Pleomorphic adenoma is the most common benign tumor of the salivary glands. About 90% of these tumors occur in the parotid gland and 10% of them occur in the minor salivary glands. The most common sites for pleomorphic adenoma of the minor salivary glands are the palate, followed by the lips and the cheeks. Pleomorphic adenoma of the palate presents clinically as a painless, slow-growing mass found on posterior lateral aspect. In this case report, we report a case of pleomorphic adenoma of the palate in a 36-year old male patient whose initial diagnosis was vascular mass such as hemangioma or lymphangiohemangioma by preoperative CT and MRI.

This paper addresses the emotion computing model for software affective agents. In this paper, emotion is represented in valence-arousal-dominance dimensions instead of discrete categorical representation approach. Firstly, a novel emotion model architecture for affective agents is proposed based on Scherer’s componential theories of human emotion, which is one of the well-known emotion models in psychological area. Then a fuzzy logic is applied to determine emotional statuses in the emotion model architecture, i.e., the first valence and arousal, the second valence and arousal, and dominance. The proposed methods are implemented and tested by applying them in a virtual training system for children’s neurobehavioral disorders.

This paper proposes an emotion classifier from EEG signals based on Bayesʼ theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

This paper presents a statistical analysis method for the selection of electroencephalogram (EEG) electrode positions and spectral features to recognize emotion, where emotional valence and arousal are classified into three and two levels, respectively. T

Recently, to improve operating efficiency with the higher in-line rate in automated production lines, a lot of cases of grouping machines and material handling system together to form a cluster has shown frequently. This article addresses the job allocati

Recently, to improve operating efficiency with the higher in-line rate in automated production lines, a lot of cases of grouping machines and material handling system together to form a cluster has shown frequently. This article addresses the job allocation and operation method of automated material handling for cluster-type production systems. First of all, the control problems of the automated material handling systems are classified into the control problem of inter-cluster material handling system and that of intra-cluster material handling system. Then, a distributed agent-based control scheme is proposed for the former, and an operational control procedure for the latter. Simulation experiment shows that the proposed method is efficient in reducing cycle times and improving utilization of material handling vehicles.