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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Mandeep Mittal Sudhir Kumar Chauhan Shivangi Rastogi Department of Mathematics School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh India Department of Mathematics AIAS Amity University Uttar Pradesh Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The sustainable inventory model is a framework designed to ensure manufacturing processes' sustainability while maintaining profitability. During manufacturing, some items may experience degradation, resulting in imperfect goods. Nevertheless, a screening process is implemented to eliminate such items before they reach the consumers. Therefore, it is critical to scrutinize items prone to deterioration thoroughly. Suppliers prioritize trade credit in payments when supplying retailers, necessitating the development of an inventory model that caters to the needs of retailers dealing with imperfect and deteriorating quality items while considering carbon emissions and trade credit as a payment. The study presents numerical illustrations and sensitivity analysis for a better understanding of the model.

关键词： Manufacturing processes Sensitivity analysis Profitability Supply chains Carbon dioxide Market research Numerical models

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Analysis of Nonlinear Processes in the P-I-N Diodes Plasma by the Perturbation Theory Methods

Analysis of Nonlinear Processes in the P-I-N Diodes Plasma b...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Andrii Bomba Igor Moroz Department of Computer Science and Applied Mathematics National University of Water and Environmental Engineering Rivne Ukraine Department of Information Technologies and Modeling Rivne State University of Humanities Rivne Ukraine

A nonlinear singularly perturbed boundary value problem of the electron-hole plasma state predicting in the p-i-n diodes active region is considered. The search for solutions to the problem is carried out by the asymptotic method of boundary corrections. The decomposition of the original problem is carried out, the results are analyzed, and the existence of its asymptotic solutions is proved.

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Looking into Concept Explanation Methods for Diabetic Retinopathy Classification

arXiv

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arXiv 2024年

作者： Storås, Andrea M. Sundgaard, Josefine V. Department of Holistic Systems Simula Metropolitan Center for Digital Engineering Oslo Norway Novo Nordisk A/S Søborg Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark

Diabetic retinopathy is a common complication of diabetes, and monitoring the progression of retinal abnormalities using fundus imaging is crucial. Because the images must be interpreted by a medical expert, it is infeasible to screen all individuals with diabetes for diabetic retinopathy. Deep learning has shown impressive results for automatic analysis and grading of fundus images. One drawback is, however, the lack of interpretability, which hampers the implementation of such systems in the clinic. Explainable artificial intelligence methods can be applied to explain the deep neural networks. Explanations based on concepts have shown to be intuitive for humans to understand, but have not yet been explored in detail for diabetic retinopathy grading. This work investigates and compares two concept-based explanation techniques for explaining deep neural networks developed for automatic diagnosis of diabetic retinopathy: Quantitative Testing with Concept Activation Vectors and Concept Bottleneck Models. We found that both methods have strengths and weaknesses, and choice of method should take the available data and the end user’s preferences into account. Our code is available at https://***/AndreaStoraas/ConceptExplanations_DR_grading. © 2024, CC BY.

关键词： Deep neural networks

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A Noise-Model-Free Hyperspectral Image Denoising Method Based on Diffusion Model

A Noise-Model-Free Hyperspectral Image Denoising Method Base...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Keli Deng Zhongshun Jiang Qipeng Qian Yi Qiu Yuntao Qian College of Computer 0Science Zhejiang University Hangzhou China Department of Applied Mathematics University of Arizona Tucson AZ College of Engineering Huzhou University Huzhou China

Hyperspectral Image (HSI) denoising is a crucial preprocessing step to ensure the accuracy of the subsequent HSI analysis and interpretation. Neural network methods recently achieve state-of-the-art performance in HSI denoising. Nevertheless, these methods are typically trained on specific noise models, which could limit their performance as noise models in HSI may vary across different spectral bands. To mitigate this problem, we introduce an HSI denoising method based on the Diffusion Model (DM) whose training process is independent of the noise model, noted as a noise-model-free method. In this method, we first introduce a DM for HSI by increasing the input and output dimensions to incorporate spectral and spatial information. We then derive a DM-based HSI denoising process for a common noise model. Moreover, to address the issue of overfitting during training, we have introduced a stochastic sampling method to more effectively balance the significance of both spatial and spectral information. Experimental results on in-distribution and out-of-distribution samples demonstrate the efficacy of our approach.

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Under-Counted Tensor Completion with Neural Incorporation of Attributes

arXiv

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arXiv 2023年

作者： Ibrahim, Shahana Fu, Xiao Hutchinson, Rebecca Seo, Eugene School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States Department of Applied Mathematics Brown University ProvidenceRI02912 United States

Systematic under-counting effects are observed in data collected across many disciplines, e.g., epidemiology and ecology. Under-counted tensor completion (UC-TC) is well-motivated for many data analytics tasks, e.g., inferring the case numbers of infectious diseases at unobserved locations from under-counted case numbers in neighboring regions. However, existing methods for similar problems often lack supports in theory, making it hard to understand the underlying principles and conditions beyond empirical successes. In this work, a low-rank Poisson tensor model with an expressive unknown nonlinear side information extractor is proposed for under-counted multi-aspect data. A joint low-rank tensor completion and neural network learning algorithm is designed to recover the model. Moreover, the UC-TC formulation is supported by theoretical analysis showing that the fully counted entries of the tensor and each entry’s under-counting probability can be provably recovered from partial observations—under reasonable conditions. To our best knowledge, the result is the first to offer theoretical supports for under-counted multi-aspect data completion. Simulations and real-data experiments corroborate the theoretical claims. Copyright © 2023, The Authors. All rights reserved.

关键词： Data Analytics

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MIXING ON GENERALIZED ASSOCIAHEDRA

arXiv

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arXiv 2024年

作者： Chang, William Defant, Colin Frishberg, Daniel Department of Applied Mathematics University of California Los Angeles United States Department of Mathematics Harvard University CambridgeMA02138 United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

Eppstein and Frishberg recently proved that the mixing time for the simple random walk on the 1-skeleton of the associahedron is O(n3 log3 n). We obtain similar rapid mixing results for the simple random walks on the 1-skeleta of the type-B and type-D associahedra. We adapt Eppstein and Frishberg's technique to obtain the same bound of O(n3 log3 n) in type B and a bound of O(n13 log2 n) in type D;in the process, we establish an expansion bound that is tight up to logarithmic factors in type B. © 2024, CC BY.

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Fine-grained Feature and Template Reconstruction for TIR Object Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liao, Donghai Shu, Xiu Li, Zhihui Liu, Qiao Yuan, Di Chang, Xiaojun He, Zhenyu Xidian University Guangzhou Institute of Technology Guangzhou510555 China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou510006 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Chongqing Normal University National Center for Applied Mathematics Chongqing401331 China University of Technology Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology Sydney2007 Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China

Thermal infrared (TIR) object tracking is a significant subject within the field of computer vision. Currently, TIR object tracking faces challenges such as insufficient representation of object texture information and underutilization of temporal information, which severely affects the tracking accuracy of TIR tracking methods. To address these issues, we propose a TIR object tracking method (called: FFTR) based on fine-grained feature and template reconstruction. Specifically, aiming at the fine-grained information of the TIR object, we employ a frequency channel attention mechanism that transforms TIR images into the frequency domain using discrete cosine transform features. By capturing the fine-grained feature of TIR images from the frequency domain, we enhance the model’s ability to comprehend these images. To better leverage temporal information, we utilize a template region reconstruction method. This method reconstructs the template from the previous frame based on the search area of the current frame, which is then incorporated into the attention computation for the subsequent frame, thereby improving the tracking capability of TIR objects. Extensive quantitative and qualitative experiments show that our method achieves competitive tracking performance on the TIR benchmarks. © 1991-2012 IEEE.

关键词： Object tracking

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A universal transfer theorem for convex optimization algorithms using inexact first-order oracles 24

A universal transfer theorem for convex optimization algorit...

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Proceedings of the 41st International Conference on Machine Learning

作者： Phillip Kerger Marco Molinaro Hongyi Jiang Amitabh Basu Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore Microsoft Research Redmond and Department of Computer Science PUC-Rio Rio de Janeiro Brazil Department of Civil and Environmental Engineering Cornell University Ithaca

Given any algorithm for convex optimization that uses exact first-order information (i.e., function values and subgradients), we show how to use such an algorithm to solve the problem with access to inexact first-order information. This is done in a "black-box" manner without knowledge of the internal workings of the algorithm. This complements previous work that consider the performance of specific algorithms like (accelerated) gradient descent with inexact information. In particular, our results apply to a wider range of algorithms beyond variants of gradient descent, e.g., projection-free methods, cutting-plane methods, or any other first-order methods formulated in the future. Further, they also apply to algorithms that handle structured nonconvexities like mixed-integer decision variables.

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A Cautionary Note on Poli's Stability Condition for Particle Swarm Optimization

A Cautionary Note on Poli's Stability Condition for Particle...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Daniel H. Von Eschwege Andries P. Engelbrecht Department of Industrial Engineering Stellenbosch University Stellenbosch South Africa Department of Industrial Engineering and Computer Science Division Stellenbosch University Stellenbosch South Africa Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Kuwait Kuwait

Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. Various methods of adapting or tuning CPs exist, but many result in exploding particle velocities and an unstable search process. Poli's stability condition ensures convergence in the mathematical limit, and is often used to inform CP configuration. However, this study shows that since it does not place any practical convergence constraints, it cannot be used to guarantee a stable search process. Velocity explosion occurs nonetheless and can lead to floating-point overflow and numerical instability. The investigation into various CP configurations across diverse functions and measurements of particle velocities provides empirical evidence of velocity explosion, and cautions against the assumption that enforcing Poli's criterion guarantees stability. The findings underline the need for comprehensive understanding of CP tuning and stability conditions in PSO, as well as the crucial role of empirical evidence in evaluating the real-world performance of swarm intelligence algorithms.

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Orthopedic Walker Fall Detection and Motion Classification Using Bidirectional Gated Recurrent Unit Neural Network 10

Orthopedic Walker Fall Detection and Motion Classification U...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Accurate fall detection for orthopedic walker users is essential for timely medical intervention and enhancing safety for elderly and mobility-impaired individuals. This paper introduces a new method using a bidirectional gated recurrent unit (BiGRU) neural network to detect falls and classify motion patterns in walker usage. We use the Walker dataset, which includes inertial measurement unit sensor data for four motion classes: idle, motion, step, and fall. The dataset consists of 6-axis IMU readings (3-axis accelerometer and 3-axis gyroscope) from sensors mounted on orthopedic walkers, with around 620 samples per class. Our BiGRU model leverages temporal dependencies in both forward and backward directions to improve classification accuracy. We assess the model's performance in two scenarios: binary classification (fall vs. non-fall) and multi-class classification of all four motion types. The proposed architecture shows strong performance, achieving 99.79% accuracy and a 99.73% F1-score in binary classification, and 98.51% accuracy and a 98.50% F1-score in multi-class classification. It outperforms traditional deep learning models like CNN, LSTM, and GRU. The experimental results confirm the effectiveness of bidirectional processing in capturing temporal motion patterns, indicating significant potential for realtime fall detection in walker-assisted mobility. The high precision and recall values in both scenarios demonstrate the model's reliability for practical healthcare monitoring applications. © 2025 IEEE.

关键词： Recurrent neural networks

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