A New Approach for Unqualified Salted Sea Cucumber Identification: Integration of Image Texture and Machine Learning under the Pressure Contact

At present, rapid, nondestructive, and objective identification of unqualified salted sea cucumbers with excessive salt content is extremely difficult. Artificial identification is the most common method, which is based on observing sea cucumber deformation during recovery after applying-removing pressure contact. This study is aimed at simulating the artificial identification method and establishing an identification model to distinguish whether the salted sea cucumber exceeds the standard by means of machine vision and machine learning technology. The system for identification of salted sea cucumbers was established, which was used for delivering the standard and uniform pressure forces and collecting the deformation images of salted sea cucumbers during the recovery after pressure removal. Image texture features of contour variation were extracted based on histograms (HIS) and gray level cooccurrence matrix (GLCM), which were used to establish the identification model by combining general regression neural networks (GRNN) and support vector machine (SVM), respectively. Contour variation features of salted sea cucumbers were extracted using a specific algorithm to improve the accuracy and stability of the model. Then, the dimensionality reduction and fusion of the feature images were achieved. According to the results of the models, the SVM identification model integrated with GLCM (GLCM-SVM) was found to be optimal, with accuracy, sensitivity, and specificity of 100%, 100%, and 100%, respectively. In particular, the sensitivity reached 100%, demonstrating an excellent identification ability to excessively salted sea cucumbers of the optimized model. This study illustrated the potential for identification of salted sea cucumbers based on pressure contact by combining image texture of contour varying with machine learning.

Recover Missing Sensor Data with GRNN-based Cascade Scheme

Background: Today, using of systems on the base of Internet of Things (ІоТ) devices is very widespread in various applications. Intellectual analysis of the data collected by similar devices is an important task for efficient and successful functioning of such systems. In particular, the reliability of such kind of analysis has greatly influence on the ability to partially or fully automate certain processes or subsystems. However, imperfect devices of data collection, transportation errors, etc. cause data missing to appear. A number of limitations cause this problem, and in the work, they makes it impossible an effective intellectual analysis for specific use. That is why the scientific and applied problem of effectively filling the missing in the data collected by the sensors of specific characteristics should be considered. Methods: The authors propose a new prediction method for solving this problem based on the use of General Regression Neural Networks (GRNN). Results: The possibility of approximation and partial elimination of the error of computational intelligence of this type has been analytically proved. A cascade of two sequentially connected GRNN was developed. The optimal parameters of the developed cascade were selected. The simulation of its work was performed to solve the problem of recover missing sensor data in the dataset for monitoring the state of air environment. A high number of missing for one reason or another characterizes this real data set, collected by IoT device. Conclusion: High accuracy of cascade operation in comparison with existing methods of this class is inserted. All advantages and disadvantages are described. Perspectives of further research are outlined.

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon.