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International Symposium on Underwater Technology

作者： Dominic Solpico Yuya Nishida Kota Mishima Kazuo Ishii Tokuo Suetsugu Yoshinori Yatsunami Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Fishery Machinery Division Belltechne Co. Ltd Fukuoka Japan

Efficient feeding is crucial in cage aquaculture, reducing the cost spent on feeds while minimizing the pollution in water and maintaining optimal fish growth. Working towards digitization of fish feeding knowledge to optimize feeding decisions, we developed a modular sensor network estimating the fish feeding activity by measuring the fish-induced flow and recording underwater videos of the activity. We designed the system to collect data at multiple depths from at least two opposing sides of a fish cage. We also designed an offshore system and automatic data collection mechanism to enable the network to perform long-term observations. Two experiments were performed in cages located at two separate aquaculture farms to demonstrate and evaluate the functionality of the developed. We observed changes of flow during feeding and compared them to the videos of fish activity. Observed differences between the results of the two experiments could be attributed to various factors such as the characteristics of the fish cages as well as the method of feeding. Improvements to the sensor network and further analysis of data are needed to achieve a more effective system for estimating fish feeding activity.

关键词： visualization Telecommunication traffic data collection Fish Water pollution Pollution measurement Reliability

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A Framework for Enabling Cloud Services to Leverage Energy data

A Framework for Enabling Cloud Services to Leverage Energy D...

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ieee International Conference on Cloud Engineering (IC2E)

作者： Vasileios Karagiannis Shievam Kashyap Nikolas Zechner Oliver Hödl Georg Hartner Manuel Llorca Tooraj Jamasb Stefan Grünberger Marc Kurz Christoph Schaffer Stefan Schulte Center for Digital Safety & Security Austrian Institute of Technology Vienna Austria Department of Smart and Interconnected Living University of Applied Sciences Upper Austria Hagenberg Austria Cooperative Systems Research Group Faculty of Computer Science University of Vienna Vienna Austria Copenhagen School of Energy Infrastructure Copenhagen Business School Copenhagen Denmark Christian Doppler Laboratory for Blockchain Technologies for the Internet of Things TU Hamburg Hamburg Germany

Cloud services have been well integrated into various sectors of contemporary societies such as transportation, healthcare, and work. However, the energy sector has made little progress in offering cloud services to energy consumers. In some cases, even basic features (such as energy consumption monitoring and visualization of households) are still unattainable. To foster innovation in cloud-based energy services, governments implement regulations that moderate access to energy data via the energy providers’ platforms. Despite these regulations, cloud service providers may still struggle to aggregate energy data because each energy provider may be using different procedures, authentication mechanisms, and data semantics. Consequently, aggregating energy data at scale becomes challenging, as it requires achieving compatibility with diverse platforms. To address this challenge, we propose a cloud-based framework that handles all interactions with energy providers and prepares the data for further processing by services. Additionally, we analyze the socioeconomic impact of our approach, and we outline novel use cases that may emerge due to the proposed framework.

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Regeneration-Constrained Self-Supervised Seismic data Interpolation

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ieee TRANSACTIONS ON GEOscience AND REMOTE SENSING 2023年 61卷 1页

作者： Song, Aoqi Wang, Changpeng Zhang, Chunxia Zhang, Jiangshe Xiong, Deng Wei, Xiaoli Changan Univ Sch Sci Xian 710054 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China BGP Geophys Technol Res & Dev Ctr Zhuozhou 072750 Hebei Peoples R China

Seismic data interpolation is an indispensable part of seismic data processing. In recent years, deep-learning-based interpolation algorithms for seismic data have become popular due to their high accuracy. However, a considerable amount of work has focused on the migration of concepts and algorithms in deep-learning-based methods while ignoring the implicit properties of seismic data itself. In this article, we propose the regeneration prior, which is an implicit property of seismic data with respect to the interpolation function, and are used for self-supervised seismic data interpolation tasks. In mathematical form, the regeneration prior can be considered as a regular term describing the structure of the seismic data. Theoretically, the regeneration prior is a necessary condition to obtain an optimal interpolation function. Experimentally, the proposed method achieves significant improvement in accuracy and intuitive visualization in comparison with advanced unsupervised or self-supervised methods. In addition, we provide an intuitive interpretation of the regeneration prior, and our study shows that the regeneration prior plays an anti-overfitting structuring role in the parameter learning process of the interpolation function. Finally, we analyze the robustness of the regeneration prior. The experimental results show that the performance of the regeneration prior is stable despite the fact that the hyperparameters associated with the regeneration prior are perturbed in a considerable range.

关键词： Interpolation Neural networks Mathematical models Image reconstruction Optimization Linear programming Task analysis Regeneration prior seismic interpolation self-supervised learning unsupervised learning

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V3Trans-Crowd: A Video-based Visual Transformer for Crowd Management Monitoring

V3Trans-Crowd: A Video-based Visual Transformer for Crowd Ma...

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Smart Mobility (SM), ieee International Conference on

作者： Yuqi Zuo Aymen Hamrouni Hakim Ghazzai Yehia Massoud King Abdullah University of Science and Technology (KAUST) Thuwal Makkah Province Saudi Arabia University of Electronic Science and Technology of China Chengdu Sichuan China

Autonomously monitoring and analyzing the behavior of the crowd is an open research topic in the transportation field. The real-time identification, tracking, and prediction of the crowd behavior is primordial to ensure smooth crowd management operations in many public areas such as public transport stations and streets. First, the complexity brought by the interaction and fusion from individual to group that needs to be assessed and analyzed. Second, the classification of these actions which might be useful in identifying danger and avoiding any undesired consequences. In this paper, we propose a transformer-based crowd management monitoring framework called V3Trans-Crowd that captures information from video data and extracts meaningful output to categorize the behavior of the crowd. We provide an improved hierarchical transformer for multi-modal tasks. Inspired by 3D visual transformer, our proposed 3D visual model, V3Trans-Crowd, has been shown to achieve great performances in terms of accuracy compared to state-of-the-art methods, all tested on the standard Crowd-11 dataset.

关键词： visualization Solid modeling Three-dimensional displays Transportation Transformers Real-time systems Behavioral sciences

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LCM Aging Detection and Analysis System Design

LCM Aging Detection and Analysis System Design

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ieee International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Meng-Chang Wu Yu-Xiang Wang Yung-Hoh Sheu Ching-Lin Li Teng-Chi Chang Jen-Yu Shieh Department of Electro-Optical Engineering National Formosa University Yunlin County Taiwan Computer Science and Information Engineering National Formosa University Yunlin County Taiwan Product Development and Management Division General Interface Solution Limited Miaoli County Taiwan

This study designs an Internet of Things (IoT) system for LCD Module (LCM) aging detection and analysis, aiming to achieve the detection and analysis functions for the LCM manufacturing process and provide intelligent quality inspection of LCM. In this system, a custom- Compound LCM Drive Measurement Circuit Board (CLDMCB) is used to drive different types of LCM. For the analysis of LCM aging status, the CLDMCB is used to measure the voltage and current values of the LCM at specific screens, and the detected voltage and current data are uploaded to the cloud platform. Finally, the voltage and current values are presented with data visualization, and defect detection and analysis of the LCM are performed.

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Network Traffic Virtualization Using Wireshark and Google Maps

Network Traffic Virtualization Using Wireshark and Google Ma...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee International Conference on

作者： Sudha Arvind S. Arvind Vamshi Kumar Silveri Govardhan Potey Parameshwar Nunavath Ranadheep Podishetty Department of Electronics and Communication Engineering CMR Hyderabad India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad India

Security analysts have the knowledge necessary to identify the types of traffic that are present in a network, identify instances of abnormally high traffic, and identify the network nodes responsible. Assisting users in locating servers and the destinations of data flows, network traffic visualization can also help researchers identify online threats. With Wireshark’s capabilities as a packet tracer and capture tool, network analysis starts and ends with the packet capture parameters selected in this study. Wireshark is used in this study to create a network tracker. In order to identify the source Internet Protocol (IP) and destination IP on a Google map, will capture the data packets using packet tracer, analyze them, and then translate the IP addresses into latitude and longitude values. This will let users visualize network traffic on a Google map and track it.

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Use of Eye Behavior With Visual Distraction for Attention Training in VR

Use of Eye Behavior With Visual Distraction for Attention Tr...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee Conference on

作者： Yi Joy Li Pranathi Ayyalaraju Ramalakshmi Chao Mei Sungchul Jung Kennesaw State University Reality Labs Meta

Concentration and attention skills are critical to human beings in almost every area of society, including school, work, and relationships. Especially, improving and increasing attention skills for certain people with social impairments such as Autism Spectrum Disorders, will assist their intervention and benefit their life quality. Virtual reality (VR) with a behavioral analysis system can help attention training with more customized scenarios for interaction and a fully controllable virtual environment that is often not available in real life. In the current phase, a system prototype was developed and a preliminary study with young adult participants was conducted to collect the baseline and validate the design. A within-subject design was used to embed four different training strategies in the training session. A total of 20 participants were involved in the preliminary experiment. The results of the data analysis show that even though eye gazing on certain objects decreased as expected, the total distraction time was rather on the opposite trend. The results have brought up new insights into the study and will inspire more improvement in future steps.

关键词： Training Human computer interaction visualization Three-dimensional displays data analysis Conferences Virtual environments

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Visual Analysis of Apple Disease And Pest Research Based on data Mining

Visual Analysis of Apple Disease And Pest Research Based on ...

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Integrated Circuits and Communication Systems (ICICACS), ieee International Conference on

作者： Dongju Guo Xiaohong Qian Shengying Yang Cong Hu Jagadevi N. Kalshetty Zhejiang University of Science and Technology Hangzhou Zhejiang China Dept. of CSE Nitte Meenakshi Institute of Technology Bengaluru India

To help researchers and decision makers understand the current research direction, hot spots and trends in the field of apple pests and diseases in China by analyzing the domestic literature on apple pests and diseases. Based on CNKI database, citation analysis was carried out on the number of publications, core authors, institutions and hot keywords in the field of apple pests and diseases from 1979 to 2022 using bibliometrics method. In CNKI database, 2054 literatures related to apple diseases and insect pests were retrieved, including 1466 valid literatures, including 1252 institutions, 248 journals and 1287 authors. Northwest A&F University and Hebei Agricultural University were the two institutions with the most publications, Keqiang Cao, Guiben Wu, Meiying Liu and other authors have published a lot of articles, and formed a number of cooperative teams, which have great influence. At present, domestic scholars pay more and more attention to apple diseases and insect pests. The number of institutions and researchers is large. The research institutions are mainly concentrated in universities and research institutes, and the cooperation and exchange between institutions need to be improved.

关键词： Integrated circuits visualization Insects Market research Physiology data mining Visual databases

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Scene-level Point Cloud Colorization with Semantics-and-geometry-aware Networks

Scene-level Point Cloud Colorization with Semantics-and-geom...

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ieee International Conference on Robotics and Automation (ICRA)

作者： Rongrong Gao Tian-Zhu Xiang Chenyang Lei Jaesik Park Qifeng Chen Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong SAR China Inception Institute of Artificial Intelligence Abu Dhabi UAE POSTECH

In robotic applications, we often obtain tons of 3D point cloud data without color information, and it is difficult to visualize point clouds in a meaningful and colorful way. Can we colorize 3D point clouds for better visualization? Existing deep learning-based colorization methods usually only take simple 3D objects as input, and their performance for complex scenes with multiple objects is limited. To this end, this paper proposes a novel semantics-and-geometry-aware colorization network, termed SGNet, for vivid scene-level point cloud colorization. Specifically, we propose a novel pipeline that explores geometric and semantic cues from point clouds containing only coordinates for color prediction. We also design two novel losses, including a colorfulness metric loss and a pairwise consistency loss, to constrain model training for genuine colorization. To the best of our knowledge, our work is the first to generate realistic colors for point clouds of large-scale indoor scenes. Extensive experiments on the widely used ScanNet benchmarks demonstrate that the proposed method achieves state-of-the-art performance on point cloud colorization.

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HBOD: A Novel dataset with Synchronized Hand, Body, and Object Manipulation data for Human-Robot Interaction

HBOD: A Novel Dataset with Synchronized Hand, Body, and Obje...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Peiqi Kang Kezhe Zhu Shuo Jiang Bin He Peter Shull State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai P.R. China College of Electronics and Information Engineering Tongji University Shanghai P.R. China Frontiers Science Center for Intelligent Autonomous Systems Shanghai P.R. China

Estimating hand and body posture is crucial for enabling human-robot collaboration, preventing occupational diseases, and training humanoid robots. Although advances in wearable motion sensors, such as Inertial Measurement Units (IMUs), have resulted in public datasets in industrial and occupational settings, these datasets rarely include movements with subjects holding and manipulating objects. However, it is crucial to have data on how humans move and interact with different objects so that we can better understand human motion intention and movement strategies in specific scenarios. We thus propose the HBOD dataset (hand-body-object dataset), which encompasses synchronized human pose data from an IMU sensor network, hand posture data from a smart data glove, and object position and attitude information obtained from a motion capture system, while subjects move and interact with a screwdriver, hammer, spanner, electric drill, and a rectangular workpiece. This paper provides an overview of the hardware setup, experimental protocol, data format, and data visualization results. This dataset provides crucial object information absent from existing datasets, thus offering highly valuable manipulation data for occupational diseases research, human-robot interaction, and robot skill acquisition.

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