Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

The present work is a revision about neural networks. Initially presents a little introduction to neural networks, fuzzy logic, a brief history, and the applications of Neural Networks on Biotechnology. The chosen sub-areas of the applications of Neural Networks on Biotechnology are, Solid-State Fermentation Optimization, DNA Sequencing, Molecular Sequencing Analysis, Quantitative Structure-Activity Relationship, Soft Sensing, Spectra Interpretation, Data Mining, each one use a special kind of neural network like feedforward, recurrent, siamese, art, among others. Applications of the Neural-Networks in spectra interpretation and Quantitative Structure-activity relationships, is a direct application to Chemistry and consequently also to Biochemistry and Biotechnology. Soft Sensing is a special example for applications on Biotechnology. It is a method to measure variables that normally can’t be directly measure. Solid state fermentation was optimized and presenting, as result, a strong increasing of production efficiency.

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A hybrid soft sensing approach of a cement mill using principal component analysis and artificial neural networks

2013 3rd IEEE International Advance Computing Conference (IACC) ◽

10.1109/iadcc.2013.6514314 ◽

2013 ◽

Cited By ~ 2

Author(s):

A. K. Pani ◽

H. K. Mohanta

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Artificial Neural Networks ◽

Principal Component ◽

Component Analysis ◽

Soft Sensing ◽

Artificial Neural

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A VISION BASED SYSTEM FOR OBJECT DETECTION IN UNDERWATER IMAGES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800140000012x ◽

2000 ◽

Vol 14 (02) ◽

pp. 167-188 ◽

Cited By ~ 32

Author(s):

G. L. FORESTI ◽

S. GENTILI

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Object Detection ◽

Water Depth ◽

Light Attenuation ◽

Input Image ◽

Geometric Reasoning ◽

Sea Bottom ◽

Color Compensation ◽

Underwater Object

In this paper, a vision-based system for underwater object detection is presented. The system is able to detect automatically a pipeline placed on the sea bottom, and some objects, e.g. trestles and anodes, placed in its neighborhoods. A color compensation procedure has been introduced in order to reduce problems connected with the light attenuation in the water. Artificial neural networks are then applied in order to classify in real-time the pixels of the input image into different classes, corresponding e.g. to different objects present in the observed scene. Geometric reasoning is applied to reduce the detection of false objects and to improve the accuracy of true detected objects. The results on real underwater images representing a pipeline structure in different scenarios are shown. The presence of seaweed and sand, different illumination conditions and water depth, different pipeline diameter and small variations of the camera tilt angle are considered to evaluate the algorithm performances.

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