MDL and Signal Change Detection in Machine Condition Monitoring

A minimum description length (MDL) based sequentially normalized maximum likelihood (SNML) approach combined with an autoregressive (AR) model is proposed for signal change detection in machine condition monitoring. The results showed the success of the method to detect signal changes and distinguish different ball bearing failures.JRC.G.4-Maritime affair

Anomaly-based Intrusion Detection Using Deep Neural Networks

Identification of network attacks is a matter of great concern for network operators due to extensive the number of vulnerabilities in computer systems and creativity of the attackers. Anomaly-based Intrusion Detection Systems (IDSs) present a significant opportunity to identify possible incidents, logging information and reporting attempts. However, these systems generate a low detection accuracy rate with changing network environment or services. To overcome this problem, we present a deep neural network architecture based on a combination of a stacked denoising autoencoder and a softmax classifier. Our architecture can extract important features from data and learn a model for detecting abnormal behaviors. The model is trained locally to denoise corrupted versions of their inputs based on stacking layers of denoising autoencoders in order to achieve reliable intrusion detection. Experimental results on real KDD-CUP'99 dataset show that our architecture outperformed shallow learning architectures and other deep neural network architectures. </p

Robust denoising of electrophoresis and mass spectrometry signals with minimum description length principle

AbstractThe need for high-throughput assays in molecular biology places increasing requirements on the applied signal processing and modelling methods. In order to be able to extract useful information from the measurements, the removal of undesirable signal characteristics such as random noise is required. This can be done in a quite elegant and efficient way by the minimum description length (MDL) principle, which treats and separates `noise' from the useful information as that part in the data that cannot be compressed. In its current form the MDL denoising method assumes the Gaussian noise model but does not require any ad hoc parameter settings. It provides a basis for high-speed automated processing systems without requiring continual user interventions to validate the results as in the conventional signal processing methods. Our analysis of the denoising problem in mass spectrometry, capillary electrophoresis genotyping, and sequencing signals suggests that the MDL denoising method produces robust and intuitively appealing results sometimes even in situations where competing approaches perform poorly

Bayesian Approach for Optimizing Forest Inventory Survey Sampling with Remote Sensing Data

In large-area forest inventories, a trade-off between the amount of data to be sampled and the corresponding collection costs is necessary. It is not always possible to have a very large data sample when dealing with sampling-based inventories. It is therefore important to optimize the sampling design with the limited resources. Whereas this sort of inventories are subject to these constraints, the availability of remote sensing (RS) data correlated with the forest inventory variables is usually much higher. For this reason, the RS and sampled field measurement data are often used in combination for improving the forest inventory estimation. In this study, we propose a model-based data sampling method founded on Bayesian optimization and machine learning algorithms which utilizes RS data to guide forest inventory sample selection. We evaluate our method in empirical experiments using real-world volume of growing stock data from the Aland region in Finland. The proposed method is compared against two baseline methods: simple random sampling and the local pivotal method. When a suitable model link is selected, the empirical experiments show on best case on average up to 22% and 79% improvement in population mean and variance estimation respectively over baselines. However, the results also illustrate the importance of model selection which has a clear effect on the results. The novelty of the study is in the application of Bayesian optimization in national forest inventory survey sampling

Image Analysis and Development of Graphical User Interface for Pole Vault Action

In recent years, motion estimation analysis has become one of the vital research areas in sport and has attracted the interest of many researchers toward events such as swimming, pole vaulting, and hurdling. In this paper, we present a novel method for determining the step length, speed, and the feet-contact-time on the running track of a pole vault athlete using a mono-camera arrangement. The step length and step frequency are essential descriptors of the approach run in pole vaulting. The approach along a linear trajectory is familiar to many throwing and jumping events. The measurement setting and image processing, as well as the step registration stages such as the block matching and optimal flow algorithm are presented and compared to alternative methods. The validation of the step size and step frequency accuracy is provided, using manually digitized step sizes as the baseline. The proposed methodology is efficient and straightforward, providing immediate feedback to the athlete and coaches. We were also successful in building a basic Graphical User Interface (GUI) to illustrate pole-vaulting actions during a performance. This research could be used as an initial step for developing a fully interactive platform that is capable of yielding supportive instructions to the athletes and the coaches on a real-time basis for self-assessment and further improvement.</p