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Electronics and Renewable Systems (ICEARS), International conference on

作者： Changbo Luo Sanya Vocational College of Aviation Tourism Sanya City China

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the continuous iteration of the ship engine technology, related technologies have achieved great development. Ship engines run for a long time in harsh environments of high temperature, high pressure and high speed, which increases the possibility of failure. Therefore, how to detect and warn faults is an important research topic. In this study, the efficient ship engine condition monitoring and fault diagnosis system based on intelligent control is studied. In the early stage of system construction, this study analyzed the basic model of ship engine condition monitoring. The model combines neural networks and sensor data extraction algorithms, and analyzes the health status of gas turbines through modeling. At the same time, machine learning algorithms such as BP neural networks are used to predict the compressor characteristic curve. This attempt improves the traditional algorithm's ability to remember samples and improves the accuracy of the system. In terms of fault diagnosis modeling, this study uses a machine learning-based model and a signal processing technology study. Specifically, the time-frequency data of the turbine equipment is studied to identify anomalies. The characteristic frequency is extracted by fast Fourier transform and the energy concentration area is analyzed to achieve the potential fault detection. Experimental results show that the system can effectively improve the accuracy of ship engine fault diagnosis and achieve good results on both small and large data sets.

关键词： Fault diagnosis Condition monitoring Analytical models Machine learning algorithms Neural networks signal processing algorithms Prediction algorithms Marine vehicles Engines Intelligent control

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Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining

Reducing the Sensitivity of Neural Physics Simulators to Mes...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Nathan Vaska Justin Goodwin Robin Walters Rajmonda S. Caceres MIT Lincoln Laboratory Lexington USA Northeastern University Boston USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Meshes are used to represent complex objects in high fidelity physics simulators across a variety of domains, such as radar sensing and aerodynamics. There is growing interest in using neural networks to accelerate physics simulations, and also a growing body of work on applying neural networks directly to irregular mesh data. Since multiple mesh topologies can represent the same object, mesh augmentation is typically required to handle topological variation when training neural networks. Due to the sensitivity of physics simulators to small changes in mesh shape, it is challenging to use these augmentations when training neural network-based physics simulators. In this work, we show that variations in mesh topology can significantly reduce the performance of neural network simulators. We evaluate whether pretraining can be used to address this issue, and find that employing an established autoencoder pretraining technique with graph embedding models reduces the sensitivity of neural network simulators to variations in mesh topology. Finally, we highlight future research directions that may further reduce neural simulator sensitivity to mesh topology.

关键词： Training Sensitivity Network topology Shape Computational modeling Neural networks Autoencoders data models Topology Physics

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signal and data processing of small targets 1991

Signal and Data Processing of Small Targets 1991

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signal and data processing of small targets 1991

ISBN: (纸本)0819405906

The Proceedings materials contain 40 papers addressing the issues of signal processing and tracking of small targets. targets of interest include point source objects, small extended objects, and clusters of point source and small extended objects. Important advances are presented in in multiple hypothesis tracking, track-before-detect, multiple spectral processing, sensor fusion, and matched filtering with the target obscuring the background.

关键词： Radar, Optical

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IBM Telum II: Next Generation 5.5GHz Microprocessor with On-Die data processing Unit and Improved AI Accelerator 72

IBM Telum II: Next Generation 5.5GHz Microprocessor with On-...

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72nd IEEE International Solid-State Circuits conference, ISSCC 2025

作者： Strevig, Gerald Berry, Chris Rao, Rahul Jungmann, Noam Sperling, Michael Becht, Michael Herkel, Eduard Pflanz, Matthias Meaney, Pat Romain, Michael Cichanowski, Mark Venton, Amanda Wolpert, David Kachir, Elazar Hopkins, Luke Bubb, Tim Arp, Andreas Kiss, Daniel Buchsenstein, Simon Wood, Michael Spear, Michael Sonnelitter, Robert Joshi, Rajiv IBM Systems AustinTX United States IBM Systems PoughkeepsieNY United States IBM Systems Bangalore India IBM Systems Tel Aviv Israel IBM Systems Böblingen Germany IBM Research Yorktown HeightsNY United States

ISBN: (纸本)9798331541019

The IBM Z microprocessor, Telum II, has been redesigned for the zNext system to improve performance and system capacity over the previous z16 system [1]. The system topology consists of four Dual-Chip Modules (DCM), each composed of two central-processor (CP) chips per drawer. The system can be configured with up to four drawers and a total of 32 CP chips in a fully coherent shared memory system. The CP (Fig. 2.2.7) is a 600mm2 die containing 43B transistors and is designed in Samsung 5nm bulk technology [2]. It contains over 24 miles of wire and 165B vias spread across 18 layers of metal: 8 narrow-width layers for local interconnect, 8 medium width-high performance layers, and two ultra-thick layers for off-chip signal routing and power/clock distribution. The combination of die size increase and library improvements allowed us to keep 8 cores per CP, while adding a new data processing Unit (DPU) onto the chip. Each CP operates at 5.5GHz. Each core has a 128KB L1 instruction cache, and a 128KB data cache. Each CP also has 2 PCIe Gen5 ×16 interfaces, an M-BUS interface to the other CP on the DCM, and an X-BUS interface to every CP in the other 3 DCMs per drawer. There is 1 A-BUS interface to connect 6 out of the 8 CPs in each drawer to the other drawers in the system. The clock network is designed with a 1:1 resonant mesh covering most of the chip and three small asynchronous non-resonant meshes for the memory and PCIe interfaces. In addition, Telum II adds an on-chip voltage control loop for improved dynamic voltage management, which maintains performance without requiring higher voltages across all workloads [3]. Key system capacity and performance improvements came from enhancements to the core and the increased cache size. Telum II increases the number of L2 cache instances from 8 to 10 and uses Samsung's high-density SRAM cell to grow the L2 cache by 40% from 32MB on Telum to 36MB per L2 cache instance. Each processor core and the DPU have a private 36MB L2 cac

关键词： Cache memory

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MuRL-DTI: A Multimodal Feature Fusion Reinforcement Learning Approach for Cold Start in Drug-Target Interactions

MuRL-DTI: A Multimodal Feature Fusion Reinforcement Learning...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yao Liu Xin Wang Ye Liu Dandan Dou Beijing Key Laboratory of Digital Media Beihang University Beijing China Department of Engineering Science University of Oxford Oxford UK The School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China The School of Biological Science and Medical Engineering Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Drug Target Interaction (DTI) focuses on exploring the interactions between specific drug molecules and their biological targets to assess the efficacy and safety of drugs. Significant advancements have been made in integrating computational techniques compared to traditional approaches, including machine learning-based DTI models, deep learning-based DTI models, and knowledge graph-based DTI models. However, these models often depend on rich contextual and structural information, which can lead to reduced prediction accuracy when dealing with sparse data and pose challenges in cold start scenarios. To address this limitation, we propose a novel DTI prediction method called MuRL-DTI. Specifically, we employ DQN-based reinforcement learning to the DTI task to solve the cold-start problem by leveraging learned knowledge to transfer to the unknown drug-target interaction. Additionally, we utilize biological information from multiple sources, including sequence features and compound features, to reinforce the learned environmental information and improve the prediction accuracy. Experimental results on three benchmark datasets demonstrate that our model outperforms the baselines, validating the effectiveness of the proposed approach.

关键词： Drugs Accuracy Biological system modeling Computational modeling Reinforcement learning Predictive models data models Compounds Standards Diffusion tensor imaging

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Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks

Implicit Neural Representations with Fourier Kolmogorov-Arno...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ali Mehrabian Parsa Mojarad Adi Moein Heidari Ilker Hacihaliloglu Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Biomedical Engineering The University of British Columbia Vancouver Canada Department of Radiology The University of British Columbia Vancouver Canada Department of Medicine The University of British Columbia Vancouver Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Implicit neural representations (INRs) use neural networks to provide continuous and resolution-independent representations of complex signals with a small number of parameters. However, existing INR models often fail to capture important frequency components specific to each task. To address this issue, in this paper, we propose a Fourier Kolmogorov-Arnold network (FKAN) for INRs. The proposed FKAN utilizes learnable activation functions modeled as Fourier series in the first layer to effectively control and learn the task-specific frequency components. The activation functions with learnable Fourier coefficients improve the ability of the network to capture complex patterns and details, which is beneficial for high-resolution and high-dimensional data. Experimental results show that our proposed FKAN model outperforms four state-of-the-art baseline schemes, and improves the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for the image representation task and intersection over union (loU) for the 3D occupancy volume representation task, respectively. The code is available at ***/Ali-Meh619/FKAN.

关键词： Solid modeling Three-dimensional displays PSNR Volume measurement Image representation Neural radiance field signal representation Fourier series Speech processing signal resolution

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A Hierarchical Compression Technique for 3D Gaussian Splatting Compression

A Hierarchical Compression Technique for 3D Gaussian Splatti...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： He Huang Wenjie Huang Qi Yang Yiling Xu Zhu Li Shanghai Jiao Tong University University of Missouri-Kansas City

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D Gaussian Splatting (GS) demonstrates excellent rendering quality and generation speed in novel view synthesis. However, substantial data size poses challenges for storage and transmission, making 3D GS compression an essential technology. Current 3D GS compression research primarily focuses on developing more compact scene representations, such as converting explicit 3D GS data into implicit forms. In contrast, compression of the GS data itself has hardly been explored. To address this gap, we propose a Hierarchical GS Compression (HGSC) technique. Initially, we prune unimportant Gaussians based on importance scores derived from both global and local significance, effectively reducing redundancy while maintaining visual quality. An Octree structure is used to compress 3D positions. Based on the 3D GS Octree, we implement a hierarchical attribute compression strategy by employing a KD-tree to partition the 3D GS into multiple blocks. We apply farthest point sampling to select anchor primitives within each block and others as non-anchor primitives with varying Levels of Details (LoDs). Anchor primitives serve as reference points for predicting non-anchor primitives across different LoDs to reduce spatial redundancy. For anchor primitives, we use the region adaptive hierarchical transform to achieve near-lossless compression of various attributes. For non-anchor primitives, each is predicted based on the k-nearest anchor primitives. To further minimize prediction errors, the reconstructed LoD and anchor primitives are combined to form new anchor primitives to predict the next LoD. Our method notably achieves superior compression quality and a significant data size reduction of over 4.5× compared to the state-of-the-art compression method on small scenes datasets.

关键词： Visualization Three-dimensional displays Octrees Termination of employment Transforms signal processing Rendering (computer graphics) Acoustics Time complexity Speech processing

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data-Efficient Low-Complexity Acoustic Scene Classification via Distilling and Progressive Pruning

Data-Efficient Low-Complexity Acoustic Scene Classification ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Bing Han Wen Huang Zhengyang Chen Anbai Jiang Pingyi Fan Cheng Lu Zhiqiang Lv Jia Liu Wei-Qiang Zhang Yanmin Qian Shanghai Jiao Tong University Shanghai China Tsinghua University Beijing China North China Electric Power University Beijing China Huakong AI Plus Company Limited Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of the acoustic scene classification (ASC) task is to classify recordings into one of the predefined acoustic scene classes. However, in real-world scenarios, ASC systems often encounter challenges such as recording device mismatch, low-complexity constraints, and the limited availability of labeled data. To alleviate these issues, in this paper, a data-efficient and low-complexity ASC system is built with a new model architecture and better training strategies. Specifically, we firstly design a new low-complexity architecture named Rep-Mobile by integrating multi-convolution branches which can be reparameterized at inference. Compared to other models, it achieves better performance and less computational complexity. Then we apply the knowledge distillation strategy and provide a comparison of the data efficiency of the teacher model with different architectures. Finally, we propose a progressive pruning strategy, which involves pruning the model multiple times in small amounts, resulting in better performance compared to a single step pruning. Experiments are conducted on the TAU dataset. With Rep-Mobile and these training strategies, our proposed ASC system achieves the state-of-the-art (SOTA) results so far, while also winning the first place with a significant advantage over others in the DCASE2024 Challenge.

关键词： Training Scene classification Analytical models Computational modeling Computer architecture Transformers Acoustics Recording Speech processing Kernel

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DF-CFAR: deep feature constant false alarm ratio detector based on feature game for sea-surface small target with robustness to sea states

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Neural Computing and Applications 2025年 1-20页

作者： Xiang, Houhong Zhu, Liangliang Chen, Yufeng Wang, Fengyu Zeng, Xiaolu School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Department of Early Warning Technology Wuhan Early Warning Academy Wuhan430019 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

The classical constant false alarm rate (CFAR) detector is optimal for target detection in Gaussian white noise but struggles with unknown, time-varying sea states. data-driven target detection methods are highly sensitive to clutter distribution, leading to poor detection performance and high false alarm rates (Pfa) under unknown sea states. This paper proposes a deep feature constant false alarm ratio (DF-CFAR) detector based on a feature game model. The input to the feature extraction network is fragmented fast time-dimensional data processed by coherent integration, incorporating a feature game mechanism. This mechanism effectively mines target echo features that are uncorrelated with the background sea clutter. By transforming the target detection problem under varying sea state conditions into a regression prediction problem, we achieve constant false alarm detection through a statistical probability threshold. Simulation and real data experiments demonstrate that, compared to commonly used algorithms, the proposed method not only achieves higher detection probability and lower false alarm rates but also exhibits excellent constant false alarm characteristics and stronger robustness to unseen sea states. Results from publicly available X-band radar data processing show that the proposed algorithm can eliminate the influence of different sea clutters and improve the detection performance of sea-surface small targets by an equivalent signal-to-clutter ratio (SCR) of about 3.1 dB. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Gaussian distribution

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Enhancing small Model Performance in Educational Classification Tasks through Knowledge Distillation

Enhancing Small Model Performance in Educational Classificat...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haoxin Xu Changyong Qi Bingqian Jiang Tong Liu Longwei Zheng Xiaoqing Gu Shanghai Institute of AI for Education East China Normal University Shanghai China Department of Education Information Technology East China Normal University Shanghai China The School of Journalism and Communication Shanghai International Studies University Shanghai China School of Education City University of Macau China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As the demand for precision, efficiency, and low-cost solutions in educational classification tasks continues to grow, enhancing model performance has become a critical focus of research. While large language models excel in these tasks, their high cost and resource requirements limit widespread application. This study proposes a Knowledge-Enhanced Distillation (KED) method, utilizing ChatGPT-4, ChatGPT-4o, and Llama3 as teacher models, and three different sizes of BERT models as student models. The method was validated across three real-world educational datasets. The results demonstrate that the KED method significantly improves the accuracy and F1 scores of small models in educational text classification tasks, while also substantially reducing computational costs and resource consumption. Notably, the KED method shows exceptional performance in scenarios involving few-shot learning and class imbalance. The innovation of this study lies in applying the KED method to educational classification tasks, filling a gap in current research and highlighting its significant potential for practical application in educational contexts.

关键词： Ethics Technological innovation Accuracy Computational modeling Text categorization Training data Numerical models Few shot learning Speech processing Standards

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