José Juan Carbajal-Hernández

Immediate water quality assessment in shrimp culture using fuzzy inference systems

The continuous monitoring of physical, chemical and biological parameters in shrimp culture is an important activity for detecting potential crisis that can be harmful for the organisms. Water quality can be assessed through toxicological tests evaluated directly from water quality parameters involved in the ecosystem; these tests provide an indicator about the water quality. The aim of this study is to develop a fuzzy inference system based on a reasoning process, which involves aquaculture criteria established by official organizations and researchers for assessing water quality by analyzing the main factors that affect a shrimp ecosystem. We propose to organize the water quality parameters in groups according to their importance; these groups are defined as daily, weekly and by request monitoring. Additionally, we introduce an analytic hierarchy process to define priorities for more critical water quality parameters and groups. The proposed system analyzes the most important parameters in shrimp culture, detects potential negative situations and provides a new water quality index (WQI), which describes the general status of the water quality as excellent, good, regular and poor. The Canadian water quality and other well-known hydrological indices are used to compare the water quality parameters of the shrimp water farm. Results show that WQI index has a better performance than other indices giving a more accurate assessment because the proposed fuzzy inference system integrates all environmental behaviors giving as result a complete score. This fuzzy inference system emerges as an appropriated tool for assessing site performance, providing assistance to improve production through contingency actions in polluted ponds.

Air quality assessment using a weighted Fuzzy Inference System

Abstract Air pollution is a current monitored problem in areas with high population density such as big cities. In this sense, environmental modelling should be accurate in order to generate better air quality evaluations; but in consequence they are complex. Nowadays, the artificial intelligence based on heuristic methods allows assessing air quality parametres, providing a partial solution to this problem. Accordingly, this paper proposes a new evaluation model using fuzzy inferences combined with an Analytic Hierarchy Process, providing a new air quality index. Environmental parametres (PM 2.5 , PM 10 , O 3 , CO, NO 2 and SO 2 ) are evaluated according to toxicological levels and then, a fuzzy reasoning process assesses different air quality situations. Additionally, individual weights are computed and assigned according to the pollutant importance on the air evaluation. Finally, the model proposed considers five score stages: excellent , good , regular , bad and dangerous , based on data from the Mexico City Atmospheric Monitoring System (SIMAT). Experimental results show a good performance of the proposed air quality index against those in literature, providing better assessments when weights are assigned according to an importance level in atmosphere pollution.

Aircraft class identification based on take-off noise signal segmentation in time

Abstract Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the aircraft type as the key element. This paper presents a new computational model to identify the aircraft class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The aircraft are grouped into classes depending on the installed engine type. The model works with 13 aircraft categories with an identification level above 85% in real environments.

Classification of unbalance and misalignment in induction motors using orbital analysis and associative memories

Fault detection in induction motors is an important task in industry when production greatly depends of the functioning of the machine. This paper presents a new computational model for detecting misalignment and unbalance problems in electrical induction motors. Through orbital analysis and signal vibrations, unbalance and misalignment motor faults can be mapped into patterns, which are processed by a classifier: the Steinbuch Lernmatrix. This associative memory has been widely used as classifier in the pattern recognition field. A modification of the Lernmatrix is proposed in order to process real valued data and improve the efficiency and performance of the classifier. Experimental patterns obtained from induction motors in real situations and with a certain level of unbalance or misalignment were processed by the proposed model. Classification results obtained in an experimental phase indicate a good performance of the associative memory, providing an alternative way for recognizing induction motor faults.

Rotor Unbalance Detection in Electrical Induction Motors Using Orbital Analysis

Deterioration in mechanical parts of a motor causes faults that generate vibrations. Those vibrations can be related with a different type of motor fault. In this work, we propose a new computational model for identifying rotor unbalance problems in electrical induction motors. Measured vibrations are preprocessed in order to create orbits which represent characteristic patterns. Those patterns are used in a recognition process using an artificial neural network. Experimental results using vibration signals extracted from real situations show a good performance and effectiveness of the proposed model, providing a new way for recognizing unbalance problems in induction motors.

Misalignment Identification in Induction Motors Using Orbital Pattern Analysis

Induction motors are the most common engine used worldwide. When they are summited to extensive working journals, e.g. in industry, faults may appear, generating a performance reduction on them. Several works have been focused on detecting early mechanical and electrical faults before damage appears in the motor. However, the main drawback of them is the complexity on the motor’s signal mathematical processing. In this paper, a new methodology is proposed for detecting misalignment faults in induction motors. Through signal vibration and orbital analysis, misalignment faults are studied, generating characteristically patterns that are used for fault identification. Artificial Neural Networks are evolved with an evolutionary algorithm for misalignment pattern recognition, using two databases (training and recovering respectively). The results obtained, indicate a good performance of Artificial Neural Networks with low confusion rates, using experimental patterns obtained from real situations where motors present a certain level of misalignment.

Single-Step-Ahead and Multi-Step-Ahead Prediction with Evolutionary Artificial Neural Networks

In recent years, Evolutionary Algorithms EAs have been remarkably useful to improve the robustness of Artificial Neural Networks ANNs. This study introduces an experimental analysis using an EAs aimed to evolve ANNs architectures the FS-EPNet algorithm to understand how neural networks are evolved with a steady-state algorithm and compare the Single-step-ahead SSP and Multiple-step-ahead MSP methods for prediction tasks over two test sets. It was decided to test an inside-set during evolution and an outside-set after the whole evolutionary process has been completed to validate the generalization performance with the same method SSP or MSP. Thus, the networks may not be correctly evaluated misleading fitness if the single SSP is used during evolution inside-set and then the MSP at the end of it outside-set. The results show that the same prediction method should be used in both evaluation sets providing smaller errors on average.

Neural Network Modelling for Dissolved Oxygen Effects in Extensive Litopenaeus Vannamei Culture

Shrimp aquaculture is an important activity currently practiced worldwide. Dissolved oxygen can be lethal in organisms when low concentrations are present in an extensive cultured pond. According to this, a new computational model for dissolved oxygen assessment using artificial neural networks is proposed. Measurements from environmental parameters related with dissolved oxygen were used, classifying those negative situations that can affect the environmental stability of the ecosystem. As a result, an indicator concerning the good or bad water quality condition is obtained. Finally, comparisons against models reported in literature show the good performance of the proposed model.

A Modification of the Lernmatrix for Real Valued Data Processing

An associative memory is a binary relationship between inputs and outputs, which is stored in an M matrix. In this paper, we propose a modification of the Steinbuch Lernmatrix model in order to process real-valued patterns, avoiding binarization processes and reducing computational burden. The proposed model is used in experiments with noisy environments, where the performance and efficiency of the memory is proven. A comparison between the proposed and the original model shows a good response and efficiency in the classification process of the new Lernmatrix.