The ground-excited ice-roller has been able to effectively solve the problem of ice-snow on the roads, but its ice breaking mechanism, operating parameters and efficiency still need to be studied and improved. This pa...
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Through sentiment analysis technology, NPCs (Non-Player-controlled Character) in virtual reality roaming system are able to recognize the user's emotional state and react accordingly. In aspect-based sentiment ana...
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ISBN:
(数字)9798350353174
ISBN:
(纸本)9798350353181
Through sentiment analysis technology, NPCs (Non-Player-controlled Character) in virtual reality roaming system are able to recognize the user's emotional state and react accordingly. In aspect-based sentiment analysis, NLP techniques often use manually constructed features combined with machine learning models for classification. However, these methods are complex in feature engineering and lack generalisation capabilities. At the same time, traditional models often ignore the importance of local context for the correct classification of the sentiment polarity of aspect term, and process all input data indiscriminately. In order to solve the above problems and improve the processing efficiency of aspect-based sentiment analysis, an attention weight decay mechanism is proposed. On this basis, a multi-module model is constructed by fusing BERT, BiLSTM and TextCNN, and the performance of the model is verified experimentally. This model can be better applied to virtual reality roaming systems.
Industrial sensor signals are essentially non-Euclidean graph structures due to the interplay between process variables;thus, graph convolutional networks (GCNs) have been widely studied and applied. However, most of ...
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Industrial sensor signals are essentially non-Euclidean graph structures due to the interplay between process variables;thus, graph convolutional networks (GCNs) have been widely studied and applied. However, most of the existing GCN-based methods may suffer from two drawbacks: 1) it is difficult to characterize multiple interactions among nodes and 2) the input graph constructed from the original data may contain errors and missing edges, which will degenerate the fault diagnosis performance. To address the abovementioned issues, this article designs a hierarchical GCN with latent structure learning for industrial fault diagnosis, which can organize hierarchical networks to collaboratively improve the quality of latent graph structure, and enhanced diagnostic performance can be guaranteed. First, a high-quality updated graph is formed by incorporating the original graph with the new graph in the graph constructing layer, which can not only eliminate the adverse effects of noise and outliers but also characterize the multiple interactions among nodes. Then, the updated graph is fed into the multilayer GCN layer for better feature learning and enhances the node representation through intra- and inter-layer convolutional operations simultaneously. After that, the produced node embeddings are used to guide the latent structure learning process for optimal graph. Finally, the proposed method is verified in both the simulated and real industrial processes. The experimental results demonstrate that the new approach has better fault diagnosis accuracy and practicability than state-of-the-art methods.
In medical imaging, accurate boundary detection of anatomical structures is crucial for disease diagnosis and treatment planning. Traditional X-ray imaging, however, generates 2D greyscale images that frequently lack ...
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ISBN:
(纸本)9798350304268
In medical imaging, accurate boundary detection of anatomical structures is crucial for disease diagnosis and treatment planning. Traditional X-ray imaging, however, generates 2D greyscale images that frequently lack the clarity and resolution required for precise boundary analysis. In order to improve the boundary analysis of anatomical structures, we provide a unique method for reconstructing greyscale X-ray pictures in this study. Our method makes better use of internal structure visualization and boundary identification by reconstructing 3D volumetric data from multiple 2D X-ray images using Deep Learning techniques and N eural Networks. This research paper focuses on the Reconstruction of Greyscale X-ray images to facilitate Boundary analysis of Anatomical Structures for orthopaedic surgeries. Our study utilizes the X-ray images in DICOM format from the LIDC-IDRI dataset obtained from the Cancer Imaging Archive, employing a series of steps to reconstruct 3D images and detect boundaries within the lung region. Basic pre-processing techniques are applied to the acquired X-ray images to enhance their quality and improve subsequent analysis. Subsequently, segmentation is performed to isolate the bounded lung area from the images. This segmentation process enables focused analysis and accurate boundary detection. Sinograms are generated from the segmented images through the utilization of the Inverse Radon Transform technique. This step captures X-ray attenuation measurements from different angles, providing the foundation for subsequent image reconstruction. The reconstruction part of our project involves the implementation of both the Algebraic Reconstruction Technique and Filtered Back Projection. FBP, a widely used algorithm in CT imaging, is used to reconstruct the 3D image based on the filtered sinogram data. The ART algorithm, known for its iterative nature, refines the image reconstruction through an iterative process that incorporates acquired and synthetic
In recent years, a new design methodology is finding fertile ground in the building and plant engineering field: BIM (Building Information modeling). BIM is an innovative modeling method that proposes a substantial ch...
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ISBN:
(纸本)9781665409186
In recent years, a new design methodology is finding fertile ground in the building and plant engineering field: BIM (Building Information modeling). BIM is an innovative modeling method that proposes a substantial change in the entire workflow of a project aimed at the digitization of information processes and supported by the transition from 2D to 3D design. The Digital Twin, final product of the entire design process, represents a valuable tool to support the management of the building but, at present, its potential is not fully exploited. Today, the market offers many solutions for building and facility management, but in none of them the information is well contextualized within the space. SCADA (Supervisory control And data Acquisition) systems, in fact, applied to the building and plant field, do not offer a representation of the building that has a decisive impact on the final user experience. The objective of this discussion is, therefore, to propose an innovative system that exploits the unexpressed potential of BIM and extends the usefulness of the project beyond the construction of the building. The ability of BIM to produce a Digital Twin suggests the possibility of integrating this model within the SCADA system. The data, acquired and processed, are, in fact, linked to the "digital twin" that from static and parametric becomes dynamic and informative. The result is what can be defined as "Dynamic Digital Twin".
The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regula...
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ISBN:
(数字)9781510688902
ISBN:
(纸本)9781510688896
The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regulators with simulation exercises, intelligent deductions, decision-making references, Q&A services, which provide intelligent reference solutions for precise regulation under new energy access scenarios with high percentage of new energy under a new type of power system. In order to enhance training effectiveness and operational efficiency, it is need to apply intelligent technology and data-driven models instead of experience and human labor. Interactive Q&A application such as Chat GPT has subversively changed the implementation mode of the training process, making the traditional teaching of theory and basic knowledge easier and faster. However, these commercial Q&A systems still have limitations in terms of accuracy, security, as well as professionalism, which makes them inefficient in professional learning area. In this paper, we use the idea of parallel control and the transformer model to construct a human-computer cooperative training system adapted to new energy grid-connected electric power systems, which realize a human-computer cooperative training model that highly integrates the Q&A services with the real trainer, the real trainees, the computer simulation system. By constructing a large model of human-computer system, a training computing experiment platform, as well as a system with a closed loop of training reality, the parallel training will help training program planning, training teaching design, training arrangements, teaching interaction and other key training links, and realize automated and intelligent training design and execution. As a training and management model adapted to the situation of artificial intelligence, parallel training will bring brand new possibilities for the development of the training industry in the intellige
The deficiency of traditional architectural decoration project cost management concept in cost control affects the position of architectural decoration industry in market competition to a certain extent. To solve such...
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The proceedings contain 150 papers. The special focus in this conference is on Computer-Aided Architectural Design Research in Asia. The topics include: COMMUNICATION WITH DETROIT: Machine Learning in Open Source Comm...
ISBN:
(纸本)9789887891796
The proceedings contain 150 papers. The special focus in this conference is on Computer-Aided Architectural Design Research in Asia. The topics include: COMMUNICATION WITH DETROIT: Machine Learning in Open Source Community Housing Design;PREDICTION AND OPTIMISATION OF THE TYPICAL AIRPORT TERMINAL CORRIDOR FAÇADE SHADING USING INTEGRATED MACHINE LEARNING AND EVOLUTIONARY ALGORITHMS;RESEARCH ON ARCHITECTURAL SKETCH TO SCHEME IMAGE BASED ON CONTEXT ENCODER;‘;TEXT-TO-GARDEN: Generating Traditional Chinese Garden Design From Text-Descriptions at Scale With Multimodal Machine Learning;INTEGRATION OF EEG AND DEEP LEARNING ON DESIGN DECISIONMAKING: A data-Driven Study of Perception in Immersive Virtual Architectural Environments;INCORPORATING PHYSICAL EXPERIMENTATION INTO CREATIVE DL-DRIVEN DESIGN SPACE EXPLORATION;AN IMAGE-BASED MACHINE LEARNING METHOD FOR URBAN FEATURES PREDICTION WITH THREE-DIMENSIONAL BUILDING INFORMATION;AN INTEGRATED APPLICATION OF BUILDING INFORMATION modeling, COMPUTER-AIDED MANUFACTURING, MACHINE LEARNING, AND THE INTERNET OF THINGS: A Hybrid Stadium as a Case Study;CROSS-DISCIPLINARY SEMANTIC BUILDING FINGERPRINTS: Knowledge Graphs To Store Topological Building Information Derived From Semantic Building Models (Bim) To Apply Methods Of Artificial Intelligence (Ai) Throughout The Life Cycle Of Buildings;SYNTHESIZING STYLE-SIMILAR RESIDENTIAL FACADE FROM SEMANTIC LABELING ACCORDING TO THE USER-PROVIDED EXAMPLE;VARIABILITY IN MACHINE LEARNING FOR MULTI-CRITERIA PERFORMANCE analysis;SKYWAYS VERSUS SIDEWALKS: Evaluating the Perceptual Qualities and Environmental Features of Elevated Pedestrian Systems in Hong Kong;BESPOKE 3D PRINTED CHAIR: Research On The Digital Design And Fabrication Method Of Multi-body Pose Fusion;TRADITIONAL CHINESE VILLAGE MORPHOLOGICAL FEATURE EXTRACTION AND CLUSTER analysis BASED ON MULTI-SOURCE data AND MACHINE LEARNING;GESTURE modeling: In Between Nature and control;PAINTERLY EXPANSION.
Ontology plays an essential role in biological research. It supports integrative analysis and interpretation by provisioning a standardized vocabulary, metadata, machine-readable axioms, and definitions. As a represen...
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The proceedings contain 120 papers. The topics discussed include: deep reinforcement learning based demand response for domestic variable volume water heater;tomato disease degree recognition based on RGB and lab colo...
ISBN:
(纸本)9798350311259
The proceedings contain 120 papers. The topics discussed include: deep reinforcement learning based demand response for domestic variable volume water heater;tomato disease degree recognition based on RGB and lab color space conversion method;adaptive neural network asymptotic tracking control for autonomous surface vehicles;exergy-related operating performance assessment for hot rolling process based on multiple imputation and multi-class support vector data description;digital twin development: mathematical modeling;a survey of few-shot learning-based compound fault diagnosis methods for industrial processes;backstepping-based anti-disturbance flight control for attitude and altitude unmanned helicopters with state constraints;integrating worker assistance systems and enterprise resource planning in industry 4.0;an efficient condition monitoring and fault diagnosis method for bearings under multiple working conditions;and sliding window-based real-time remaining useful life prediction for milling tool.
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