48,937 research outputs found
Comparing Statistical Feature and Artificial Neural Networks for Control Chart Pattern Recognition: A Case Study
Control chart has been widely used for monitoring production process, especially in
evaluating the quality performance of a product. An uncontrolled process is usually known by
recognizing its chart pattern, and then performing some actions to overcome the problems. In high
speed production process, real-time data is recorded and plotted almost automatically, and the control
chart pattern needs to be recognized immediately for detecting any unusual process behavior. Neural
networks for automatic control chart recognition have been studied in detecting its pattern. In the field
of computer science, the performance of its automatic and fast recognition ability can be a substitution
for a conventional method by human. Some researchers even have developed newer algorithm to
increase the recognition process of this neural networks control chart. However, artificial approaches
have some difficulties in implementation, especially due to its sophisticated programming algorithm.
Another competing method, based on statistical feature also has been considered in recognition
process. Control chart is related to applied statistical method, so it is not unreasonable if statistical
properties are developed for its pattern recognition. Correlation coefficient, one of classic statistical
features, can be applied in control chart recognition. It is a simpler approach than the artificial one. In
this paper, the comparison between these two methods starts by evaluating the behavior of control
chart time series point, and measured for its closeness to some training data that are generated by
simulation and followed some unusual control chart pattern. For both methods, the performance is evaluated by comparing their ability in detecting the pattern of generated control chart points. As a sophisticated method, neural networks give better recognition ability. The statistical features method simply calculate the correlation coefficient, even with small differences in recognizing the generated pattern compared to neural networks, but provides easy interpretation to justify the unusual control chart pattern. Both methods are then applied in a case study and performances are then measured
Quantitative infrared thermography resolved leakage current problem in cathodic protection system
Leakage current problem can happen in Cathodic Protection
(CP) system installation. It could affect the performance of
underground facilities such as piping, building structure, and
earthing system. Worse can happen is rapid corrosion where
disturbance to plant operation plus expensive maintenance
cost. Occasionally, if it seems, tracing its root cause could be
tedious. The traditional method called line current
measurement is still valid effective. It involves isolating one
by one of the affected underground structures. The recent
methods are Close Interval Potential Survey and Pipeline
Current Mapper were better and faster. On top of the
mentioned method, there is a need to enhance further by
synthesizing with the latest visual methods. Therefore, this
paper describes research works on Infrared Thermography
Quantitative (IRTQ) method as resolution of leakage current
problem in CP system. The scope of study merely focuses on
tracing the root cause of leakage current occurring at the CP
system lube base oil plant. The results of experiment
adherence to the hypothesis drawn. Consequently, res
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Sequential Complexity as a Descriptor for Musical Similarity
We propose string compressibility as a descriptor of temporal structure in
audio, for the purpose of determining musical similarity. Our descriptors are
based on computing track-wise compression rates of quantised audio features,
using multiple temporal resolutions and quantisation granularities. To verify
that our descriptors capture musically relevant information, we incorporate our
descriptors into similarity rating prediction and song year prediction tasks.
We base our evaluation on a dataset of 15500 track excerpts of Western popular
music, for which we obtain 7800 web-sourced pairwise similarity ratings. To
assess the agreement among similarity ratings, we perform an evaluation under
controlled conditions, obtaining a rank correlation of 0.33 between intersected
sets of ratings. Combined with bag-of-features descriptors, we obtain
performance gains of 31.1% and 10.9% for similarity rating prediction and song
year prediction. For both tasks, analysis of selected descriptors reveals that
representing features at multiple time scales benefits prediction accuracy.Comment: 13 pages, 9 figures, 8 tables. Accepted versio
Non-contact Microelectronic Device Inspection Systems And Methods
Non-contact microelectronic device inspection systems and methods are discussed and provided. Some embodiments include a method of generating a virtual reference device (or chip). This approach uses a statistics to find devices in a sample set that are most similar and then averages their time domain signals to generate the virtual reference. Signals associated with the virtual reference can then be correlated with time domain signals obtained from the packages under inspection to obtain a quality signature. Defective and non-defective devices are separated by estimating a beta distribution that fits a quality signature histogram of inspected packages and determining a cutoff threshold for an acceptable quality signature. Other aspects, features, and embodiments are also claimed and described.Georgia Tech Research Corporatio
Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods
International audienceControl charts are among the main tools in statistical process control (SPC) and have been extensively used for monitoring industrial processes. Currently, besides the single control charts, there is an interest in the concurrent ones. These graphics are characterized by the simultaneous presence of two or more single control charts. As a consequence, the individual patterns may be mixed, hindering the identification of a non-random pattern acting in the process; this phenomenon is refered as concurrent charts. In view of this problem, our first goal is to investigate the importance of an efficient separation step for pattern recognition. Then, we compare the efficiency of different Blind Source Separation (BSS) methods in the task of unmixing concurrent control charts. Furthermore, these BSS methods are combined with shape and statistical features in order to verify the performance of each one in pattern classification. In additional, the robustness of the better approach is tested in scenarios where there are different non-randomness levels and in cases with imbalanced dataset provided to the classifier. After simulating different patterns and applying several separation methods, the results have shown that the recognition rate is widely influenced by the separation and feature extraction steps and that the selection of efficient separation methods is fundamental to achieve high classification rates
Life jacket
Anyone who cannot swim well should wear life jacket whether they are in the water or around the water. Even those who are can swim well should wear the life jacket when they are doing activity such as swimming, fishing, boating or while doing any water-related activity. Life jacket is a kind of safety jacket that keeping the wearer float the in the water. The wearer may be in the conscious or unconscious condition but by wearing the life jacket we can minimize the risk of drowning because life jacket assist the wearer to keep floating in the water
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