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Journal articles on the topic 'Address-vector compression'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Nasrabadi, N. M., and Y. Feng. "Image compression using address-vector quantization." IEEE Transactions on Communications 38, no. 12 (1990): 2166–73. http://dx.doi.org/10.1109/26.64659.

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Canta, Gerardo R., and Giovanni Poggi. "Compression of multispectral images by address-predictive vector quantization." Signal Processing: Image Communication 11, no. 2 (1997): 147–59. http://dx.doi.org/10.1016/s0923-5965(96)00043-4.

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Liu, Baichen, Dongwei Wang, Qi Lv, Zhi Han, and Yandong Tang. "Towards Super Compressed Neural Networks for Object Identification: Quantized Low-Rank Tensor Decomposition with Self-Attention." Electronics 13, no. 7 (2024): 1330. http://dx.doi.org/10.3390/electronics13071330.

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Deep convolutional neural networks have a large number of parameters and require a significant number of floating-point operations during computation, which limits their deployment in situations where the storage space is limited and computational resources are insufficient, such as in mobile phones and small robots. Many network compression methods have been proposed to address the aforementioned issues, including pruning, low-rank decomposition, quantization, etc. However, these methods typically fail to achieve a significant compression ratio in terms of the parameter count. Even when high

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Xu, Qian, Juan Yang, Feng Zhang, et al. "Tribase: A Vector Data Query Engine for Reliable and Lossless Pruning Compression using Triangle Inequalities." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–28. https://doi.org/10.1145/3709743.

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Approximate Nearest Neighbor Search (ANNS) is a critical problem in vector databases. Cluster-based index is utilized to narrow the search scope of ANNS, thereby accelerating the search process. Due to its scalability, it is widely employed in real-world vector search systems. However, existing cluster-based indexes often suffer from coarse granularity, requiring query vectors to compute distances with vectors of varying quality, thus increasing query complexity. Existing work aim to represent vectors with minimal cost, such as using product quantization (PQ) or linear transformations, to spee

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Zhou, Qifei, Lin Yan, Zihao Wang, Na Ren, and Changqing Zhu. "Multi-Instance Zero-Watermarking Algorithm for Vector Geographic Data." ISPRS International Journal of Geo-Information 14, no. 2 (2025): 54. https://doi.org/10.3390/ijgi14020054.

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To address the variability and complexity of attack types, this paper proposes a multi-instance zero-watermarking algorithm that goes beyond the conventional one-to-one watermarking approach. Inspired by the class-instance paradigm in object-oriented programming, this algorithm constructs multiple zero watermarks from a single vector geographic dataset to enhance resilience against diverse attacks. Normalization is applied to eliminate dimensional and deformation inconsistencies, ensuring robustness against non-uniform scaling attacks. Feature triangle construction and angle selection are furt

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SANKARAGOMATHI, B., L. GANESAN, and S. ARUMUGAM. "ENCODING VIDEO SEQUENCES IN FRACTAL-BASED COMPRESSION." Fractals 15, no. 04 (2007): 365–78. http://dx.doi.org/10.1142/s0218348x0700371x.

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With the rapid increase in the use of computers and the Internet, the demand for higher transmission and better storage is increasing as well. This paper describes the different techniques for data (image-video) compression in general and, in particular, the new compression technique called fractal image compression. Fractal image compression is based on self-similarity, where one part of an image is similar to the other part of the same image. Low bit rate color image sequence coding is very important for video transmission and storage applications. The most significant aspect of this work is

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Pang, Zaixiang, Tongyu Wang, Junzhi Yu, Shuai Liu, Xiyu Zhang, and Dawei Jiang. "Design and Analysis of a Flexible, Elastic, and Rope-Driven Parallel Mechanism for Wrist Rehabilitation." Applied Bionics and Biomechanics 2020 (November 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/8841400.

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This paper proposes a bionic flexible wrist parallel mechanism to simulate human wrist joints, which is characterized by a rope-driven, compression spring-supported hybrid mechanism. Specifically, to realize the movement of the wrist mechanism, a parallel structure is adopted to support the mobile platform and is controlled by a cable, which plays the role of wrist muscles. Because the compression spring is elastic, it is difficult to directly solve inverse kinematics. To address this problem, the external force acting on the moving platform is firstly equivalent to the vector force and torque

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Bacon, Ian C., Micah Shepherd, and Scott D. Sommerfeldt. "An overview of symmetry-based techniques used for sound power application with illustrative case studies." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A143. http://dx.doi.org/10.1121/10.0027099.

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This work investigates the growing application of vibration-based sound power (VBSP) measurements in structural acoustics. The VBSP method involves multiplying a measured velocity vector by a geometry-dependent radiation resistance matrix to compute the frequency-dependent sound power. However, computational challenges arise during matrix construction, even for simple geometries. To address this, inherent symmetries in the radiation resistance matrix for both baffled and unbaffled geometries are exploited. Multi-layered Toeplitz symmetries emerge for baffled conditions, while centrosymmetric s

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Bilal, Muhammad, Hafiz Muhammad Azib Khan, Abdul Mueed, Kamran Liaqat Bhatti, Syed Safdar Raza, and Muhammad Siddique. "Interference Analysis of Very Fast Transient Overvoltages in Gas-Insulated Substations Using a Hybrid Algorithm." Sukkur IBA Journal of Emerging Technologies 7, no. 2 (2025): 91–99. https://doi.org/10.30537/sjet.v7i2.1559.

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The reliable and efficient computational approach for the cable interference in gas insulated substations (GIS) is presented. The hybrid domain decomposition method-multilevel fast multi-pole algorithm (DDM-MLFMA) is presented herein. This technique determines the specifications of GIS cables for RG58 and AWG23. Self and mutual interference are identified utilizing the very fast transient overvoltage (VFTO) transient interference signal. The hybrid technique offers a numerical simulation to replicate the impact of the VFTO interference signal. Utilizing the matrix-vector multiplication product

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Qiu, Shiyu, Chunlei Wu, Zhenghao Wan, and Siyuan Tong. "High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity." Applied Sciences 15, no. 3 (2025): 1535. https://doi.org/10.3390/app15031535.

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Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. Despite these advantages, the large number of Gaussian points and associated internal parameters result in hig

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D. Patil, Dinesh, R. P. Singh, Vilas M. Thakare, and Avinash K. Gulve. "Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network." International Journal of Engineering & Technology 7, no. 2.17 (2018): 27. http://dx.doi.org/10.14419/ijet.v7i2.17.11553.

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This paper tried to address several topics concerning the analysis, synthesis and compression of the electrocardiogram signal (ECG) using the MIT database. We detect the R-wave by identifying the location of each interval delineating a QRS complex using unbiased and biased estimators. In the second part of the work, we segmented the signal into RR periods constituting the vectors of a data matrix, where we extracted its main components in order to reduce the size of the cardiac information, and then further reduced in addition the size by the use of a threshold on the signal. Then the classifi

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Wu, Weijia, Lizhong Gu, Yuefeng Zhang, Xianping Huang, and Weihe Zhou. "Pulmonary Nodule Clinical Trial Data Collection and Intelligent Differential Diagnosis for Medical Internet of Things." Contrast Media & Molecular Imaging 2022 (May 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/2058284.

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In this paper, the medical Internet of things (IoT) is used to pool data from clinical trials of pulmonary nodules, and on this basis, intelligent differential diagnosis techniques are investigated. A filtered orthogonal frequency division multiplexing model based on polarisation coding is proposed, where the input data are fed to a modulator after polarisation cascade coding, and the system performance is analysed under a medical Internet of things modulated additive Gaussian white noise channel. The above polarisation-coded filtered orthogonal frequency division multiplexing system component

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Aruna, Kumar Bananaik Thulajanaik, and Manjanaik Naganaik. "Video perception enhancement using effective encoding optimization in future generation wireless network." Video perception enhancement using effective encoding optimization in future generation wireless network 32, no. 1 (2023): 392–402. https://doi.org/10.11591/ijeecs.v32.i1.pp392-402.

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The cumulative effects of network transmission link imperfections and realtime limitations of video data result in multiple challenges for video transmission in massive millimeter wave (mmwave) network. The challenges, together with the increasing expectations of users for goodquality videos, further extend the complexity particularly in dense areas. To address the issues this work proposes perceptual quality aware video encoding (PQAVE) scheme in two phases: in phase 1 an effective perceptual quality encoding method leveraging low-rank approximation to reduce overall video size is done. In ph

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Veena R. "Topic Driven Text Extraction for Kannada Document Summarization Using LDA." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 138–48. https://doi.org/10.52783/jisem.v10i36s.6324.

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Automatic Text Summarization (ATS) compacts source content into a concise format while preserving core information. While extensively studied for resource-rich languages, ATS remains challenging for low-resource languages like Kannada due to limited corpora and NLP tools. This work introduces an extractive, topic-driven method for summarizing Kannada news articles from multiple documents. We developed a custom dataset of 100 Kannada news story sets (3 articles per set) to address the lack of standardized benchmarks. The proposed approach leverages Latent Dirichlet Allocation (LDA) to identify

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He, Peisong, Hongxia Wang, Ruimei Zhang, and Yue Li. "A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency." Security and Communication Networks 2021 (April 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/5568351.

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Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properti

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Feizi, Sepehr, Mohammad Zare, and Bahram Einollahi. "Simultaneous Phacoemulsification and Graft Refractive Surgery in Penetrating Keratoplasty Eyes." ISRN Ophthalmology 2011 (September 20, 2011): 1–5. http://dx.doi.org/10.5402/2011/495047.

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Purpose. To report outcomes of graft refractive surgery (GRS) along with clear-cornea phacoemulsification and intraocular lens (IOL) implantation in penetrating keratoplasty (PKP) eyes. Methods. Fourteen eyes of 13 patients who had received PKP underwent simultaneous GRS (relaxing incisions with or without counter-quadrant compression sutures) and clear-cornea phacoemulsification with IOL implantation. To calculate IOL power, preoperative keratometry readings and the SRK-T formula were used. Results. Mean patient age and follow-up period were years and months, respectively. A significant incre

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Işık, Mustafa, Martin Rünz, Markos Georgopoulos, et al. "HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion." ACM Transactions on Graphics 42, no. 4 (2023): 1–12. http://dx.doi.org/10.1145/3592415.

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Representing human performance at high-fidelity is an essential building block in diverse applications, such as film production, computer games or videoconferencing. To close the gap to production-level quality, we introduce HumanRF 1 , a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints. Our novel representation acts as a dynamic video encoding that captures fine details at high compression rates by factorizing space-time into a temporal matrix-vector decomposition. This allows us

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Hou, Hanting, Huan Bao, Kaimin Wei, and Yongdong Wu. "Universal Low-Frequency Noise Black-Box Attack on Visual Object Tracking." Symmetry 17, no. 3 (2025): 462. https://doi.org/10.3390/sym17030462.

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Adversarial attacks on visual object tracking aim to degrade tracking accuracy by introducing imperceptible perturbations into video frames, exploiting vulnerabilities in neural networks. In real-world symmetrical double-blind engagements, both attackers and defenders operate with mutual unawareness of strategic parameters or initiation timing. Black-box attacks based on iterative optimization show excellent applicability in this scenario. However, existing state-of-the-art adversarial attacks based on iterative optimization suffer from high computational costs and limited effectiveness. To ad

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Zhang, Qingyong, Changhuan Song, and Yiqing Yuan. "Fault Diagnosis of Vehicle Gearboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM." Applied Sciences 14, no. 3 (2024): 1212. http://dx.doi.org/10.3390/app14031212.

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Vehicle gearboxes are subject to strong noise interference during operation, and the noise in the signal affects the accuracy of fault identification. Signal denoising and fault diagnosis processes are often conducted independently, overlooking their synergistic potential in practical applications. This article proposes a gearbox fault identification method that integrates improved adaptive modified wavelet function noise reduction, logarithmic transformation on principal component analysis (LT-PCA), and support vector machines (SVMs) to mitigate the influence of noise and feature outliers on

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Zhang, Hao, Jing Wang, Xuanyuan Wang, et al. "CPS-LSTM: Privacy-Sensitive Entity Adaptive Recognition Model for Power Systems." Energies 18, no. 8 (2025): 2013. https://doi.org/10.3390/en18082013.

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With the widespread application of Android devices in the energy sector, an increasing number of applications rely on SDKs to access privacy-sensitive data, such as device identifiers, location information, energy consumption, and user behavior. However, these data are often stored in different formats and naming conventions, which poses challenges for consistent extraction and identification. Traditional taint analysis methods are inefficient in identifying these entities, hindering the realization of accurate identification. To address this issue, we first propose a high-quality data constru

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R. Maheshwari, Dr. D. Banumathy, Dr. P. Thiyagarajan, and P. Rajeshwaran. "Kernel Block Based Morphing Identification and Elimination In Server." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 281–90. http://dx.doi.org/10.32628/ijsrset24113130.

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The primary stage of duplicated region identification for investigating duplicate and insert image forgeries does the image block match. Because of evolution of highly sophisticated tools, numerous image modifications have been made. When images are difficult to duplicate and transfer, forgeries of those parts may happen. The images are examined closely in the area where they were forged. By using the suggested Gaussian RBF kernel PCA, the segment of the image that is copied and pasted will be known. One of the most significant issues in this step is the high processing time required to locate

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Choi, Hyunmin, Simon S. Woo, and Hyoungshick Kim. "Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 21976–85. http://dx.doi.org/10.1609/aaai.v38i20.30200.

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Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic encryption to address these privacy concerns. Homomorphic encryption allows computations on encrypted data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations.

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Ye, Zhen, Peiwen Sun, Jiahe Lei, et al. "Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25697–705. https://doi.org/10.1609/aaai.v39i24.34761.

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Recent advancements in audio generation have been significantly propelled by the capabilities of Large Language Models (LLMs). The existing research on audio LLM has primarily focused on enhancing the architecture and scale of audio language models, as well as leveraging larger datasets, and generally, acoustic codecs, such as EnCodec, are used for audio tokenization. However, these codecs were originally designed for audio compression, which may lead to suboptimal performance in the context of audio LLM. Our research aims to address the shortcomings of current audio LLM codecs, particularly t

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Guan, Hong, Lei Yu, Lixi Zhou, et al. "Privacy and Accuracy-Aware AI/ML Model Deduplication." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–28. https://doi.org/10.1145/3725340.

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With the growing adoption of privacy-preserving machine learning algorithms, such as Differentially Private Stochastic Gradient Descent (DP-SGD), training or fine-tuning models on private datasets has become increasingly prevalent. This shift has led to the need for models offering varying privacy guarantees and utility levels to satisfy diverse user requirements. Managing numerous versions of large models introduces significant operational challenges, including increased inference latency, higher resource consumption, and elevated costs. Model deduplication is a technique widely used by many

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Liang, Zhaowei, and Wenzhou Shu. "Ethical Reconstruction of Linguistic Signs: An Ethical Study of Machine Translation Based on Linguistic Ontology." Journal of Literature & Language 1, no. 1 (2025): 66–75. https://doi.org/10.71204/859k3b53.

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This study confronts the ethical crisis in machine translation (MT) caused by the systematic erosion of cultural semantics and symbolic integrity. While current MT systems achieve high technical performance (e.g., 72.3 BLEU scores in WMT2022), they fail to preserve cultural-contextual nuances, with 34.8% mistranslation rates for culturally loaded terms and 73% semantic reduction for low-resource language symbols. Through deconstructive analysis of Saussure’s arbitrariness principle and Wittgenstein’s language game theory, we demonstrate how algorithmic compression of dynamic semantic networks

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Mojtaba, Ahmadi Maleki, and Emami Mohammad. "Application of SVM for Investigation of Factors Affecting Compressive Strength and Consistency of Geopolymer Concretes." Journal of Civil Engineering and Materials Application 3, no. 2 (2019): 101–7. https://doi.org/10.22034/JCEMA.2019.92507.

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A solution for synthesizing environmentally friendly concrete is to reduce the conventional Portland cement (OPC) content and utilize activated pozzolanic binders. Geopolymers are a sort of mineral polymers, so that their chemical composition resembles zeolites and their microscopic structure is not crystalline, but rather amorphous. In this study, it is attempted to address the behavior of synthetic geopolymers through the investigation of their base materials, e.g. blast furnace slag, metakaolin, fly ash and other curing agents such as potassium hydroxide or sodium hydroxide solutions. It is

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A, Dr Usman Usman, Salim Idris Malami, Mahmud Muhammad Jibril, Ismail A. Mahmoud, Umar Jibrin Muhammad, and Jabir Zakari Yau. "Design of machine learning model for predicting the compressive strength of fabric fiber-reinforced Portland cement." Techno-computing Journal 1, no. 1 (2025): 14–26. https://doi.org/10.71170/tecoj.2025.1.1.pp14-26.

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Mortar is a combination of cement, sand, and water used in civil engineering works; for joining building blocks, forming structure members, and plastering and masonry. Despite these vast uses, it is linked with structural failures mainly low compressive strength and brittle behavior under tensile stress. To address this, incorporating fabric fiber into the mortar mix has been explored to improve its mechanical performance. This study aims to predict mortar compressive strength a critical mechanical property of a mortar prepared with PC using three soft computing models i.e. feed-forward neural

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More, Supriya Siddharth, and Ajaykumar R. Kambekar. "Performance evaluation of compressive strength of concrete using different machine learning algorithms." Challenge Journal of Concrete Research Letters 16, no. 2 (2025): 60. https://doi.org/10.20528/cjcrl.2025.02.002.

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Accurately predicting the compressive strength of concrete is crucial for ensuring structural integrity, optimizing material usage, and reducing construction costs. Conventional experimental methods, though reliable, are often labour-intensive and time-consuming. To address these limitations, this study investigates the effectiveness of machine learning (ML) algorithms as efficient alternatives for predicting concrete compressive strength. Four ML algorithms—Linear Regression (LR), Multilayer Perceptron (MLP), M5 Rule-Based Model, and Support Vector Machines (SVM)—were evaluated based on their

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Zhao, Luping, Weihao Li, and Wentao Xu. "A Multi-Mode Dynamic Fusion Mach Number Prediction Framework." Aerospace 12, no. 7 (2025): 569. https://doi.org/10.3390/aerospace12070569.

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The precise control of Mach numbers in supersonic and hypersonic compressor wind tunnel systems is a critical challenge in aerodynamic research. Although existing studies have improved prediction accuracy to some extent through machine learning methods, they generally neglect the multi-mode characteristics of complex wind tunnel systems, limiting the generalizability of the models. To address this issue, the present study proposes a multi-mode dynamic fusion Mach number prediction framework that integrates strategies of segmented modeling and cross-modal information fusion. First, single-mode

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Phoeuk, Menghay, and Minho Kwon. "Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset." Advances in Civil Engineering 2023 (May 17, 2023): 1–23. http://dx.doi.org/10.1155/2023/5076429.

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The use of alternative materials and recycling in construction has gained popularity in recent years as part of the industry’s commitment to sustainability. One such material, recycled aggregates, has been extensively studied over the past two decades for its potential to replace natural aggregates in cement-based composites. However, the unique properties of recycled aggregates make traditional concrete mix design methods ineffective in determining their target compressive strength. To address this challenge, four machine learning models based on ensemble learning algorithms, including CatBoo

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Huang, Shuai, Chuanqi Li, Jian Zhou, Xiancheng Mei, and Jiamin Zhang. "Use of BOIvy Optimization Algorithm-Based Machine Learning Models in Predicting the Compressive Strength of Bentonite Plastic Concrete." Materials 18, no. 13 (2025): 3123. https://doi.org/10.3390/ma18133123.

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The combination of bentonite and conventional plastic concrete is an effective method for projecting structures and adsorbing heavy metals. Determining the compressive strength (CS) is a crucial step in the design of bentonite plastic concrete (BPC). Traditional experimental analyses are resource-intensive, time-consuming, and prone to high uncertainties. To address these challenges, several machine learning (ML) models, including support vector regression (SVR), artificial neural network (ANN), and random forest (RF), are generated to forecast the CS of BPC materials. To improve the predictio

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Liu, Jiandong, Guichen Li, Sen Yang, and Jiandong Huang. "Prediction Models for Evaluating the Strength of Cemented Paste Backfill: A Comparative Study." Minerals 10, no. 11 (2020): 1041. http://dx.doi.org/10.3390/min10111041.

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Cemented paste backfill (CPB) is widely used in underground mining, and attracts more attention these years as it can reduce mining waste and avoid environmental pollution. Normally, to evaluate the functionality of CPB, the compressive strength (UCS) is necessary work, which is also time and money consuming. To address this issue, seven machine learning models were applied and evaluated in this study, in order to predict the UCS of CPB. In the laboratory, a series of tests were performed, and the dataset was constructed considering five key influencing variables, such as the tailings to cemen

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Belhe, Yash, Michaël Gharbi, Matthew Fisher, Iliyan Georgiev, Ravi Ramamoorthi, and Tzu-Mao Li. "Discontinuity-Aware 2D Neural Fields." ACM Transactions on Graphics 42, no. 6 (2023): 1–11. http://dx.doi.org/10.1145/3618379.

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Neural image representations offer the possibility of high fidelity, compact storage, and resolution-independent accuracy, providing an attractive alternative to traditional pixel- and grid-based representations. However, coordinate neural networks fail to capture discontinuities present in the image and tend to blur across them; we aim to address this challenge. In many cases, such as rendered images, vector graphics, diffusion curves, or solutions to partial differential equations, the locations of the discontinuities are known. We take those locations as input, represented as linear, quadra

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Shen, Fangfang, Xuyang Chen, Yanming Liu, Yaocong Xie, and Xiaoping Li. "Strip-Map SAR Image Formulation Based on the Modified Alternating Split Bregman Method." Remote Sensing 13, no. 21 (2021): 4231. http://dx.doi.org/10.3390/rs13214231.

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Conventional compressive sensing (CS)-based imaging methods allow images to be reconstructed from a small amount of data, while they suffer from high computational burden even for a moderate scene. To address this problem, this paper presents a novel two-dimensional (2D) CS imaging algorithm for strip-map synthetic aperture radars (SARs) with zero squint angle. By introducing a 2D separable formulation to model the physical procedure of the SAR imaging, we separate the large measurement matrix into two small ones, and then the induced algorithm can deal with 2D signal directly instead of conve

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Gupta, Deepak, Saurabh Dubey, and Mainak Mallik. "Extreme learning machine to foretell the deflection in plain cement concrete beam, steel reinforced, and bamboo-reinforced concrete beams." Emerging Materials Research 14, no. 1 (2025): 1–35. https://doi.org/10.1680/jemmr.24.00046.

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In mountainous regions, bamboo has emerged as a favored local reinforcement material for concrete owing to its accessibility and cost-effectiveness. Despite their potential, the absence of proper design guidelines for bamboo-reinforced structures complicates the assessment of their deflection behavior under varying loads. Issues such as bond failure, shrinkage, corrosion, and uncertainties in the material strength further exacerbate these challenges. To address these issues, this study introduces a novel approach using an extreme learning machine to predict the deflection in plain cement concr

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Haruna, Sadi I., Yasser E. Ibrahim, and Ibrahim Khalil Umar. "Machine Learning Approach for Prediction and Reliability Analysis of Failure Strength of U-Shaped Concrete Samples Joined with UHPC and PUC Composites." Journal of Composites Science 9, no. 1 (2025): 23. https://doi.org/10.3390/jcs9010023.

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To meet the increasing demand for resilient infrastructure in seismic and high-impact areas, accurate prediction and reliability analysis of the performance of composite structures under impact loads is essential. Conventional techniques, including experimental testing and high-quality finite element simulation, require considerable time and resources. To address these issues, this study investigated individual and hybrid models including support vector regression (SVR), Gaussian process regression (GPR), and improved eliminate particle swamp optimization hybridized artificial neural network (

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Shi, Xuyang, Shuzhao Chen, Qiang Wang, Yijun Lu, Shisong Ren, and Jiandong Huang. "Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete." Gels 10, no. 2 (2024): 148. http://dx.doi.org/10.3390/gels10020148.

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As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources to prepare the cementitious component of the product. The challenging issue with employing geopolymer concrete in the building business is the absence of a standard mix design. According to the chemical composition of its components, this work proposes a thorough system or framework for estimating the compressive strength of fly ash-based geopolymer concrete (FAGC). It could be possible to construct a system for predicting the compressive strength of FAGC using soft co

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Nashed, Samuel, Srijan Lnu, Abdelali Guezei, Oluchi Ejehu, and Rouzbeh Moghanloo. "Downhole Camera Runs Validate the Capability of Machine Learning Models to Accurately Predict Perforation Entry Hole Diameter." Energies 17, no. 22 (2024): 5558. http://dx.doi.org/10.3390/en17225558.

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In the field of oil and gas well perforation, it is imperative to accurately forecast the casing entry hole diameter under full downhole conditions. Precise prediction of the casing entry hole diameter enhances the design of both conventional and limited entry hydraulic fracturing, mitigates the risk of proppant screenout, reduces skin factors attributable to perforation, guarantees the presence of sufficient flow areas for the effective pumping of cement during a squeeze operation, and reduces issues related to sand production. Implementing machine learning and deep learning models yields imm

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Hu, Y., J. Chuang, C. Lo, and C. Li. "Efficient greyscale image compression technique based on vector quantization." Opto-Electronics Review 19, no. 1 (2011). http://dx.doi.org/10.2478/s11772-010-0073-0.

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AbstractIn this paper, a novel greyscale image coding technique based on vector quantization (VQ) is proposed. In VQ, the reconstructed image quality is restricted by the codebook used in the image encoding/decoding procedures. To provide a better image quality using a fixed-sized codebook, the codebook expansion technique is introduced in the proposed technique. In addition, the block prediction technique and the relatively address technique are employed to cut down the required storage cost of the compressed codes. From the results, it is shown that the proposed technique adaptively provides

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Zhao, Zhixiang, Yanxia Wu, Guoyin Zhang, Yiqing Yang, and Ruize Hong. "Recursive Hybrid Compression for Sparse Matrix‐Vector Multiplication on GPU." Concurrency and Computation: Practice and Experience 37, no. 4-5 (2025). https://doi.org/10.1002/cpe.8366.

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ABSTRACTSparse Matrix‐Vector Multiplication (SpMV) is a fundamental operation in scientific computing, machine learning, and data analysis. The performance of SpMV on GPUs is crucial for accelerating various applications. However, the efficiency of SpMV on GPUs is significantly affected by irregular memory access patterns, high memory bandwidth requirements, and insufficient exploitation of parallelism. In this paper, we propose a Recursive Hybrid Compression (RHC) method to address these challenges. RHC begins by splitting the initial matrix into two portions: an Ellpack (ELL) portion and a C

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Lan, Tian, Xian-Ling Mao, Wei Wei, Xiaoyan Gao, and Heyan Huang. "Towards Efficient Coarse-grained Dialogue Response Selection." ACM Transactions on Information Systems, August 4, 2023. http://dx.doi.org/10.1145/3597609.

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Coarse-grained response selection is a fundamental and essential subsystem for the widely used retrieval-based chatbots, aiming to recall a coarse-grained candidate set from a large-scale dataset. The dense retrieval technique has recently been proven very effective in building such a subsystem. However, dialogue dense retrieval models face two problems in real scenarios: (1) the multi-turn dialogue history is re-computed in each turn, leading to inefficient inference; (2) the index storage of the offline index is enormous, significantly increasing the deployment cost. To address these problem

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Megahed, Khaled, Nabil Said Mahmoud, and Saad Elden Mostafa Abd-Rabou. "Prediction of the axial compression capacity of stub CFST columns using machine learning techniques." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-53352-1.

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AbstractConcrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models—Gaussian process regression

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Khushal Das, Kamlish, and Fazeel Abid. "Advancements in Word Embeddings: A Comprehensive Survey and Analysis." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 61, no. 3 (2024). http://dx.doi.org/10.53560/ppasa(61-3)842.

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In recent years, the field of Natural Language Processing (NLP) has seen significant growth in the study of word representation, with word embeddings proving valuable for various NLP tasks by providing representations that encapsulate prior knowledge. We reviewed word embedding models, their applications, cross-lingual embeddings, model analyses, and techniques for model compression. We offered insights into the evolving landscape of word representations in NLP, focusing on the models and algorithms used to estimate word embeddings and their analysis strategies. To address this, we conducted a

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Zhao, Zhixiang, Guoyin Zhang, Yanxia Wu, Ruize Hong, Yiqing Yang, and Yan Fu. "Block-wise dynamic mixed-precision for sparse matrix-vector multiplication on GPUs." Journal of Supercomputing, March 11, 2024. http://dx.doi.org/10.1007/s11227-024-05949-6.

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AbstractSparse matrix-vector multiplication (SpMV) plays a critical role in a wide range of linear algebra computations, particularly in scientific and engineering disciplines. However, the irregular memory access patterns, extensive memory usage, high bandwidth requirements, and underutilization of parallelism hinder the computational efficiency of SpMV on GPUs. In this paper, we propose a novel approach called block-wise dynamic mixed-precision (BDMP) to address these challenges. Our methodology involves partitioning the original matrix into uniformly sized blocks, with each block’s size det

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Isleem, Haytham F., Tang Qiong, Naga Dheeraj Kumar Reddy Chukka, Rakesh Kumar, T. Vamsi Nagaraju, and Asmaa Y. Hamed. "Machine learning and nonlinear finite element analysis of fiber‐reinforced polymer‐confined concrete‐steel double‐skin tubular columns under axial compression." Structural Concrete, June 21, 2024. http://dx.doi.org/10.1002/suco.202300835.

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AbstractFiber‐reinforced polymer (FRP)‐confined double‐skin tubular columns (DSTCs) are an innovative type of hybrid columns that consist of an outer tube made of FRP, an inner circular steel tube, and a concrete core sandwiched between them. Available literature focuses on hollow DSTCs with limited research on DSTCs made with inner steel tubes filled with concrete. Overall, DSTCs have many applications, highlighting the importance of studying the effects of concrete filling and strength on the composite system. To address this gap, finite element models (FEMs) and both traditional and innovat

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Li, Ping, Yanru Zhang, Jiming Gu, and Shiwei Duan. "Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-57131-w.

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AbstractThere are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the bas

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Liu, Cong, Yunqing Liu, Qiong Zhang, Xiaolong Li, Tong Wu, and Qi Li. "A two-stage classification algorithm for radar targets based on compressive detection." EURASIP Journal on Advances in Signal Processing 2021, no. 1 (2021). http://dx.doi.org/10.1186/s13634-021-00719-5.

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AbstractAlgorithms are proposed to address the radar target detection problem of compressed sensing (CS) under the conditions of a low signal-to-noise ratio (SNR) and a low signal-to-clutter ratio (SCR) echo signal. The algorithms include a two-stage classification for radar targets based on compressive detection (CD) without signal reconstruction and a support vector data description (SVDD) one-class classifier. First, we present the sparsity of the echo signal in the distance dimension to design a measurement matrix for CD of the echo signal. Constant false alarm rate (CFAR) detection is per

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Wang, Jing, Ruichen Lu, and Ming Cheng. "Application of ensemble model in capacity prediction of the CCFST columns under axial and eccentric loading." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-36576-5.

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AbstractUnderstanding the load-carrying capacity of circular concrete-filled steel tube (CCFST) columns is crucial for designing CCFST structures. However, traditional empirical formulas often yield inconsistent results for the same scenario, causing confusion for decision makers. Additionally, simple regression analysis is unable to accurately predict the complex mapping relationship between input and output variables. To address these limitations, this paper proposes an ensemble model that incorporates multiple input features, such as component geometry and material properties, to predict CC

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Guo, Chaochao, Youchao Sun, Rourou Yu, and Chong Peng. "Metamodeling of turbofan engine compressor characteristics using SVM-based improved Kriging method." Aircraft Engineering and Aerospace Technology, December 19, 2024. https://doi.org/10.1108/aeat-03-2024-0064.

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Purpose The purpose of this paper is to overcome the inherent lack of precision in commonly used interpolation procedures when solving the mathematical model of turbofan engines, as well as to address the issue that the theoretical variogram model in traditional Kriging models is prone to subjective selection bias, which makes it impossible to accurately capture the inherent fluctuation patterns in compressor data. Design/methodology/approach To mitigate this challenge, based on the spatial distribution characteristics of the compressor characteristic data of a certain type of turbofan engine,

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Zhang, Hengming, Da Li, and Feng Li. "Buckling critical load prediction of pultruded fiber-reinforced polymer columns and feature analysis by machine learning." Advances in Structural Engineering, June 6, 2024. http://dx.doi.org/10.1177/13694332241260129.

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For slender FRP columns, predicting the global buckling critical loads is crucial in structural design. However, there is a lack of a consensus prediction method based on specialized domain knowledge. To address this issue, this study created a comprehensive database by collecting 365 experimental data related to global buckling of axially loaded pultruded FRP columns to predict buckling critical loads using such machine learning methods as extreme gradient boosting, artificial neural network, and support vector regression. The prediction accuracy and stability of the machine learning predicti

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