Change Detection and Prognostics for Transient Real-World Processes Using Streaming Data

2018 ◽  
pp. 279-315
Author(s):  
Ashif Sikandar Iquebal ◽  
Satish Bukkapatnam
Keyword(s):  
Change Detection ◽  
Real World ◽  
Streaming Data

scholarly journals Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

2019 ◽  
Vol 8 (6) ◽  
pp. 272 ◽  
Author(s):  
Iq Reviessay Pulshashi ◽  
Hyerim Bae ◽  
Hyunsuk Choi ◽  
Seunghwan Mun ◽  
Riska Asriana Sutrisnowati
Keyword(s):  
South Korea ◽  
Real World ◽  
Experimental Evaluation ◽  
Streaming Data ◽  
Harsh Environments ◽  
Noise Filtering ◽  
New Approach ◽  
Detection Algorithms ◽  
Outlying Point

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach.

scholarly journals Active Recurrence of Lighting Condition for Fine-Grained Change Detection

2018 ◽  
Author(s):  
Qian Zhang ◽  
Wei Feng ◽  
Liang Wan ◽  
Fei-Peng Tian ◽  
Ping Tan
Keyword(s):  
Change Detection ◽  
Light Source ◽  
Real World ◽  
Reference Image ◽  
Light Sources ◽  
Lighting Condition ◽  
Fine Grained ◽  
Lighting Conditions ◽  
Point Light ◽  
Change Monitoring

This paper addresses active lighting recurrence (ALR), a new problem that actively relocalizes a light source to physically reproduce the lighting condition for a same scene from single reference image. ALR is of great importance for fine-grained visual monitoring and change detection, because some phenomena or minute changes can only be clearly observed under particular lighting conditions. Hence, effective ALR should be able to online navigate a light source toward the target pose, which is challenging due to the complexity and diversity of real-world lighting \& imaging processes. We propose to use the simple parallel lighting as an analogy model and based on Lambertian law to compose an instant navigation ball for this purpose. We theoretically prove the feasibility of this ALR strategy for realistic near point light sources and its invariance to the ambiguity of normal \& lighting decomposition. Extensive quantitative experiments and challenging real-world tasks on fine-grained change monitoring of cultural heritages verify the effectiveness of our approach. We also validate its generality to non-Lambertian scenes. 

Memory for centrally attended changing objects in an incidental real-world change detection paradigm

2002 ◽  
Vol 93 (3) ◽  
pp. 289-302 ◽  
Author(s):  
Daniel T. Levin ◽  
Daniel J. Simons ◽  
Bonnie L. Angelone ◽  
Christopher F. Chabris
Keyword(s):  
Change Detection ◽  
Real World

scholarly journals Continuous Online Sequence Learning with an Unsupervised Neural Network Model

2016 ◽  
Vol 28 (11) ◽  
pp. 2474-2504 ◽  
Author(s):  
Yuwei Cui ◽  
Subutai Ahmad ◽  
Jeff Hawkins
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real World ◽  
Sequence Learning ◽  
Streaming Data ◽  
Variable Order ◽  
Unsupervised Neural Network ◽  
Sequence Memory ◽  
Prediction Problems ◽  
Temporal Sequences

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods—autoregressive integrated moving average; feedforward neural networks—time delay neural network and online sequential extreme learning machine; and recurrent neural networks—long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

scholarly journals A Framework for Anomaly Detection under Dynamic and Distributed Scenarios

2021 ◽  
Author(s):  
◽  
Murugaraj Odiathevar
Keyword(s):  
Anomaly Detection ◽  
Real World ◽  
Detection System ◽  
Traffic Monitoring ◽  
Streaming Data ◽  
Multiple Sources ◽  
Real World Data ◽  
Detection Model ◽  
Healthcare Data ◽  
Robust Model

<p><b>Anomaly Detection is an important aspect of many application domains. It refers to the problem of finding patterns in data that do not conform to expected behaviour. Hence, understanding of expected behaviour well is fundamental to performing effective anomaly detection. However, data profiles constantly evolve in certain domains such as computer networks. In other domains such as traffic monitoring and healthcare, data are distributed and are either too large or there are privacy concerns in transmitting them to a central location. These situations pose a challenge to obtain an accurate understanding of non-anomalous profiles. Changing profiles undermine existing anomaly detection models and make them less effective. Training a robust model with data from multiple sources is also challenging. Moreover, in real world scenarios, it is not apparent how an anomaly detection model can be built to address the problem.</b></p> <p>This thesis focuses on the building of a robust anomaly detection system where data profiles evolve and/or are distributed. It proposes a novel Online Offline Framework to separate existing expected behaviour, new possible expected behaviour and anomalies in streaming data. It also addresses the distributed scenario using a theoretically sound fully Bayesian approach. These methods improve performances of anomaly detection systems and work well with biased and uneven data partitions.</p> <p>The proposed methods are validated using real world data in three different domains. This thesis identifies the implementation difficulties in these domains and produces three novel methodologies to address each of the core anomaly detection problems.</p>

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