This paper introduces a simulation-based approach to enhance quality defect detection in a prominent French textile company's manufacturing process. The production workflow is initially simulated and analyzed to i...
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This paper introduces a simulation-based approach to enhance quality defect detection in a prominent French textile company's manufacturing process. The production workflow is initially simulated and analyzed to identify key parameters influencing defect creation and detection. After assessing the existing controlprocess using performance metrics, a novel Inspection Quality Plan (IQP) is formulated. To validate the effectiveness of the proposed IQP, a simulation model is developed and calibrated to mirror real-world conditions. This model, initially validated against the current solution, acts as a benchmark for evaluating predefined scenarios and determining optimal configurations. The implemented scenario, tested in the workshop under authentic conditions, results in a remarkable 42% improvement in defect detection within the knitting process. The positive outcomes are meticulously examined and discussed. This study, employing a holistic approach that integrates simulation modeling, performance evaluation, and real-world deployment, establishes an optimized strategy for enhancing defect detection in the textile industry. It underscores the effectiveness of the proposed IQP, showcasing significant improvements observed in practical settings based on a simulation model.
Financial sentiment analysis, the task of discerning market sentiment from financial texts, plays a crucial role in investment decisions, risk assessment, and understanding economic trends. Traditional sentiment analy...
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ISBN:
(纸本)9798400709760
Financial sentiment analysis, the task of discerning market sentiment from financial texts, plays a crucial role in investment decisions, risk assessment, and understanding economic trends. Traditional sentiment analysis techniques have often faced limitations in handling the complexities and nuances of financial language. The advent of large language models (LLMs) has brought a paradigm shift in this field. With their remarkable ability to process and understand natural language, LLMs are enabling new approaches that increase the accuracy and sophistication of financial sentiment analysis. This paper provides a comparative overview of cutting-edge LLM-based techniques for financial sentiment analysis. We introduce a six-pronged classification framework covering data types, sentiment granularity, model architectures, training approaches, methodological focus, and evaluation metrics. This framework aims to provide a structured perspective for understanding recent research trends. Our analysis reveals several key developments in the field. We discuss the challenges and opportunities associated with advanced techniques, like Instruction-tuning approaches and Retrieval-augmented methods. While LLMs offer clear advantages, ensuring data quality, mitigating bias, enhancing model explainability, and scaling these models to real-world applications remain active research areas. This review offers investors and financial researchers a comprehensive guide to the evolving landscape of financial sentiment analysis, facilitating well-informed choices for different use cases and laying the groundwork for future research.
This article describes the FModal tool that has been designed to bridge the gap between the structural dynamics and guidance and control domains to facilitate the development and use of high-fidelity flexible body dyn...
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ISBN:
(纸本)9781665490320
This article describes the FModal tool that has been designed to bridge the gap between the structural dynamics and guidance and control domains to facilitate the development and use of high-fidelity flexible body dynamics models. FModal streamlines the process of generating modal data-including residual vectors and modal integral terms-from component NASTRAN structural dynamics models. The data generation process can be tailored to meet simulation fidelity and performance needs. FModal's output is a portable HDF5 file with hierarchical, well-organized, and labeled data that can be used to automate, simplify, and speed up the creation of flexible multibody dynamics models-resulting in faster design iterations and reduced costs. This paper uses an interface between FModal and the DARTS flexible multibody dynamics tool to carry out several numerical studies to exercise and validate the FModal pipeline.
Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and ...
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Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and findable, accessible, interoperable, and reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object-relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch processcontrol (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from the literature and laboratory experiments, to enable data-driven learning of process-structure-property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community.
A general data inspection software based on the basic data of multi-source radar was designed, which was the central part of the data pre-processing. In this paper, software requirements analysis, overall architecture...
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With the deepening of the energy transition, the electro-hydrogen synergistic system, as an important energy solution, is facing challenges such as prominent light abandonment and low utilization of photovoltaic (PV),...
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This paper focuses on the modeling and identification of a Twin Rotor Multi-Input-Multi-Output Systems (TRMS). It begins with an overview of TRMS, followed by the formulation and analysis of a comprehensive nonlinear ...
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ISBN:
(纸本)9783031702846;9783031702853
This paper focuses on the modeling and identification of a Twin Rotor Multi-Input-Multi-Output Systems (TRMS). It begins with an overview of TRMS, followed by the formulation and analysis of a comprehensive nonlinear model based on first principles. The static characteristics of main-elevation and tail-azimuth are explored, considering the influence of rotors on each other. Factors affecting real-time model measurements are investigated. The paper then proceeds to identify TRMS parameters using the "fminsearch" algorithm, the Autoregressive Exogenous Input (ARX) model and Auto-Regressive Moving Average with Exogenous Input (ARMAX) models. Comparative analyses of results from each method are presented. Finally, a comprehensive comparison is made between the nonlinear model, linear models, and real-time experimental data for elevation /pitch and azimuth /yaw angles. This work significantly contributes to understanding TRMS systems, providing insights into nonlinear modeling, identification, and analysis. The findings establish a foundation for advancing comprehension of plant behavior and designing requisite control systems.
The development of a smart irrigation system is critical in addressing water conservation and enhancing agri-cultural practices. The design and implementation of a control system aimed at regulating water delivery to ...
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Despite the rapid development of edge and fog computing technologies, including significant improvements in the characteristics of communication channels and computing devices themselves, the problems associated with ...
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In order to satisfy different production needs,working modes are often adjusted in real industrial processes,which may lead to the emergence of new working modes with a small amount of modeling ***,most of the traditi...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In order to satisfy different production needs,working modes are often adjusted in real industrial processes,which may lead to the emergence of new working modes with a small amount of modeling ***,most of the traditional process monitoring algorithm requires that there are sufficient data to establish the reliable *** address the above issue,a process monitoring algorithm is proposed in this work to transfer the common information from the known mode with sufficient modelingdata to the new mode with limited data in multimode ***,a reference mode is selected by evaluating the similarity between new mode and the known ***,the common information is extracted and projected into a manifold subspace by using transfer component *** on the transfer features,two statistics,i.e.,T and SPE,are defined to analyze the process status and identify the *** effectiveness of the proposed method is verified by Tennessee Eastman（TE） process.
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