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Artificial Intelligence techniques for space experiments 33

Artificial Intelligence techniques for space experiments

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33rd Euromicro International conference on Parallel, distributed, and network-Based processing, PDP 2025

作者： Cuna, Federica Bossa, Maria Gargano, Fabio Mazziotta, Nicola Mario Betti, Pietro Istituto Nazionale di Fisica Nucleare ICSC Bari Italy Istituto Nazionale di Fisica Nucleare ICSC Napoli Italy Istituto Nazionale di Fisica Nucleare Firenze Italy

ISBN: (纸本)9798331524937

The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a groundbreaking advancement in data analysis and experimental design. As space missions become increasingly complex, integrating AI technologies is essential for optimizing their performance and enhancing their scientific outcomes. This study focuses on the use of Graph neural networks (GNNs) for tracking systems and Transformers for calorimeters in space-based experiments. A significant challenge for trackers in space experiments is the high-noise environment, characterized mainly by backscattering tracks. We will present a novel approach that employs GNNs for node-level tasks, specifically aimed at classifying backscattering hits versus signal hits and reconstructing tracks. The goal for the space calorimeter experiment is to distinguish between particle types, such as electrons and protons. By capturing the dependencies within these features, Transformers can achieve robust classifications, even when the data spans thousands of channels or dimensions. By addressing these challenges, we aim to enhance the accuracy and reliability of data interpretation in astroparticle physics through the application of advanced Artificial Intelligence techniques. © 2025 IEEE.

关键词： Graph neural networks

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distributed Denial of Service Attack Detection in IoT Utilizing Attention Mechanism Based Long Short-Term Memory

Distributed Denial of Service Attack Detection in IoT Utiliz...

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International conference on 6G Communications networking and Signal processing, SGCNSP 2023

作者： Nijaguna, G.S. Ramadan, Ghazi Mohamad Prabu, S. Pranavakumar, R. Ramachandra, A.C. Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India College of MLT Ahl Al Bayt University Karbala Iraq Department of ECE Mahendra Institute of Technology Namakkal India Bangalore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Internet of Things (IoT) is a highly impactful approach which has become ubiquitous in our daily lives, particularly when it comes to safeguarding user data and personal information. Protecting the IoT infrastructure with a traditional distributed Denial of Service (DDoS) is a highly challenging task due to the vast variety and number of IoT devices. This research proposes the Attention-based Long Short-Term Memory (A-LSTM) for DDoS attack detection in IoT system. The proposed A-LSTM method utilized the two IoT datasets named Bot-IoT and UNSWNB15 for estimate the performance. In this research, a pre-processing step is performed for handling the missing values and normalization in the collected dataset. Then, pre-processed data is selected by using Particle Swarm Optimization (PSO) approach. The A-LSTM is utilized to classify DDoS attack into malicious or normal. The proposed A-LSTM approach accomplishes superior results like accuracy of 99.72 and 97.91% in both Bot-IoT and UNSWNB15 dataset respectively when compared to the previous approaches named Deep neural network (DNN), Feedforward neural network (FNN) and LSTM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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Internet of Things Enabled Smart Energy Management of distributed Energy Resources using Self-Adaptive Physics-Informed neural network Optimized with Green Anaconda Optimization 4

Internet of Things Enabled Smart Energy Management of Distri...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ghongade, Rahul D. Arunaa, J. Sri Latha Mageshwari, P.S. Vinoth, K. Shanmugathai, M. Mercy Rani, J. Femila P. R. Pote Patil College of Engineering and Management Department of Electronics and Telecommunication Engineering Maharashtra Amravati444602 India Dr. M. G. R Educational and Research Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai600095 India Tamil Nadu Kavaraipettai601206 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India Sri Sairam Engineering College Department of English Tamil Nadu Chennai600044 India Department of Mathematics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798331523923

Internet of Things (IoT)-enabled Smart Energy Management (SEM) in distributed Energy Resources (DERs), while crucial for optimizing energy distribution and resource management faces challenges such as data inconsistencies and the variability of renewable energy generation. These issues result in inaccurate demand forecasts, leading to suboptimal allocation of resources and inefficient Energy Management (EM). Additionally, as energy demand and generation patterns are influenced by factors like weather conditions, time of day, and energy consumption behaviors, prediction models may struggle to capture these dynamic changes, affecting the reliability of forecasts. To address these limitations, this manuscript proposes a novel approach for energy demand prediction. Data is collected from IoT-enabled sensors monitoring DERs. The data undergoes pre-processing, where the Fast Resampled Iterative Filtering (FRIF) method is used to eliminate missing values and normalize the inputs. The Self-Adaptive Physics-Informed neural network (SAPINN) model then utilizes the processed data to forecast energy demand, renewable energy generation, and storage levels. Green Anaconda Optimization (GAO) is applied to optimize the weight parameters of the SAPINN model. The proposed SAPINN-GAO method is implemented using the MATLAB platform and compared with existing models, such Stacked Convoluted Bi-Directional Gated Attention network-Hybrid Darts Seagull Optimizer (SConBGAN-HDSO), Recurrent neural network (RNN), and Support Vector Machine-Particle Swarm Optimization (SVM-PSO). The SAPINN-GAO method achieves an accuracy of 99.2%, precision of 99.2%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in energy demand prediction. The SAPINN-GAO method's higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for energy demand, renewable energy generation, and storage levels forecasting in IoT-enabled SEM syst

关键词： Recurrent neural networks

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Collaborative Inference Acceleration with Non-Penetrative Tensor Partitioning

Collaborative Inference Acceleration with Non-Penetrative Te...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liu, Zhibang Xu, Chaonong Lv, Zhenjie Liu, Zhizhuo Zhao, Suyu Beijing Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

ISBN: (纸本)9798350368741

The inference of large-sized images on Internet of Things (IoT) devices is commonly hindered by limited resources, while there are often stringent latency requirements for Deep neural network (DNN) inference. Currently, this problem is generally addressed by collaborative inference, where the large-sized image is partitioned into multiple tiles, and each tile is assigned to an IoT device for processing. However, since significant latency will be incurred due to the communication overhead caused by tile sharing, the existing collaborative inference strategy is inefficient for convolutional computation, which is indispensable for any DNN. To reduce it, we propose Non-Penetrative Tensor Partitioning (NPTP), a fine-grained tensor partitioning method that reduces the communication latency by minimizing the communication load of tiles shared, thereby reducing inference latency. We evaluate NPTP with four widely-adopted DNN models. Experimental results demonstrate that NPTP achieves a 1.44-1.68× inference speedup relative to CoEdge, a state-of-the-art (SOTA) collaborative inference algorithm. © 2025 IEEE.

关键词： collaborative inference distributed computing edge intelligence

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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Research on Abnormal Power Consumption Behavior Detection and Load Forecasting System of Power Big Data Based on FCM and Wavelet neural network 14th

Research on Abnormal Power Consumption Behavior Detection an...

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14th International conference on Frontier Computing, FC 2024

作者： Pan, Jingni Chai, Yufeng Lv, Jiayu Wang, Xiaotian State Grid Shizuishan Power Supply Co. Ningxia China State Grid Ningxia Power Supply Co. Ningxia China State Grid Wuzhong Power Supply Co. Ningxia China

ISBN: (纸本)9789819627974

With the progress of smart grid, power systems are faced with more and more complex data challenges, including large-scale data, multi-sample types and low resource integration. This research takes power big data as the object, carries out abnormal power consumption behavior detection and load prediction based on Hadoop platform, and successfully realizes the visualization processing of power data. We thoroughly analyzed the needs of power big data visualization and designed the overall Hadoop-based architecture. In terms of key technologies, we adopt the visualization method based on geographic information, and use MapReduce to carry out parallel computation, and effectively identify and display different electrical behaviors. In addition, we proposed a wavelet neural network power load forecasting method based on FCM, and personalized classification of power users through FCM clustering. Combined with the prediction ability of wavelet neural network, accurate power load prediction was successfully realized, and parallel calculation was carried out on the distributed platform to further improve the prediction accuracy. Finally, we built a Hadoop big data experiment platform to verify the experimental results and performance of the proposed method. The experimental results show that our research has the ability to effectively cope with the big data challenges of power system, and provides important support and guidance for the safe and stable operation of power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abnormal Power Consumption Behavior Detection FCM Hadoop Platform Power Big Data Wavelet neural network

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Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on distributed Current Measurement and neural network Model 1st

Prediction and Evaluation Method of Modification Effect of L...

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1st Electrical Artificial Intelligence conference, EAIC 2024

作者： Yizhuo, Wang Zhonlian, Li Long, Li Runhua, Li Xinglei, Cui Zhi, Fang School of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9789819648559

Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD excitation voltage amplitude, repetition frequency, discharge working gas flow rate and reaction medium flow rate were changed. The discharge current at different positions was measured by self-made current coil. The water contact angle and flashover voltage at the corresponding position were tested experimentally as the evaluation criteria of the modification effect. The feature extraction of distributed current is carried out by manual and image recognition methods, and the prediction and evaluation models are established by BP neural network and convolutional neural network (CNN) respectively. The accuracy and generalization ability of the two models are compared. The results show that the CNN model based on image recognition has higher accuracy and generalisation ability in predicting the water contact angle and flashover voltage on the material surface compared to the BP neural network model based on manual feature extraction. Compared with the BP neural network, the CNN model reduces the mean absolute error (MAE) by 41.3% and the root mean square error (RMSE) by 36.1% in predicting the water contact angle of the material surface, and the mean absolute error (MAE) reduces by 47.7% and the RMSE reduces by 40.2% in the flashover voltage. The experimental results with different processing distances are used to examine the generalisation ability of the two models, and the results show that

关键词： Electric insulating materials

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Benchmarking Quantum Convolutional neural networks for Signal Classification in Simulated Gamma-Ray Burst Detection 33

Benchmarking Quantum Convolutional Neural Networks for Signa...

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33rd Euromicro International conference on Parallel, distributed, and network-Based processing, PDP 2025

作者： Farsian, Farida Parmiggiani, Nicolo Rizzo, Alessandro Panebianco, Gabriele Bulgarelli, Andrea Schilliro, Francesco Burigana, Carlo Cardone, Vincenzo Cappelli, Luca Meneghetti, Massimo Murante, Giuseppe Sarracino, Giuseppe Scaramella, Roberto Testa, Vincenzo Trombetti, Tiziana OACT INAF Via S. Sofia 78 Catania Italy OAS INAF Via Pietro Gobetti 93/3 Bologna Italy IRA INAF Via Pietro Gobetti 101 Bologna Italy OAR INAF Via Frascati 33 Monteporzio Catone Italy OATs INAF Via Tiepolo 11 Trieste Italy OACN INAF Via Moiariello 16 Napoli Italy

ISBN: (纸本)9798331524937

This study evaluates the use of Quantum Convolutional neural networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional neural networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis. © 2025 IEEE.

关键词： network coding

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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STB-GraCapsNet: A Novel Capsule network Structure with Swin Transformer Block 25th

STB-GraCapsNet: A Novel Capsule Network Structure with Swin...

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25th International conference on Parallel and distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Chunying Dong, Ziao Wang, Liya Liu, Lu Ren, Jing Ma, Jiang Liu, Bin College of Science North China University of Science and Technology 21 Bohai Road Caofeidian Xincheng Hebei Tangshan063210 China Big data and Social Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang0500198 China

ISBN: (纸本)9789819642069

Capsule network is a new type of neural network encoding features into capsules and constructing the part-whole relationships, which demonstrated good performance in image classification. However, it has some issues such as weak feature extraction ability and large number of training parameters, leading to performance decline when processing images with complex background. To address these issues, this article proposes a novel capsule network structure called STB-GraCapsNet, with key improvements as follows: 1) It uses Swin Transformer Block with the shift window attention mechanism for data preprocessing to obtain richer features;2) the features are then processed through Linear Embedding and passed into the Primary Capsule Layer to generate high-dimension feature capsules, which reduces primary capsules number and improves feature extraction ability of the network. Ablation and comparative experiments based on multiple datasets show that STB-GraCapsNet performs better on complex datasets: 77.8% and 36.2% reduction in parameters number of capsule structure, and 12.07% and 5.22% improvement in model accuracy, as compared to CapsNet and GraCapsNet, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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