Compositional study of the lost temples in instructional design

The article presents the design and research work of the authors and first-year architecture students of Irkutsk National Research Technical University concerning compositional study of lost temples of Irkutsk with the reconstruction of their architectural appearance. The illustrative material was prepared using the students’ works. The complex of Siberian Baroque temples in Irkutsk in the mid-18th – late 19th centuries and various types of church buildings were studied. The work uses modeling as a tool for predicting the architectural appearance of the temple. Sketch drawings and models of the Miracle-Working, Tikhvinsky and Annunciation temples were completed, and the model of the evolution of Siberian Baroque temples was recreated. The main stages of the term project, from building functional, planning and volumetric models to designing image and structural characteristics of the object on the sample board, were presented.

Neural networks and mechatronics for detecting carpet pile direction

Determining carpet pile direction is necessary in the manufacture of sample boards and when installing carpet because it is desirable for any shading effects to be homogenous. Depending on the style of the carpet, determining pile direction can be time-consuming and difficult. Two approaches to automating it are developed and tested in order to improve sample board production rates. Significant research has already been performed in the automatic detection of pile lay orientation in carpet and fiber orientation in nonwovens. These methods are insufficient for the present application because they yield the angle of a line parallel to the carpet pile but not the direction along that line in which the pile points. In this work, labeled carpet samples of varying styles are used to train and validate a convolutional neural network (CNN). These samples are also used to test an electromechanical solution. The CNN is shown to provide 100% accuracy when determining pile lay orientation and 93% accuracy when determining pile direction. The electromechanical method for determining pile direction is 65% accurate when used alone and 90% accurate when combined with prior knowledge of the pile lay orientation. These values fall short of the 99% accuracy of an expert operator detecting pile direction but compare favorably to that of a beginner.

Mining Experiential Patterns from Game-Logs of Board Game

In board games, game-logs record past game processes, which can be regarded as an accumulation of experience. Similar to a real person, a computer player can gradually increase its skill by learning from game-logs. Therefore, the game becomes more interesting. This paper proposes an extensible approach to mine experiential patterns from increasing game-logs. The computer player improves its strategies by utilizing these growing patterns, just as it acquires experience. To evaluate the effect and performance of the approach, we designed a sample board game as a test platform and elaborated an experiment consisting of a series of tests. Experimental results show that our approach is effective and efficient.