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riskroot:Multi-dimensional root Causes Location Model Based on risk Score and Iterative Search 6

RiskRoot:Multi-Dimensional Root Causes Location Model Based ...

引用

6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Zhang, Kaiqin Zhang, dong Guo, Feng Wang, Xiaotong Ma, Hongchao duan, Yihai Qi, Kaiyuan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Inspur Electronic Information Industry Co. Ltd. System Software R&d Dept. Jinan China

ISBN: (纸本)9798350339994

For internet companies, multi-dimensional KPIs (Key Performance Indicators) are crucial monitoring data and it is essential for operations and maintenance personnel to rapidly and precisely identify the attribute combination causing anomalies in these KPIs. In this paper, a model named riskroot is proposed to address the problem of small fluctuations of anomalies or the interaction of two changes that make anomalies inconspicuous and thus difficult to locate the root cause, as well as the impact of too large search space on computational efficiency. First, all elements are divided and weighted, risk score is defined to identify root causes and non-obvious abnormalities are located through cumulative weights. Then, the hierarchical data is searched iteratively and each iteration is filtered using element pruning to ensure operational efficiency. Finally, we validate the effectiveness of riskroot on multiple datasets, the experimental results show that riskroot outperforms the state-of-the-art baseline. Its F1-score is on average 3.54 % higher than the next best method. In particular, the F1-score improves by 10.1 % in more challenging localization scenarios. © 2023 IEEE.

关键词： anomaly localization Multi-dimensional KPIs ripple effect root cause location

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Self-superviesed sleep staging model based on contrast learing 3

Self-superviesed sleep staging model based on contrast leari...

引用

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： ding, Huilong Zhang, dong Qi, Kaiyuan Wu, dong School of Electrical and Information Engineering Zhengzhou University Zhengzhou China System Software R&D Dept. Inspur Electronic Information Industry Co. Ltd. Jinan China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In the method of sleep staging task, the artificial sleep staging process is complicated, while the traditional machine learning only realizes feature classification and relies on manual feature extraction. This paper presents a deep learning method which can automatically extract and classify EEG features. At the same time, in view of the slow improvement of staging accuracy and high model complexity of existing studies, and the high cost of data due to the dependence on annotated data sets, this paper constructs the end-to-end self-supervised sleep staging model for these problems and continuously improves it. An automatic sleep staging model based on Inception structure and BILSTM was proposed. The convolutional neural network of Inception structure extracts the corresponding features from different time scales, combines the application of attention mechanism and residual structure to carry out the EEG signal characterization learning, and uses BILSTM to carry out the sequence information feature learning. Finally, a self-supervised learning method based on comparison is used to learn the potential feature representation of EEG data from a large number of unlabeled data. In the experiment, the sleep-EdF dataset was used, and the results showed that self-supervision improved the overall classification accuracy of the model. Meanwhile, the classification results of various categories were equalized, and the overall performance of the Sleep staging model was improved. © 2023 SPIE.

关键词： deep learning

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design and development of Intelligent Lighting Control system for Urban Tunnel

Design and Development of Intelligent Lighting Control Syste...

引用

IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Bao ding R&D Center of System Software Jiangsu Jinzhi Technology Co. Ltd. Nanjing PRC

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The realization of the national strategic goal of carbon peak and carbon neutral "3060" relies on the adoption of advanced technology in various industries to save energy and reduce carbon emissions. At present, the urban tunnel lighting system is characterized by inefficiency and inappropriate light distribution, which is not well adapted to the needs of safe driving and energy saving and carbon reduction. An intelligent tunnel lighting control system is presented in this paper with a rule-based reasoning engine that matches and processes collected information based on the user's predefined set of rules to generate corresponding lighting control commands. With such an intelligent light control system, urban tunnel can provides drivers with a more comfortable and safer driving environment, and saves the operating costs of energy, the workload of maintenance personnel can also be dramatically reduced meanwhile.

关键词： Power demand Energy conservation Lighting Process control system integration Control systems Lighting control Cognition Safety Engines

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A Cross-Company Architecture for Cyber-Physical Production systems and its Industrial Use 64

A Cross-Company Architecture for Cyber-Physical Production S...

引用

2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of riga Technical University, ITMS 2023

作者： Meyer, Friedrich Benisch, Michael F. Baueregger, Florian Zimmermann, Armin R&d System Software Rohde & Schwarz GmbH & Co. Kg Teisnach Germany Deggendorf Germany Digital Factory Rohde & Schwarz GmbH & Co. Kg Teisnach Germany Systems and Software Engineering Group Technische Universität Ilmenau Ilmenau Germany

ISBN: (纸本)9798350370294

Cyber-physical systems are expected to be an enabler for better control of production systems. They require a combination of heterogeneous sensors and systems processing data from various sources. We present an architecture for cyber-physical production systems for cross-company sensor integration, ensuring the necessary data safety. Its design targets generalizability and performance. A real-life industrial application in the area of optical components polishing demonstrates the benefits. © 2023 IEEE.

关键词： data handling

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Forecasting of Low Visibility Using Weather and Air Quality data for Safe and Smooth Transportation Operation

Forecasting of Low Visibility Using Weather and Air Quality ...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Topon Paul Vidhisha reddy Sai Prem Kumar Ayyagari ryusei Shingaki Kaneharu Nishino System AI Laboratory Corporate R&D Center Toshiba Corporation Kanagawa Japan R&D Division Toshiba Software (India) Pvt. Ltd. Bangalore KA India

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Low visibility condition affects the safety and operation of road, sea, and air traffic, which causes disruption in transportation and sometimes leads to serious accidents. Therefore, forecasting of low visibility accurately is of utmost importance for transportation agencies and aviation authorities. The conventional approach of visibility forecasting using time-series weather data cannot forecast low visibility with high accuracy due to imbalanced and clipped visibility data used to build a forecasting model. To address this issue, we propose a new approach that combines forecasting and classification methods with discrete and kernel density estimation (KdE)-based weights to build forecasting models for multi-step ahead forecasting. We show the effectiveness of our proposed approach by performing experiments with four publicly available visibility datasets of weather and air quality data from the USA and Japan and evaluating in terms of forecasting errors and accuracy of forecasting of low visibility. Experimental results suggest that our proposed approach can improve rMSE and MAE in the low visibility range by respectively 29–54 % and 40–59 % and F-score of forecasting of low and high visibility by 0.20-0.26 on the average over existing technologies for 3–24 hour ahead forecasting, which implies that it is well-suited for forecasting of low visibility.

关键词： Accuracy Atmospheric modeling Weather forecasting Transportation Predictive models Air quality data models Forecasting Wind forecasting Meteorology

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design of X-ray Generator Simulator 1

Design of X-ray Generator Simulator

引用

1st International Conference on recent Trends in Microelectronics, Automation, Computing and Communications systems, ICMACC 2022

作者： Shinde, Akash Mali, Swapnil G Thekkuttu, Sreekumar College of Engineering Pune Dept. of Extc Maharashtra Pune411005 India System Software Architect Igt Systems Hic R&D Philips Pune India

ISBN: (纸本)9781665496049

X-ray machine systems are used to acquire X-ray images of internal human body parts. A C-arm is a type of X-ray machine system used to produce X-ray images of desired body parts. The creation of X-rays is essential to the development of X-ray images. An X-ray generator built within the C-arm system produces the X-rays. The Master Controller is the brain of C-arm systems, which monitors and controls all the components of the C-arm system. Communication between the Master Controller and the X-ray generator is required in order to regulate the various functionalities of the X-ray generator. Using an actual X-ray generator to test the C-arm system's performance can be expensive and time-consuming. Thus, it is necessary to create a simulator that may take the role of an X-ray generator. In this research, a novel method for creating an X-ray generator simulator that uses an Arduino board in place of the X-ray generator is discussed. The Arduino board is programmed to perform like an X-ray generator, and communication between the Arduino board and Master Controller is obtained to simulate the functionalities of an X-ray generator. © 2022 IEEE.

关键词： Controllers

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Log Anomaly detection Based on Bi-LSTM Feature Extraction 5

Log Anomaly Detection Based on Bi-LSTM Feature Extraction

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5th International Conference on data Science and Information Technology, dSIT 2022

作者： Zhang, Yue Zhang, dong Guo, Feng Wang, Xiaotong duan, Yihai Zhang, Xiaonan School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Inspur Electronic Information Industry Co. Ltd. System Software R&D Dept. Jinan China Inspur Electronic Information Industry Co. Ltd. System Software R&D Dept. Zhengzhou China Compute Science and Technology Harbin Institute of Technology Weihai Weihai China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

With the development of the Internet and the digital age continues to advance, traditional operation and maintenance methods can no longer meet the maintenance of large-scale distributed systems, AIOps emerges as the times require. In AIOps, finding anomalies is a top priority. PCA (Principal Component Analysis) is a traditional machine learning algorithm, which is used for log anomaly detection using the dimensionality reduction feature of PCA algorithm, the advantages of this detection method are: fast detection and simple model. However, the anomaly detection effect is not satisfactory, because when extracting the features of log data, the sequence features and semantic features cannot be sufficiently mined, resulting in a lower accuracy rate. For this problem, we propose a feature extraction improvement model. This model is based on Bi-LSTM (Bidirectional Long Short Term Memory) deep network to improve the feature extraction method of PCA model, extracting semantic features of log sequences by exploiting the ability of long short-term memory network to grasp contextual information, and feed the vector representation matrix containing the semantic features into the PCA anomaly detection network for anomaly detection. The model is validated on HdFS open source dataset and Inspur server dataset, and the results show that the F1-measure of this model is 36.3 % higher than that of the traditional PCA model, and 1.1 % higher than that of the single Bi-LSTM model. © 2022 IEEE.

关键词： Long short-term memory

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Social Media Multi-Emotion detection 2

Social Media Multi-Emotion Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ibrahiem, Soha S. El-Sayed Mohammed, Bahy Ibrahim El Sayed Ahmed, Mohammed Ahmed Elbahnasy, Khaled Menofia University Computality R&D System Analyst & Nlp Researcher Faculty of Science Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Jeddah International College Computer Science and Information Technology Department Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states;however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic regression (Lr), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection. © 2025 IEEE.

关键词： Tweets

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residual Signature Analysis for Wind Turbine Failure Prognosis

Residual Signature Analysis for Wind Turbine Failure Prognos...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Topon Paul Vidhisha reddy Sai Prem Kumar Ayyagari Kaneharu Nishino System AI Laboratory Corporate R&D Center Toshiba Corporation Kanagawa Japan R&D Division Toshiba Software (India) Pvt. Ltd. Bangalore KA India

In operation and maintenance of wind turbines, predictive maintenance is widely used to reduce the downtime and low operating efficiency due to failure of a wind turbine, in which the condition of a wind turbine is continuously monitored and measured by attaching a number of sensors, sign of near-future failure or degradation is detected early before the actual failure occurs, and faulty parts are replaced or maintenance is performed. In this context, automatic prognosis of failure by analyzing the time-series data of the attached sensors is very important. recently, various machine learning techniques are proposed where sign of failure is predicted based on the residual errors between the actual condition and predicted condition of a wind turbine with a prediction model built by using the time-series data. However, when the residual errors fluctuate due to external factors, this conventional approach may generate many false alarms and/or overlook potential points of failure prognosis. In this paper, we propose residual Signature Analysis (reSA) that generates anomaly scores by applying signal processing and clustering techniques on the segmented residual errors, which are generated by applying a prediction method on the time-series data. To show the effectiveness of the proposed method, we apply reSA to three wind turbines of two publicly available datasets and show the experimental results. Our experimental results suggest that our proposed method can detect sign of failure much earlier than actual time of failure with a very low rate (<0.75%) of false alarms compared to conventional residual analysis-based approach.

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Methods for Improving the Performance of Tactical Information displays

Methods for Improving the Performance of Tactical Informatio...

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Byoung-Kug Kim Yonghoon Cha Jae-Hwan Lim Keun-Woong ryu Heon-Jei Park Sung-Hwa Hong dept. of computer software Induk University Seoul Korea dept. of R&D Center Koreanair Daejeon Korea dept. of R&D Center Link Nine System Daejeon Korea CEO Link Nine System Daejeon Korea dive of Nav. Info. System Mokpo National Maritime Univ. Mokpo Korea

In Tactical situation display, lots of tactical information are expressed as symbols with an electronic map. There are various symbols depending on the situation throughout the mission (i.e., planning/operating/debriefing), and coordinate calculation is essential to display them in the appropriate location on the electronic map. Many tactical displays implement appropriate functionality to determine the location of each symbol, but they are affected by the performance of the computer processor. This proposal designs a method to decrease the overall display latency through fast positioning.

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