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A Retrieval-Augmented Generation-Based Method for Aviation Accident data Analysis 4

A Retrieval-Augmented Generation-Based Method for Aviation A...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Yang, Jianzhong Xiang, Xinyu Chen, Xiyuan Civil Aviation University of China College of Safety Science and Engineering Tianjin China

ISBN: (纸本)9798331531225

In response to the growing demand for more intelligent and efficient methods of analyzing aviation accidents, this paper explores an AI-based approach to data mining and utilization in aviation accident investigation reports. Based on the framework of Retrieval-Augmented Generation (RAG), we constructed a benchmark dataset of aviation accident reports, we also built an external knowledge base from the Air Transport Association Specification 100 (ATA100) and enhanced the content with components in Aircraft Manual Maintenance (AMM). Experiments of sensitive factors were conducted to find out the most influential factors of RAG. the results show that the LLM, the retrieved number of external knowledge segments (top-k), and especially the embedding model have the greatest influence on RAG. the optimal factors reached scores of 0.75 in recall coverage rate, 0.74 in two-digit codes accuracy and 0.53 in four-digit codes accuracy. Based on the optimal sensitive factors, we conducted experiment of external knowledge enhancement. the results indicate that a more comprehensive external knowledge base can benefit both LLM and embedding model as recall coverage rate improved 8%, two-digit codes accuracy improved 11%, and four-digit codes improved 16%. the outcome of our work indicates that the RAG-base analysis method can significantly enhance the efficiency and accuracy of aviation knowledge extraction task while being highly automated and self-iterative, demonstrating its potential for future applications in the aviation industry. © 2024 IEEE.

关键词： Embeddings

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Vision Transformer-based Deep learning Model for Leather Species Identification 11

Vision Transformer-based Deep Learning Model for Leather Spe...

引用

11th international conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Rajan, S. Sundar Jawahar, Malathy Prince, A. Amalin BITS Pilani K K Birla Goa Campus Sancoale403726 India Central Leather Research Institute Chennai600020 India

ISBN: (纸本)9798331518127

Identifying the species of animal leather becomes a prominent factor in assessing the quality of the leather goods and also in defending the endangered species. Unlike convolutional neural network (CNN)-based deep learning (DL) architectures, the present work employs vision transformer (ViT)based modelling for leather species identification with a diverse set of images from different regions of the leather. It uses a self-attention mechanism to relate the whole image region enabling the model to analyse the complex pattern efficaciously. An input leather image is divided into disjoint patches and each patch is transformed into vectors. Positional encodings are added to patch embeddings to furnish the spatial hierarchies of each patch inside the image. the resulting vector series are passed through a transformer encoder which aids in capturing the global dependencies. Finally, the species prediction is done by passing the output of a transformer encoder to a multilayer perceptron head. the proposed model achieved a prediction accuracy of 96.3%. Moreover, the experimental analysis compares and proves the superiority of the ViT-based approach over the state-of-the-art CNNs. © 2024 IEEE.

关键词： Leather

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Land Cover Extraction from Satellite Imagery using U-Net Model

Land Cover Extraction from Satellite Imagery using U-Net Mod...

引用

international conference on Computing for Sustainable Global Development (INDIACom)

作者： K. Pushpa Rani Pogula Sreedevi C. Dastagiriah K. Srinivas Rao G. V. Subbaraju Jasti Padmavathi Dept. of Computer Science and Engineering MLR Institute of Technology Hyderabad India Dept. of Computer Science and Engineering G. Pulla Reddy Engineering College Kurnool India Dept. of Computer Science and Engineering Anurag University Hyderabad India Dept. of Computer Science and Engineering SCETW Hyderabad India Malla Reddy Institute of Technology and Science (Autonomous) Hyderabad India

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. the trained model achieved an F1 score of 92% and an impressive precision score of 95%. this automated method can be scaled to large sets of data, reducing the need for human labeling.

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An Ant Colony System Algorithm for Multiple Traveling Salesman Problem with Hub Cities 11

An Ant Colony System Algorithm for Multiple Traveling Salesm...

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11th international conference on Machine Intelligence theory and Applications, MiTA 2024

作者： Chen, Ying-Xin Tang, Fei-Yi South China Normal University School of Computer Science Guangzhou China Guangzhou Panyu Polytechnic School of Information Engineering Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9798350385670

Logistics serves as the bridge for commodities to flow from production sites to consumption sites, which is important to industrial development and economic growth. Efficient organization of logistics scheduling can reduce the costs of production and sales of commodities. Logistics scheduling can be modeled as a multiple traveling salesman problem (MTSP). Traditional MTSPs do not take the different business requirements of cities into account, resulting in potentially large disparities in the amount of business for each traveling salesman. In this paper, we focus on the scenario where some cities have high requirements for commodities and thus they should be visited by multiple traveling salesmen. Such cities are termed hub cities. the objective of the scenario is to minimize the sum of the path lengths of multiple traveling salesmen and to make the difference between the path lengths of the multiple traveling salesmen as small as possible. To address the above problem, we propose an ACS-MTSP algorithm. the ACS-MTSP selects the next city to be visited alternately by multiple traveling salesmen, and the construction of the path is completed when all cities have been visited. In order to minimize the difference between the path lengths of the multiple traveling salesmen, the current path lengths of the multiple traveling salesmen are taken into account when selecting the next city to visit. Experimental results for different MTSP instances with different numbers of cities and hub cities show that the ACS-MTSP is effective in reducing traveling salesmen costs. © 2024 IEEE.

关键词： Traveling salesman problem

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Globular Cluster Detection in M33 Using Multiple Views Representation learning 24th

Globular Cluster Detection in M33 Using Multiple Views Repr...

引用

24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Singlor, Taned thawatdamrongkit, Phonphrm Techa-Angkoon, Prapaporn Suwannajak, Chutipong Bootkrajang, Jakramate Department of Computer Science Faculty of Science Chiang Mai University Chiang Mai Thailand Chiang Mai Thailand

ISBN: (纸本)9783031482311

Globular clusters (GC) are crucial for understanding galaxy formation and evolution. However, identifying them in large imagery datasets is a time-consuming task. this prompts the development of an automated GC detection algorithm. Although GC detection is fundamentally an object detection problem, the state-of-the-art object detection algorithms are unable to produce accurate results. Motivated by how GCs are identified by astronomers, we propose a deep neural network that fuses multiple views of raw imaging data and learns a better representation of the input image. the proposed network is then combined with YOLO object detection algorithm resulting in YOLO for Globular Cluster detection (YOLO-GC) model. Experimental results based on a real catalog of GCs in the M33 Galaxy showed that the proposed multi-view representation learning technique helps improve detection performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Object detection

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Predicting Stock Market using CNN and BiLSTM Model 11

Predicting Stock Market using CNN and BiLSTM Model

引用

11th international conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Pawitra, Mohammad Tyas Fakhrurroja, Hanif Abdurrahman, Lukman School of Industrial Engineering Telkom University Bandung Indonesia School of Industrial Engineering Telkom University National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

this study explores the efficacy of two deep learning models, Convolutional Neural Network (CNN) is a deep learning model primarily designed for recognizing patterns in data through convolutional layers. and Bidirectional Long Short-Term Memory (BiLSTM) is an advanced variant of the Long Short-Term Memory (LSTM) network, designed to handle sequential data in predicting stock prices. Utilizing historical stock price data from NVIDIA spanning January 1, 2013, to December 31, 2023, the dataset was normalized and structured into sequences with a time step of 10 days. After that, the data was divided using an 80/20 split between the training set and the testing set. the CNN model architecture included multiple convolutional layers with batch normalization and dropout to prevent overfitting, followed by dense layers. Conversely, the BiLSTM model comprised a bidirectional LSTM layer with dense layers for output. Both models were use Mean Squared Error (MSE) as the loss function and trained using the Adam optimizer 0.001 learning rate. Model performance was evaluated using evaluation metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). the BiLSTM model significantly outperformed the CNN model, achieving lower MAE (1.675), RMSE (2.237), and MSE (5.008) values, and a higher R2 value (0.963), indicating superior predictive accuracy. these findings underscore the BiLSTM model's enhanced capability in capturing temporal dependencies and intricate patterns within stock price data, suggesting its greater effectiveness for time series forecasting tasks. For the purpose of improving the result of stock price predictions, studies in the future should take into consideration hybrid models that combine the advantages of CNN and BiLSTM. © 2024 IEEE.

关键词： Long short-term memory

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Research on CNN Based Ultrasonic Guided Wave Multi-bolt Connection Looseness Detection 11

Research on CNN Based Ultrasonic Guided Wave Multi-bolt Conn...

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2023 IEEE 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Tian, Zhenxiong Jiang, Liangliang Xing, Sisi Du, Fei Xu, Chao Northwestern Polytechnical University College of Astronautics Shaanxi Xi'an China Beijing Institute of Astronautical Systems Engineering Beijing China Xian Aerospace Propulsion Institute Shaanxi Xi'an China

ISBN: (纸本)9798350314014

Ultrasonic guided wave is one of the most potential structural health monitoring technologies. In recent years, artificial intelligence technologies such as deep learning have flourished, and it is possible to establish more effective structural damage detection techniques using deep learning combined with guided wave damage detection principles. However, for complex multi bolt connection structures, when bolts become loose, the guided wave signal changes more complex, often requiring multiple sets of sensors to detect simultaneously. the traditional machine learning methods have limited feature extraction capabilities, and the determination of the bolt loose position of the bolt group is limited. Based on this research background, this paper focuses on the construction and verification of guided wave damage detection methods for structures based on deep learning. Taking bolt looseness detection as the research object, a detailed study has been conducted. Two multi-sensor information processing methods based on convolutional neural networks have been proposed: multi-channel input convolution method and high-level feature fusion model method. Taking a 14-bolt-connection component as the research object, using Hankel matrix to convert one-dimensional data to two-dimensional matrix, the effectiveness of the two methods was verified, and their performance was compared. © 2023 IEEE.

关键词： Structural health monitoring

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Development of a deep learning system for preventive detection of heart failure due to pulmonary disease 11

Development of a deep learning system for preventive detecti...

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11th international conference on Software Process Improvement (CIMPS) - Applications in Software engineering

作者： Felix Jimenez, Axel Frederick Ibarra Belmonte, Isaul Sanchez Lee, Vania Stephany Jaramillo Avila, Uziel Parra Gonzalez, Ezra Federico Natl Polytech Inst Comp Syst Engn Zacatecas Zacatecas Mexico Ctr Res Math Zacatecas Zacatecas Mexico Ctr Res Math Dept Comp Sci Zacatecas Zacatecas Mexico

ISBN: (纸本)9798350398960

In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.

关键词： heart failure pulmonary conditions condition prediction preventive diagnosis classification algorithm

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Optimizing Intersection Signal Via Reinforcement learning for Cooperative Adaptive Cruise Control 8

Optimizing Intersection Signal Via Reinforcement Learning fo...

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8th IEEE international conference on intelligent Transportation engineering, ICITE 2023

作者： Yang, Shu Yuan, Rui Wang, Miao Sun, Liang Wu, Jingyuan China Communications Information Technology Group Co. Ltd. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport Beijing China

ISBN: (纸本)9798350314212

With the continuous advancement of urbanization, traffic congestion becomes a major challenge for social development. intelligent driving technology can ease the traffic pressure at intersections, which is the trend of future traffic development. Cooperative Adaptive Cruise Control (CACC) is a specific application of intelligent driving technology and a necessary step toward autonomous driving. this paper proposes a signal control method for CACC homogeneous traffic flow at single intersections based on deep reinforcement learning, which considers the operation characteristics of CACC vehicles at intersections and uses a grid-based state space input to improve the model prediction accuracy. On this basis, this paper proposes a variable phase duration strategy to enhance the phase control flexibility and a platoon clearance time to reduce the vehicle travel delay. Simulation experiments show that the signal control method based on deep reinforcement learning has a significant advantage over traditional signal control methods in the CACC homogeneous traffic flow environment and is more suitable for intersections with high traffic volume. the research results of this paper not only contribute to enriching the related research field but also offer a valuable reference for improving traffic safety and efficiency at intersections. © 2023 IEEE.

关键词： Adaptive cruise control

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CMIF: A Cross-Modal Information Fusion Approach for Molecular Property Prediction 11th

CMIF: A Cross-Modal Information Fusion Approach for Molecul...

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11th international Joint conference on Rough Sets, IJCRS 2025

作者： Zang, Maoyuan Gong, Xu Han, Rui Liu, Qun Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9783031927409

Molecular property prediction is vital in bioinformatics, as accurate and efficient predictions can greatly speed up drug discovery and lower R&D costs. In recent years, the development of deep learning has driven the innovation of data-driven molecular representation learning, which has derived sequence-based, graph-based, and geometry-based molecular representations. Many methods have been proposed to fuse molecular representations of different modalities to improve the performance of molecular property prediction. However, most recent studies have focused on one or two modes and combined them in a simple way, they also neglected the alignment of heterogeneous features. To address these challenges, we propose CMIF, a cross-modal information fusion framework for molecular property prediction, which integrates the three modal representations of sequence, graph, and geometry. the proposed CMIF encodes each modal feature separately through a specific neural network and innovatively employs a cross-attention mechanism for deep feature fusion to avoid the limitations of traditional linear operations. To further solve the problem of heterogeneous feature alignment, CMIF introduces a dual self-supervised strategy of contrast learning and cross-modal matching to strengthen the inter-modal semantic consistency. We evaluated the performance of CMIF on seven molecular datasets, and the results show that our method outperforms baseline models in most cases, validating the effectiveness of CMIF in comprehensively capturing molecular features. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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