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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37040 条记录，以下是4681-4690 订阅

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Identifying Ordinary Differential Equations for data-efficient Model-based Reinforcement Learning

Identifying Ordinary Differential Equations for Data-efficie...

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international Joint conference on Neural Networks (IJCNN)

作者： Tobias Nagel Marco F. Huber Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system’s governing equations, especially in complex environments that combine physical laws of different disciplines. In this paper, we present a new approach that allows identifying an ordinary differential equation by means of a physics-informed machine learning algorithm. Our method introduces a special neural network that allows exploiting prior human knowledge to a certain degree and extends it autonomously, so that the resulting differential equations describe the system as accurately as possible. We validate the method on a Duffing oscillator with simulation data and, additionally, on a cascaded tank example with real-world data. Subsequently, we use the developed algorithm in a model-based reinforcement learning framework by alternately identifying and controlling a system to a target state. We test the performance by swinging-up an inverted pendulum on a cart.

关键词： knowledge engineering Machine learning algorithms Neural networks Process control Reinforcement learning Ordinary differential equations Mathematical models

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Investigation of Microgels and Double Crosslinked Microgels Containing 2-Carboxyethyl Acrylate (CEA) 5th

Investigation of Microgels and Double Crosslinked Microgels ...

引用

5th international conference on Advances in manufacturing and Materials engineering, ICAMME 2022

作者： Zaki, Syazwani Mohd Musa, Sharan Department of Manufacturing and Materials Engineering Kuliyyah of Engineering International Islamic University Malaysia Gombak Kuala Lumpur53100 Malaysia School of Materials and Mineral Resources Engineering Universiti Sains Malaysia Nibong Tebal Pulau Pinang14300 Malaysia

ISBN: (纸本)9789811995088

In this work, a new pH-responsive microgel (MGs) containing 2-carboxyethyl acrylate (CEA) as a comonomer. MGs were synthesized by emulsion polymerization and denoted as CEAX, whereby X shows different CEA content in MGs. These MGs samples were denoted as CEA14, CEA21 and CEA28. The CEA MGs were vinyl-functionalized with GMA and used to prepare three DX MGs gels denoted as DX CEA21, DX CEA28 and a blended system that contained both CEA21 and CEA28 to observe the effect of a binary system. The latter system is denoted as DX BL-CEA. The z-average diameters (dz) obtained from DLS measurements for these three systems are 39 nm for CEA14, 84 nm for CEA21 and 255 nm for CEA28. The performance of the gels was further studied by rheology analysis and from rheology, G’ values decreased in the order: DX MAA (70.2 kPa) > DX BL-CEA (37.3 kPa) > DX CEA28 (34.5 kPa) > DX CEA21 (4.68 kPa). The MG size increased strongly with increased CEA content used to prepare the MGs. The modulus (E) value of the blend system was remarkably very close to the DX CEA28 gel indicating that CEA28 distributed more stress within these blend gels. The ductility of DX CEA gels was greater than the established DX MAA gels. The data in this study are the first example of DX MGs prepared using a carboxylic acid monomer other than MAA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Gels

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Challenges of Developing Digital Twin for Additive Layer manufacturing in the Aerospace Industry

Challenges of Developing Digital Twin for Additive Layer Man...

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2022 international conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Kozhay, Kaisar Turarbek, Shyngys Ali, Md. Hazrat Shehab, Essam School of Engineering and Digital Science Nazarbayev University Nur-Sultan Kazakhstan

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Additive layer manufacturing (ALM) and digital twin (DT) play a vital role in the fourth industrial revolution. Both additive manufacturing (AM) and digitalization have received attention from the research community as independent research areas. AM creates an opportunity for manufacturing intricate shapes, parts consolidation and optimization that is very beneficial for the aerospace industry;however, it has a significant drawback in the repeatability of the process due to many production variables. Process monitoring and supervision through DT could alleviate this problem. Furthermore, there is lack of knowledge about integrating these two technologies. Therefore, this paper aims to formulate the main challenges of developing a digital twin for ALM in the aerospace industry. The paper focuses on the data and physical layer challenges of DT technology. Also, it proposes the implementation process of database HDF5 format and Artificial Intelligence that are part of DT for ALM that can change the certification of aerospace industry products by qualifying the process of production. The paper employs the method of in-situ production process monitoring by comparing the correspondence of apparent and latent characteristics of printed parts with required standards. It would create an opportunity for safety improvement and broader application of ALM in the aerospace industry. © 2022 IEEE.

关键词： Aerospace industry

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Modeling of Metaheuristics with Deep Transfer Learning Based Visual data Mining for the Healthcare Sector 6

Modeling of Metaheuristics with Deep Transfer Learning Based...

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6th Iraqi international conference on engineering Technology and its Applications, IICETA 2023

作者： Abed, Z. Shnawa, Ammar H. Hadi, M.R. Ibrahim, Karrar Adnan, Myasar Mundher Hameed, Waleed Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq Altoosi University College Najaf Iraq Al-Zahraa University for Women Karbala Iraq The Islamic University Najaf Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350303339

data mining is a process that interacts with a large dataset to determine complex interesting patterns from unknown structured data. Visual data mining (VDM) is considered a combination of two disciplines: data mining and visualization to explore useful and implicit knowledge from a huge dataset. Also, it is strongly associated with multimedia systems, high-performance computing, computer graphics, human-computer interaction, and pattern recognition. Recently, VDM approaches find useful in the healthcare sector to aid the decision-making process. In addition, the design of VDM approaches for healthcare applications needs special consideration to make sure security of data. Artificial intelligence (AI) methods play a vital role in achieving scalability and accurate analysis from real-time environments. Therefore, this study develops a blockchain-assisted quantum bacterial colonial optimization with deep learning (BAQBCO-DL) algorithm for VDM in the healthcare environment. The proposed BAQBCO-DL model exploits BC technology for secured data communication in the healthcare sector. In addition, the BAQBCO-DL model designs a U-Net-based segmentation with an optimal densely connected network (DenseNet) model for feature extraction, and the hyperparameter tuning process is performed using the QBCO algorithm. Finally, a probabilistic neural network (PNN) classifier is utilized to define appropriate class labels. An extensive experimental analysis is conducted to report the superior performance of the BAQBCO-DL technique and the outcomes are measured under several aspects. The simulation outcomes underlined the supremacy of the BAQBCO-DL algorithm over recent methodologies. © 2023 IEEE.

关键词： data mining

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4th international conference on Trends in Cognitive Computation engineering, TCCE 2022

4th International Conference on Trends in Cognitive Computat...

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4th international conference on Trends in Cognitive Computation engineering, TCCE 2022

ISBN: (纸本)9789811994821

The proceedings contain 43 papers. The special focus in this conference is on Trends in Cognitive Computation engineering. The topics include: Descriptive Analysis for Electric Bus During Non-Operational Stage;the Effectiveness Level on the Electric Buses Operation: Case Study for Affordability and Accessibility;IoMT-based Android Application for Monitoring COVID-19 Patients Using Real-Time data;A Cost-Effective Unmanned Ground Vehicle (UGV) Using Swarm Robotics Technology for Surveillance and Future Combat;neural Network-Based Obstacle and Pothole Avoiding Robot;a Comparative Study of Psychiatric Characteristics Classification for Predicting Psychiatric Disorder;Material Named Entity Recognition (MNER) for knowledge-Driven Materials Using Deep Learning Approach;An Improved Optimization Algorithm-Based Prediction Approach for the Weekly Trend of COVID-19 Considering the Total Vaccination in Malaysia: A Novel Hybrid Machine Learning Approach;analyzing the Effectiveness of Several Machine Learning Methods for Heart Attack Prediction;Ensemble Machine Learning Technique for Identifying COVID-19 from CT Scan Images;solving the Royalty Payment Problem Through Shooting Method;ECG Signal Classification Using Transfer Learning and Convolutional Neural Networks;partitional Technique for Searching Initial Cluster Centers in K-means Algorithm;a Novel Ensemble Methodology to Validate Fuzzy Clusters of Big data;Model Analysis for Predicting Prostate Cancer Patient’s Survival: A SEER Case Study;quantum-Inspired Neural Network on Handwriting datasets;an Efficient and Secure data Deduplication Scheme for Cloud Assisted Storage systems with Access Control;priority-Based Intelligent Reflecting Surface for Uplink 6G Communication;identifying Duplicate Questions Leveraging Recurrent Neural Network;stegoPix2Pix: Image Steganography Method via Pix2Pix Networks;machine Learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features;low-Cost Energy Efficient Encryption Algorithm for P

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Semantic-guided Unknown-aware Rare Disease Diagnosis System

Semantic-guided Unknown-aware Rare Disease Diagnosis System

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Luo, Yiwen Yuan, Yixuan City University of Hong Kong Department of Electrical Engineering Hong Kong Chinese University of Hong Kong Department of Electronic Engineering Hong Kong

ISBN: (纸本)9798350337488

The significant challenge posed by rare disease diagnoses has recently motivated researchers to explore computer-aided solutions. While deep learning approaches have shown potential in developing automatic diagnosis systems, their effectiveness diminishes when addressing rare diseases with limited data. Moreover, existing diagnostic models typically fail to detect unknown diseases, making them inappropriate for real-world applications. In this paper, we address the above challenges by proposing a Semantic-guided unknown-aware Rare Disease Diagnosis (SRDD) model. SRDD aims to tackle the performance degradation of classification models in low-data regimes, as well as their inability to distinguish unknown diseases. Specifically, we propose a Semantic-guided Saliency Discovery (SSD) module to explore semantic saliency information within images by aligning image regions with the semantic knowledge embedded within category labels. The image content is subsequently decomposed into semantically related information (SRI) and image instance template (IIT). Then, we design a Reciprocal samples Synthetic Strategy (RSS) to create known and unknown reciprocal points using SRI and IIT. This facilitates a compact feature space for known classes while preserving space for unknown data, promoting accurate known disease diagnosis and unknown disease detection. We validate SRDD on the public skin disease dataset SD-260. SRDD achieves state-of-the-art performance in both known disease classification and unknown disease identification. © 2023 IEEE.

关键词： Rare disease diagnosis Semantic-guided model Unknown disease detection

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Electric Power Gateway Metering Monitoring and Analysis Platform Based on Random Forest Algorithm

Electric Power Gateway Metering Monitoring and Analysis Plat...

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2022 IEEE international conference on knowledge engineering and Communication systems, ICKES 2022

作者： Li, Zhongcheng Sun, Yining Jiao, Tong Sun, Yue Hou, Rui Su, Ying Samanvita, N. Marketing Service Center of State Grid Liaoning Electric Power Co. Ltd Liaoning Shenyang110000 China College of General Education Shenyang City University Liaoning Shenyang110112 China Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9781665456371

Under the concept of 'emerging Internet of Things', computing and intelligence have become the guideline for the development of smart grids. On the one hand, communication technology is used to realize large-scale communication between underlying electronic devices and cloud platforms;on the other hand, with the gradual development of artificial intelligence and data mining technology, the value of power grid data must be further indexed. The main purpose of this paper is to design and study the power gateway metering monitoring and analysis platform based on the random forest (RF) algorithm. This question is mainly aimed at the problem that most devices on the power user side cannot directly transmit data to the cloud platform, and designed and implemented a power gateway device system. In addition, in terms of power data analysis, relying on machine learning theory, through the research and analysis on the detection of abnormal user behavior, a multi-classifier combination-based abnormal power behavior detection model is proposed. In this paper, the function and performance of the researched and designed intelligent power distribution monitoring system are tested. The accuracy of its functionality and technical indicators is verified in the laboratory environment, and it is found that for a single classifier, the RF algorithm is more suitable for the detection of abnormal electrical behavior, which meets the design requirements. © 2022 IEEE.

关键词： data mining

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Analysis the Impact of Artificial Intelligence on College Student Employment Based on data Mining

Analysis the Impact of Artificial Intelligence on College St...

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international conference on Fuzzy systems and knowledge Discovery (FSKD)

作者： Zhuowen Ma Jiazhao Ma Shaohong Zhang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou Guangdong China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

With the continuous development of artificial intelligence(AI), its influence is becoming more and more extensive. The development of AI and the employment of college students are both hot topics in today's society. In order to analyze the situation of college students using AI technology and the impact of AI on the employment of college students. We crawled 2398 job recruitment information from the website and conducted a questionnaire survey of college students. Identifying the relatively popular professional fields under the context of AI, and binary logistic regression was used to model the factors affecting students' use of artificial intelligence technology. The research results provide a good reference for students' career planning.

关键词： Surveys Logistic regression engineering profession Employment knowledge discovery Planning data mining Artificial intelligence Recruitment Fuzzy systems

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Revolutionizing Healthcare Intelligence Multisensory data Fusion with Cutting-Edge Machine Learning and Deep Learning for Patients’ Cognitive knowledge

Revolutionizing Healthcare Intelligence Multisensory Data Fu...

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knowledge engineering and Communication systems (ICKECS), international conference on

作者： Samatha R Swamy K S Nandini Prasad Dept. of ISE RV Institute of Technology & Management(RVITM) Bengaluru India Dept. of ISE Dayananda Sagar Academy of Technology & Management(DSATM) Bengaluru India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The study explores the recent advancements in healthcare that have witnessed an unprecedented integration of multisensory data, ranging from wearable devices to medical imaging and electronic health records. This paper explores the transformative impact of cutting-edge machine learning algorithms and deep learning algorithms in processing and interpreting this multisensory data, offering unparalleled insights into patients’ cognitive well-being. The review delves into the types and challenges of multisensory data, evaluating performance of supervised and supervised machine learning models for disease detection and pattern recognition respectively. Additionally, it scrutinizes the importance of deep learning applications that are based on convolutional neural networks as well as recurrent neural networks for medical image classification. The paper also mentions the importance of the integration of multisensor data fusion with these advanced algorithms, emphasizing real-time monitoring systems and ethical considerations. Summaries from research data shows effectiveness of remote patient care and predictive analysis and personalized treatment plans. The exploration of future directions and opportunities underscores the prime importance for further advancements in explainable AI, collaborative learning, and edge computing, consolidating the vision of a healthcare paradigm revolutionized by intelligent data fusion and cutting-edge technologies.

关键词： Deep learning knowledge engineering Ethics Machine learning algorithms data integration Medical services data models

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A Semantic Approach to Clinical Risk Detection: Early Warning systems with knowledge Graphs

A Semantic Approach to Clinical Risk Detection: Early Warnin...

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Cybernetics and Innovations (ICCI), international conference on

作者： Nutpraphut Prapatsirikul Siwasit Seangnikun Worayot Liamkeaw Jirawat Thawornphanthip Tharathon Utasri Akkharawoot Takhom Faculty of Engineering Thammasat School of Engineering Pathum Thani Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

Detecting early signs of clinical deterioration is vital for ensuring patient safety and enabling timely intervention. This paper introduces a hybrid Early Warning System (EWS) that integrates physiological monitoring with semantic symp-tom reasoning to support clinical decision-making. The system leverages automated machine learning to identify nonlinear patterns in historical vital sign data, while a curated knowledge base links patient-reported symptoms to standardized clinical concepts. A conversational large language model guides user interaction, asking context-aware questions to clarify ambiguous input and improve data completeness. Once both physiological and symptom-based inputs are harmonized, the system generates an interpretable EWS score and suggests potential diagnoses or symptom clusters. This approach enhances predictive accuracy while maintaining transparency and promoting trust among clin-icians. By combining data-driven modeling with semantic reasoning and interactive AI, the proposed framework demonstrates a promising direction for human-centered, next-generation clinical decision support systems.

关键词： Technological innovation Large language models Semantics knowledge graphs Alarm systems Automated machine learning Cognition Safety Biomedical monitoring Next generation networking

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