• International conference on construction engineering and project management
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• 2022.06a
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• pp.1085-1092
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• 2022

Travel distance is a parameter mainly used in the objective function of Construction Site Layout Planning (CSLP) automation models. To obtain travel distance, common approaches, such as linear distance, shortest-distance algorithm, visibility graph, and access road path, concentrate only on identifying the shortest path. However, humans do not necessarily follow one shortest path but can choose a safer and more comfortable path according to their situation within a reasonable range. Thus, paths generated by these approaches may be different from the actual paths of the workers, which may cause a decrease in the reliability of the optimized construction site layout. To solve this problem, this paper adopts reinforcement learning (RL) inspired by various concepts of cognitive science and behavioral psychology to generate a realistic path that mimics the decision-making and behavioral processes of wayfinding of workers on the construction site. To do so, in this paper, the collection of human wayfinding tendencies and the characteristics of the walking environment of construction sites are investigated and the importance of taking these into account in simulating the actual path of workers is emphasized. Furthermore, a simulation developed by mapping the identified tendencies to the reward design shows that the RL agent behaves like a real construction worker. Based on the research findings, some opportunities and challenges were proposed. This study contributes to simulating the potential path of workers based on deep RL, which can be utilized to calculate the travel distance of CSLP automation models, contributing to providing more reliable solutions.

• Asia pacific journal of information systems
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• v.29 no.1
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• pp.35-49
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• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.145-150
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• 2010

The paper is describing work being developed in the frame of a 5-year IAEA Coordinated Research Programme (CRP) started in late 2005. Participants gained knowledge of modelling methodologies and experience in the development and use of rather sophisticated simulation tools in support of site characterization and performance assessment calculations. These goals were achieved by a coordinated effort, in which the advantages and limitations of numerical models are examined and demonstrated through a comparative analysis of simplified, illustrative test cases. This knowledge and experience should help them address these issues in their own country's nuclear waste program. Coordination efforts during the first three years of the project aimed at enabling this transfer of expertise and maximizing the learning experience of the participants as a group. This was accomplished by identifying common interests of the participants (i.e., Process Modelling and Total System Performance Assessment methodology), and by defining complementary tasks that are solved by the members. Synthesis of all available results by comparative assessments is planned in the coming months. The project will be completed end of 2010. This paper is summarizing activities up to November 2009.

• Korean Institute of Interior Design Journal
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• v.16 no.3 s.62
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• pp.3-9
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• 2007

Taliesin West is a meaningful historic site in architecture field, because its place had still been existing only one in the world as a ideal architectural community for working and living as well as learning by doing for Taliesin Fellowships and others based on F. L. Wright's idea of Organic Architecture. The main purpose of this study was to follow up the architectural thought and its construction shown in F. L. Wright's 'Taliesin West'. A study on the key notes are as follows; 1) The key-clue of the construction background for Taliesin West was based on the project of 'Complex Campus Building' which was early planed by F. L. Wright. 2) A basic design idea for Taliesin West was admiring from its own site characters as well as the Experimental construction methods and materials. 3) Design motive of Taliesin West was based on American Indian's movable shelter which called 'Tepee.' 4) A construction of F. L. Wright's temporary studio, Ocotilla, was a good opportunity to construct for Taliesin West which construction methods, covered and framed, were same as Ocotilla. 5) A concept idea of the master plan for Taliesin West came from combining Taliesin's Hillside Home School and Complex Campus Building project. 6) Construction of Taliesin West was a final accomplished place as F. L. Wright's utopia architecture and community.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• Journal of Korean Elementary Science Education
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• v.40 no.1
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• pp.113-125
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• 2021

In this study, a problem-centered learning based STEAM field experience learning program was developed and the effects of applying it were investigated. The program was composed of 8 sessions by using problem-centered learning education method and integrating STEAM elements between disciplines. The contents of program are as follow. In the step of sharing problems and making a problem-solving plan, they understood the various examples and meanings of endangered species, explored the project activities, and made an inquiry plan. In the search and re-exploration phase, a smart device was used to investigate the appearance, habitat environment and cause of extinction for Clithon retropictus, and a site inquiry plan was established for each group. Then, they moved to the field to explore brackish-headed gallops and discuss ways to protect endangered species. In the step of creating a solution, a web-based report was produced as the final product using smart devices based on the results of the inquiry. In the presentation and evaluation stage, the produced web-based report was used to present each group, conduct mutual evaluation, and organize project activities. The developed program was applied to 6th grade 29 students enrolled in the J University Gifted Education Center. In order to find out the effectiveness of the program, tests of science process skill, creative problem-solving ability, and scientific attitude were conducted before and after of program learning, and the results were statistically analyzed by t-test. In addition, a STEAM program satisfaction test was conducted after project in order to find out the satisfaction of the class. As a result of application of the program, the results were significantly improved in openness, criticism, and creativity among the sub-factors of creative problem-solving ability and scientific attitude. Satisfaction with the STEAM program was also high, but no significant result was found in science process skill. Therefore, the program of this study could be influenced on improvement of creative problem-solving ability and scientific attitude of gifted students in elementary science.

• Educational Technology International
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• v.21 no.2
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• pp.217-247
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• 2020

Many developing countries consider ICT as a key enabler to improve their educational systems and teachers are viewed as change agents. This paper aims to present policy suggestions concerning how to foster teachers' ICT competencies in developing countries based on the outcomes of an ODA project case in Peru. This study was conducted through three stages: Literature survey, site visit, and policy suggestions. To draw relevant policy suggestions, we employed the framework of the 'macro-meso-micro' level of teacher professional development. The following policy suggestions are discussed: (a) macro level: to develop the national framework of teacher ICT competencies and competency-based teacher training, (b) meso-level: to promote teacher communities of practices and school-based research programs, and (c) micro-level: to redesign teacher professional development programs to help teachers better understand the complex relationships between content, pedagogy, and technology, beyond learning about basic ICT literacy skills. This study contributes to the understanding of how ODA projects can approach the issue of teacher ICT capacity building at multiple levels.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.839-846
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• 2022

Traditionally, the construction industry has shown low labor productivity and productivity growth. To improve labor productivity, it must first be accurately measured. The existing method uses work-sampling techniques through observation of workers' activities at certain time intervals on site. However, a disadvantage of this method is that the results may differ depending on the observer's judgment and may be inaccurate in the case of a large number of missed scenarios. Therefore, this study proposes a model to automate labor productivity measurement by monitoring workers' actions using a deep learning-based pose estimation method. The results are expected to contribute to productivity improvement on construction sites.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.56.2-56.2
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• 2015

The KMTNet Supernovae Project utilizes the large $2^{\circ}{\times}2^{\circ}$ field of view of the three KMTNet telescopes to search and monitor supernovae, especially early ones, and other optical transients. A key component of the project is to build a data pipeline with a descent latency and an early alerting system that can handle the large volume of the data in an efficient and a prompt way, while minimizing false alarms, which casts a significant challenge to the software development. Here we present the current status of their development. The pipeline utilizes a difference image analysis technique to discover candidate transient sources after making correction of image distortion. In the early phase of the program, final selection of transient sources from candidates will mainly rely on multi-filter, multi-epoch and multi-site screening as well as human inspection, and an interactive web-based system is being developed for this purpose. Eventually, machine learning algorithms, based on the training set collected in the early phase, will be used to select true transient sources from candidates.