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Articles de revues sur le sujet « Modal normalization »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Valliappan, Nachiappan, Fabian Ruch, and Carlos Tomé Cortiñas. "Normalization for fitch-style modal calculi." Proceedings of the ACM on Programming Languages 6, ICFP (2022): 772–98. http://dx.doi.org/10.1145/3547649.

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Fitch-style modal lambda calculi enable programming with necessity modalities in a typed lambda calculus by extending the typing context with a delimiting operator that is denoted by a lock. The addition of locks simplifies the formulation of typing rules for calculi that incorporate different modal axioms, but each variant demands different, tedious and seemingly ad hoc syntactic lemmas to prove normalization. In this work, we take a semantic approach to normalization, called normalization by evaluation (NbE), by leveraging the possible-world semantics of Fitch-style calculi to yield a more m

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Zhang, Heng, Yuanyuan Pu, Rencan Nie, Dan Xu, Zhengpeng Zhao, and Wenhua Qian. "Multi-modal image synthesis combining content-style adaptive normalization and attentive normalization." Computers & Graphics 98 (August 2021): 48–57. http://dx.doi.org/10.1016/j.cag.2021.04.030.

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ZHU, Shaojie, Lei ZHANG, Bailong LIU, Shumin CUI, Changxing SHAO, and Yun LI. "Mode Normalization Enhanced Recurrent Model for Multi-Modal Semantic Trajectory Prediction." IEICE Transactions on Information and Systems E103.D, no. 1 (2020): 174–76. http://dx.doi.org/10.1587/transinf.2019edl8130.

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Pereira, Sérgio, Edwin Reynders, Filipe Magalhães, Álvaro Cunha, and Jorge P. Gomes. "The role of modal parameters uncertainty estimation in automated modal identification, modal tracking and data normalization." Engineering Structures 224 (December 2020): 111208. http://dx.doi.org/10.1016/j.engstruct.2020.111208.

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Yang, Q. W. "A Flexibility-Based Method for Structural Damage Identification Using Ambient Modal Data." International Journal of Space Structures 24, no. 3 (2009): 153–59. http://dx.doi.org/10.1260/026635109789867616.

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Structural damage identification using ambient vibration modes has become a very important research area in recent years. The main issue surrounding the use of ambient vibration modes is the mass normalization of the measured mode shapes. This paper presents a promising approach that extends the flexibility sensitivity technique to tackle the ambient vibration case. By introducing the mass normalization factors, manipulating the flexibility sensitivity equation, the unknown damage parameters and mass normalization factors can be computed simultaneously by the least-square technique. The effect

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Martins, Ana Teresa, and Lília Ramalho Martins. "Natural Deduction for Full S5 Modal Logic with Weak Normalization." Electronic Notes in Theoretical Computer Science 143 (January 2006): 129–40. http://dx.doi.org/10.1016/j.entcs.2005.05.027.

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Ye, Xi Jun, Tian Feng Zhu, Quan Sheng Yan, and Wei Feng Wang. "Experimental Verification of Decentralized Approach for Modal Identification Based on Wireless Smart Sensor Network." Advanced Materials Research 291-294 (July 2011): 3–11. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.3.

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This paper provides an experimental verification of decentralized approach for modal test and analysis of a 30 meters long railway overpass bridge. 11 Imote2 smart sensor nodes were implemented on the WSSN. In order to compare the identification precision of different topologies, acceleration responses were obtained under centralized and 3 different decentralized topologies. Local modal parameters were estimated by NExT/ERA within each local group; true modes were then distinguished from spurious modes by EMAC and finite-element analysis. In order to estimate global mode shape, a least square

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Medeiros, Maria Da Paz N. "A new S4 classical modal logic in natural deduction." Journal of Symbolic Logic 71, no. 3 (2006): 799–809. http://dx.doi.org/10.2178/jsl/1154698578.

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AbstractWe show, first, that the normalization procedure for S4 modal logic presented by Dag Prawitz in [5] does not work. We then develop a new natural deduction system for S4 classical modal logic that is logically equivalent to that of Prawitz, and we show that every derivation in this new system can be transformed into a normal derivation.

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Jahani, K., and A. S. Nobari. "Assessment of the Performance of Modal Based Decoupling Method in Identifying Structural Joints Characteristics." Applied Mechanics and Materials 50-51 (February 2011): 125–29. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.125.

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In this paper, the performance of modal based decoupling method in structural joint identification is investigated. This method does not need any iteration and is independent from updating techniques. Because of these advantages, in present work it is tried to assess the effect of experimental and numerical issues of using modal data in this method. Issues such as effect of number of digits of precision of modal pairs, effects of lower hand higher eliminated modes, effect of any normalization of modal vectors and effect of possible noisy experimentally extracted modal parameters on identified

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Gratzer, Daniel. "A Modal Deconstruction of Löb Induction." Proceedings of the ACM on Programming Languages 9, POPL (2025): 864–92. https://doi.org/10.1145/3704866.

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We present a novel analysis of the fundamental Löb induction principle from guarded recursion. Taking advantage of recent work in modal type theory and univalent foundations, we derive Löb induction from a simpler and more conceptual set of primitives. We then capitalize on these insights to present Gatsby, the first guarded type theory capturing the rich modal structure of the topos of trees alongside Löb induction without immediately precluding canonicity or normalization. We show that Gatsby can recover many prior approaches to guarded recursion and use its additional power to improve on pr

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Chen, Weiyi, Lingjuan Miao, Yuhao Wang, Zhiqiang Zhou, and Yajun Qiao. "Infrared–Visible Image Fusion through Feature-Based Decomposition and Domain Normalization." Remote Sensing 16, no. 6 (2024): 969. http://dx.doi.org/10.3390/rs16060969.

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Infrared–visible image fusion is valuable across various applications due to the complementary information that it provides. However, the current fusion methods face challenges in achieving high-quality fused images. This paper identifies a limitation in the existing fusion framework that affects the fusion quality: modal differences between infrared and visible images are often overlooked, resulting in the poor fusion of the two modalities. This limitation implies that features from different sources may not be consistently fused, which can impact the quality of the fusion results. Therefore,

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Rakesh Raushan. "Mathematical Modelling for Failure Analysis of Composite Drive Shaft Using Modal Flexibility and Curvature Method." Communications on Applied Nonlinear Analysis 31, no. 1s (2024): 42–55. http://dx.doi.org/10.52783/cana.v31.555.

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Early damage identification in composite structures has attracted a lot of attention in post-failure examinations in recent years. In the research, torsional fatigue test of composite drive shafts has been done and numerical methodologies have been employed for the validation for premature failure using modal flexibility and modal curvature methods. Initially, the eigenvalue and eigenvectors of healthy and fractured drive shafts are evaluated using a numerical model and verified by fatigue experimental approach. Flexibility variance and the absolute curvature difference between the drive shaft

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Li, Yubin, Weida Zhan, Yichun Jiang, and Jinxin Guo. "RDCRNet: RGB-T Object Detection Network Based on Cross-Modal Representation Model." Entropy 27, no. 4 (2025): 442. https://doi.org/10.3390/e27040442.

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RGB-thermal object detection harnesses complementary information from visible and thermal modalities to enhance detection robustness in challenging environments, particularly under low-light conditions. However, existing approaches suffer from limitations due to their heavy dependence on precisely registered data and insufficient handling of cross-modal distribution disparities. This paper presents RDCRNet, a novel framework incorporating a Cross-Modal Representation Model to effectively address these challenges. The proposed network features a Cross-Modal Feature Remapping Module that aligns

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Li, Shi Qi, Zhe Zhang, Shi Ping Liu, and Heng Zhang. "Mass and Stiffness Matrices Updating Using the First Modal Data." Applied Mechanics and Materials 851 (August 2016): 834–39. http://dx.doi.org/10.4028/www.scientific.net/amm.851.834.

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This paper presents a new direct approach to simultaneously update the analytical mass and stiffness matrices in an undamped model .First, we assume that the difference between the finite element model and the real model is little. Second, only by utilizing the first order modal data orthogonal normalization conditions and eigen equations , we derive the updated matrices. Third, the method can maintain matrices symmetry and sparsity and preserve system connectivity. The merit of the proposed method is simple and accurate in a practical engineering computation. Finally,a numerical example is gi

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Liu, Leijia, Ping Chen, Yanbing Wang, and Lu Han. "A Short-term Power Load Forecasting Method Based on An Improved VMD-LSTM Algorithm." Journal of Physics: Conference Series 2532, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1742-6596/2532/1/012004.

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Abstract Accurate power load prediction is a crucial basis to ensure the balance of power supply and demand. This paper provides a modified variational modal decomposition (VMD) - long short-term memory (LSTM) algorithm, using genetic algorithm (GA) to iteratively optimize the number of decomposition layers and penalty factor parameters, and using VMD to obtain the modal components, fuse them with the filtered features. Then the reconstruction data is obtained through normalization according to the parameters of the original load characteristic data set. Finally, LSTM is constructed to excavat

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HAFS, Toufik, Hatem ZEHIR, and Ali HAFS. "Enhancing Recognition in Multimodal Biometric Systems: Score Normalization and Fusion of Online Signatures and Fingerprints." Romanian Journal of Information Science and Technology 2024, no. 1 (2024): 37–49. http://dx.doi.org/10.59277/romjist.2024.1.03.

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Multimodal biometrics employs multiple modalities within a single system to address the limitations of unimodal systems, such as incomplete data acquisition or deliberate fraud, while enhancing recognition accuracy. This study explores score normalization and its impact on system performance. To fuse scores effectively, prior normalization is necessary, followed by a weighted sum fusion technique that aligns impostor and genuine scores within a common range. Experiments conducted on three biometric databases demonstrate the promising efficacy of the proposed approach, particularly when combine

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Pei, Xue-Yang, Hai-Bin Huang, and Peng Cao. "An Improved Gaussian Mixture Model-Based Data Normalization Method for Removing Environmental Effects on Damage Detection of Structures." Buildings 15, no. 3 (2025): 359. https://doi.org/10.3390/buildings15030359.

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In structural health monitoring, effectively eliminating the influence of variable environmental conditions on modal frequencies remains a critical challenge for accurate damage identification. Nonstationary and nonlinear variations in modal frequencies, commonly induced by environmental changes, tend to overshadow the effects caused by structural damage. An improved Gaussian mixture model (GMM) is proposed in this paper to normalize nonlinear and nonstationary frequency data, enabling effective structural damage detection under variable environmental conditions. As the effectiveness of the GM

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PLATO, JAN VON, and ANNIKA SIDERS. "NORMAL DERIVABILITY IN CLASSICAL NATURAL DEDUCTION." Review of Symbolic Logic 5, no. 2 (2012): 205–11. http://dx.doi.org/10.1017/s1755020311000311.

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A normalization procedure is given for classical natural deduction with the standard rule of indirect proof applied to arbitrary formulas. For normal derivability and the subformula property, it is sufficient to permute down instances of indirect proof whenever they have been used for concluding a major premiss of an elimination rule. The result applies even to natural deduction for classical modal logic.

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Pascucci, Matteo. "Propositional quantifiers in labelled natural deduction for normal modal logic." Logic Journal of the IGPL 27, no. 6 (2019): 865–94. http://dx.doi.org/10.1093/jigpal/jzz008.

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Abstract This article concerns the treatment of propositional quantification in a framework of labelled natural deduction for modal logic developed by Basin, Matthews and Viganò. We provide a detailed analysis of a basic calculus that can be used for a proof-theoretic rendering of minimal normal multimodal systems with quantification over stable domains of propositions. Furthermore, we consider variations of the basic calculus obtained via relational theories and domain theories allowing for quantification over possibly unstable domains of propositions. The main result of the article is that f

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Benedicto, David, M. Victoria Collados, Juan C. Martín, Omel Mendoza-Yero, Juan A. Vallés, and Jesús Atencia. "Contribution to the improvement of the correlation filter method for modal analysis with a spatial light modulator." Journal of Physics: Conference Series 2407, no. 1 (2022): 012013. http://dx.doi.org/10.1088/1742-6596/2407/1/012013.

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Abstract Modal decomposition of light is essential to study propagation properties of waveguides and photonic devices. Modal analysis can be carried out by implementing a computer generated hologram acting as a match filter in a spatial light modulator. In this work, a series of aspects to be taken into account in order to get the most out of this method are presented, aiming to provide operational procedures. First of all, the influence of the mode normalization in the complex amplitude encoding inherent noise is investigated. Then, a method for filter size adjustment based on the LP-modes sy

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Li, Xi, Joseph Smalley, Zhitong Li, and Qing Gu. "Effective Modal Volume in Nanoscale Photonic and Plasmonic Near-Infrared Resonant Cavities." Applied Sciences 8, no. 9 (2018): 1464. http://dx.doi.org/10.3390/app8091464.

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We survey expressions of the effective modal volume, Veff, commonly used in the literature for nanoscale photonic and plasmonic cavities. We apply different expressions of Veff to several canonical cavities designed for nanoscale near-infrared light sources, including metallo-dielectric and coaxial geometries. We develop a metric for quantifying the robustness of different Veff expressions to the different cavities and materials studied. We conclude that no single expression for Veff is universally applicable. Several expressions yield nearly identical results for cavities with well-confined p

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Liu, Jiabo. "Research on Forest Fire Detection Method Based on Multi-Modal Data." Applied and Computational Engineering 120, no. 1 (2025): 181–86. https://doi.org/10.54254/2755-2721/2025.19482.

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Forest fire is one of the major disasters that threaten the ecological environment and the sustainable development of social economy. Aiming at the limitations of traditional fire monitoring methods in terms of real-time, accuracy and environmental adaptability, this paper proposes a multimodal data fusion forest fire detection method based on Transformer architecture. This method realizes fire classification and precise positioning by fusing the feature information of visible light images and infrared images. The model design includes multimodal input encoding, cross-modal attention mechanism

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Benedicto, David, María Victoria Collados, Juan C. Martín, Jesús Atencia, Omel Mendoza-Yero, and Juan A. Vallés. "Contribution to the Improvement of the Correlation Filter Method for Modal Analysis with a Spatial Light Modulator." Micromachines 13, no. 11 (2022): 2004. http://dx.doi.org/10.3390/mi13112004.

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Modal decomposition of light is essential to study its propagation properties in waveguides and photonic devices. Modal analysis can be carried out by implementing a computer-generated hologram acting as a match filter in a spatial light modulator. In this work, a series of aspects to be taken into account in order to get the most out of this method are presented, aiming to provide useful operational procedures. First of all, a method for filter size adjustment based on the standard fiber LP-mode symmetry is presented. The influence of the mode normalization in the complex amplitude encoding-i

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Jia, Xuzhong, Chenyu Hu, and Guancong Jia. "Cross-modal Contrastive Learning for Robust Visual Representation in Dynamic Environmental Conditions." Academic Journal of Natural Science 2, no. 2 (2025): 23–34. https://doi.org/10.70393/616a6e73.323833.

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This paper proposes a novel cross-modal contrastive learning framework for robust visual representation under dynamic environmental conditions. We address the challenge of maintaining consistent performance across varying environments by introducing a dual-stream architecture that leverages complementary information from visual and contextual modalities. Our framework incorporates three key components: (1) a cross-modal contrastive learning mechanism that establishes correspondences between modalities while preserving their semantic structure, (2) a feature alignment module with cross-modal at

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Toleu, Alymzhan, Gulmira Tolegen, Alexandr Pak, Jaxylykova Assel, and Bagashar Zhumazhanov. "End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models." Applied Sciences 15, no. 8 (2025): 4324. https://doi.org/10.3390/app15084324.

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In this work, we propose a multi-modal speaker change detection (SCD) approach with focal loss, which integrates both audio and text features to enhance detection performance. The proposed approach utilizes pre-trained large-scale models for feature extraction and incorporates a self-attention mechanism to optimize useful features related to speaker change. The extracted features are fused and processed through a fully connected classification network, with layer normalization and dropout for stability and generalization. To address class imbalance, we apply focal loss, which reduces errors fo

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Chan, Sixian, Feng Du, Yanjing Lei, Zhounian Lai, Jiafa Mao, and Chao Li. "Learning Identity-Consistent Feature for Cross-Modality Person Re-Identification via Pixel and Feature Alignment." Mobile Information Systems 2022 (October 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/4131322.

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RGB-IR cross-modality person re-identification (ReID) can be seen as a multicamera retrieval problem that aims to match pedestrian images captured by visible and infrared cameras. Most of the existing methods focus on reducing modality differences through feature representation learning. However, they ignore the huge difference in pixel space between the two modalities. Unlike these methods, we utilize the pixel and feature alignment network (PFANet) to reduce modal differences in pixel space while aligning features in feature space in this paper. Our model contains three components, including

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Martini, Simone, Andrea Masini, and Margherita Zorzi. "From 2-Sequents and Linear Nested Sequents to Natural Deduction for Normal Modal Logics." ACM Transactions on Computational Logic 22, no. 3 (2021): 1–29. http://dx.doi.org/10.1145/3461661.

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We extend to natural deduction the approach of Linear Nested Sequents and of 2-Sequents. Formulas are decorated with a spatial coordinate, which allows a formulation of formal systems in the original spirit of natural deduction: only one introduction and one elimination rule per connective, no additional (structural) rule, no explicit reference to the accessibility relation of the intended Kripke models. We give systems for the normal modal logics from K to S4. For the intuitionistic versions of the systems, we define proof reduction, and prove proof normalization, thus obtaining a syntactical

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Chen, Binyue, and Guohua Liu. "D4Care: A Deep Dynamic Memory-Driven Cross-Modal Feature Representation Network for Clinical Outcome Prediction." Applied Sciences 15, no. 11 (2025): 6054. https://doi.org/10.3390/app15116054.

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With the advancement of information technology, artificial intelligence (AI) has demonstrated significant potential in clinical prediction, helping to improve the level of intelligent medical care. Current clinical practice primarily relies on patients’ time series data and clinical notes to predict health status and makes predictions by simply concatenating cross-modal features. However, they not only ignore the inherent correlation between cross-modal features, but also fail to analyze the collaborative representation of multi-granularity features from diverse perspectives. To address these

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Elsas, Robbe, Dries Van Leemput, Jeroen Hoebeke, and Eli De Poorter. "3MSF: A Multi-Modal Adaptation of the 6TiSCH Minimal Scheduling Function for the Industrial IoT." Sensors 24, no. 8 (2024): 2414. http://dx.doi.org/10.3390/s24082414.

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Although wireless devices continuously gain communication capabilities, even state-of-the-art Industrial Internet of Things (IIoT) architectures, such as Internet Protocol version 6 over the Time-Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4 (6TiSCH), continue to use network-wide, fixed link configurations. This presents a missed opportunity to (1) forego the need for rigorous manual setup of new deployments; and (2) provide full coverage of particularly heterogeneous and/or dynamic industrial sites. As such, we devised the Multi-Modal Minimal Scheduling Function (3MSF) for the TSCH lin

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Wang, Yi. "Multimodal Brain Tumor Segmentation Based on Multi-Scale Feature Extraction Network." Applied and Computational Engineering 157, no. 1 (2025): 64–70. https://doi.org/10.54254/2755-2721/2025.po23836.

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This study aims to develop a multi-modal brain tumor segmentation technique based on a multi-scale feature extraction network to enhance the accuracy of brain tumor segmentation and assist in the clinical diagnosis of neurological diseases. Built upon the advanced U-Net architecture, the study designed a network framework with residual connections, downsampling and upsampling modules, and multi-branch combination mechanisms, achieving the extraction and integration of features from multiple scales. By integrating deep learning techniques such as depthwise separable convolutions, spatial pyrami

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Cury, Alexandre, Christian Cremona, and John Dumoulin. "Long-term monitoring of a PSC box girder bridge: Operational modal analysis, data normalization and structural modification assessment." Mechanical Systems and Signal Processing 33 (November 2012): 13–37. http://dx.doi.org/10.1016/j.ymssp.2012.07.005.

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Champagne, Allen A., Nicole S. Coverdale, Andrew Ross, et al. "Multi-modal normalization of resting-state using local physiology reduces changes in functional connectivity patterns observed in mTBI patients." NeuroImage: Clinical 26 (2020): 102204. http://dx.doi.org/10.1016/j.nicl.2020.102204.

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Tsiapoki, Stavroula, Moritz W. Häckell, Tanja Grießmann, and Raimund Rolfes. "Damage and ice detection on wind turbine rotor blades using a three-tier modular structural health monitoring framework." Structural Health Monitoring 17, no. 5 (2017): 1289–312. http://dx.doi.org/10.1177/1475921717732730.

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The increasing number of installed wind turbines has led to a greater need for monitoring of their subcomponents. In particular, damages on rotor blades should be detected as early as possible, since they can cause long and hence expensive standstill times. In this work, a three-tier structural health monitoring framework is employed on the experimental data of a 34-m rotor blade for damage and ice detection. The structural health monitoring framework includes the functions of data normalization by clustering according to environmental and operational conditions, feature extraction, and hypoth

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Adelusi, Bamidele Samuel, Abel Chukwuemeke Uzoka, Yewande Goodness Hassan, and Favour Uche Ojika. "Developing Predictive Technographic Clustering Models Using Multi-Modal Consumer Behavior Data for Precision Targeting in Omnichannel Marketing." International Journal of Management and Organizational Research 2, no. 2 (2023): 206–14. https://doi.org/10.54660/ijmor.2023.2.2.206-214.

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This paper presents a comprehensive framework for developing predictive technographic clustering models using multi-modal consumer behavior data to enable precision targeting in omnichannel marketing. With the proliferation of digital platforms and evolving consumer expectations, traditional segmentation approaches fail to capture the dynamic and heterogeneous nature of consumer interactions. This study integrates behavioral analytics, device usage patterns, and contextual engagement signals to form robust consumer clusters using unsupervised machine learning algorithms such as K-Means, DBSCAN

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Admojo, Fadhila Tangguh, Made Leo Radhitya, Hamada Zein, and Ahmad Naswin. "Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach." Indonesian Journal of Data and Science 5, no. 3 (2024): 243–50. https://doi.org/10.56705/ijodas.v5i3.199.

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This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poiso

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Steele, Christopher D., Ludmil Alexandrov, and Azhar Khandekar. "Abstract 3563: Multi-modal mutational signatures elucidate the comprehensive consequences of mutational processes." Cancer Research 84, no. 6_Supplement (2024): 3563. http://dx.doi.org/10.1158/1538-7445.am2024-3563.

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Abstract The development of mutational signatures defined individually in the contexts of single/double base substitutions, insertions and deletions, structural variants and copy number variation have led to improved understanding of cancer mutagenesis and potential for improved patient stratification. We hypothesized that combining these contexts into a multi-modal mutational signature framework may improve the accuracy of assigning mutational processes to samples, and provide a holistic view of the full repertoire of mutations conferred from such processes. In order to develop multi-modal si

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Wang, Liu, Wang, Zhou, Jiang, and Li. "Numerical Simulation of the Sound Field of a Five-Stage Centrifugal Pump with Different Turbulence Models." Water 11, no. 9 (2019): 1777. http://dx.doi.org/10.3390/w11091777.

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To study the influence of the turbulence model on the sound field of pumps, the standard k-ε, Re-normalization Group (RNG) k-ε and Shear Stress Transfer (SST) k-ω models were employed to simulate flow and sound fields of a five-stage centrifugal pump with a vaned-diffuser. The vibration characteristics of the pump were simulated with the modal response method. A vibration experiment in the pump was carried out to verify the feasibility of the numerical simulation of the hydrodynamic noise in the pump. Results show that in the spectrum of internal and external noise, the peak value appears at a

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Both, S., and T. Weiss. "Resonant states and their role in nanophotonics." Semiconductor Science and Technology 37, no. 1 (2021): 013002. http://dx.doi.org/10.1088/1361-6641/ac3290.

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Abstract Resonant phenomena have been extensively used in micro- and nanophotonics. Mathematically, these phenomena originate in a discrete set of basis functions known as resonant states or quasi-normal modes. Therefore, it is extremely beneficial to develop theoretical approaches that use these resonant states as a physically meaningful basis in order to describe the light–matter interaction in micro- and nanoresonators. However, the question of how to normalize resonant states correctly for such an expansion initially hampered many theoretical attempts. Only recently, this problem of normal

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Kranjc, Tadej, Janko Slavič, and Miha Boltežar. "The mass normalization of the displacement and strain mode shapes in a strain experimental modal analysis using the mass-change strategy." Journal of Sound and Vibration 332, no. 26 (2013): 6968–81. http://dx.doi.org/10.1016/j.jsv.2013.08.015.

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Abel, Andreas, Nils Anders Danielsson, and Oskar Eriksson. "A Graded Modal Dependent Type Theory with a Universe and Erasure, Formalized." Proceedings of the ACM on Programming Languages 7, ICFP (2023): 920–54. http://dx.doi.org/10.1145/3607862.

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We present a graded modal type theory, a dependent type theory with grades that can be used to enforce various properties of the code. The theory has Π-types, weak and strong Σ-types, natural numbers, an empty type, and a universe, and we also extend the theory with a unit type and graded Σ-types. The theory is parameterized by a modality, a kind of partially ordered semiring, whose elements (grades) are used to track the usage of variables in terms and types. Different modalities are possible. We focus mainly on quantitative properties, in particular erasure: with the erasure modality one can

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Gromyko, Dmitrii, Ilia Fradkin, Sergey Dyakov, and Nikolay Gippius. "Resonant Mode Coupling Method for the Description of Oscillating Dipoles Emission inside Stacked Photonic Nanostructures." Materials Proceedings 4, no. 1 (2020): 34. http://dx.doi.org/10.3390/iocn2020-07928.

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Resonant modes are important characteristics of the optical properties of photonic crystals since they are responsible for the features in the transmission and reflection spectra as well as the emissivity of quantum emitters inside such structures. We present a resonant modes expansion method applied to a problem of radiating dipoles inside a photonic crystal. In stacked photonic crystal slabs, there is a coupling between the resonances of distinct subsystems and Fabry–Perot resonances. We propose a technique to calculate the coefficients of resonant mode expansion based on the scattering matr

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Pan, Aiming. "Application of multi-modal data fusion based on deep learning in diagnosis of depression." Data and Metadata 4 (April 4, 2025): 863. https://doi.org/10.56294/dm2025863.

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Depression is a frequent mental condition requiring precise diagnosis in its early onset. Traditional methods are less than accurate and occur late. Following these deficits, this investigates the multi-modal data fusion and Deep Learning (DL) with the purpose of enhancing accuracy for diagnosis. A new DL model, Dynamic Dolphin Echolocation-tuned Effective Temporal Convolutional Networks (DDE-ETCN), is utilized for depression diagnosis. Different sources of data, such as physiological signals (EEG, heart rate), behavioral indicators (facial expressions), and biometric data (activity levels), a

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Anastasopoulos, Dimitrios, Maure De Smedt, Lucie Vandewalle, Guido De Roeck, and Edwin P. B. Reynders. "Damage identification using modal strains identified from operational fiber-optic Bragg grating data." Structural Health Monitoring 17, no. 6 (2017): 1441–59. http://dx.doi.org/10.1177/1475921717744480.

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Vibration-based structural health monitoring of civil structures relies on the repeated identification of dynamic structural characteristics of the structure from output-only vibration data. Natural frequencies and displacement mode shapes are the most commonly employed dynamic characteristics; yet their sensitivity to local damage of moderate severity is rather low with respect to their sensitivity to other factors such as temperature, necessitating data normalization. Strain mode shapes offer a higher sensitivity to local damage, but their accurate identification in a dense grid is challengi

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Silva, Moisés, Adam Santos, Reginaldo Santos, Eloi Figueiredo, Claudomiro Sales, and João CWA Costa. "Deep principal component analysis: An enhanced approach for structural damage identification." Structural Health Monitoring 18, no. 5-6 (2018): 1444–63. http://dx.doi.org/10.1177/1475921718799070.

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The structural health monitoring relies on the continuous observation of a dynamic system over time to identify its actual condition, detect abnormal behaviors, and predict future states. The regular changes in environmental factors have been reported as one of the main challenges for the application of structural health monitoring systems. These influences in the structural responses are in general nonlinear, affecting the damage-sensitive features in the most varied forms. The usual process to remove these normal changes is referred to as data normalization. In that regard, principal compone

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Kang, Xinxin, and Yong Nie. "Design and analysis of teaching early warning system based on multimodal data in an intelligent learning environment." PeerJ Computer Science 11 (March 4, 2025): e2692. https://doi.org/10.7717/peerj-cs.2692.

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In online teaching environments, the lack of direct emotional interaction between teachers and students poses challenges for teachers to consciously and effectively manage their emotional expressions. The design and implementation of an early warning system for teaching provide a novel approach to intelligent evaluation and improvement of online education. This study focuses on segmenting different emotional segments and recognizing emotions in instructional videos. An efficient long-video emotional transition point search algorithm is proposed for segmenting video emotional segments. Leveragi

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Hafs, Toufik, Hatem Zehir, Ali Hafs, and Amine Nait-Ali. "Multimodal Biometric System Based on the Fusion in Score of Fingerprint and Online Handwritten Signature." Applied Computer Systems 28, no. 1 (2023): 58–65. http://dx.doi.org/10.2478/acss-2023-0006.

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Abstract Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out

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Muhammad Hamza Saleem, Ayesha Bibi, Naeem Aslam, Muhammad Fuzail, Mueed Ahmed Mirza, and Hira Javaid. "OPTIMIZED DEEP LEARNING FRAMEWORK FOR BRAIN TUMOR CLASSIFICATION USING GENETIC ALGORITHM-DRIVEN HYPERPARAMETER SELECTION." Kashf Journal of Multidisciplinary Research 2, no. 04 (2025): 159–84. https://doi.org/10.71146/kjmr404.

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Early and precise brain tumor detection is crucial to optimal treatment planning and patient outcome. Traditional MRI diagnosis tends to be time-consuming and subject to interobserver variations. For accurate and efficient diagnosis, we outline a deep learning framework with incorporation of Convolutional Neural Networks (CNN) coupled with Genetic Algorithm (GA)-assisted hyperparameter optimization for brain tumor automatic classification. The model is trained on a dataset of 7,023 MRI scans into Glioma, Meningioma, Pituitary, and No Tumor classes. For enhanced model robustness, Brain Data Aug

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Odusami, Modupe, Rytis Maskeliūnas, and Robertas Damaševičius. "Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer’s Disease Diagnosis: Enhancing Data Integration and Feature Extraction." Journal of Personalized Medicine 13, no. 10 (2023): 1496. http://dx.doi.org/10.3390/jpm13101496.

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Multimodal neuroimaging has gained traction in Alzheimer’s Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overcoming the challenges posed by multiscale transform methods remains a significant hurdle. This article proposes a novel approach to address these challenges. To harness the power of diverse neuroimaging data, we employ a strategy that leverages optimized convolution techniques. These optimizations include varying kernel sizes and the incorporation of inst

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Yang, Nan, Guihong Bi, Yuhong Li, Xiaoling Wang, Zhao Luo, and Xin Shen. "A BiGRUSA-ResSE-KAN Hybrid Deep Learning Model for Day-Ahead Electricity Price Prediction." Symmetry 17, no. 6 (2025): 805. https://doi.org/10.3390/sym17060805.

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In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage,

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Hu, Jason Z. S., and Brigitte Pientka. "A Dependent Type Theory for Meta-programming with Intensional Analysis." Proceedings of the ACM on Programming Languages 9, POPL (2025): 416–45. https://doi.org/10.1145/3704851.

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In this paper, we introduce DeLaM, a dependent layered modal type theory which enables meta-programming in Martin-Löf type theory (MLTT) with recursion principles on open code. DeLaM includes three layers: the layer of static syntax objects of MLTT without any computation; the layer of pure MLTT with the computational behaviors; the meta-programming layer extends MLTT with support for quoting an open MLTT code object and composing and analyzing open code using recursion. We can also execute a code object at the meta-programming layer. The expressive power strictly increases as we move up in a

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