检索结果-内蒙古大学图书馆

long short-term memory autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting

引用

ENERGIES 2024年第15期17卷 3666-3666页

作者： Moon, Yeeun Lee, Younjeong Hwang, Yejin Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 South Korea Sungkyunkwan Univ Dept Biomechatron Engn 2066 Seobu Ro Suwon 16419 South Korea

Electricity consumption prediction is crucial for the operation, strategic planning, and maintenance of power grid infrastructure. The effective management of power systems depends on accurately predicting electricity usage patterns and intensity. This study aims to enhance the operational efficiency of power systems and minimize environmental impact by predicting mid to long-term electricity consumption in industrial facilities, particularly in forging processes, and detecting anomalies in energy consumption. We propose an ensemble model combining Extreme Gradient Boosting (XGBoost) and a long short-term memory autoencoder (LSTM-AE) to accurately forecast power consumption. This approach leverages the strengths of both models to improve prediction accuracy and responsiveness. The dataset includes power consumption data from forging processes in manufacturing plants, as well as system load and System Marginal Price data. During data preprocessing, Expectation Maximization Principal Component Analysis was applied to address missing values and select significant features, optimizing the model. The proposed method achieved a Mean Absolute Error of 0.020, a Mean Squared Error of 0.021, a Coefficient of Determination of 0.99, and a Symmetric Mean Absolute Percentage Error of 4.24, highlighting its superior predictive performance and low relative error. These findings underscore the model's reliability and accuracy for integration into Energy Management Systems for real-time data processing and mid to long-term energy planning, facilitating sustainable energy use and informed decision making in industrial settings.

关键词： factory energy management system electricity consumption prediction deep learning long short-term memory autoencoder extreme gradient boosting

来源：评论

学校读者我要写书评

暂无评论

Wear anomaly detection method of tunnel boring machine disc cutters based on anomaly-attention improved long short-term memory autoencoder

引用

MEASUREMENT 2025年 253卷

作者： Zhang, Kai Wang, Junren Zheng, Qing Ding, Guofu Li, Zhixuan Zhang, Zhonghua Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China Southwest Jiaotong Univ Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China China Railway Engn Serv Co Ltd Chengdu Peoples R China

Wear anomaly detection method of tunnel boring machine disc cutters based on anomaly-attention improved long short-term memory autoencoder

关键词： Tunnel boring machine Disc cutter Anomaly detection long short-term memory autoencoder Anomaly attention mechanism

来源：评论

学校读者我要写书评

暂无评论

MOLA: Enhancing Industrial Process Monitoring Using a Multi-Block Orthogonal long short-term memory autoencoder

引用

PROCESSES 2024年第12期12卷 2824-2824页

作者： Ma, Fangyuan Ji, Cheng Wang, Jingde Sun, Wei Tang, Xun Jiang, Zheyu Oklahoma State Univ Sch Chem Engn 420 Engn North Stillwater OK 74078 USA Beijing Univ Chem Technol Coll Chem Engn Beijing 100029 Peoples R China Louisiana State Univ Cain Dept Chem Engn Baton Rouge LA 70803 USA

In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to constrain the latent space output. This helps eliminate the redundancy in the features identified, thereby improving the overall monitoring performance. On top of this, a multi-block monitoring structure is proposed, which categorizes the process variables into multiple blocks by leveraging expert process knowledge about their associations with the overall process. Each block is associated with its specific orthogonal long short-term memory autoencoder model, whose extracted dynamic orthogonal features are monitored by distance-based Hotelling's T2 statistics and quantile-based cumulative sum (CUSUM) designed for multivariate data streams that are nonparametric and heterogeneous. Compared to having a single model accounting for all process variables, such a multi-block structure significantly improves overall process monitoring performance, especially for large-scale industrial processes. Finally, we propose an adaptive weight-based Bayesian fusion (W-BF) framework to aggregate all block-wise monitoring statistics into a global statistic that we monitor for faults. Fault detection speed and accuracy are improved by assigning and adjusting weights to blocks based on the sequential order in which alarms are raised. We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman process and comparing the performance with various benchmark methods.

关键词： process monitoring fault detection long short-term memory autoencoder Bayesian fusion CUSUM

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using long short-term memory autoencoder

引用

ENERGIES 2023年第13期16卷 4965页

作者： Akins, Alexandra Kultgen, Derek Heifetz, Alexander Argonne Natl Lab Nucl Sci & Engn Div Lemont IL 60439 USA North Carolina State Univ Dept Nucl Engn Raleigh NC 27006 USA

Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold trap to prevent corrosion and obstructing small orifices. We have developed a deep learning long short-term memory (LSTM) autoencoder for continuous monitoring of a cold trap and detection of operational anomaly. Transient data were obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The cold trap purification at METL is monitored with 31 variables, which are sensors measuring fluid temperatures, pressures and flow rates, and controller signals. Loss-of-coolant type anomaly in the cold trap operation was generated by temporarily choking one of the blowers, which resulted in temperature and flow rate spikes. The input layer of the autoencoder consisted of all the variables involved in monitoring the cold trap. The LSTM autoencoder was trained on the data corresponding to cold trap startup and normal operation regime, with the loss function calculated as the mean absolute error (MAE). The loss during training was determined to follow log-normal density distribution. During monitoring, we investigated a performance of the LSTM autoencoder for different loss threshold values, set at a progressively increasing number of standard deviations from the mean. The anomaly signal in the data was gradually attenuated, while preserving the noise of the original time series, so that the signal-to-noise ratio (SNR) averaged across all sensors decreased below unity. Results demonstrate detection of anomalies with sensor-averaged SNR < 1.

关键词： sodium cooled fast reactors liquid sodium purification artificial intelligence long short-term memory autoencoder anomaly detection cold trap

来源：评论

学校读者我要写书评

暂无评论

short-term Fault Prediction of Wind Turbines Based on Integrated RNN-LSTM

引用

IEEE ACCESS 2024年 12卷 22465-22478页

作者： Rama, V. Siva Brahmaiah Hur, Sung-Ho Yang, Jung-Min Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

This paper presents a data-driven approach to short-term wind turbine fault prediction and condition monitoring based on a hybrid architecture of recurrent neural network and long short-term memory. The proposed architecture is established by utilizing time series data from the supervisory control and data acquisition system and a Bladed model of a 5 MW wind turbine to predict faults occurring to the wind generator. The recurrent neural network-long short-term memory training procedure is enhanced with self-organizing maps and long short-term memory auto encoder so as to describe the complex interaction between the mechanical system and unpredictable wind speed. To verify the performance of the proposed scheme, we conduct in-depth numerical experiments by applying the hybrid architecture to the Bladed 5 MW wind turbine model with rated wind speed of 11.8 m/s. Experimental results confirm that the proposed scheme has superior accuracy and practicality of fault prediction compared with eminent existing machine learning algorithms such as extreme gradient boost and random forest regressor.

关键词： Extreme gradient boosting fault prediction long short-term memory autoencoder random forest regressor self-organizing maps wind turbine

来源：评论

学校读者我要写书评

暂无评论

Unsupervised outlier detection for time-series data of indoor air quality using LSTM autoencoder with ensemble method

引用

JOURNAL OF BIG DATA 2023年第1期10卷 66页

作者： Park, Junhyeok Seo, Youngsuk Cho, Jaehyuk Soongsil Univ Dept Elect & Informat Engn Seoul South Korea Soongsil Univ Dept Math Seoul South Korea Jeonbuk Natl Univ Dept Software Engn Jeonju Si Jeollabuk Do South Korea

The proposed framework consists of three modules as an outlier detection method for indoor air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based reconstruction error detector, which designs the LSTM layer in the shape of an autoencoder, to build a reconstruction error-based outlier detection model and extract latent features. The latent feature class-assisted vector machine detector constructs an additional outlier detection model using previously extracted latent features. Finally, the ensemble detector combines the two independent classifiers to define a new ensemble-based decision rule. Furthermore, because real-time anomaly detection proceeds with unsupervised learning, more stable and consistent external detection rules are defined than when using a single ensemble model. Laboratory tests with five random cases were performed for objective evaluation. Thus, we propose a framework that can be applied to various industrial environments by detecting and defining stable outlier decision rules.

关键词： long short-term memory autoencoder Environmental sensor Anomaly detection Multivariate time series Condition monitoring

来源：评论

学校读者我要写书评

暂无评论

Two-Level Optimal Bidding Strategy for Load Aggregator Based on a Data-Driven Approach Combined With LSTM-Based Forecasting and Agent-Based Models

引用

IEEE ACCESS 2023年 11卷 89249-89264页

作者： Ryu, Han Seok Kim, Hyung Joon Kim, Mun Kyeom Chung Ang Univ Dept Energy Syst Engn Seoul 06974 South Korea

Demand response (DR) is an economical way of addressing the challenges faced by the massive penetration of distributed energy resources, such as renewable energy. Residential consumers account for a significant proportion of electricity consumption. However, their behavior is highly random and uncertain, meaning it is difficult to quantify the impact of DR programs in which they participate. This paper presents a two-level optimal bidding strategy framework for load aggregators that combines a data-driven forecasting model and a data-driven agent-based model (D-ABM) to provide a realistic estimate of the impact of DR. First, the aggregated load of all consumers and market prices are predicted via a long short-term memory (LSTM) autoencoder forecasting model. Then, the proposed D-ABM estimates and quantifies the difference rate in terms of total load due to DR. Since D-ABM is a bottom-up approach, each consumer can be treated as a heterogeneous agent and changes in individual electricity usage patterns due to DR can be estimated. Changes in collective electricity consumption patterns can also be quantified by considering the estimated individual behavior and the interactions defined by the basic rules. In addition, assumptions about biases and preferences that explain the irrationality of individual decision-making are given to agents, and the uncertainty of DR participation is considered more realistically. Finally, based on these uncertainties addressed at each level, various bidding strategies for load aggregators can be obtained. The numerical simulation results indicate that our framework provides a more realistic estimation of the impact of total load under DR, minimizes any deviations from bidding strategies, and ensures maximum profits for load aggregators.

关键词： Bidding strategy load aggregator demand response long short-term memory autoencoder data-driven agent-based model

来源：评论

学校读者我要写书评

暂无评论

Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach

引用

SENSORS 2024年第1期24卷 82页

作者： Diraco, Giovanni Manni, Andrea Leone, Alessandro Natl Res Council Italy Inst Microelect & Microsyst P Lecce Monteroni km 1-200 I-73100 Lecce Italy

Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and long short-term memory autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system's performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection.

关键词： Abnormal Gait Detection Ambient Assisted Living RGB-D Camera Temporal Convolutional Network Gated Recurrent Unit long short-term memory autoencoder

来源：评论

学校读者我要写书评

暂无评论

Analyzing and Interpreting Students' Self-regulated Learning Patterns Combining Time-series Feature Extraction, Segmentation, and Clustering

引用

JOURNAL OF EDUCATIONAL COMPUTING RESEARCH 2022年第5期60卷 1130-1165页

作者： Zhang, Mingyan Du, Xu Hung, Jui-long Li, Hao Liu, Mengfan Tang, Hengtao Zhejiang Normal Univ Coll Teacher Educ Jinhua Zhejiang Peoples R China Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan Peoples R China Boise State Univ Dept Educ Technol 1910 Univ Dr Boise ID 83725 USA Cent China Normal Univ Natl Engn Lab Educ Big Data Wuhan 430079 Peoples R China Univ South Carolina Dept Educ Studies Columbia SC USA

In online learning, students' learning behavior might change as the course progresses. How students adjust learning behaviors aligned with course requirements reflects their self-regulated learning strategies. Analyzing students' learning patterns can help instructors understand how the course design or activities shape students' learning behaviors, including their learning beliefs and motivation, and facilitate teaching decision makings accordingly. This study aims to propose a scientific analytic method to understand students' self-regulated learning (SRL) patterns. The whole process includes the following four steps: (1) encoding behavioral patterns;(2) detecting turning points and chunking behavioral patterns;(3) grouping similar patterns;and (4) interpreting results. A case study with 4604 K-12 students from 476 courses was conducted to validate the proposed method. Five successful patterns, three at-risk patterns, and three average patterns were identified. The case study indicated that successful students showed at least one of the following characteristics: (1) Balanced, (2) Proactive and Balanced, and (3) Balanced with one highly engaged behavior. The at-risk students showed the following characteristics: (1) Oscillatory and (2) Low Engaged. Patterns which led to successful or at-risk conditions are compared and connected with corresponding SRL strategies. Practical and research implications are discussed in the article as well.

关键词： long short-term memory autoencoder learning pattern time-series turning points learning performance

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Unsupervised Learning Architecture to Enhance User Behavior Anomaly Detection 14

A Lightweight Unsupervised Learning Architecture to Enhance ...

引用

IEEE Latin-American Conference on Communications (LATINCOM)

作者： Molina, Andre L. B. Goncalves, Vinicius P. de Sousa Jr, Rafael T. Pividal, Marcel Meneguette, Rodolfo, I Rocha Filho, Geraldo P. Univ Brasilia Elect Engn Dept Brasilia DF Brazil Amazon Web Serv World Wide Specialist Org AI ML Miami FL USA Univ Sao Paulo Inst Math & Comp Sci Sao Carlos Brazil Univ Brasilia Comp Sci Dept Brasilia DF Brazil

ISBN: (纸本)9781665482257

In recent years, user behavior anomaly detection has been gaining attention in cybersecurity. A crucial challenge that has been discussed in the literature is that supervised models that use vast amounts of data for training do not apply to real scenarios for anomaly detection. Within this context, the requirement to gather datasets with labeled behavior anomalies has proven to be a significant limiting factor for evaluating different models. This paper presents WEAPON, an unsupervised learning-based architecture for user behavior anomaly detection that requires a small amount of data for building behavior profiles considering the individuality of each user. WEAPON implements the weak supervision-based behavior anomaly labeling approach using Snorkel. When compared to other approaches, WEAPON proved to be more efficient, surpassing the ROC curve of the second best model by 4.31%. Furthermore, WEAPON outperforms rule-based methods by finding anomalies that an expert would not anticipate.

关键词： User Behavior Anomaly Detection autoencoders Wide and Deep Neural Networks long short-term memory autoencoder temporal convolution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：