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24th Eurographics/ieee VGTC Conference on visualization (EuroVis)

作者： Huesmann, Karim Linsen, Lars Univ Munster Munster Germany

Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio-temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio-temporal ensemble data without re-training. The latent-space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large-scale spatio-temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in-depth basic understanding of the underlying temporal evolution of a given ensemble.

关键词： Deep neural networks

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A Feature Extraction and analysis Method for Battery Health Monitoring

A Feature Extraction and Analysis Method for Battery Health ...

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ieee International symposium on Industrial Electronics (ISIE)

作者： Jilun Tian Jiusi Zhang Hao Luo Congsheng Huang Mo-Yuen Chow Yuchen Jiang Shen Yin Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan R.O.C Department of Electrical and Computer Engineering North Carolina State University Raleigh NC U.S.A Faculty of Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Batteries are widely applied in industrial systems, promoting industrial production efficiency. The system prognosis needs to establish a battery health monitoring model and maintain regular maintenance, improving reliability and safety while reducing unnecessary economic losses. An accurate data-driven state-of-health (SOH) estimation method is crucial, as it mainly relies on the information related to battery SOH values in the extracted features. Therefore, we propose a statistical-based feature extraction method, which makes it physically meaningful and effective. To explore more information, we propose a feature analysis framework to analyze battery features, including visualization, correlation, and importance analysis. We utilize a random forest regression model as an effective learner and validate it based on NASA’s real-world dataset. The feature analysis contents are beneficial for understanding the role and importance of features in SOH estimation tasks, which can also provide an extended scenario for feature extraction of batteries.

关键词： Training visualization Correlation Estimation Voltage Feature extraction Batteries

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Dimension Reduction of Multi-Source Time Series Sensor data for Industrial Process

Dimension Reduction of Multi-Source Time Series Sensor Data ...

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ieee International symposium on Industrial Electronics (ISIE)

作者： Jiao Meng Xin Huo Changchun He Chao Zhu Control and Simulation Center Harbin Institute of Technology Harbin China Shanghai Rising Digital Co. Ltd Intelligent Research Institute Shanghai China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

With the advent of big data era, industrial process data has become increasingly large, which are characterized by large-scale, multi-source, and it’s difficult to analyze these high-dimensional data directly. In order to solve this problem, dimension reduction of high-dimensional data is necessary, which saves a lot of resources for subsequent data processing. This paper proposes a dimension reduction method to reduce scale for time series and analyze the correlation between multi-source sensors. For time series with large time scales, an adaptive largesttriangle-three-buckets method is proposed, which adaptively selects the optimal bucket number according to the similarity between downsampled data and original data of multi-source data. Further, robust principal component analysis is used to decompose the high-dimensional data into low-rank matrix and sparse matrix. The low-rank matrix represents the low-dimension principal component of multi-source data, and correlation analysis reduces its dimension further. Experiments are carried out on industrial excavator dataset to verify the effectiveness and preponderance of the method.

关键词： Dimensionality reduction Industrial electronics Correlation Time series analysis Excavation data processing Sensors

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Crop Detection Method using Relative Positional Relationships for Small Weeding Robots

Crop Detection Method using Relative Positional Relationship...

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ieee/SICE International symposium on System Integration

作者： Yusuke Iuchi Atsuki Koshigoe Soki Nishiwaki Takanori Emaru Graduate School of Engineering Hokkaido University Hokkaido Japan Faculty of Engineering Hokkaido University Hokkaido Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Automation in agriculture is increasingly critical for addressing global food security and sustainability challenges. This paper presents a novel crop detection method using relative positional relationships, specifically designed for small weeding robots. Unlike traditional approaches that rely heavily on visual characteristics and large annotated datasets, our method leverages the spatial arrangement of plants to distinguish crops from weeds, thereby reducing the dependency on extensive data collection and annotation efforts. We implemented a multi-stage detection system that first identifies all plants using an object detection algorithm and then classifies them based on their positional and size information. Experimental results on soybean datasets demonstrate that our approach achieves AP of 71.3% for soy crops and 27.2% for weeds in environments not included in the training dataset, showing comparable effectiveness to traditional visual-based detection methods in scenarios with limited data. This advancement offers potential for enhancing the adaptability and efficiency of agricultural automation technologies.

关键词： Training visualization Automation Crops Object detection System integration Feature extraction Agriculture Sustainable development Robots

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Beidou Carrier Phase Difference Reliability Improvement Method Applied to Medium and large Fixed Wing UAV in Complex Environment 21

Beidou Carrier Phase Difference Reliability Improvement Meth...

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21st International symposium on Communications and Information Technologies, ISCIT 2022

作者： Ren, Qi Wan, Xiang Wu, Jiajia Space Star Technology Co. Ltd Navigation Department Beijing China

ISBN: (数字)9781665498517

ISBN: (纸本)9781665498517

This paper aims at the problem of how to obtain the position information of self-height accuracy in real time when the medium and large UAV is affected by complex electromagnetic environment, high dynamics, large angle, high jitter and other factors in the process of task execution. Based on the Beidou carrier phase difference method, a method of integrity monitoring and a posteriori weight dynamic allocation is proposed to ensure the reliability of positioning results in complex environments. It is verified by simulation analysis and using the actual flight data of a certain UA V. The verification results show that the method introduced in this paper can make the UA V maintain high-precision and stable positioning in only 9 hours of mission. © 2022 ieee.

关键词： Reliability

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A Prototypical Knowledge Oriented Adaptation Framework for Semantic Segmentation

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ieee TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 149-163页

作者： Tian, Haitao Qu, Shiru Payeur, Pierre Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Univ Ottawa Sensing & Machine Vis Automat & Robot Intelligenc Ottawa ON K1N 6N5 Canada

A prevalent family of fully convolutional networks are capable of learning discriminative representations and producing structural prediction in semantic segmentation tasks. However, such supervised learning methods require a large amount of labeled data and show inability of learning cross-domain invariant representations, giving rise to overfitting performance on the source dataset. Domain adaptation, a transfer learning technique that demonstrates strength on aligning feature distributions, can improve the performance of learning methods by providing inter-domain discrepancy alleviation. Recently introduced output-space based adaptation methods provide significant advances on cross-domain semantic segmentation tasks, however, a lack of consideration for intra-domain divergence of domain discrepancy remains prone to over-adaptation results on the target domain. To address the problem, we first leverage prototypical knowledge on the target domain to relax its hard domain label to a continuous domain space, where pixel-wise domain adaptation is developed upon a soft adversarial loss. The development of prototypical knowledge allows to elaborate specific adaptation strategies on under-aligned regions and well-aligned regions of the target domain. Furthermore, aiming to achieve better adaptation performance, we employ a unilateral discriminator to alleviate implicit uncertainty on prototypical knowledge. At last, we theoretically and experimentally demonstrate that the proposed prototypical knowledge oriented adaptation approach provides effective guidance on distribution alignment and alleviation on over-adaptation. The proposed approach shows competitive performance with state-of-the-art methods on two cross-domain segmentation tasks.

关键词： Image segmentation Semantics Task analysis Adaptation models Training visualization Image classification Domain adaptation semantic segmentation prototypical knowledge unsupervised learning transfer learning

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Financial Fine-Tuning a large Time Series Model

Financial Fine-Tuning a Large Time Series Model

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2025 ieee symposium on Computational Intelligence for Financial Engineering and Economics, CiFer 2025

作者： Fu, Xinghong Hirano, Masanori Imajo, Kentaro Massachusetts Institute of Technology CambridgeMA United States Preferred Networks Inc. Tokyo Japan

ISBN: (纸本)9798331508319

large models have shown unprecedented capabilities in natural language processing, image generation, and most recently, time series forecasting. This leads us to ask the question: treating market prices as a time series, can large models be used to predict the market? In this paper, we answer this by evaluating the performance of the latest time series foundation model TimesFM on price prediction. We find that due to the irregular nature of price data, directly applying TimesFM gives unsatisfactory results and propose to fine-tune TimeFM on financial data for the task of price prediction. This is done by continual pre-training of the latest time series foundation model TimesFM on price data containing 100 million time points, spanning a range of financial instruments spanning hourly and daily granularities. The fine-tuned model demonstrates higher price prediction accuracy than the baseline model. We conduct mock trading for our model in various financial markets and show that it outperforms various benchmarks in terms of returns, sharpe ratio, max drawdown and trading cost. © 2025 ieee.

关键词： Cost benefit analysis

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Self-Evolutionary Group-wise Log Parsing Based on large Language Model 35

Self-Evolutionary Group-wise Log Parsing Based on Large Lang...

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35th ieee International symposium on Software Reliability Engineering, ISSRE 2024

作者： Pei, Changhua Liu, Zihan Li, Jianhui Zhang, Erhan Zhang, Le Zhang, Haiming Chen, Wei Pei, Dan Xie, Gaogang University of Chinese Academy of Sciences Hangzhou Institute for Advanced Study China Chinese Academy of Sciences Computer Network Information Center China University of Chinese Academy of Sciences China Tencent China Tsinghua University China

ISBN: (纸本)9798350353884

Log parsing involves extracting appropriate templates from semi-structured logs, providing foundational information for downstream log analysis tasks such as anomaly detection and log comprehension. Initially, the task of log parsing was approached by domain experts who manually designed heuristic rules to extract templates. However, the effectiveness of these manual rules deteriorates when certain characteristics of a new log dataset do not conform to the pre-designed rules. To address these issues, introducing large language models (LLM) into log parsing has yielded promising results. Nevertheless, there are two limitations: one is the reliance on manually annotated templates within the prompt, and the other is the low efficiency of log processing. To address these challenges, we propose a self-evolving method called SelfLog, which, on the one hand, uses similar pairs extracted by LLM itself in the historical data to act as the prompt of a new log, allowing the model to learn in a self-evolution and labeling-free way. On the other hand, we propose an N-Gram-based grouper and log hitter. This approach not only improves the parsing performance of LLM by extracting the templates in a group-wise way instead of a log-wise way but also significantly reduces the unnecessary calling to LLMs for those logs whose group template is already extracted in history. We evaluate the performance and efficiency of SelfLog on 16 public datasets, involving tens of millions of logs, and the experiments demonstrate that SelfLog has achieved state-of-The-Art (SOTA) levels in 0.975's GA, and 0.942's PA. More importantly, without sacrificing accuracy, the processing speed has reached a remarkable 45,000 logs per second. © 2024 ieee.

关键词： Modeling languages

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A visualization Method for Association analysis and Fusion Based on large-Scale Polymorphic Information

A Visualization Method for Association Analysis and Fusion B...

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ieee International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Chao-Nan Song Yi Cao Jin Jin Qiong-Ying Fu Rong-Sen Li Hai-Bo Wang State Key Laboratory of Intelligent Game Beijing China China People's Liberation Army National Defence University Beijing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Based on mining the association relationship in the War Game data, this study extracts the key actions to provide support for the auxiliary operation deduction, operation process description and review evaluation. Based on the construction of evaluation system, relationship mining and target value ranking, this paper proposes a technical route to realize the analysis and fusion display method of operational relationship and action impact in war game data. According to the needs of different levels and angles, the distribution display and trend of data situation are presented on the visual interface. It provides theoretical guidance and practical reference for the design of deduction data fusion presentation, which is helpful to reduce the cognitive load of decision-makers, enhance situation awareness and improve decision-making efficiency.

关键词： Training visualization Accuracy Reviews Decision making data integration Games Software quality Software reliability Sorting

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Principal Component analysis visualization and State Discovery with Soil data

Principal Component Analysis Visualization and State Discove...

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ieee International Workshop on Intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Miki Sirola Markku Koskinen Tatu Polvinen Mari Pihlatie University of Helsinki Helsinki Finland

data exploration helps to gain understanding of the dataset and the system itself. There are methodologies to handle large number of sensors as well. In this paper operational states are defined to interpret physical behaviour in a soil ecosystem. Dimensionality reduction is achieved with Principal Component analysis (PCA) method giving another view to the soil dataset from spring term. K-means algorithm groups data densities by clustering the data. This grouping is the basis for defining operational states in the system. Soil data as a part of an ecosystem involves specific features. In the applied approach dynamic visualization including animations constitute an important exploration view. All experiments are realized in Jupyter programming environment with Python 3 programming language. Related literature about data visualization is reviewed. Combining methods and tools with this data as a result soil ecosystem features are recognized.

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