A major source of endoscopic tissue tracking errors during deformations stems from wrong data association between observed sensor measurements with previously tracked scene. To mitigate this issue, we present a surgic...
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ISBN:
(纸本)9798350377712;9798350377705
A major source of endoscopic tissue tracking errors during deformations stems from wrong data association between observed sensor measurements with previously tracked scene. To mitigate this issue, we present a surgical perception framework, SuPerPM, that leverages learning-based non-rigid point cloud matching for data association, thus accommodating larger deformations than previous approaches which relied on Iterative Closest Point (ICP) for point associations. The learning models typically require training data with ground truth point cloud correspondences, which is challenging or even impractical to collect in surgical environments. Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth. This was achieved by employing a position-based dynamics (PBD) simulation to ensure that the correspondences adhered to physical constraints. The proposed framework is demonstrated on several challenging surgical datasets that are characterized by large deformations, achieving superior performance over advanced surgical scene tracking algorithms. (1)
Case-based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computer-aided diagnoses systems. This Technology directly addresses the problems found in the traditional Arti...
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With the development of intelligent manufacturing in the context of Industry 4.0, more and more data from industrial sites is urgently required to be collected and analyzed accordingly. However, faced with multiple he...
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ISBN:
(纸本)9798350379860;9798350379877
With the development of intelligent manufacturing in the context of Industry 4.0, more and more data from industrial sites is urgently required to be collected and analyzed accordingly. However, faced with multiple heterogeneous devices and different types of on-site systems, factors such as diverse protocol types, large data volumes, and tight on-site debugging time have caused difficulties in obtaining data from industrial equipment. This article proposes a universal framework for dataacquisition of industrial field equipment and systems, and designs a flexible and configurable method for dataacquisition based on this framework. To achieve this method, a dataacquisition tool called Digital acquisition Tool(DATool) has been designed and developed. Finally, this article established a simulation experimental environment and implemented dataacquisition and processing on devices within Modbus protocols. The experiment proves that this method is convenient and effective in dataacquisition.
Diabetes has become one of the most relevant concerns of the century due to the large number of users who are affected by it. Devices that control blood glucose are very diverse and the user and the healthcare profess...
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ISBN:
(纸本)9798350319552;9798350319545
Diabetes has become one of the most relevant concerns of the century due to the large number of users who are affected by it. Devices that control blood glucose are very diverse and the user and the healthcare professionals in charge need to analyze their output to help them in the most agile way possible. In this proposal, an intelligentdata platform based on IoMT devices is presented to retrieve data from diabetic patients in real time. This data is processed and summarized using generation of linguistic descriptions of time series (GLiDTS) techniques, together with Fuzzy Logic and the Computation with Words paradigm, to highlight the most relevant information for a full day. In the application layer of the platform, a web application is provided to visualize raw glucose data and summaries generated for each patient's Time series (TS) of glucose data. This service is intended to serve users as a help to understand their condition and health professionals to speed up the detection of possible problems or wrong habits in their patients using natural language (NL).
With the rise of big data, efficient data handling techniques have become increasingly crucial. Hard drive failure prediction is a critical domain where accurate and timely data handling is paramount. This research pa...
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The main problems of processing recommendations in intelligentdatabases (IDBs) were considered in the paper. The Funk SVD algorithm was modified to enhance the accuracy and efficiency of recommender systems in IDBs. ...
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In response to Japan's imminent labor shortage crisis and its sluggish integration of IT into industry, an innovative AI-Based Skill Analysis and Matching System is proposed. With projections indicating a demand f...
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There exist various multi-objective optimization problems(MOPs) in the real world that require acquiring diverse solutions efficiently. Various multi-objective evolutionary algorithms (MOEAs) have been proposed to sol...
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To better improve the production capacity and control efficiency of discrete manufacturing workshops, a decision system architecture based on online acquisition and data analysis is proposed, and its architecture and ...
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data-driven fault diagnostics and failure prediction in the industry are dominated by numerical data from sensors. Other data, such as inspection notes, are often discarded due to complexity in feature extraction, flu...
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ISBN:
(纸本)9798350355376;9798350355369
data-driven fault diagnostics and failure prediction in the industry are dominated by numerical data from sensors. Other data, such as inspection notes, are often discarded due to complexity in feature extraction, fluctuations in information quality, and subjectivity due to human factors. However, for systems where degradation evolves slowly, these data could offer critical insight into the fault diagnostic process. Particularly in the task of quantifying the degradation level of inspected components, the observations made by technicians could hold critical information. Extracting useful information from text data has long remained a challenging task, especially with industrial reports. Therefore, this study presents a method utilizing large language models (LLMs) for extracting machine degradation information from brief on-site technician inspection notes. Industrial proprietary texts serve as background knowledge, with LLMs acting as the embedding medium. The proposed method's performance is investigated through a real industrial case study, where identification of machine degradation level is enhanced using text data and domain knowledge.
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