This paper proposes a music/lyrics composing system consisting of two sections, a lyric composing section and a music composing section, which reflects user's impressions of theme of songs to music/lyrics. Love and nature are considered as theme in this paper. First of all, a user has a theme and image of lyrics to compose. The lyric composing section presents initial lyrics selected at random from database that is generated using existent lyrics and Markov Chain. If presented lyrics do not fit user's image, a part of lyrics not fitting user's image is changed by some other lyrics. When satisfied four lines lyrics are obtained, the music composition section starts. This section composes music fitting lyrics generated by the lyric composing section with the music composition system. The section presents combinations of four lines lyrics and 16 measures music. A subject evaluates each combination of lyrics and music whether they fit his/her image of a song. According to subject's evaluation music melody is changed by Genetic Algorithms and a part of lyrics are changed. These procedures are repeated until satisfied combination of lyrics and music melody is generated. In order to verify the validity of the presented system, subject experiments are performed.

Music/lyrics composition system with user's impressions of theme

This paper introduces a neural fuzzy network based on improved particle swarm optimization (IPSO) algorithm to use human body posture classification, and the proposed IPSO method can strengthen we search global solution. The IPSO is hybrid improvement evolutionary direction operator (IEDO) and migration. Four main human body postures are used for posture classification, including standing, bending, sitting and lying. After the classification features are extracted from the projected silhouette onto horizontal and vertical axes, and used magnitudes of significant Fourier Transform Coefficients together with silhouette length-width ratio are used as features. The four postures can be classified with high accuracy according to experimental results. Classification results are also applicable to home care emergency detection of a person who suddenly falls and remains in the lying posture.

Human Body Posture Classification Using a Neural Fuzzy Network based on Improved Particle Swarm Optimization

In our daily life, we often use some forecasting techniques to predict weather, temperature, stock, earthquake, economy, ..., etc. Based on these forecasting results, human can prevent damages to occur or get benefits from the forecasting activities. In facts, an event in the real-world can be affected by many factors. The more the factors we consider, the higher the forecasting accuracy rate. Moreover, the length of each interval in the universe of discourse also affects the forecasting results. In this paper, we present a new method for temperature prediction, based on automatic clustering techniques and two-factors high-order fuzzy time series. The average forecasting accuracy rates of the proposed method are higher than the existing methods.

Handling Forecasting Problems Based on Automatic Clustering Techniques and Two-Factors High-Order Fuzzy Time Series

Electroencephalographic (EEG) is used to record activities of human brain in the area of psychology for many years. As technology developed, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection (IFS) for the feature selection whose method is based on the reinforcement learning.

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Emotion Recognition Method for Driver Services

We examine how a toroidal sequence of the six fundamental colors affects human color impression. In order to investigate the different effects of spatial color sequences, we consider a hexagonal diagram that is a projection of RGB color space. The hexagonal diagram corresponds roughly to the hue circle indicated by both hue and saturation. The toroidal sequence is similar to the hue circle. The projected route area indicates the magnitude of naturalness (as in rainbows) for color sequences. The minimum sequence is similar to the order of colors in rainbows, whereas the non-minimum sequence is completely different. Therefore, we propose a simple fuzzy model of human color impression using the projected route area assisted by route complexity. The complexity is defined by the ratio of the square of envelope route distance to route area. The relationship between route complexity and the number of subjects for projected route area is investigated. We clarified that the majority (approximately over 26%) of subjects of nearly all ages have natural impressions when the minimum route area is large, and propose a simple fuzzy model of the human color impression. This model provides the natural (or unnatural) order of spatial color sequences of several colors.

Effect of Route Area and Route Complexity of Spatial Color Sequence on Human Color Impression and Simple Fuzzy Model

This paper realizes a system that recognizes traffic safety signs by applying the principle used for game in reverse. The game used for this paper is one that expresses the shape of temporary objects intended by the maker when the maker sees the numerical image provided on (x, y) coordinates and then expresses it on the mesh. After separating the traffic safety sign image from the input image, the system is realized by outputting the content of the sign into letters by recognizing the forms and colors constituting the sign using the puzzle game above. Our system has fast process time and better rate of recognition than the existing system with black-and-white image processing and recognition without any penciling progress.

Young-Sun Sohn

Papers

Recognition of Traffic Safety Signs Using Numerical Value Information of Pixel