• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.103-109
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• 2018

This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.613-618
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• 2022

효과적인 자연어 처리를 위해 제안된 Transformer 구조의 등장 이후, 이를 활용한 대규모 언어 모델이자 사전학습 모델인 BERT, GPT, OPT 등이 공개되었고, 이들을 한국어에 보다 특화한 KoBERT, KoGPT 등의 사전학습 모델이 공개되었다. 자연어 처리 모델의 확보를 위한 학습 자원이 늘어나고 있지만, 사전학습 모델을 각종 응용작업에 적용하기 위해서는 데이터 준비, 코드 작성, 파인 튜닝 및 저장과 같은 복잡한 절차를 수행해야 하며, 이는 다수의 응용 사용자에게 여전히 도전적인 과정으로, 올바른 결과를 도출하는 것은 쉽지 않다. 이러한 어려움을 완화시키고, 다양한 기계 학습 모델을 사용자 데이터에 보다 쉽게 적용할 수 있도록 AutoML으로 통칭되는 자동 하이퍼파라미터 탐색, 모델 구조 탐색 등의 기법이 고안되고 있다. 본 연구에서는 한국어 사전학습 모델과 한국어 텍스트 데이터를 사용한 자연어 처리 모델 산출 과정을 정형화 및 절차화하여, 궁극적으로 목표로 하는 예측 모델을 자동으로 산출하는 시스템의 설계를 소개한다.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.79-91
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• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.113-124
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• 2020

Social movements to improve the performance of buildings through remodeling of aging apartment houses are being captured. To this end, the remodeling construction cost analysis, structural analysis, and political institutional review have been conducted to suggest ways to activate the remodeling. However, although the method of analyzing construction cost for remodeling apartment houses is currently being proposed for research purposes, there are limitations in practical application possibilities. Specifically, In order to be used practically, it is applicable to cases that have already been completed or in progress, but cases that will occur in the future are also used for construction cost analysis, so the sustainability of the analysis method is lacking. For the purpose of this, we would like to suggest an automated estimating method. For the sustainability of construction cost estimates, Deep-Learning was introduced in the estimating procedure. Specifically, a method for automatically finding the relationship between design elements, work types, and cost increase factors that can occur in apartment remodeling was presented. In addition, Monte Carlo Simulation was included in the estimation procedure to compensate for the lack of uncertainty, which is the inherent limitation of the Deep Learning-based estimation. In order to present higher accuracy as cases are accumulated, a method of calculating higher accuracy by comparing the estimate result with the existing accumulated data was also suggested. In order to validate the sustainability of the automated estimates proposed in this study, 13 cases of learning procedures and an additional 2 cases of cumulative procedures were performed. As a result, a new construction cost estimating procedure was automatically presented that reflects the characteristics of the two additional projects. In this study, the method of estimate estimate was used using 15 cases, If the cases are accumulated and reflected, the effect of this study is expected to increase.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.744-747
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• 2008

Quantity information focused on a design drawing plays a critical role in a decision making related to cost for project participants during project life cycles. Related participants absolutely depend on quantity take-off working which produces the quantity information by hand, and then a worker's mistake often causes many errors. The difference of quantity by the know-how of the person in charge of the estimation also occurs. In addition, the worker passes through the whole quantity take-off processes again in case of re-working for quantity take-off produced by a change order. The requirements about the automated estimation increase because of the needs for the accurate quantity take-off and dealing with the change order and recently, the studies about the automated estimation working process based on 34 cad model from 3d cad modeler are attempted in various viewpoints. However, the existing studies reach the limits such as common quantity data type framework for getting Quantity information. Focused on a certain 34 cad modeler and BIM based automation of estimation using it Therefore, the objective of this study is to develop the a series of system which can extract, analyze, and verify Quantity Data Type in modeler to automate quantity take-off originated from various 3d cad modelers as a foundation study for BIM based automation of estimation framework.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.63-68
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• 2017

Although automated metro subway systems have the advantage of operating a train without a train driver, it is difficult to detect an immediate fault condition and take countermeasures when an unusual situation occurs. Therefore, it is important to construct a maintenance information system (MIS) that detects the vehicle failure/status information in real time and maintains it efficiently in the depot of the railway's vehicles. This paper proposes a conceptual design method that realizes the interface between the train control system (TCS), the operation control center train control monitoring system (OCC-TCMS) console, and the MIS using wireless communication network in real-time. To transmit a large amount of information on 800,000 occurrences per day during operation, data was collected in a 56 byte data table using a data processing algorithm. This state information was classified into 4 hexadecimal codes and transmitted to the MIS by mapping the status and the fault information on the vehicle during the main line operation. Furthermore, the transmission and reception data were examined in real time between the TCS and MIS, and the implementation of the failure information screen was then displayed.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.642-650
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• 2000

The pilot (human actor) involved in the control loop of the highly automated aircraft systems (machine actor) must be able to monitor these systems just as the machine actor must also be able to monitor the human actor. For its safety and better performance of the human machine system, each of the two elements must be knowledgeable about the other's intentions or goals. In fact, several recent accidents occurred due to goal conflicts between human and machines in a modern avionic system. To facilitate the coordination of these actors, a computational aid was developed. The aid, which operates in a part-task simulator environment, attempts to facilitate the management of the goals and functions being performed to accomplish them. To provide an accurate knowledge of both actors' goals and their function statuses, the aid uses agent-based objects representing the elements of the cockpit operations. This paper describes the development of the flightdeck goals and functions called Agenda Management.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.1
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• pp.61-69
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• 2013

In an evaluation process of aircraft flying qualities, a clear and concise application interface is important since an evaluation process requires numerous repeated evaluation. This flight evaluation program have implemented efficient flight evaluation user interface along with changed trim condition interface and composed of comprehensive evaluation interface have mounted all automated FQ evaluation modules that was selected to be compose of 14 items in respect of an unmanned fixed-wing aircraft. Accordingly when it is necessary to design the flight control system as well as to develop a FQ considered aircraft, this S/W can be utilized as a tool that is a useful test evaluation S/W with scalability and enable to reduce the time and the cost of verification and evaluation process.