In recent years, because of the development of computers, it has been possible to analyze, that is to say the data mining. Due to this, there are a lot of studies about stock market prediction using past stock data. Almost all these methods, however, use only predicting brand information. There are a lot of factors of the price, but it is possible to think that other brands affect the price. In this paper, we propose a prediction method that uses not only the predicting brand information but also other brands information. We show the effectiveness of our method through the experiment of stock market prediction.
Recently, the automation of picking work has advanced in the factory for the reduction of labor costs. When picking work is automatic, it is very important to estimate posture of target components. Therefore, an algorithm to automatically estimate posture is expected. We propose an efficient method to improve Differential Evolution (DE). Then, we applied the proposal method and DE to experiment to estimate posture of each four three-dimensional (3-D) objects by using the 3-D CAD data. The experimental results show that the proposal method achieves high success rate and efficiency than DE.
Automatic construction methods for image processing proposed till date approximate adequate image transformation from original images to their target images using a combination of several known image processing filters by evolutionary computation techniques. Genetic Image Network (GIN) is a recent automatic construction method for image transformation. The representation of GIN is a network structure. In this paper, we propose a method of automatic construction of image classifiers based on GIN, designated as Genetic Image Network for Image Classification (GIN-IC). The representation of GIN-IC is a feed-forward network structure. GIN-IC is composed of image transformation nodes, feature extraction nodes, and arithmetic operation nodes. GIN-IC transforms original images to easier-to-classify images using image transformation nodes, and selects adequate image features using feature extraction nodes. We apply GIN-IC to test problems involving multi-class categorization of texture images and two-class categorization of pedestrian and non-pedestrian images. Experimental results show that the use of image transformation nodes is effective for image classification problems.
Point cloud registration is an important part of 3-dimensional information processing. Low overlap ratio, noise, outliers, and missing points considerably influence the registration results. In this paper, we propose a fast and robust point cloud registration method to reduce the impact of these factors. First, the point groups are resampled by point clouds as basic elements for point cloud registration. Second, singular value decomposition is used to decompose the point groups. Third, the depth image of the point groups is calculated, and the sparse feature is obtained using the depth image. Finally, the sparse feature is used to obtain registration results through sparse representation. Under the premise of robustness to low overlap ratio, noise, outliers, and missing points, experimental results show that our algorithm is faster and more accurate than extant methods.
Much research in augmented reality (AR) technology attempts to match the textures of virtual objects with real world. However, the textures of real objects must also be rendered consistent with virtual information. This paper proposes a method for representing the degradation of real objects in virtual time. Real-world depth information, used to build three-dimensional models of real objects, is captured by a RGB-D camera. The degradation of real objects is then represented by superimposing the degradation texture onto the real object.
It is thought that human being recognizes a complicated figure by combining simple figures. This is "figure alphabet hypothesis" and these simple figures are called "figure alphabet". We considered "the mechanism in which a complicated figure is recognized with the combination of the figure chosen from comparatively simple figure groups", and applies it to a pattern classification. The proposed method assumes the figure alphabet to be the dot pattern (Alphabet Dot Pattern, ADP) of an N × N pixels. Because there are many kinds of ADP, ADP group is optimized by Genetic Algorithm (GA). And, the euclidean distance of an input figure and an ADP group is calculated, and classifies the figure. The proposal technique was previously applied to the classification problem of the binary multifont figure, and the validity was shown. In this research, the result applied to the gradation images. As a result, the classification of the face image and the pedestrian image obtained a high correct answer rate.
Cleaning is inseparable in life, but it is impossible to see with the naked eye where the room was actually cleaned. For this reason, if information on the location where the cleaning was performed cannot be shared when cleaning by multiple people, there is a possibility that an unclean area is remained. Therefore, if Augmented Reality (AR) can be used to visualize the passing area of the hand or cleaning tool being cleaned, it will lead to improve cleaning efficiency and increase motivation by visualizing the cleaning area. The purpose of this research is to obtain and superimpose the location information of the passing area using Simultaneous Localization and Mapping (SLAM) in order to visualize the passing area of the hand or the cleaning tool using AR.
Many methods that construct image classification algorithms automatically using evolutionary computation have been studied. Although these classifiers are very effective, several problems have been pointed out. For example, it is difficult to analyze or modify classifiers, and they are too complicated for humans to understand. In this paper, we propose a new method for classification using an evolutionary decision network (EDEN) that emphasizes good human-readability. EDEN automatically constructs an adequate network for classification by combining simple nodes using evolutionary computation. This network is composed of a set of nodes that changes the branches of the decision flow in accordance with the feature values of the input data. We build an effective classifier by optimizing a set of nodes and their threshold values for branching. In experiments, we evaluate EDEN by applying it to image classification problems. The experimental results show EDEN is able to build an effective classifier with a human-readable structure and achieve satisfactory performance.
