazemZadeh Hosna Tavakoli Department of Electrical and Computer Engineering The University of Tehran Tehran Iran Department of Neuroscience Monash University Melbourne Australia Computational and Artificial Intelligence Department Institute of Cognitive Science Studies Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

This study introduces a novel diagnostic method for schizophrenia using causal discovery and node embedding techniques on resting-state fMRI data. Data from 148 subjects (27 schizophrenia patients, 121 healthy controls) were analyzed across 116 brain regions, applying several causal discovery techniques to assess directional connectivity and uncover schizophrenia-related patterns. By combining advanced causal discovery with node embedding techniques and a rigorous data-balancing pipeline, our method improves sensitivity to subtle network variations critical for schizophrenia diagnosis. Our approach addresses key challenges in the literature: the difficulty of extracting effective neurobiological markers from noisy and complex fMRI data, the limited interpretability of connectivity networks, and the imbalance in datasets that often skews classification performance. Experimental results demonstrate the pipeline's effectiveness in overcoming imbalanced data challenges and extracting features from noisy and complex data, with Regression Dynamic Causal Modeling (RDCM) achieving the highest classification metrics, including 90.06% accuracy and 89.09% sensitivity. This method identified 15 brain regions with strong diagnostic potential, offering improved sensitivity and specificity for schizophrenia diagnosis Compared to contemporary domain techniques.

关键词： Sensitivity Accuracy Pipelines Machine learning Schizophrenia Functional magnetic resonance imaging Feature extraction Brain modeling Noise measurement Data mining

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Streaming quantum state purification for general mixed states

arXiv

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arXiv 2025年

作者： Grier, Daniel Leung, Debbie Li, Zhi Pashayan, Hakop Schaeffer, Luke Department of Mathematics Department of Computer Science and Engineering University of California San Diego United States Institute for Quantum Computing University of Waterloo Canada Perimeter Institute for Theoretical Physics Canada National Research Council of Canada Canada Dahlem Center for Complex Quantum Systems Freie Universität Berlin Germany Research Institute Taiwan Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland United States

Given multiple copies of a mixed quantum state with an unknown, nondegenerate principal eigenspace, quantum state purification is the task of recovering a quantum state that is closer to the principal eigenstate. A streaming protocol relying on recursive swap tests has been proposed and analysed for noisy depolarized states with arbitrary dimension and noise level. Here, we show that the same algorithm applies much more broadly, enabling the purification of arbitrary mixed states with a nondegenerate principal eigenvalue. We demonstrate this through two approaches. In the first approach, we show that, given the largest two eigenvalues, the depolarized noise is the most difficult noise to purify for the recursive swap tests, thus the desirable bounds on performance and cost follow from prior work. In the second approach, we provide a new and direct analysis for the performance of purification using recursive swap tests for the more general noise. We also derive simple lower bounds on the sample complexity, showing that the recursive swap test algorithm attains optimal sample complexity (up to a constant factor) in the low-noise regime. © 2025, CC BY.

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The expanding frontier of optical elastography and diagnostic biomechanics 10

The expanding frontier of optical elastography and diagnosti...

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Optical Elastography and Tissue Biomechanics X 2023

作者： Parker, Kevin J. Ge, Gary R. Rolland, Jannick P. Nedergaard, Maiken University of Rochester Department of Electrical and Computer Engineering 724 Computer Studies Building Box 270231 RochesterNY14627 United States Institute of Optics University of Rochester 480 Intercampus Drive Box 270186 RochesterNY14627 United States University of Rochester Center for Translational Neuroscience 601 Elmwood Ave Box 645 RochesterNY14642 United States

ISBN: (纸本)9781510658677

The term "elastography" covers a dynamic and expanding set of diagnostic imaging techniques which probe the biomechanical properties of tissues. This overview covers some of the major approaches that have evolved and their role in improving clinical diagnoses. A deeper level of study is also emerging linking our estimates of viscoelasticity to the multiscale structure and composition of living tissue in normal and diseased states. These studies can be undertaken at the highest spatial resolution with optical techniques, and examples in cornea, brain, and skin will be covered. © 2023 SPIE.

关键词： Shear waves

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Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers

arXiv

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arXiv 2023年

作者： Ince, Osman Batur Zeraati, Tanin Yagcioglu, Semih Yaghoobzadeh, Yadollah Erdem, Erkut Erdem, Aykut Koç University KUIS AI Center Turkey Koç University Computer Engineering Department Turkey University of Tehran Iran Hacettepe University Computer Engineering Department Turkey Tehran Institute for Advanced Studies Khatam University Iran

Neural networks have revolutionized language modeling and excelled in various downstream tasks. However, the extent to which these models achieve compositional generalization comparable to human cognitive abilities remains a topic of debate. While existing approaches in the field have mainly focused on novel architectures and alternative learning paradigms, we introduce a pioneering method harnessing the power of dataset cartography (Swayamdipta et al., 2020). By strategically identifying a subset of compositional generalization data using this approach, we achieve a remarkable improvement in model accuracy, yielding enhancements of up to 10% on CFQ and COGS datasets. Notably, our technique incorporates dataset cartography as a curriculum learning criterion, eliminating the need for hyperparameter tuning while consistently achieving superior performance. Our findings highlight the untapped potential of dataset cartography in unleashing the full capabilities of compositional generalization within Transformer models. Our code is available at https://***/cyberiada/ cartography-for-compositionality. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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LTC-SE: Expanding the Potential of Liquid Time-Constant Neural Networks for Scalable AI and Embedded Systems

arXiv

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arXiv 2023年

作者： Bidollahkhani, Michael Atasoy, Ferhat Abdellatef, Hamdan Department of Computer Engineering Institute of Graduate Studies Karabuk University Karabuk Turkey Gesellschaft für Wissenschaftliche DatenverarbeitungmbH Göttingen Germany Department of Electrical and computer engineering Lebanese American University Byblos Lebanon

We present LTC-SE, an improved version of the Liquid Time-Constant (LTC) neural network algorithm originally proposed by Hasani et al. in 2021. This algorithm unifies the Leaky-Integrate-and-Fire (LIF) spiking neural network model with Continuous-Time Recurrent Neural Networks (CTRNNs), Neural Ordinary Differential Equations (NODEs), and bespoke Gated Recurrent Units (GRUs). The enhancements in LTC-SE focus on augmenting flexibility, compatibility, and code organization, targeting the unique constraints of embedded systems with limited computational resources and strict performance requirements. The updated code serves as a consolidated class library compatible with TensorFlow 2.x, offering comprehensive configuration options for LTCCell, CTRNN, NODE, and CTGRU classes. We evaluate LTC-SE against its predecessors, showcasing the advantages of our optimizations in user experience, Keras function compatibility, and code clarity. These refinements expand the applicability of liquid neural networks in diverse machine learning tasks, such as robotics, causality analysis, and time-series prediction, and build on the foundational work of Hasani et al. © 2023, CC BY-NC-ND.

关键词： Ordinary differential equations

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A Novel Deep Learning Approach for Capturing Time Series Dependencies and Improving Short-term Weather Forecasting 7

A Novel Deep Learning Approach for Capturing Time Series Dep...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Johri, Shiva Divyajyothi, M.G. Anitha, S. Rani, M. Sandya Murari, Thejovathi Shirisha, N. Oriental College of Management Oriental Group of Institutes Department of Management Madhya Pradesh Bhopal462022 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Vels Institute of Science Technology And Advanced Studies Department of Computer Science Engineering Pallavaram Krishnapuram Tamil Nadu Chennai India St. Martin's Engineering College Secunderabad Telangana Dhulapally500100 India Acharya Nagarjuna University Department of Computer Science And Engineering Andhra Pradesh Guntur India Mlr Institute of Technology Department of Cse Hyderabad Dundigal India

ISBN: (纸本)9798350371406

Weather forecasting is a critical and challenging task that requires accurate predictions based on historical data and intricate dependencies between time series. Traditional neural networks, such as Back Propagation through Time (BPTT) trained RNNs, struggle to effectively capture these dependencies, leading to suboptimal performance in weather forecasting. In this study, we suggest a novel Integrated-Stack based Bi-LSTM Model, which associations the strengths of LSTM and Bi-LSTM networks, to address these limitations and enhance the accuracy of short-term weather predictions. The primary objective of this research is to develop a versatile deep learning model capable of maintaining long-term dependances and reading any length of sequence, while simultaneously providing real-time short-term weather forecasts. To achieve this, the Integrated-Stack based Bi-LSTM Model is designed to exploit the versatility and non-linear adaptive processing ability of neural networks. The proposed model's architecture involves the integration of LSTM and Bi-LSTM layers. By incorporating both forward and reverse direction processing, the model gains a comprehensive understanding of complex patterns present in time-series data. This bidirectional approach enables the model to capture dependencies effectively, which is essential for accurate weather predictions. To estimate the presentation of the Integrated-Stack based Bi-LSTM Model, we conducted experiments using real-world weather data. We compared the model's predictive capabilities against traditional LSTM models and other state-of-the-art weather forecasting methods. The assessment metrics included accuracy, root mean square error (RMSE), and mean absolute error (MAE). © 2023 IEEE.

关键词： Weather forecasting

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Experimental demonstration of a flexible-grid 1×(2×3)mode-and wavelength-selective switch using silicon microring resonators and counter-tapered couplers

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Chinese Optics Letters 2024年第1期22卷 52-58页

作者：孔德军鲁昊汪鹏君符强戴世勋陈伟伟王栎沣张波豪马凌霄李军戴庭舸杨建义 Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou 325035China College of Science and Technology Ningbo UniversityNingbo 315300China Laboratory of Infrared Materials and Devices The Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Department of Information Science and Electronics Engineering and Cyrus Tang Center for Sensor Materials and Applications Zhejiang UniversityHangzhou 310027China

A flexible-grid 1×(2×3)mode-and wavelength-selective switch which comprises counter-tapered couplers and silicon microring resonators has been proposed,optimized,and demonstrated experimentally in this *** carefully thermally tuning phase shifters and silicon microring resonators,mode and wavelength signals can be independently and flexibly conveyed to any one of the output ports,and different bandwidths can be generated as *** particle swarm optimization algorithm and finite difference time-domain method are employed to optimize structural parameters of the twomode(de)multiplexer and crossing *** bandwidth-tunable wavelength-selective optical router composed of12 microring resonators is studied by taking advantage of the transfer matrix *** results show that,for the fabricated module,cross talk less than-10.18 dB,an extinction ratio larger than 17.41 d B,an in-band ripple lower than0.79 dB,and a 3-dB bandwidth changing from 0.38 to 1.05 nm are obtained,as the wavelength-channel spacing is 0.40 *** corresponding response time is measured to be 13.64μs.

关键词： integrated optics optical waveguide mode-and wavelength-selective switch

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Navigating the Edge: UAS Boundary Tracing for Efficient Volcanic Plume Monitoring

Navigating the Edge: UAS Boundary Tracing for Efficient Volc...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： John Ericksen Abir Islam Carter Frost Kevin Aubert G. Matthew Fricke Varsha Dani Rafael Fierro Tobias Fischer Scott Nowicki Jared Saia Melanie Moses Department of Computer Science University of New Mexico Department of Electrical Engineering University of New Mexico Center for Advanced Research Computing University of New Mexico Computer Science Department Rochester Institute of Technology Earth and Planetary Sciences University of New Mexico Santa Fe Institute Santa Fe

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

We present the implementation and validation of Sketch, an algorithm that uses two Unpiloted Aerial Systems (UASs) to trace the boundary of volcanic plumes. Sketchguarantees asymptotically optimal flight distance and turning by maintaining a sandwich invariant where one UAS stays inside the plume boundary (defined by a $\textbf{CO}_{2}$ concentration threshold) and the other UAS stays outside. The UASs adjust their flight paths based on real-time CO 2 measurements to maintain this invariant. This paper details the implementation of Sketchon a real-world UAS platform, the Dragonfly drone. We evaluate the efficacy of Sketchthrough extensive testing in physics-based simulations and real-world outdoor environments using virtual plumes. The algorithm is compared to a single-UAS baseline algorithm called Zigzag. Results show that Sketchmeets the expectations set by theory, and it is more efficient than Zigzag, achieving shorter flight paths, less turning, and faster mapping times. While Zigzagexhibits slightly higher accuracy in estimating plume area and boundary, Sketchoffers a more efficient real-time volcanic plume monitoring approach, especially in time-sensitive situations. These results demonstrate the feasibility and efficacy of Sketchfor real-time volcanic plume monitoring, paving the way for accurate $\textbf{CO}_2$ emission estimation in hazardous and challenging environments.

关键词： Accuracy Navigation Volcanic activity Estimation Turning Real-time systems Safety Security Monitoring Testing

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Transfer Learning Approach on Bacteria Classification from Microscopic Images 5

Transfer Learning Approach on Bacteria Classification from M...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Singh, Anupam Kumar, Abhishek Salman, Hayder M. Rawat, Navneet Jain, Sanjiv Kumar Rao, Annam Takshitha Shobhit University UP Meerut India Al-Turath Universiy College Department of Computer Science Baghdad10013 Iraq Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Medi-Caps University Electrical Engineering Department Indore India Symbiosis Institute of Technology SIT Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350398267

The ability to identify and categorize bacteria is crucial in modern medicine for disease diagnosis, infection treatment, and epidemic investigation. However, manually identification and categorization of bacteria requires a lot of time and effort from humans. As technology has progressed, computer systems-based techniques are now doing the duty of identifying images captured by digital electron microscopes. On top of that, modern Deep Learning (DL) methods have shown remarkable improvement in the area of image classification. In this research, we explore a method for using a DL model to automate the identification and categorization of bacteria. To develop the DL model, we used a dataset consisting of more than 600 images of 33 distinct bacteria taken with a microscope and the 'transfer learning' technique. GoogLeNet and AlexNet are two examples of transfer learning models used in this research. The DL classification accuracy was evaluated using 20% randomly selected and isolated images from the dataset. Experimental findings of prediction obtained an accuracy of roughly 98.67% by GoogLeNet, and both transfer learning models recognized and classified all 33 bacterial species with better success rates. © 2022 IEEE.

关键词： Bacteria

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