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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

共 38182 条记录，以下是951-960 订阅

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Design of a Seedling Planting Robot Using a CNC Controller

Design of a Seedling Planting Robot Using a CNC Controller

引用

2023 IEEE International conference on Agrosystem engineering, Technology and Applications, AGRETA 2023

作者： Rafiee, Pooriya Sorouri, Majid Islamic Azad University Science and Research Branch Department of Computer and Mechatronics Engineering Tehran Iran

ISBN: (纸本)9798350347333

Planting large numbers of seedlings in a short period improves the quality of life and meets basic human needs, especially in areas where forests have been damaged by fires or human activities. Planting seedlings has always been a very exhausting and time-consuming process that requires a significant amount of human effort. This article discusses the steps of designing and building an autonomous seedling planting robot (known as Nahal) that performs pit digging, precise planting, and irrigating are discussed. In addition, the idea of using a closed-loop control system of CNC machines to control the seedling robot's operation is proposed. In this approach, the robot controller initially receives the CAD file of the land where the seedlings are to be planted along with the details of the land and the points marked for planting the seedlings and, finally, the CNC control software is used for the robot to navigate the robot through the entire planting stages. © 2023 IEEE.

关键词： Machine design

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Optimizing Uncertainty Estimation on scientific Visualizations Using Learning Models

Optimizing Uncertainty Estimation on Scientific Visualizatio...

引用

2024 Workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Almeida, Clara J. Guerrero-Pantoja, David Pautsch, Erik Rizzi, Silvio Thiruvathukal, George K. Pantoja, Maria University of California Santa Cruz Robotics and Electrical Engineering Santa Cruz United States University of California Santa Cruz Computer Science and Game Design Santa Cruz United States Loyola University Chicago Computer Science Chicago United States Argonne National Laboratory Argonne Leadership Computing Facility Lemont United States Computer Science and Software Engineering California Polytechnics State University San Luis Obispo United States

ISBN: (纸本)9798350355543

scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can lead to misclassifications where the model still produces high confidence scores, reflected in elevated softmax output values, even though its prediction is incorrect. Ideally, in such cases, the model should indicate uncertainty, effectively saying, "I do not know."In this work, we enhance the trustworthiness of DL models by quantifying their uncertainty and enabling selective classification, where the model abstains from making predictions when uncertainty is high. Instead of providing a single prediction, our model outputs a prediction distribution, allowing users to determine whether human intervention or other actions are necessary for handling high-uncertainty outputs. This paper evaluates uncertainty quantification across three different tasks: predicting airfoil pressure and velocity using a Reynolds-Averaged Navier-Stokes (RANS) model on a custom airfoil dataset, image classification using the ImageNet1K dataset, and digit recognition using the MNIST handwritten digits dataset. © 2024 IEEE.

关键词： deep learning ensemble learning evidential learning scientific visualization uncertainty

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Research on bearing-only target tracking of UUV under underwater strong noise disturbance 20

Research on bearing-only target tracking of UUV under underw...

引用

20th IEEE International conference on mechatronics and Automation, ICMA 2023

作者： You, Aodi Wang, Hongjian Liu, Hongzhi Zhangsun, Mengwei Zhang, Yanbin Jin, Ridong Department of Harbin Engineering University NanTong Street HeiLongJiang Harbin China

ISBN: (纸本)9798350320831

Aiming at the problem of bearing-only target tracking (BOTT) for UUVs in complex and dynamic ocean environments, the system is susceptible to interference from strong noise. This interference can lead to large noise covariance, poor tracking accuracy, and even filter divergence. An algorithm based on robust adaptive cubature Kalman filter (RACKF) is proposed. The algorithm consists of a noise statistics estimator (NSE) and a cubature Kalman filter (CKF). To ensure the robustness of the NSE, the paper builds a fault-tolerant NSE consisting of an unbiased NSE and a biased NSE. The simulation results show that the algorithm improves the filtering accuracy and robustness under the condition of underwater strong noise disturbance, which proves the effectiveness of the algorithm proposed in this paper. © 2023 IEEE.

关键词： Kalman filters

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A Future Approach For Energy Harvesting In Trains Using Piezoelectricity

A Future Approach For Energy Harvesting In Trains Using Piez...

引用

2023 International conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Majeed, Salih Rashid Al-Thaedan, Abbas Shakir, Zaenab Shafy, Amir A. Omran Alsabah, Ruaa Al-Sabbagh, Ali Canadian University Dubai College of Engineering Mechatronics Dubai United Arab Emirates Al-Muthanna University Samawah Iraq Al-Taff University College Computer Engineering Technical Kerbala Iraq University of Kerbala College of Computer Science and Information Technology Kerbala Iraq Al-Taff University College Ministry of Communication Itpc Kerbala Iraq

ISBN: (纸本)9798350322309

In the last decades increased the need for renewable energy sources to use in different life activities such as airspace filed, transportation filed, and many other fields. This research displays a new vision for railway trains;this would be accomplished by using piezoelectric material properties. These materials would have the ability to generate electricity after applying mechanical kinetic stress which would be useful for supplying the train with the required energy. Also, this work presents the circuit diagram for the proposed approach in detail. This method has a potential utility from human footsteps to generate optimal electrical energy. © 2023 IEEE.

关键词： Piezoelectricity

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Research on the Application of Intelligent Analysis System in the Promotion of Evaluation Indicators for the Development of New Rural Cultural Industries - Analysis of the Promotion Path and Key Elements Integrating TPB, TAM and DOI 24

Research on the Application of Intelligent Analysis System i...

引用

8th International conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Li, Jiayao Hu, Yubo Ji, Haokang Zhang, Yuchen College of Art and Communication China Jiliang University Zhejiang Hangzhou China Hubei University of Science and Technology Hubei Xianning China FutureFront Interdisciplinary Research Institute Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798400710094

This article examines the application of intelligent analysis systems in the promotion of evaluation indicators for the development of the cultural industry in new rural areas. As China places emphasis on rural revitalization and the integration of cultural industries, the lack of scientific and quantitative evaluation standards has become a challenge to the development of the cultural industry in new rural areas. This study employs the Analytic Hierarchy Process (AHP) to establish a set of evaluation indicators and constructs a hybrid model combining the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and Diffusion of Innovations (DOI) to analyze the acceptance, adaptability, and effectiveness of these indicators in the promotion process. Through a pilot study in Wenzhou City, Zhejiang Province, this research utilizes an intelligent analysis system to collect and analyze data, aiming to provide strategic recommendations to enhance the acceptance and usage rate of evaluation indicators for the development of the cultural industry in new rural areas. © 2024 Copyright held by the owner/author(s).

关键词： Evaluation Indicators

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Exploring Chakra Balancing Practices in Software Applications

Exploring Chakra Balancing Practices in Software Application...

引用

2024 International conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Rajput, Gajendra Singh Khan, Mubeen Ahmad Jain, Tanmay Khandelwal, Tanmay Sharma, Ujjwal Medi-caps University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

The concept of chakra originates from Indian spiritual traditions, particularly within the framework of yoga and Ayurveda. In recent years, therehas been a growing interest in understanding chakra from a scientific perspective. The value of awakening Chakras and Kundalini is that as the inner and outer world connect, becoming aligned- spiritually, mentally, emotionally, and physically. The exploration of chakras within Western esotericism brought new dimensions to the ancient concept, blending it with mystical traditionsand occult practices. Throughout the 20th century, the New Age movement emerged as a cultural phenomenon, characterized by a renewed interest in spirituality, alternative healing modalities, and personal growth. Influential figures like Carl Jung, the renowned psychologist, explored the archetypal symbolism of chakras within analytical psychology, viewing them as representations of psychic energy centers associated with psychological development and individuation. This psychological interpretation of chakras resonated with many in the West, offering a bridge between Eastern metaphysics and Western psychology. In addition to their spiritual and psychological dimensions, chakras also found expression in the realm of holistic health and wellness. Alternative healing modalities such as acupuncture, acupressure, and Reiki draw upon the conceptof chakras and energy meridians to promote physical, emotional, and spiritual well-being. Practitioners of these modalities believe that blockages or imbalances in the chakras can manifest as physical ailments or emotional disturbances, and by clearing these blockages, one can restore harmony and vitality to the body- mind-spirit complex. In recent years, scientific inquiry has intersected with the study of chakras, albeit with varying degrees of acceptance and skepticism within the academic and scientific communities. Some researchers have explored potential correlations between the chakra system and the body's neur

关键词： Economic and social effects

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Graph Isomorphic Network with Denoising Attention Mechanism for Session-Based Recommendation 4

Graph Isomorphic Network with Denoising Attention Mechanism ...

引用

4th International conference on computer science and Blockchain, CCSB 2024

作者： Lu, Dingkun Lu, Xiaoang Wang, Zhixin Huang, Jinyu Sichuan University of Science & Engineering Yibin China

ISBN: (纸本)9798350356502

The session-based recommendation system aims to predict items that users may interact with in the future by analyzing historical behaviors in anonymous sessions. This field has long faced two major challenges: first, the integration of non-adjacent item relationships is insufficient, which leads to the neglect of higher-order relationship information of items not directly connected;second, the uncertainty in user behavior introduces noise, negatively affecting prediction accuracy. These two issues collectively hinder the recommendation system's ability to accurately capture users' true intentions. To address these problems, this paper proposes a new model named IGANRec. IGANRec learns two-level item embeddings through session graphs and relational graphs, utilizing the powerful aggregation capabilities of Graph Isomorphism Networks (GIN) for initial encoding. Furthermore, by introducing multiple information highways, the model can select effective information before and after GIN and uses a self-attention mechanism to generate target representations. Then, the model performs denoising on the global representation. In the relational graph, a target-aware global attention mechanism is used to generate the denoised relational representation. By integrating information from both graphs and considering the similarity of sessions from the perspective of user intent through the Intent Neighborhood Collaboration Module, the session's intent representation is obtained. Experimental results on three benchmark datasets show that the proposed IGANRec exhibits the improvement of performance compared to existing state-of-the-art models. © 2024 IEEE.

关键词： Graph neural networks

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Development of A Novel Deep Neural Network Approach to Diabetic Retinopathy Diagnosis 1

Development of A Novel Deep Neural Network Approach to Diabe...

引用

1st International conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Gopu, Vijayalaxmi Selvi, M. Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376517

Diabetic retinopathy (DR) is a prevalent ocular condition and a major contributor to vision loss in those with diabetes. The most efficient method to control the condition is by regular screening using fundus photography and prompt action. The substantial number of individuals with diabetes and their extensive need for screening has sparked interest in a computer-assisted and entirely automated detection of diabetic retinopathy (DR). Deep neural networks have made significant advancements in numerous tasks in the past few decades. In order to streamline the process of diagnosing DR and provide suitable recommendations to DR patients, we have constructed a dataset consisting of DR fundus images that have been annotated with the corresponding treatment technique necessary. We utilized this dataset to build deep convolutional neural network models for the purpose of grading the seriousness of DR fundus pictures. In the studies, we attained an accuracy of 90% during a four-degree classification job. The models we developed were implemented on a server in the cloud and used to offer pilot DR screening services to many hospitals. During the scientific assessment, the system demonstrated a high level of accuracy, achieving an accuracy rate of 93% when compared to ophthalmologists. This indicates the usefulness of our research. © 2024 IEEE.

关键词： Convolutional neural networks

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Deploying ChatGPT for Automated Tagging of Greek Dialogue Data of University Students 20th

Deploying ChatGPT for Automated Tagging of Greek Dialogue Da...

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20th International conference on Intelligent Tutoring Systems (ITS)

作者： Gavros, Andreas Demetriadis, Stavros Tegos, Stergios Aristotle Univ Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783031630309;9783031630316

In this study, we propose a methodology of automated labelling for dialogues. We analyze dialogue data by deploying ChatGPT 3.5 model. Large language models (LLMs) have recently been in the focus of the scientific world and public since the release of ChatGPT. The approaches we used in this study were zero-shot, one-shot and few-shot learning. The model was asked to assign a label to each turn of dialogue using a tweaked version of the Issue-Based Information System (IBIS), and its labels were compared to those assigned by human raters, to evaluate its performance. Several different versions of prompts were used to investigate their effect on the model's performance, and it was also investigated whether the number of different labels affected the model's predictions. The results provide evidence that LLMs are not still able to fully process highly contextualized human dialogues but can provideworthwhile results if the assigned task is simplified. Also, we discuss on the reliability of labels created by a model, compared to labels created by a human annotator.

关键词： Automated Tagging Large Language Models Prompt engineering

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Attention with Mixture Experts model for Multivariate Time Series Imputation 4

Attention with Mixture Experts model for Multivariate Time S...

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4th International conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Zhang, Di Yin, Huanpu Liu, Ruijun Li, Haisheng School of Computer and Artificial Intelligence Beijing Technology and Business University China Beihang University School of Software China

ISBN: (纸本)9798331534622

Multivariate time series data often suffer from missing data problems due to sensor failures and environmental impacts, which destroy the integrity of the time series and seriously affect the interpretability of the data. Insufficient long-term dependency capture and difficulty in capturing complex non-linear relationships between multiple features in existing imputation models. To address these issues, we propose an attention with mixture experts model (AME) for multivariate time series imputation. The model consists of two core modules: the diagonal attention based on time bias (DATB) module enhances temporal modeling capability by introducing time bias to capture temporal dependencies;and the mixture of experts (MoE) module selects appropriate expert combinations for different features through routing mechanisms to capture the complex non-linear relationships between features better. We conducted extensive experiments on four real-world datasets, and the results showed that the AME model significantly outperformed the existing five methods in imputation accuracy, validating the effectiveness of the proposed method. © 2024 IEEE.

关键词： Spatio-temporal data

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