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Comparison of Data-Driven Approaches to Rotorcraft Store Separation Modeling

JOURNAL OF AIRCRAFT 2024年第1期61卷 229-244页

作者： Peters, Nicholas Wissink, Andrew Ekaterinaris, John Embry Riddle Aeronaut Univ Dept Aerospace Engn 1 Aerospace Blvd Daytona Beach FL 32114 USA US Army Combat Capabilities Dev Command Aviat & Missile Ct Moffett Field CA 94035 USA

In this study, two surrogate modeling approaches will be investigated for their ability to predict store surface pressure distributions as a store is launched from a rotorcraft in hover. To generate these surrogate models, three computational fluid dynamics (CFD) simulations were completed while varying the propulsive force assigned to the store. Once the surrogate models were generated, an additional CFD simulation with a new propulsive force was assigned to the store. The results of this validation indicate that although the proper orthogonal decomposition (POD) surrogate model struggled to provide detailed predictions of store-distributed loads, mean load variations could be modeled well. The results further indicated that by leveraging the convolutional neural network (CNN)-based surrogate model, a significant improvement in distributed load modeling could be obtained. It was further identified that once distributed loads were integrated, both POD- and CNN-based surrogate models provided a viable path for the generation of a trajectory prediction-based surrogate model for rotorcraft applications. Both POD- and CNN-based surrogate models provided significantly reduced computational costs. For each rotorcraft store separation CFD simulation, the computational cost required 10 days of simulation time across 880 central processing unit cores. While using the surrogate model, comparable predictions could be produced in under 3 min on a single core.

关键词： Rotorcrafts Proper Orthogonal Decomposition Uncertainty Quantification Convolutional neural network Aerodynamic Loads Vortex Dynamics Unsteady Reynolds Averaged Navier Stokes Aerodynamic Performance Fluid Structure Interaction

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Fingerprint-Based Deduplication for Renewable Energy Data On-Site Analyzing

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 2730-2740页

作者： Zeng, Zeng Gao, Liyuan Ma, Hongyue Li, Wenjing State Grid Jiangsu Elect Power Co Ltd Informat & Commun Branch Nanjing Jiangsu Peoples R China State Grid Informat & Telecommun Grp Co Ltd Beijing 102211 Peoples R China

Electric power, as a primary source of energy, is crucial to ensuring the healthy development of socio-economic systems. With the rapid growth of the economy, the energy demand continues to rise, necessitating the reduction of environmental pollution. Consequently, renewable energy sources such as wind, photovoltaic, and solar-thermal have been widely adopted. The advancement of microgrid autonomy and smart IoT technologies has improved energy utilization efficiency, optimized the energy consumption structure, reduced transmission losses, and alleviated environmental impacts through the use of renewable sources. Currently, smart power IoT focuses on the real-time collection and distribution of data, facing significant challenges due to the imperfections in edge IoT technologies and reliance on centralized cloud server processing, which substantially increases the computational load on central servers. We propose an edge IoT architecture that enhances the consumption capacity of distributed renewable energy at the microgrid level. By studying fine-grained data fingerprinting and neural network-based renewable energy data edge-analysis technologies, we have enabled edge processing of data, reducing latency and privacy issues, and enhancing the system's real-time processing capabilities and responsiveness.

关键词： Renewable energy sources Fingerprint recognition Microgrids Servers Power systems Predictive models Data models Wind power generation Real-time systems Energy consumption Data fingerprint neural network on-site analyzing renewable energy

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Fundamental Estimation for Tire Road Friction Coefficient: A Model-Based Learning Framework

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 481-493页

作者： Wang, Yan Yin, Guodong Hang, Peng Zhao, Jing Lin, Yilun Huang, Chao Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Tongji Univ Dept Traff Engn Shanghai 201804 Peoples R China Univ Macau Dept Electromech Engn Taipa 999078 Peoples R China Shanghai Artificial Intelligence Lab Urban Comp Lab Shanghai 20030 Peoples R China

Accurate tire-road friction coefficient (TRFC) is crucial for enhancing both the motion performance and safety of vehicles. In this article, a model-based learning approach, incorporating event-triggered cubature Kalman filtering (ETCKF) and extended Kalman neural network (EKFNet), is proposed for identifying TRFC. Firstly, an event-triggered mechanism is designed to assess whether measurement data is lost, and it is fused with the cubature Kalman filtering to construct an ETCKF for processing sensor data. Subsequently, these processed data are fed into a nonlinear tire model to compute normalized tire forces. Next, an EKFNet, composed of an EKF and a four-layer neural network, utilizes the tire force information and vehicle model for the estimation of TRFC. Multiple virtual experiment results demonstrate that the estimation performance of the model-based learning framework outperforms conventional extended Kalman filter and unscented Kalman filter. Furthermore, the proposed method is applicable not only to distributed drive electric vehicles but also to traditional fuel vehicles.

关键词： Estimation Tires Computational modeling Kalman filters Wheels neural networks Vehicle dynamics Mathematical models Data models Safety Event-triggered cubature Kalman filtering extended Kalman neural network tire road friction coefficient (TRFC)

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LiWi-HAR: Lightweight WiFi-Based Human Activity Recognition Using distributed AIoT

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IEEE INTERNET OF THINGS JOURNAL 2024年第1期11卷 597-611页

作者： Liang, Weixi Tang, Rongshan Jiang, Sihan Wang, Ruqi Zhao, Yubin Xu, Cheng-Zhong Long, Xudong Chen, Zhuolong Li, Xiaofan Sun Yat Sen Univ Sch Microelect Sci & Technol Zhuhai 519082 Peoples R China Univ Macau State Key Lab IoTSC Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 519070 Peoples R China

Human activity recognition (HAR) based on WiFi channel state information (CSI) has received a lot of attentions recently due to its nonintrusive nature. Most CSI-based HAR systems use a WiFi router and a computing terminal for centralized processing, which makes it difficult to achieve real-time wide-range recognition. Recently, lightweight Artificial Intelligence Internet of Things (AIoT) devices are widely deployed. The equipped WiFi chips within such devices can collect and process CSI data in a distributed way. Thus, the AIoT devices extend the detection range of collecting CSI and enrich the applications. However, the memories of the AIoT devices are constrained and lack of appropriate lightweight CSI processing strategies. To address these challenges, we propose the LiWi-HAR system which employs a comprehensive lightweight CSI processing strategy in WiFi-based AIoT devices. The proposed lightweight CSI processing strategy extracts the main related features while compressing the data size. Then, a double hidden layer BP neural network based on particle swarm optimization (PSO-BPNN) algorithm is developed for HAR. In this case, the computing memory occupation of the device is effectively reduced, and the real-time high-accurate recognition is achieved. Extensive experimental results present that the efficiency of our system significantly outperforms other centralized deep learning-based systems and the recognition accuracy achieves 91.7%.

关键词： Wireless fidelity Feature extraction Real-time systems Wireless communication Deep learning neural networks Monitoring Activity recognition activity segmentation channel state information (CSI) parameter optimization WiFi chip

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Islanding detection and power quality disturbance classification in multi DG based microgrid using down sampling empirical mode decomposition and multilayer neural network

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2024年第4期25卷 429-445页

作者： Choudhury, Anasuya Roy Nayak, Pravati Mallick, Ranjan Kumar Agrawal, Ramachandra Mishra, Sairam Panda, Gayadhar Siksha O Anusandhan Deemed Univ ITER Dept Elect Engn Bhubaneswar 751030 India NIT Meghalaya Dept Elect Engn Shillong 793003 India

Power Quality, Equipment and Personnel safety of any distributed generation (DG) system connected to utility Grid merely depends on accurate detection of Islanding and non-islanding Power quality disturbances. The main objective of the proposed research is to detect islanding events with very narrow non-detection zone (NDZ) and classification of power quality disturbances with higher accuracy using signal processing and intelligent method together. A noise robust down sampling empirical mode decomposition (DEMD) is used to extract signature of islanding and power quality (PQ) disturbance features from the collected voltage signals and multilayer perceptron neural network (MLNN) is proposed to classify islanding and non-islanding (PQ) events. The performance of the proposed (DEMD-MLNN) technique is verified with IEEE-9 bus distributed generation system dominated by solar &wind energy penetration. The simulation work is carried out in MATLAB/Simulink platform. The efficacy of the proposed DEMD-MLNN is verified by large number of numerical experimentations with and without noise and comparing with existing competitive well-known techniques.

关键词： distributed generation down sampling empirical mode decomposition islanding detection multilayer neural networks power quality

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On-Orbit DNN distributed Inference for Remote Sensing Images in Satellite Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5687-5703页

作者： Qiao, Ying Teng, Shuyang Luo, Juan Sun, Peng Li, Fan Tang, Fengxiao Hunan Univ Coll Informat Sci & Engn Changsha 410082 Peoples R China Cent South Univ Coll Comp Sci & Engn Changsha 410017 Peoples R China

In satellite Internet of Things (IoT), the remote sensing satellites capture images and then transmit them to a ground station through low Earth orbit (LEO) communication satellites for model inference. However, this process results in significant transmission latency and communication overhead. In response, researchers have proposed various satellite on-orbit model inference methods. Nonetheless, the limited computation capacity and memory space of a single remote sensing satellite impose processing delays when dealing with large quantities of high-resolution images, thereby making it difficult to ensure real-time service. To tackle this issue, we propose an on-orbit deep neural network (DNN) distributed inference framework for remote sensing images in satellite IoT, leveraging the availability of numerous LEO computing satellites. Designing such a framework involves two crucial questions: first, determining which LEO satellites should participate in distributed DNN inference, and second, how to partition the images among the selected LEO satellites. To address these questions, we formulate the distributed inference process as a mixed integer nonlinear optimization problem, which is known to be NP-hard. The objective is to minimize overall energy consumption while ensuring that the distributed inference is accomplished when the satellite dynamic network remains unchanged. We initially propose a dynamic optimization algorithm that derives the optimal solution with rigorous theoretical guarantees. Subsequently, to reduce computational complexity, we introduce an approximate solution based on an improved simulated annealing algorithm. We demonstrate that the approximate algorithm performs within a limited range of the optimal algorithm. Finally, we build a heterogeneous testbed based on Kubernetes and conduct extensive experiments to validate that our proposed algorithms reduce energy consumption by an average of 24.63% and 25.98% on the Faster-RCNN inference model, 4

关键词： Satellites Low earth orbit satellites Remote sensing Heuristic algorithms Computational modeling Collaboration Inference algorithms Delays Data models Energy consumption Deep neural network (DNN) inference dynamic programming Kubernetes low Earth orbit (LEO) satellites satellite Internet of Things (IoT) simulated annealing algorithm

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RDA-Net: A multi-cascade network for DAS background noise attenuation

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 242卷

作者： Zhong, Tie Xi, Xi Dong, Xintong Wang, Xinyang Li, Feng Minist Educ Key Lab Modern Power Syst Simulat & Control & Rene Jilin Peoples R China Northeast Elect Power Univ Dept Commun Engn Jilin Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Jilin Peoples R China

The increasing attention to seismic data collection under complex geological conditions is attributed to the utilization of distributed acoustic sensing (DAS), which has gained significant prominence due to the rapid development of exploration techniques. However, the presence of background noise in DAS has adverse effects on the quality of DAS data and significantly affects the accuracy of the subsequent migration and imaging processing. In addition, these interferences have a different generation mechanism from random noise in geophone-acquired seismic data, resulting in the degradation of traditional denoising techniques. Recent advancements have introduced convolutional neural networks (CNNs) for DAS background noise attenuation, showcasing their superiority over conventional techniques in terms of denoising capability and processing accuracy. Unfortunately, most CNN-based methods only have a single-scale architecture and cannot precisely accomplish denoising tasks when confronted with weak reflection signals in DAS records. In this research, we introduce a residual dense-connection attention network called RDA-Net to further augment the denoising ability. More precisely, the multi-scale structure in the backbone of RDA-Net is employed to capture informative features from the DAS data. Simultaneously, we employ a self-enhanced spatial attention module to refine and strengthen the primary features, thereby positively impacting feature representation and denoising performance. Furthermore, dense connections are also leveraged to reinforce the feature-interaction capability and effective features. Compared with typical conventional methods and CNN-based frameworks, RDA-Net can improve the signal-to-noise ratio over 24 dB and show superiority in weak signal recovery and intense DAS noise attenuation, both for synthetic and field records.

关键词： distributed acoustic sensing (DAS) Seismic noise attenuation Convolutional neural network (CNN) Dense connection Spatial attention mechanism Weak signal recovery

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Weak Signals Recovery of Downhole DAS With Scale-Weighted Nonlocal Selective Attention

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Yao, Guijin Sun, Decheng Zhao, Yuxing Sui, Jilei Tian, Yanan Li, Yue Jilin Univ Coll Commun Engn Dept Informat Changchun 130012 Peoples R China

The comprehensive requirements of effectively suppressing various noises, preserving signal amplitudes, recovering weak signals, balancing the effects between local and global, and reducing computational costs have always been the crucial task of data processing of downhole distributed acoustic sensing (DAS). Traditional methods, single-mechanism convolutional neural network (CNN) models, and training methods with a single patch size, due to their simplistic structures, limited data processing capabilities, and a large number of network layers often struggle to meet the aforementioned comprehensive requirements. In this article, using a progressive training method, we propose a novel nonlocal selective attention multiscale residual network (NSKMRNet). At its core lies an iterative multiscale residual block (MRB): multiple resolution convolution streams allow for the transfer of contextual information from the low-resolution stream to consolidate high-resolution features;parallel nonlocal contextual processing uses a distillation mechanism for extracting useful information, and weighted attention handling between adjacent scale streams for merging information. The feature maps selected from a randomly chosen training iteration clearly reflect the noise suppression process and the importance of iterative process. In experiments conducted on both synthetic and field DAS data, our method achieved a signal-to-noise ratio (SNR) improvement of around 30 dB, surpassing other methods. It maximally preserves signal amplitudes, resulting in minimal signal residuals in the amplified field records. Moreover, in terms of floating point operations (FLOPs) and training time, our method is within an acceptable range but significantly lower than more complex networks. The proposed network holds promise as a potentially valuable tool in meeting the comprehensive requirements for processing of downhole DAS data.

关键词： Noise Training Kernel Streams Convolution Noise reduction Signal to noise ratio Attentional mechanism distributed acoustic sensing (DAS) multiscale seismic data denoising

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Optimizing Multi-Level Checkpointing for distributed Deep Learning Workloads on Cloud Spot VM Clusters

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IEEE ACCESS 2024年 12卷 116891-116904页

作者： Cho, Yonghyeon Kim, Yoochan Kim, Kihyun Kim, Jinwoo Kim, Hong-Yeon Kim, Youngjae LG Elect webOS SW Dev Grp Seoul 07336 South Korea Sogang Univ Dept Comp Sci & Engn Seoul 04107 South Korea Elect & Telecommun Res Inst ETRI Daejeon 34129 South Korea

Spot Virtual Machines (Spot VMs) offer access to underutilized computing resources at significant discounts, sometimes up to 90% off regular on-demand pricing. For budget-conscious organizations, using clusters of Spot VMs is an effective strategy for training large-scale distributed deep learning (DDL) models. However, the risk of preemption by cloud providers poses a challenge, as it can result in the loss of unsaved data in memory and local storage. To mitigate this risk, one solution involves using networked storage systems for checkpoints, though their low write throughput can slow down training. An alternative approach is to use the memory of a remote, on-demand computing node for temporary checkpoint storage, balancing data protection with training efficiency. In this paper, we propose a novel approach, ACUTE, to optimize temporary checkpointing in the memory of on-demand nodes during DDL training. ACUTE includes three key optimizations: 1) Check-Mem, which reduces memory copying overhead on the training node;2) Check-Trans, which accelerates checkpoint data transfer through parallel processing;and 3) Check-Pack, which eliminates unnecessary data unpacking and repacking. Implemented using PyTorch's distributed data-parallel library, ACUTE was evaluated against two other checkpointing schemes on AWS VM instances. Results show that ACUTE reduces makespan delay to nearly zero and achieves, on average, 43.30% faster checkpointing compared to a baseline multi-level checkpointing scheme, without compromising the precision of Deep neural network (DNN) models.

关键词： Checkpointing Artificial neural networks Parallel processing Deep learning Cloud computing Graphics processing units Fault tolerance distributed deep learning cloud computing fault-tolerant systems checkpoint and restart

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Mobileware: distributed Architecture With Channel Stationary Dataflow for MobileNet Acceleration

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第9期43卷 2661-2673页

作者： Ryu, Sungju Jang, Jaeyong Oh, Youngtaek Kim, Jae-Joon Sogang Univ Dept Syst Semicond Engn Seoul 04107 South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea POSTECH Dept Convergence IT Engn Pohang 37673 South Korea

The depthwise separable convolution, a key feature of the MobileNet models, has a different input reuse pattern from the conventional standard convolution, and a smaller number of input/weight pairs are used for a dot product, thereby leading to extremely low MAC utilization. This article proposes a Mobileware architecture for the high-performance acceleration of the MobileNet workloads. A new channel stationary dataflow architecture distributes the on-chip buffers, and the distributed SRAMs are placed near each processing element (PE). By doing so, PEs and SRAMs can communicate with high bandwidth. Our Mobileware architecture shows 1.4 x- 29.5 x higher throughput than conventional weight stationary-based hardware architecture, and the proposed design was verified on the Xilinx ZCU102 FPGA evaluation board.

关键词： Convolution Random access memory Standards Computer architecture Design automation System-on-chip Computational modeling Dataflow deep neural network depthwise convolution distributed memory hardware accelerator MobileNet processing element (PE)

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