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When used as an image processing method, convolutional neural networks (CNNs) cannot verify conditions that can achieve high performance; however, they show sufficiently high performance and are used in various fields. The architecture of a CNN and various parameters determine its performance, and it is impossible to verify the number of all cases that determine the performance of the CNN. Therefore, well-known CNN models are generally used. Recently, various methods for adjusting the parameters of CNNs or generating CNN architectures have been studied in various ways. Methods using metaheuristic algorithms often focus on parameter tuning, or the use of simple hierarchical architectures. This paper proposes a method to create a CNN model with a complex CNN architecture that can be applied to different datasets using the harmony search (HS) algorithm from among metaheuristic algorithms. This study aimed to generate a CNN architecture using fewer computing resources and to verify the results. To make the CNN model in units of cells, the internal and hierarchical architecture of the cell was created based on the learning of the CIFAR image dataset through HS and the performance of the created CNN model was tested by applying it to the classification of a damaged sewer pipe image dataset. The 10-GPU-day computed result showed that the accuracy of classifying the damaged sewers was at least 5% higher than that of VGGNet, the highest among the existing CNN models. Keywords: convolutional neural network; auto machine learning; metaheuristic algorithm; harmony search algorithm; heuristic algorithm, optimization, artificial neural network, image classification.

In this paper, we propose an advanced parameter-setting-free (PSF) scheme to solve the problem of setting the parameters for the harmony search (HS) algorithm. The use of the advanced PSF method solves the problems of the conventional PSF scheme that results from a large number of iterations and shows good results compared to fixing the parameters required for the HS algorithm. In addition, unlike the conventional PSF method, the advanced PSF method does not use additional memory. We expect the advanced PSF method to be applicable to various fields that use the HS algorithm because it reduces the memory utilization for operations while obtaining better results than conventional PSF schemes.

This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.