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미아오 쉬(Xu Miao),반상규(Sang-Kyu Bahn),강보영(Bo-Yeong Kang) 대한기계학회 2012 대한기계학회 춘추학술대회 Vol.2012 No.11
Population based incrmental learning(PBIL) is an algorithm of evolutionary optimization approach combined with probabilistic distribution model, providing effective and efficient optimization performance in a variety of research areas. The distinction between PBIL and conventional evolutionary approach, genetic algorithm(GA), is in the process of reproducing the genetic information for offspring: GA relies on crossover and mutation operators, while PBIL adops a probabilistic model. In this paper, we used PBIL algorithm for robot path planning as a probabilistic evolutionary approach, which is a first try in robot path planning field to our knowledge, and proposed probabilistic model of nodes and edge garage to generate promising offsprings for PBIL algorithm application. When we evaluate its performance on three maps with different population sizes, the results indicate that the proposed PBIL gives markedly better performance than conventional evolutionary approach, GA.
미아오 쉬(Miao, Xu),엄신조(Eom, Shin-Jo) 대한건축학회 2023 대한건축학회논문집 Vol.39 No.12
In construction projects, optimizing material selection is crucial, as over half of the construction cost is allocated to materials. To achieve this, an integrated material information system becomes essential. Creating an efficient material list requires significant investment in manpower and time to register and manage diverse material information. This study introduces a system developed through deep learning-based intelligent material extraction. The system builds a database of building material information from real projects, utilizing a classifier trained with standard construction codes using the FastText method and LSTM model. Through experiments on 40 buildings, the system demonstrated an 86% accuracy rate. The resulting building material information serves as a foundational resource for future applications such as artificial intelligence-based automation of design economic evaluation and design safety assessment.