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Method for Detecting Disorder of a Nonlinear Dynamic Plant

SENSORS 2025年第4期25卷 1256-1256页

作者： Wang, Xuechun Eliseev, Vladimir Natl Res Univ Dept Control & Intelligent Technol Moscow Power Engn Inst Krasnokazarmennaya 14 Moscow 111250 Russia Infotecs JSC Mishina 56 Moscow 127083 Russia

This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input-output relation using a cross-correlation function and neural network autoencoder. The CCF-AE method does not use the reference model of the dynamic object, but only considers real-time behavior changes, given by input and output time series. The proposed method was used to detect disorder in the process of a nonlinear pH neutralization reaction, and was compared with the cumulative sum control chart (CUsum) and the exponentially weighted moving variance control chart (EWMV). The CCF-AE method demonstrates a better true detection rate and lower false alarm rate than CUsum and EWMV. Also, CCF-AE has more advantages in detecting disorder of complex nonlinear processes.

关键词： disorder detection time series dynamic plant cross-correlation function neural network autoencoder cumulative sum algorithm exponentially weighted moving variance pH neutralization reaction

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Monitoring Tree Cover Change Using the Cusum algorithm: A Case Study in the Southern Western Ghats of Kerala

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年第3期53卷 803-814页

作者： Anjitha, A. S. Nitish Sri Surya, N. Reddy, C. Sudhakar Asok, Smitha V. All Saints Coll Dept Environm Sci Thiruvananthapuram 695007 Kerala India Indian Space Res Org Forest Biodivers & Ecol Div Natl Remote Sensing Ctr Forestry & Ecol Grp Hyderabad 500037 Telangana India

The world's forest ecosystem plays a crucial role in maintaining biodiversity, regulating climate, and providing numerous ecosystem services essential for human well-being. Changes in tree cover due to deforestation and degradation have become a critical global concern, making it essential to monitor and address disturbances for effective ecosystem management. Change detection methods employing remote sensing technology offer cost-effective and near-real-time monitoring of the changes in tree cover. In the present study, we evaluated the cumulative sum (Cusum) algorithm-based change detection method in combination with bootstrap analysis to monitor forest disturbance in two wildlife sanctuaries (WLSs), Neyyar and Peppara, in Thiruvananthapuram district, Kerala. The HV polarization backscatter coefficient of the time series ALOS PALSAR data (L-band) was utilized. A cluster of pixels with changes was detected in 2020 and 2021 by the algorithm in the southern part of Neyyar WLS. Through the visual interpretation of the change detected pixels with the corresponding areas in Sentinel-2 images and Google Earth images, the detected change was validated with the loss of tree cover. By analyzing the subsequent images from Sentinel-2, Google Earth, the areas that were deforested were observed to have been partly afforested or regenerated while the rest of the area remained unaltered. The study highlights the need for continuous monitoring of the protected areas to prevent damage to the forest ecosystem. Moreover, the results of the study give valuable insights into the reliability of the algorithm and ALOS PALSAR time series data in detecting changes in tree cover, with immense application in the change detection of dense tropical forest ecosystems.

关键词： Tree cover change cumulative sum algorithm ALOS PALSAR Sentinel-2 Google Earth

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ReCusum: A polyvalent method to monitor tropical forest disturbances

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2023年第1期203卷 358-372页

作者： Bertrand, Ygorra Frederic, Frappart Jean-Pierre, Wigneron Christophe, Moisy Thibault, Catry Benjamin, Pillot Jonas, Courtalon Anna, Kharlanova Serge, Riazanoff INRAE UMR1391 ISPA F-33140 Villenave Dornon France VisioTerra F-77420 Champs Sur Marne France Univ Bordeaux F-33400 Talence France Univ Reunion Univ Guyane Univ Antilles Univ MontpellierESPACE DEV Montpellier France

Change detection methods based on Earth Observations are increasingly used to monitor rainforest in the intertropical band. Until recently, deforestation monitoring was mainly based on remotely sensed optical images which often face limitations in humid tropical areas due to frequent cloud coverage. This leads to late detections of disturbance events. Since the launch of Sentinel-1 acquiring images with a revisit time of 12 days and a spatial resolution of 5 x 20 m in Brazil, Synthetic Aperture Radar (SAR) images have been increasingly used to monitor deforestation. In this study, we propose a multitemporal version of the change detection method we previously applied to timeseries of Sentinel-1 SAR images, to monitor deforestation/degradation in the Congo rainforest. Our approach is based on a cumulative sum (Cusum) method combined with a spatial recombination of Critical Thresholds (Cusum cross-Tc). The newly developed multitemporal Cusum method (ReCusum) was applied to a time-series of 82 dual polarization (VH, VV) Ground Range Detected (GRD) Sentinel-1 images acquired in the Para State, in the Brazilian Amazonia, from 29/09/2016 to 01/07/2019. The ReCusum method consists of iteratively applying the Cusum cross-Tc to monitor multiple changes in a time-series by splitting the time-series at each date of detected change and by independently iterating over the time periods resulting from the splits. The number of changes in the time-series was then analysed according to the vegetation type (Forest, non-forest vegetation) determined by visual inspection of optical Sentinel-2 image and PlanetScope monthly mosaic. This showed a difference between non-forest vegetation and forested areas. A threshold based on the number of changes (Tnbc) was then developed to differentiate forest from non-forest disturbances. The ability to monitor non-forest vegetation was analysed: the Cusum cross-Tc detected up to 90% of the total non-forest vegetation area over the study region in th

关键词： Remote Sensing Sentinel-1 cumulative sum algorithm Tropical Deforestation Multiple Change Detection Forest/Non-Forest map

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Improving the capability of detecting joints and fractures in rock mass from roof bolt drilling data by using wavelet analysis

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INTERNATIONAL JOURNAL OF OIL GAS AND COAL TECHNOLOGY 2019年第1期20卷 97-112页

作者： Liu, Wenpeng Saab, Samer S., Jr. Rostami, Jamal Ray, Asok Colorado Sch Mines Earth Mech Inst Dept Min Engn Golden CO 80401 USA Penn State Univ Dept Elect Engn State Coll PA 16802 USA Penn State Univ Dept Mech & Nucl Engn State Coll PA 16802 USA

To optimise ground supporting and mitigate ground instability, a proper understanding of the ground conditions is critical. The concept of monitoring drilling parameters of a bolter for ground characterisation, which refers to identifying geological features included locations of joints and strengths of rock layers, has been studied in the past few decades. Several intelligent drilling units have been developed for joint detection but have limited capabilities. For instance, the existing systems fail to discriminate joints with the aperture of less than 3.175 mm and tend to generate false alarms. The objective of this research was to develop more efficient and sensitive detection programs for joint detection. To achieve this objective, a series of full-scale drilling tests with various simulated joint conditions have been conducted, and new detection programs have been proposed based on pattern recognition algorithms. Moreover, wavelet analysis has been applied to pre-process data to further promote detection programs.

关键词： wavelet analysis cumulative sum algorithm CUsum algorithm roof bolter drilling parameters joint detection ground support optimisation

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Low-Complexity Quickest Change Detection in Linear Systems With Unknown Time-Varying Pre- and Post-Change Distributions

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第3期67卷 1804-1824页

作者： Zhang, Jiangfan Wang, Xiaodong Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Columbia Univ Dept Elect Engn New York NY 10027 USA

Motivated by the sequential detection of false data injection attacks (FDIAs) in a dynamic smart grid, we consider a more general problem of sequentially detecting a time-varying change in a dynamic linear regression model. To be specific, when the change occurs, a time-varying unknown vector is added in the linear regression model. The parameter vector of the linear regression model is also assumed to be unknown and time-varying. Thus, the pre- and post-change distributions are both unknown and time-varying. This imposes a significant challenge for designing a computationally efficient sequential detector. We first propose two cumulative-sum-type algorithms to address this challenge. One is called generalized cumulative-sum (GCUsum) algorithm, and the other one is called relaxed generalized cumulative-sum (RGCUsum) algorithm, which is a modified version of the GCUsum. It can be shown that the computational complexity of the proposed RGCUsum algorithm scales linearly with the number of observations. Next, considering Lordon's setup, for any given constraint on the expected false alarm period, a lower bound on the threshold employed in the proposed RGCUsum algorithm is derived, which provides a useful guideline for the design of the proposed RGCUsum algorithm to achieve any prescribed performance requirement in practice. In addition, for any given threshold employed in the proposed RGCUsum algorithm, an upper bound on the expected detection delay is also provided. The performance of the proposed RGCUsum algorithm is numerically studied in the context of an IEEE standard power system under FDIAs. Moreover, the numerical results demonstrate the superiority of the proposed RGCUsum in computational efficiency.

关键词： Smart grids Heuristic algorithms Linear regression Time-varying systems Meters Data models Computational modeling cumulative sum algorithm false data injection attack dynamic linear regression model smart grid cybersecurity

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Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2007年第5-6期34卷 567-583页

作者： Lin, Hong-Dar Ho, Duan-Cheng Chaoyang Univ Technol Dept Ind Engn & Management Taichung 41349 Taiwan

This paper presents a global approach for the automatic inspection of tiny pinhole defects in randomly textured surfaces of surface barrier layer (SBL) chips. By means of a discrete cosine transform (DCT)-based image restoration scheme, the proposed method is independent of textural features and thus not confined by the limitations of feature extraction based methods. Through properly decomposing the frequency matrix of an image in the DCT domain and selecting the best radius of the sector filter for the high-pass filtering operation, we effectively attenuate the global random texture pattern and accentuate only tiny pinhole defects in the restored image. We also develop two accumulative sum detection procedures that automatically determine the best high-pass filtering parameters based on the abrupt changes of the frequency coefficients in the decomposed matrix. Experimental results show that the proposed method outperforms the traditional approach in reducing the Type I error by 70-80% and in decreasing the deviation of the defect areas by 95%. Moreover, the proposed method can be applied to various types of passive components in large-batch production because no precise positioning of the target chip or template matching is required.

关键词： pinhole defect detection computer vision system discrete cosine transform cumulative sum algorithm enhancement of frequency domain

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Autocorrelation-Based Quickest Change Detection

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IEEE COMMUNICATIONS LETTERS 2020年第12期24卷 2913-2916页

作者： Afser, Huseyin Yabaci, Seyhun Barbaros Adana Alparslan Turke Sci & Technol Univ Dept Elect & Elect Engn TR-01250 Adana Turkey Cukurova Univ Dept Elect & Elect Engn TR-01330 Adana Turkey

We consider the utilization of the autocorrelation information for aiding the quickest detection problem. Specifically, we investigate the problem of quickly detecting a Gaussian source with autocorrelation such that some of its symbols are repeated as cyclic prefixes. Based on the cumulative sum algorithm, we propose a method which takes advantage of this autocorrelation in order to provide performance improvement compared to the classical energy based detection of the uncorrelated source.

关键词： Correlation Signal to noise ratio Delays OFDM AWGN Change detection algorithms Probability Quickest detection cumulative sum algorithm autocorrelation detection energy detection

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A semi-supervised inattention detection method using biological signal

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ANNALS OF OPERATIONS RESEARCH 2017年第1期258卷 59-78页

作者： Choi, Yerim Park, Jonghun Shin, Dongmin Kyonggi Univ Dept Ind & Management Engn Suwon 16227 Gyeonggi South Korea Seoul Natl Univ Dept Ind Engn Seoul 151744 South Korea Seoul Natl Univ Inst Ind Syst Innovat Seoul 151744 South Korea Hanyang Univ Dept Ind & Management Engn Ansan 425791 Gyeonggi South Korea

Recently, operations research methods have been utilized for biological data analysis as a huge amount of biological data becomes available. One of popular applications of the data analysis is inattention detection of operators in human-machine interaction systems using electroencephalography (EEG) signal. Most of the previous studies on the inattention detection employed supervised learning approaches, but their results have potential bias since they rely on imperfect assumptions for the acquisition of mental state labels, attention and inattention, due to the absence of the standardized measure for the mental states. Instead, we consider unsupervised learning approach, where no labeled data is required. In order to address the low performance of unsupervised learning approaches, attention duration for which an operator sustains his/her attention from the beginning of performing a task and relevance levels between four attributes of EEG signal and mental states are exploited. In this regard, we propose a semi-supervised inattention detection method (SID), in which attention duration and attributes-weights of EEG signal are respectively utilized as a small portion of labeled data for semi-supervised learning and weights for similarity calculation. Specifically, cumulative sum algorithm is used for the determination of the attention duration, and constrained attributes-weighting clustering algorithm is used for the estimation of attributes-weights. From experiments using real-world dataset, SID outperformed the compared methods, and it is expected that the adoption of SID will contribute to the enhancement of the operators' safety.

关键词： Inattention detection Biological signal Semi-supervised method cumulative sum algorithm Constrained attributes-weighting clustering algorithm

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Auxiliary signal design for detecting changes towards unknown hypotheses

Auxiliary signal design for detecting changes towards unknow...

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1997 IEEE International Symposium on Intelligent Control

作者： Kerestecioglu, F Cetin, I Bogazici Univ Istanbul Turkey

ISBN: (纸本)0780341171

The effects of auxiliary input signals on detecting changes in CARMA models via statistical tests are discussed. It is assumed that parameters of the system before the change is known whereas those describing the after-change situation is unknown. The performance criteria for the input design are based on the local and asymptotic properties of the test. It is concluded that the optimal input spectrum consist of a finite number of frequencies and that a suitable choice for the input can drastically improve the detection performance.

关键词： change detection statistical tests cumulative sum algorithm input design

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Monitoring loss of tropical forest cover from Sentinel-1 time-series: A Cusum-based approach

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2021年 103卷 102532-102532页

作者： Ygorra, B. Frappart, F. Wigneron, J. P. Moisy, C. Catry, T. Baup, F. Hamunyela, E. Riazanoff, S. INRAE UMR1391 ISPA F-33140 Villenave Dornon France VisioTerra F-77420 Champs Sur Marne France Univ Bordeaux F-33400 Talence France Univ Toulouse LEGOS UMR CNES CNRS IRD UPS F-31400 Toulouse France Univ Reunion Univ Guyane Univ Antilles ESPACE DEVUniv MontpellierIRD Montpellier France CESBIO F-31400 Toulouse France Univ Namibia Private Bag 13301 Windhoek Namibia

The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the cumulative sum algorithm (Cusum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

关键词： Deforestation Remote sensing Sentinel-1 cumulative sum algorithm Tropical forest Change detection

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