STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table, and its effectiveness have been confirmed by simulation experiments. However, the previous work has only pointed out the problems of the conventional methods and simply proposed the method to overcome them, and STRIM has yet to be applied to several problems and/or has contained some limitations in STRIM which must be solved before it can be applied to real-world datasets. This paper further examines the limitations of STRIM as presently introduced, and considers several conditions for its application and utilization. Specifically,these are to eliminate the limitation of the number of the decision attribute values, and to clarify the principle for STRIM to induct true rules, the size of the dataset needed, and the relationships and differences between STRIM and the conventional methods. Real-world datasets often contain missing values in the condition attributes, and contaminated values in the decision attribute, from various reasons. Based on the above considerations, this paper reports simulation experiments to examine the capacity of STRIM in such circumstances. The results show the method seems to be sufficiently robust for application to real-world datasets.

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Considering that humans perform handwriting task with small powers by contacting elbow or wrist on a table, it is reasonable to deem that manipulators can save energy and simultaneously accomplish tasks precisely like humans by bracing intermediate links. First this paper discusses equation of motion of robot under bracing condition, based on the robot’s dynamics with constraint condition including motor dynamics. Then a control method is proposed to control simulateneously bracing force and hand’s trajectory tracking, followed by optimization of the elbow-bracing position that minimizes energy consumption.

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In this study, we investigate the control problem of the load frequency of electric power systems that consist of consumers, suppliers, generators and the independent system operator. For this purpose, we adopt the model predictive control method, in which the control performance over a finite future is optimized repeatedly. The objective of this study is to provide an optimal electric price operation for achieving the steady-state of electric power systems with taking into account both benefits of consumers and suppliers.

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In this paper, we proposed a computer aided diagnosis system (CADS) that supports to detect the existence of tumor in the area of the brain from a given FDG-PET/CT image. Firstly, tilt correction was applied to brain image based on a computation of the mid-sagittal plane in the head. Secondly, tumor detection was performed by detecting asymmetrical regions between images of the left and right cerebral hemispheres. In this step, an improved SURF algorithm was utilized to detect the asymmetrical regions between the left and right sides in a 2D axial slice of the brain image. In cases where the 2D axial slice data was identified as symmetrical, one supplementary method based on typical illegal shape identification was applied so that the symmetrical tumor could be detected. 80 cases (10 cases with brain tumors and 70 normal cases) were tested in this experiment. The results showed this system could detect brain tumors effectively and was capable of identifying a health brain pattern and mostly avoided the incorrect identification of tumors.

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