• 한국산업융합학회 논문집
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• 제25권6_2호
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• pp.1017-1027
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• 2022

Development of social overhead capital (SOC) requires huge national finance, and performance issues such as cost-efficiency, safety, and environment have been constantly raised. However, currently each construction client has limited access to its own projects' performance without analytic methodology for industry-level comparisons and benchmarking for improvement. To overcome this problem, this study proposes a comprehensive performance analysis framework for post-evaluation of large-scale construction projects. To this end, this study performed a case study of advanced countries (the U.S., the U.K. and Japan) and consultation with related experts to develop a tailored performance analysis framework for the Post- Construction Evaluation and Management system in Korea. The developed framework covers three categories (project performance, project efficiency, and ripple effect), nine areas (cost, schedule, change, safety, quality, demand, benefit-cost ratio, civil complaint, and defect), and 31 detailed metrics. Using industry-level project performance database and statistical techniques, the proposed framework can be used not only to diagnose excellent and unsatisfactory performance areas for completed construction projects, but also to provide reference data for future similar projects. This study can contribute to the improvement of clients' performance management practices and effectiveness of construction projects.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.253-253
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• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.

• 대한산업공학회지
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• 제23권1호
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• pp.95-113
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• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].

• 청정기술
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• 제28권2호
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• ETRI Journal
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• 제36권5호
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• Journal of Information Technology Applications and Management
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• 제9권3호
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• pp.129-139
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• 2002

There exist a number of information systems planning (ISP) methodologies. Historically these methodologies have been evolving to reflect new technologies and business requirements. In fact, it is an uneasy task to select a methodology that fits a business need. Though there have been a number of studies proposing new ISP approaches, we are unable to find much research doing a comparative analysis on existing ISP methodologies. Our study, therefore, is to present a classification scheme for ISP approaches and to provide a guideline framework for selecting an approach most suitable to a particular firm's need. Our classification utilizes types of components covered in ISP deliverables and the peculiarity of these components. Such classification scheme and selection framework would help derive an IT-driven new enterprise model more effectively.

• 한국IT서비스학회지
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• 제9권2호
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• pp.179-192
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• 2010

In order to resolve the problems of traditional Enterprise Application Integration (EAI) for system integration and to establish flexible enterprise IT environments, Enterprise Service Bus(ESB) which have distributed architecture and support Service Oriented Architecture(SOA) has introduced. Particularly, JBI which developed by the Java Community Process is most widely used to implement ESB for advantages of Java technology. In ESB based on JBI, reliable message delivery is very important to ensure stability of services and systems because it is a message driven architecture. But, it is difficult to verify messages and trace messages when system fault or service error occurred because JBI specification is not enough to address them. In this paper we has proposed the Message Monitoring Framework for JBI-based ESBs which for using in monitoring messages efficiently. It provides foundations for gathering and tracing message-related information about component installation, message exchange, service deploy by using proxy-based change tracking and delegation mechanism for data processing. The proxy which used in our solutions collects data about message automatically when it changed, and the delegation mechanism provides users flexibility for data processing. Also, we describe the performance evaluation results of our solution which is acceptable. We expect to it enables users to ensure reliability and stability of the JBI-based ESB by systematic monitoring and managing messages being used to interact among components.

• Asia pacific journal of information systems
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• 제30권4호
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• pp.847-878
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• 2020

In the era of disruptive change, a data-driven approach is vital to Human Resource Management (HRM) of any leading organization, for it is used to gain a competitive advantage. HR analytics (HRA) has emerged as innovative technologies since advanced analytics, i.e., predictive or prescriptive analytics, were widely used in the High Performing Organizations (HPOs). Therefore, many organizations elevate themselves to become HPOs through Data Science on the "people side." This paper proposes a systematic literature review using the Literature Weighted Scoring (LWS) to develop a conceptual framework based on three adoption theories, which are the Technology-Organization-Environment (TOE), Diffusion of Innovation (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that a total of 13 theory-derived factors are determined as influential factors affecting HRA adoption, and the top three factors are "Quantitative Self-Efficacy," "Top Management Support," and "Data Availability." The conceptual framework with hypotheses is proposed to provide a foundation for further studies on organizational HRA adoption.

• 산업경영시스템학회지
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• 제47권2호
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• pp.21-29
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• 2024

Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the "drone" keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI's power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.