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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Wang, Yan South West Minzu University School of Public Adminisration Chengdu China

ISBN: (纸本)9798350360660

A web based education management system is established to develop education system by enhancing quality of education and teaching model. However, the existing resource allocation model and teaching in web-based education system has limitations such as ineffective teaching due to inappropriate resource allocation affect the education system. To overcome this problem, a Split and Kernel based Residual network (SK-ResNet) is proposed to improve quality of education by allocating resources effectively and enhance teaching model in the web based education system. The split technique in ResNet model, divide the tasks and distributed across multiple servers or devices. By splitting and kernel optimizing the tasks, the load on any single system allows the model for faster content processing and delivery. At first, the data are acquired and fed into functional structure of education system, and finally analyzed and integrated into cloud computing based on SK-ResNet model. The experimental results of the proposed method achieved accuracy of 87.29% which is greater when compared to existing method such as Deep neural network (DNN) and Hybrid Deep Learning (HDL) model. © 2024 IEEE.

关键词： Cloud platforms

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Survey and Enhancements on Deploying LSTM Recurrent neural networks on Embedded Systems

Survey and Enhancements on Deploying LSTM Recurrent Neural N...

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IEEE International Conference on Communications (IEEE ICC)

作者： Abib, Ghalid Castel, Florian Satouri, Nissrine Afifi, Hossam Said, Adel Mounir Inst Polytech Paris SAMOVAR Telecom SudParis Palaiseau France Inst Polytech Paris Telecom SudParis Palaiseau France Natl Telecommun Inst NTI Switching Dept Cairo Egypt

ISBN: (纸本)9781538674628

The real implementation of a recurrent neural network (RNN) in a low complexity IoT device is evaluated in order to predict the time series of power consumption in tertiary buildings. The RNN type long short-term memory (LSTM) algorithm is adapted for a 32-bit microcontroller unit (MCU) and the backpropagation (BP) algorithm is implemented in-house. We therefore demonstrate that Intelligent IoT (IIoT) devices, such as the Espressif ESP32 MCU, not only implement neural networks (NNs), but also learn on their own. The resulting IIoT architecture has been proven to operate efficiently and compared to the traditional computer-based learning platform. The selected results confirm that stand-alone IoT devices are a truly efficient solution that adds flexibility to the architecture, reduces storage and computation costs, and is more energy-friendly. As a conclusion, it is practically more efficient to exploit low-power and processing-time IIoT for our prediction use case rather than relying on server based distributed systems.

关键词： Intelligent IoT Machine Learning Recurrent neural networks Long Short-Term Memory Edge AI Embedded Systems

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Multi-Fingered In-Hand Manipulation With Various Object Properties Using Graph Convolutional networks and distributed Tactile Sensors

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2102-2109页

作者： Funabashi, Satoshi Isobe, Tomoki Fei Hongyi Hiramoto, Atsumu Schmitz, Alexander Sugano, Shigeki Ogata, Tetsuya Waseda Univ Shinjuku Ku Tokyo 1698555 Japan

Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of objects. However, tactile sensors on multi-fingered hands have a variety of sizes and shapes. Convolutional neural networks (CNN) can be useful for processing tactile information, but the information from multi-fingered hands needs an arbitrary pre-processing, as CNNs require a rectangularly shaped input, which may lead to unstable results. Therefore, how to process such complex shaped tactile information and utilize it for achieving manipulation skills is still an open issue. This letter presents a control method based on a graph convolutional network (GCN) which extracts geodesical features from the tactile data with complicated sensor alignments. Moreover, object property labels are provided to the GCN to adjust in-hand manipulation motions. distributed tri-axial tactile sensors are mounted on the fingertips, finger phalanges and palm of an Allegro hand, resulting in 1152 tactile measurements. Training data is collected with a data-glove to transfer human dexterous manipulation directly to the robot hand. The GCN achieved high success rates for in-hand manipulation. We also confirmed that fragile objects were deformed less when correct object labels were provided to the GCN. When visualizing the activation of the GCN with a PCA, we verified that the network acquired geodesical features. Our method achieved stable manipulation even when an experimenter pulled a grasped object and for untrained objects.

关键词： Deep learning in grasping and manipulation dexterous manipulation force and tactile sensing multifingered hands

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Individual differences in the neural architecture in semantic processing

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Liu, Xin Hu, Yiwen Hao, Yaokun Yang, Liu Air Force Med Univ Air Force Med Ctr 28Fucheng St Beijing 100142 Peoples R China

neural mechanisms underlying semantic processing have been extensively studied by using functional magnetic resonance imaging, nevertheless, the individual differences of it are yet to be unveiled. To further our understanding of functional and anatomical brain organization underlying semantic processing to the level of individual humans, we used out-of-scanner language behavioral data, T1, resting-state, and story comprehension task-evoked functional image data in the Human Connectome Project, to investigate individual variability in the task-evoked semantic processing network, and attempted to predict individuals' language skills based on task and intrinsic functional connectivity of highly variable regions, by employing a machine-learning framework. Our findings first confirmed that individual variability in both functional and anatomical markers were heterogeneously distributed throughout the semantic processing network, and that the variability increased towards higher levels in the processing hierarchy. Furthermore, intrinsic functional connectivities among these highly variable regions were found to contribute to predict individual reading decoding abilities. The contributing nodes in the overall network were distributed in the left superior, inferior frontal, and temporo-parietal cortices. Our results suggested that the individual differences of neurobiological markers were heterogeneously distributed in the semantic processing network, and that neurobiological markers of highly variable areas are not only linked to individual variability in language skills, but can predict language skills at the individual level.

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Advanced scene text recognition and application for dynamic driving scenes 4

Advanced scene text recognition and application for dynamic ...

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4th International Conference on Signal Image processing and Communication, ICSIPC 2024

作者： Wang, Jiahao Lu, Yahao Wu, Lianpei School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781510682467

In the mobile driving scenario, insufficient data has become a major challenge for the application of scene text recognition models. An alternative to reduce the cost of data annotation is the active learning method, which improves the performance of the model by screening the data with the largest annotation information entropy for training. However, the calibration deviation of modern convolutional neural network is large, and the confidence cannot accurately reflect the real situation of model prediction. In view of the above problems, a scene text recognition framework based on active learning is proposed. The strategy of generating identically distributed heterogeneous data based on anti-aliasing operation is introduced into the framework. A confidence evaluation method based on prediction invariance is proposed. Combined with the active learning method, the confidence of sample prediction is evaluated and corrected. In addition, a text recognition dataset for mobile driving scenarios is established. This method tested SAR, SVTR, and RobustScanner models on the DSO dataset, with accuracy improvements of 11.28%, 15.12%, and 11.66%, respectively. Compared with experiments with randomly annotated data, the accuracy gains of each model were 5.32%, 5.88%, and 6.53%, respectively. The results confirm that this method significantly reduces manual annotation costs while enhancing model performance and robustness. © 2024 SPIE.

关键词： Convolutional neural networks

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Deep Monocular Relative 6D Pose Estimation for Ship-Based Autonomous UAV

Deep Monocular Relative 6D Pose Estimation for Ship-Based Au...

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AIAA SciTech Forum

作者： Wickramasuriya, Maneesha Lee, Taeyoung Snyder, Murray George Washington Univ Dept Mech & Aerosp Engn 800 22nd St NW Washington DC 20052 USA George Washington Univ Dept Mech & Aerosp Engn 800 22nd St NW Washington DC 20052 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper presents a transformer neural network (TNN) model for estimating the relative pose of an autonomous unmanned aerial vehicle (UAV) with respect to a ship from monocular RGB (Red, Green, and Blue) camera images. To address the challenge of collecting rich training data in ocean, synthetic images were generated by rendering a 3-D model of the ship with randomly distributed poses of the camera under various textured environments and backgrounds. These images were used to train our TNN model with the Detection Transformer (DETR) architecture, which identifies predefined keypoints of the ship in the images. Then, the relative pose with respect to the ship is determined by the perspective-n-point algorithm. The proposed model was evaluated successfully with in-situ flight experiments performed at a US Naval Academy research vessel. Compared to traditional methods, the proposed method achieved better accuracy and consistency, even in challenging scenarios with varying light conditions. Particularly, the estimated 6D pose predictions exhibit remarkable accuracy, specifically near the landing area, when compared with an independent position measurement from a real time kinematic GPS.

关键词： Autonomous Unmanned Aerial Vehicles Global Positioning System Convolutional neural network Transformer Real Time Kinematic Flight Deck CAD model Computer Vision Natural Language processing Probability Density Functions

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Automated Search for Deep neural network Inference Partitioning on Embedded FPGA

Automated Search for Deep Neural Network Inference Partition...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Kress, Fabian Hoefer, Julian Hotfilter, Tim Walter, Iris El Annabi, El Mahdi Harbaum, Tanja Becker, Juergen Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9783031236174;9783031236181

Deep neural networks (DNNs) are currently making their way into a broad range of applications. While until recently they were mainly executed on high-performance computers, they are now also increasingly found in hardware platforms of edge applications. In order to meet the constantly changing demands, deployment of embedded Field Programmable Gate Arrays (FPGAs) is particularly suitable. Despite the tremendous advantage of high flexibility, embedded FPGAs are usually resource-constrained as they require more area than comparable Application-Specific Integrated Circuits (ASICs). Consequently, coexecution of a DNN on multiple platforms with dedicated partitioning is beneficial. Typical systems consist of FPGAs and Graphics processing Units (GPUs). Combining the advantages of these platforms while keeping the communication overhead low is a promising way to meet the increasing requirements. In this paper, we present an automated approach to efficiently partition DNN inference between an embedded FPGA and a GPU-based central compute platform. Our toolchain focuses on the limited hardware resources available on the embedded FPGA and the link bandwidth required to send intermediate results to the GPU. Thereby, it automatically searches for an optimal partitioning point which maximizes the hardware utilization while ensuring low bus load. For a low-complexity DNN, we are able to identify optimal partitioning points for three different prototyping platforms. On a Xilinx ZCU104, we achieve a 50% reduction of the required link bandwidth between the FPGA and GPU compared to maximizing the number of layers executed on the embedded FPGA, while hardware utilization on the FPGA is only reduced by 7.88% and 6.38%, respectively, depending on the use of DSPs and BRAMs on the FPGA.

关键词： distributed sensor systems neural network inference partitioning Design space exploration Embedded FPGA

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Identification of Mangrove Leaf Disease in Deep Convolutional neural network Approach

Identification of Mangrove Leaf Disease in Deep Convolutiona...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Rahmat, Romi Fadillah Sitompul, Opim Salim Lydia, Maya Silvi Fahmi, Fahmi Almashura, Raska Lubis, Fahrurrozi Universitas Sumatera Utara Department of Information Technology Medan Indonesia Universitas Sumatera Utara Department of Electrical Engineering Medan Indonesia

ISBN: (纸本)9798331511166

Mangrove forests in Indonesia are distributed over many coastal regions, exhibiting significant biodiversity and diverse structural forms. Mangroves serve as a mitigating factor against wave action and intense wind energy resulting from adverse weather conditions. Another purpose of mangroves is to provide as a food source for various marine species. Mangroves consist of many principal components, including roots, stems, leaves, and blooms. For optimal growth, some portions of mangrove plants must be safeguarded from several diseases, whether induced by parasites/pests or by environmental conditions. The leaves of the mangrove are the most vulnerable to disease;yet, pests visible on the leaves are typically inadequately managed, resulting in the plant being immediately removed when all its leaves are infested. This study uses a Deep Convolutional neural network to recognize these pests effectively. The image processing methods used in this study include smoothing and sharpening. The study findings indicate that the used approach effectively identifies mangrove leaf diseases, with an accuracy of 99.2%. © 2024 IEEE.

关键词： Plant diseases

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Analysis of Effective E-Commerce Coordination Big Data processing Strategies under Infinite Deep neural network Topology

Analysis of Effective E-Commerce Coordination Big Data Proce...

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Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)

作者： Zhang, Ailing Yao, Fuxiao Shandong Vocat & Tech Univ Int Studies Rizhao 276800 Shandong Peoples R China

ISBN: (纸本)9781728176499

This paper researches and analyses the effective e-commerce coordination big data processing strategy through the infinite-depth neural network topology. This paper firstly proposes a neural network training model neural network-Storm (NN-S) based on Storm streaming distributed architecture, which decomposes the neural network training task into multiple computing units by data-parallel method, the parameters are updated synchronously after the training of a single batch of data is completed. In the Storm architecture, a Zookeeper network is used for multi-server distributed deployment. The training results show that the NN-S model can significantly improve the training speed of neural networks. At the same time, the NN-S architecture can quickly recover from node failures and network resource scheduling abnormalities with strong robustness. In this paper, we investigate the streaming-based distributed neural network training and design a Storm-based distributed neural network training model and optimized training algorithms, which are of reference significance for distributed neural network training.

关键词： infinite depth neural network topology effective e-conunerce coordination big data processing strategy analysis

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Accelerating Deep neural networks on Mobile Multicore NPUs 2023

Accelerating Deep Neural Networks on Mobile Multicore NPUs

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21st ACM/IEEE International Symposium on Code Generation and Optimization (CGO)

作者： Jung, Hanwoong Ji, Hexiang Pushchin, Alexey Ostapenko, Maxim Niu, Wenlong Palachev, Ilya Qu, Yutian Fedin, Pavel Gribov, Yuri Nam, Heewoo Lim, Dongguen Kim, Hyunjun Song, Joonho Lee, Seungwon Han, Hwansoo Samsung Adv Inst Technol Suwon South Korea Samsung R&D Inst Xian Peoples R China

ISBN: (纸本)9798400701016

neural processing units (NPUs) have become indispensable parts of mobile SoCs. Furthermore, integrating multiple NPU cores into a single chip becomes a promising solution for ever-increasing computing power demands in mobile devices. This paper addresses techniques to maximize the utilization of NPU cores and reduce the latency of on-device inference. Mobile NPUs typically have a small amount of local memory (or scratch pad memory, SPM) that provides space only enough for input/output tensors and weights of one layer operation in deep neural networks (DNNs). Even in multicore NPUs, such local memories are distributed across the cores. In such systems, executing network layer operations in parallel is the primary vehicle to achieve performance. By partitioning a layer of DNNs into multiple sub-layers, we can execute them in parallel on multicore NPUs. Within a core, we can also employ pipelined execution to reduce the execution time of a sub-layer. In this execution model, synchronizing parallel execution and loading/storing intermediate tensors in global memory are the main bottlenecks. To alleviate these problems, we propose novel optimization techniques which carefully consider partitioning direction, execution order, synchronization, and global memory access. Using six popular convolutional neural networks (CNNs), we evaluate our optimization techniques in a flagship mobile SoC with three cores. Compared to the highest-performing partitioning approach, our techniques improve performance by 23%, achieving a speedup of 2.1x over single-core systems.

关键词： neural networks Multicore neural processing unit Layer partitioning Scheduling optimization

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