Time-Series Monitoring Method of Commercial Accumulations Using Digital Yellow Page Data

2011 ◽  
Author(s):  
Hiroaki Sengoku ◽  
Yuki Akyama ◽  
Ryosuke Shibasaki
Keyword(s):  
Time Series ◽  
Monitoring Method ◽  
Yellow Page

scholarly journals A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Reference Model ◽  
Big Data Analysis ◽  
Monitoring Method ◽  
Manufacturing Equipment

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.

scholarly journals Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems

2020 ◽  
Vol 4 (3) ◽  
pp. 88 ◽  
Author(s):  
Vadim Kapp ◽  
Marvin Carl May ◽  
Gisela Lanza ◽  
Thorsten Wuest
Keyword(s):  
Time Series ◽  
Real World ◽  
Manufacturing Systems ◽  
Multivariate Time Series ◽  
Series Data ◽  
Smart Manufacturing ◽  
Monitoring Method ◽  
Data Set ◽  
Failure Patterns ◽  
Smart Manufacturing Systems

This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques. The use case revolves around the auxiliary polymer manufacturing process of drying and feeding plastic granulate to extrusion or injection molding machines. The overall framework presented in this paper includes a comparison of two different approaches towards the identification of unique patterns in the real-world industrial data set. The first approach uses a subsequent heuristic segmentation and clustering approach, the second branch features a collaborative method with a built-in time dependency structure at its core (TICC). Both alternatives have been facilitated by a standard principle component analysis PCA (feature fusion) and a hyperparameter optimization (TPE) approach. The performance of the corresponding approaches was evaluated through established and commonly accepted metrics in the field of (unsupervised) machine learning. The results suggest the existence of several common failure sources (patterns) for the machine. Insights such as these automatically detected events can be harnessed to develop an advanced monitoring method to predict upcoming failures, ultimately reducing unplanned machine downtime in the future.

scholarly journals Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions

2019 ◽  
Vol 11 (6) ◽  
pp. 640 ◽  
Author(s):  
Beibei Wang ◽  
Zhenjie Chen ◽  
A-Xing Zhu ◽  
Yuzhu Hao ◽  
Changqing Xu
Keyword(s):  
Time Series ◽  
Environmental Changes ◽  
Impervious Surface ◽  
Impervious Surfaces ◽  
Spatiotemporal Resolution ◽  
Monitoring Method ◽  
Expansion Time ◽  
Trajectory Features ◽  
Surface Expansion ◽  
Multi Level

As urbanization has profound effects on global environmental changes, quick and accurate monitoring of the dynamic changes in impervious surfaces is of great significance for environmental protection. The increased spatiotemporal resolution of imagery makes it possible to construct time series to obtain long-time-period and high-accuracy information about impervious surface expansion. In this study, a three-step monitoring method based on time series trajectory segmentation was developed to extract impervious surface expansion using Landsat time series and was applied to the Xinbei District, Changzhou, China, from 2005 to 2017. Firstly, the original time series was segmented and fitted to remove the noise caused by clouds, shadows, and interannual differences, leaving only the trend information. Secondly, the time series trajectory features of impervious surface expansion were described using three phases and four types with nine parameters by analyzing the trajectory characteristics. Thirdly, a multi-level classification method was used to determine the scope of impervious surface expansion, and the expansion time was superimposed to obtain a spatiotemporal distribution map. The proposed method yielded an overall accuracy of 90.58% and a Kappa coefficient of 0.90, demonstrating that Landsat time series remote sensing images could be used effectively in this approach to monitor the spatiotemporal expansion of impervious surfaces.

scholarly journals Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1233 ◽  
Author(s):  
Chen ◽  
Xie ◽  
Yuan ◽  
Huang ◽  
Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Tool Wear ◽  
Real Time ◽  
Original Data ◽  
Attention Mechanism ◽  
Cnc Machining ◽  
Numerical Control ◽  
Real Time Monitoring ◽  
Monitoring Method

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

Automatic Monitoring Method for Surface Deformation of Coastal Area Based on Time Series Analysis

2019 ◽  
Vol 93 (sp1) ◽  
pp. 194
Author(s):  
Lei Liu ◽  
Chao Wang ◽  
Hong Zhang ◽  
Yixian Tang ◽  
Ziwen Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Coastal Area ◽  
Surface Deformation ◽  
Automatic Monitoring ◽  
Monitoring Method ◽  
Series Analysis

scholarly journals Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series

2021 ◽  
Vol 13 (2) ◽  
pp. 289
Author(s):  
Misganu Debella-Gilo ◽  
Arnt Kristian Gjertsen
Keyword(s):  
Land Use ◽  
Time Series ◽  
Deep Learning ◽  
Agricultural Land ◽  
Satellite Image ◽  
Agricultural Land Use ◽  
Monitoring Method ◽  
Multiple Observations ◽  
Land Use Types ◽  
Sentinel 2

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.

Time Series Data Inversion and Monitoring Method for Cross-Hole ERT Based on an Improved Extended Kalman Filter

2021 ◽  
Vol 26 (3) ◽  
pp. 209-225
Author(s):  
Zhengyu Liu ◽  
Yongheng Zhang ◽  
Xinxin Zhang ◽  
Huaihong Wang ◽  
Lichao Nie ◽  
...  
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Errors ◽  
Field Experiments ◽  
Series Data ◽  
Inversion Method ◽  
Time Step ◽  
Monitoring Method ◽  
Rapid Changes

In recent decades, the DC resistivity method has been applied to geophysical monitoring because of its sensitivity to hydrogeological properties. However, existing inversion algorithms cannot give a reasonable image if fluid migration is sudden and unpredictable. Additionally, systematic or measurement errors can severely interfere with accurate object location. To address these issues, we propose an improved time series inversion method for cross-hole electrical resistivity tomography (cross-hole ERT) based on the Extended Kalman Filter (EKF). Traditional EKF includes two steps to obtain the current model state: prediction and correction. We improved the prediction step by introducing the grey time series prediction method to create a new regular model sequence that can infer the potential trend of underground resistivity changes and provide a prior estimation state for reference during the next moment. To include more current information in the prior estimation state and decrease the non-uniqueness, the prediction model needs to be further updated by the least-squares method. For the correction step, we used single time-step multiple filtering to better deal with the case of sudden and rapid changes. We designed three different numerical tests simulating rapid changes in a fluid to validate the proposed method. The proposed method can capture rapid changes in the groundwater transport rate and direction of the groundwater movement for real-time imaging. Model and field experiments were performed. The inversion results of the model experiment were generally consistent with the results of dye tracing, and the groundwater behavior in the field experiment was consistent with the predicted groundwater evolution process.

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