• Journal of KIBIM
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• v.12 no.1
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• pp.23-34
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• 2022

In the current 2D-based design, design reliability is lowered due to interference and inconsistency between plans, errors in drawings and quantities, etc. At the time of transition to BIM-based 3D design, it is necessary to expand the reliability and usability of BIM by eliminating these errors from the design stage through securing the quality of the BIM digital model. Therefore, in the railway infrastructure design stage, the quality management process and standards of the BIM digital model were defined and quality management index were developed. Based on the rule extracted from the quality management index, a pilot quality management was conducted in connection with the commercial Model-Checker rule, problems and improvement plans were derived, and a rule-based automated quality management plan was prepared.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6C
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• pp.369-376
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• 2006

Similarity rule in order to predict the field settlement and consolidation time from oedometer test is not clear because of the thickness, loading time, rate of loading increase, dependence on strain inherent of clay. To investigate the one-dimensional consolidation tests with permeability tests varied loading period and specimen thickness were carried out the application of similarity rule. Main conclusions are 1) f(=1+e)-logk line is a unique property of the soil, 2) $c_{\nu}$, k need no correction, 3)similarity rule is depends on the positions of f-logp line and primary consolidation line.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.65-70
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• 2020

PA(postero-anterior) and AP(antero-posterior) chest projections are the most sought-after types of all kinds of projections. But if a radiological technologist puts wrong information about the position in the computer, the orientation of left and right side of an image would be reversed. In order to solve this problem, we utilized CNN(convolutional neural network) which has recently utilized a lot for studies of medical imaging technology and rule-based system. 70% of 111,622 chest images were used for training, 20% of them were used for testing and 10% of them were used for validation set in the CNN experiment. The same amount of images which were used for testing in the CNN experiment were used in rule-based system. Python 3.7 version and Tensorflow r1.14 were utilized for data environment. As a result, rule-based system had 66% accuracy on evaluating whether the orientation reversal on chest x-ray image. But the CNN had 97.9% accuracy on that. Being overcome limitations by CNN which had been shown on rule-based system and shown the high accuracy can be considered as a meaningful result. If some problems which can occur for tasks of the radiological technologist can be separated by utilizing CNN, It can contribute a lot to optimize workflow.

• Journal of the Korean Society of Safety
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• v.31 no.2
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• pp.133-142
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• 2016

The purpose of this study was to investigate the factors that influence safety rule violation at work. Semi-structured interviews were conducted with 337 participants from nine manufacturing plants. The results of the content analysis revealed the following six categories: Individual characteristics, safety commitment, safety support and resources, safety competence and communication, production pressure, and problems with rules. Among the 14 factors in the six categories above, indirect accident experience in the individual characteristics category and no action for complying with laws in the problems with rules category had not been identified in previous studies. However, some factors, such as age, peer pressure, pay type, the lowering of risk, a masculine way of working, and supervisor position were not found in this study. The implications and limitations are discussed.

• Coupled systems mechanics
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• v.9 no.3
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• pp.281-287
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• 2020

Nanotechnology is an upcoming technology that can provide solution for combating pollution by controlling shape and size of materials at the nanoscale. This review provides comprehensive information regarding the role of nanotechnology in pollution control at concrete structures. Titanium dioxide (TiO2) nanoparticles are a good item for concrete structures for diminishing the air polluting affect by gasses of exhaust. In this article, the mixture rule is presented for the effect of nanoparticles in environmental pollution reduction in concrete structures. The compressive strength, elastic modulus and reduction of steel bars in the concrete structures are studied. The Results show that TiO2 nanoparticles have significant effect on the reduction of environmental pollution and increase of stiffness in the concrete structures. In addition, the nanoparticles can reduce the use of steel bars in the concrete structure.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.111-114
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• 2018

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.279-292
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• 2011

Credibility theory is one of the most important theories of actuarial science to calculate the proper insurance premium. In this paper, the rule of relative exposure volume, the square root rule, the B$\"{u}$hlmann credibility and B$\"{u}$hlmann-Straub credibility with the basic concept of credibility have been introduced, Also, we estimate new premiums based on these methods for real data. As a result, the rule of relative exposure volume provides the highest accuracy.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.275-282
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• 2005

In order to increase extendibility and reusability of components during component design, the variability discovered in a business application development needs to be defined to separate rules. That is because component adaptation techniques through redefinition of implementation classes and interface wrapping have limits to support the component reusability. Therefore, It's essential to design the component which takes into account the future reusability in the component development. In this paper, we extended the existing component architecture to incorporate rule components by separating variable properties from the components and defined the necessary syntax for the rule definition. In the case study, we built the business components for an insurance sales application and verified the component reusability through the rule redefining.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.