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2023 ieee International Conference on Mechatronics (ICM)

作者： Lingzi Xie Darong Huang Zhenyu Lu Ning Wang Chenguang Yang College of Automation Science and Engineering South China University of Technology Guangzhou China Bristol Robotics Laboratory University of the West of England Bristol UK

Precise leader-follower control is critical for teleop- eration. This paper designs and implements a low-cost leader device for unilateral teleoperation scenario. Monocular vision based on fiducial markers and MEMS Inertial Measurement Unit (IMU) are utilized to constitute the pose sensing system of the handheld device. To increase the positioning output frequency and deal with the motion blur of the fiducial markers, the IMU data and vision message are fused by an Error State Kalman Filter (ESKF). An assembly experiment was carried out to verify the effectiveness of the proposed device and the feasibility of the fusion algorithm.

关键词： Micromechanical devices visualization Tracking Position measurement Robot sensing systems Fiducial markers Sensors

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Visual Analysis of Precision Teaching Research Based on VOS Viewer in data-Driven Perspective

Visual Analysis of Precision Teaching Research Based on VOS ...

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International Conference on Educational and Information Technology (ICEIT)

作者： Wan Xin Wen Bin School of Information and Science Technology Yunnan Normal University Kunming China

With the dilution of teaching space boundaries, complex and diversified teaching forms, the rapid progress of modern information technology, and the emergence of new educational concepts, traditional teaching from the design of objectives to the formulation of evaluation constantly reveals many problems that need to be solved. At the moment, the combination of precision teaching and data-driven allows teaching and learning problems to be solved in a new digital form. This paper analyzes the current situation of data-driven precision teaching at home and abroad with VOS viewer software, discovers the new direction of data-driven teaching model to promote teaching practice, based on the development and application of technology, data literacy concerns and enhancements to promote the realization of accurate teaching and learning. Finds that data-driven in precision teaching has the characteristics of wide application, multiple choices, and significant effect, and proposes future research directions in combination with reality.

关键词： visualization Analytical models Education Software data models Information technology

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Demo: SGX-MR-Prot: Efficient and Developer-Friendly Access-Pattern Protection in Trusted Execution Environments

Demo: SGX-MR-Prot: Efficient and Developer-Friendly Access-P...

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International Conference on Distributed Computing Systems

作者： A K M Mubashwir Alam Justin Boyce Keke Chen Department of Computer Science Marquette University Milwaukee WI Department of Computer Science University of Arkansas Fayetteville AR

Trusted Execution Environments, such as Intel SGX, use hardware supports to ensure the confidentiality and integrity of applications against a compromised cloud system. However, side channels like access patterns remain for adversaries to exploit and obtain sensitive information. Common approaches use oblivious programs or primitives, such as ORAM, to make access patterns oblivious to input data, which are challenging to develop. This demonstration shows a prototype SGX-MR-Prot for efficiently protecting access patterns of SGX-based data-intensive applications and minimizing developers' efforts. SGX-MR-Prot uses the MapReduce framework to regulate application dataflows to reduce the cost of access-pattern protection and hide the data oblivious details from SGX developers. This demonstration will allow users to intuitively understand the unique contributions of the framework-based protection approach via interactive exploration and visualization.

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Self-Supervised Multiple Faults Detection Method Based on Time-Frequency Feature Fusion With Unlabeled Wind Turbine Samples

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Xu, Qing Ma, Dazhong Liu, Yaobo Wang, Qingchen Northeastern Univ Coll Informat Sci & Engn Liaoning110004 Shenyang Peoples R China Northeastern Univ Foshan Grad Sch Innovat Foshan 528311 Guangdong Peoples R China

Fault detection is an essential aspect of power generation in wind turbines (WTs). However, existing fault detection methods are developed specifically for identifying a single type of fault and rely on a sufficient amount of labeled data. These approaches tend to be less accurate when used to detect many types of faults with limited data. To solve those problems, this article proposes a self-supervised fault detection method based on a time-frequency feature fusion module (TF-FFM). First, a periodicity-based hybrid data augmentation is presented in order to expand the number and diversity of fault samples. Second, TF-FFM can fully extract the features of fault in both time and frequency domains. Third, a fault detection method based on self-supervised learning is proposed during the training process to reduce the cost of data collection and labeling. Meanwhile, the loss function is optimized based on the energy conservation theorem in the time-frequency domain. This optimization leads to an advanced accuracy of the fault detection method for WTs by establishing a power consistency relationship. Finally, this article evaluates the effectiveness of the proposed method through a comparative analysis with various fault diagnosis techniques, feature visualization, and ablation experiments. The accuracy of the proposed method achieves 95% in the context of multiple fault detection, which is 3% higher than the results of existing methods.

关键词： Feature extraction Fault detection Vibrations Time-frequency analysis Blades Wind turbines Generators Hybrid data augmentation multiple faults power consistency self-supervised learning time-frequency feature fusion

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Unsupervised extraction, classification and visualization of clinical note segments using the MIMIC-III dataset

Unsupervised extraction, classification and visualization of...

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ieee International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Petr Zelina Jana Halámková Vít Nováček Faculty of Informatics Masaryk University Brno Czechia Masaryk Memorial Cancer Institute Brno Czechia Data Science Institute NUI Galway Galway Ireland

This paper presents a text-mining approach to extracting and organizing segments from unstructured clinical notes in an unsupervised way. Our work is motivated by the real challenge of poor semantic integration between clinical notes produced by different doctors, departments, or hospitals. This can lead to clinicians overlooking important information, especially for patients with long and varied medical histories. This work extends a previous approach developed for Czech breast cancer patients and validates it on the publicly accessible MIMIC-III English dataset, demonstrating its universal and language-independent applicability. Our work is a stepping stone to a broad array of downstream tasks, such as summarizing or integrating patient records, extracting structured information, or computing patient embeddings. Additionally, the paper presents a clustering analysis of the latent space of note segment types, using hierarchical clustering and an interactive treemap visualization. The presented results demonstrate that this approach generalizes well for MIMIC and English.

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data Integration Framework for Virtual Reality Enabled Digital Twins

Data Integration Framework for Virtual Reality Enabled Digit...

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ieee World Forum on Internet of Things (WF-IoT)

作者： Florian Stadtmann Hary Pirajan Mahalingam Adil Rasheed Department of Engineering Cybernetics NTNU Trondheim Norway Department of Computer Science NTNU Trondheim Norway

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Digital twins are becoming increasingly popular across many industries for real-time data streaming, processing, and visualization. They allow stakeholders to monitor, diagnose, and optimize assets. Emerging technologies used for immersive visualization, such as virtual reality, open many new possibilities for intuitive access and monitoring of remote assets through digital twins. This is specifically relevant for floating wind farms, where access is often limited. However, the integration of data from multiple sources and access through different devices including virtual reality headsets can be challenging. In this work, a data integration framework for static and real-time data from various sources on the assets and their environment is presented that allows collecting and processing of data in Python and deploying the data in real-time through Unity on different devices, including virtual reality headsets. The integration of data from terrain, weather, and asset geometry is explained in detail. A real-time data stream from the asset to the clients is implemented and reviewed, and instructions are given on the code required to connect Python scripts to any Unity application across devices. The data integration framework is implemented for a digital twin of a floating wind turbine and an onshore wind farm, and the potential for future research is discussed.

关键词： Headphones Wind energy data integration data visualization Virtual reality Wind farms Real-time systems

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Live Demonstration: Audio Inference using Neuromorphic Cochlea on RAMAN Accelerator

Live Demonstration: Audio Inference using Neuromorphic Cochl...

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ieee Biomedical Circuits and Systems (BIOCAS)

作者： Adithya Krishna Hitesh Pavan Oleti Anand Chauhan H Shankaranarayanan André van Schaik Mahesh Mehendale Chetan Singh Thakur Department of Electronic Systems Engineering Indian Institute of Science Bangalore India International Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Australia

The setup includes a host PC, microphone, and FPGA board. The CAR model and RAMAN are implemented on the FPGA, while the maze game and cochleagram visualization software run on the PC. The microphone captures sound from its surrounding environment and transmits the audio data to the FPGA bit-by-bit. The I 2 S (Inter-IC Sound) module combines this data into 16-bit audio samples and forwards it to the CAR model. The output from the CAR model is then fed into RAMAN. The outputs from the CAR model and RAMAN are transferred to the host PC via Arduino Nano 33 BLE to control the maze game.

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A Novel Directed Graph Convolutional Neural Network for Rolling Bearings in Fault Diagnosis

A Novel Directed Graph Convolutional Neural Network for Roll...

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ieee International Conference on Unmanned Systems (ICUS)

作者： Shoupeng Gao Yueyang Li Dong Zhao School of Electrical Engineering University of Jinan Jinan China School of Cyber Science and Technology Beihang University Beijing China

As deep learning is broadly operationalized, fault detection and diagnosis are becoming increasingly intelligent. This has resulted in a significant increase in research efforts aimed at understanding and learning the different characteristics of data structured as graphs. However, in fault diagnosis, due to the vast and complex relationships between the data, we often overlook the important feature of the direction of relationships between data, resulting in confusion of some fault categories when performing classification. To address this problem, we first transform time series data into a directed graph structure using a weighted directed visualization graph (WDVG) method. To reduce the impact of distant nodes perceived as noise, the edges are assigned weights based on the difference between the sampling points. Secondly, in order to mine the directional features of relationships between nodes in large-scale graph structures, our proposed approach named DSGIN integrates graph isomorphism network (GIN) and GraphSAGE to extract directional features. Ultimately, we achieve the graph classification task. We validate the effectiveness of WDVG and DSGIN through actual datasets.

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Deep Learning based Approach for Fast, Effective visualization of Voluminous Gridded Spatial Observations

Deep Learning based Approach for Fast, Effective Visualizati...

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Cluster, Cloud and Internet Computing Workshops (CCGridW), ieee/ACM International Symposium on

作者： Paahuni Khandelwal Menuka Warushavithana Sangmi Lee Pallickara Shrideep Pallickara Department of Computer Science Colorado State University Fort Collins Colorado

Gridded spatial datasets arise naturally in environmental, climatic, meteorological, and ecological settings. Each grid point encapsulates a vector of variables representing different measures of interest. Gridded datasets tend to be voluminous since they encapsulate observations for long timescales. Visualizing such datasets poses significant challenges stemming from the need to preserve interactivity, manage I/O overheads, and cope with data volumes. Here we present our methodology to significantly alleviate I/O requirements by leveraging deep neural network-based models.

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Seismic landslide detection based on Transformer and RNN algorithm

Seismic landslide detection based on Transformer and RNN alg...

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Sensors, Electronics and Computer Engineering (ICSECE), ieee International Conference on

作者： Qingru Yang Xin Zhang Yuxuan Xie Shuhong Huang Junfeng Kong Xiaoyu Zhang College of Computer Science and Cyber Security (Oxford Brookes College) Chengdu University of Technology Chengdu China College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

This paper proposes a seismic landslide detection method based on Transformer and RNN algorithm. This method uses seismic data and topographic data as inputs, encodes the data through the Transformer model, and then inputs the encoded data into the RNN model for classification. In the training process, we use the cross entropy loss function and Adam optimizer. To verify the effectiveness of the method, we used public data sets for experiments, and the results show that our proposed method can significantly improve the accuracy of seismic landslide detection. In addition, we interpret the results by visualization method and analyze the performance of the model under different parameters. Our method is expected to provide effective support for earthquake landslide prediction and early warning.

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