• Journal of computational fluids engineering
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• 제14권3호
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• pp.115-122
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• 2009

Numerical methods are applied to simulate the smoke behavior in a ventilated tunnel using large eddy simulation (LES) which is incorporated in FDS (Fire Dynamics Simulator) with proper combustion and radiation model. In this study, present numerical results are compared with data obtained from experiments on pool fires in a ventilated tunnel. The model tunnel is $182m(L){\times}5.4m(W){\times}2.4m(H)$. Two fire scenarios with different ventilation rates are considered with two different fire strengths. The present results are analyzed with those from LES without combustion and radiation model and from RANS ($\kappa-\epsilon$) model as well. Temperature distributions caused by fire in tunnel are compared with each other. It is found that thermal stratification and smoke back-layer can be predicted by FDS and the temperature predictions by FDS show better results than LES without combustion and radiation model. The FDS solver, however, failed to predict correct flow pattern when the high ventilation rate is considered in tunnel because of the defects in the tunnel-inlet turbulence and the near-wall turbulence.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.517-527
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• 2021

While the two principles of lean manufacturing and time-driven activity-based costing (TDABC) have been established out of multiple incentives and do not follow the same particular targets, there is substantial commonality between them. In these conditions, the supply management of a multi-product system needs a rigorous production model to minimize costs. In this sense, this paper proposes an interactive model with the consideration of optimizing product-mix decisions using both lean development tools and TDABC. This paper proposes a qualitative approach using the case study of the Iraqi state company for battery production. The suggested model decreased manufacturing time and costs, along with some substantial reduction in idle production capacity by 26 percent in 2019, based on the findings of the case study. On the other hand, the proposed model gives two side advantages: an efficient division of costs on goods due to the use of time spent as a cost factor for products and cost savings due to the introduction of the lean manufacturing approach that reduces all additional costs and increases product-mix decisions. Furthermore, the analytical data gathered here suggests that the incorporation of lean management concepts and TDABC has a strong and important influence on product-mix decisions.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• Journal of Korea Multimedia Society
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• 제19권8호
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• pp.1587-1596
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• 2016

This paper presents an interactive locomotion controller using motion capture data and inverted pendulum model. Most of the data-driven character controller using motion capture data have two kinds of limitation. First, it needs many example motion capture data to generate realistic motion. Second, it is difficult to make natural-looking motion when characters navigate dynamic terrain. In this paper, we present a technique that uses dimension reduction technique to motion capture data together with the Gaussian process dynamical model (GPDM), and interpolates the low-dimensional data to make final motion. With the low-dimensional data, we can make realistic walking motion with few example motion capture data. In addition, we apply the inverted pendulum model (IPM) to calculate the root trajectory considering the real-time user input upon the dynamic terrain. Our method can be used in game, virtual training, and many real-time applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2222-2240
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• 2023

Brand fandom refers to a collection of consumers with strong emotions toward a brand. Studying the dynamics of brand fandom can help brands understand which services or strategies influence their consumers to become a part of brand fandom. However, existing literature on fandom in the last three decades has mainly used qualitative methods, and there is still a lack of research on fandom using quantitative methods. Specifically, previous studies lack a framework for locating fandoms from online textual data and analyzing their dynamics. This study proposes a framework for exploring brand fandom dynamics based on online textual data. This framework consists of four phases based on the design thinking model: Preparing Data, Defining Fandom Categories, Generating Fandom Dynamics, and Analyzing Fandom Dynamics. This framework uses techniques such as social network analysis and process mining, combined with brand personality theory. We demonstrate the applicability of this framework using case studies of two Korean home appliance brands. The dataset contains 14,593 posts by consumers in 374 online communities. The results show that the proposed framework can analyze brand fandom dynamics using textual customer data. Our study contributes to the interdisciplinary research at the intersection of data-driven service design and consumer culture quantification.

• Journal of Wetlands Research
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• 제26권2호
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권10호
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• pp.2473-2492
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• 2012

Energy conservation is a vital issue in wireless sensor networks. Recently, employing mobile sinks for data gathering become a pervasive trend to deal with this problem. The sink can follow stochastic or pre-defined paths; however the controlled mobility pattern nowadays is taken more into consideration. In this method, the sink moves across the network autonomously and changes its position based on the energy factors. Although the sink mobility would reduce nodes' energy consumption and enhance the network lifetime, the overhead caused by topological changes could waste unnecessary power through the sensor field. In this paper, we proposed EEDARS, an energy-efficient dual-sink algorithm with role switching mechanism which utilizes both static and mobile sinks. The static sink is engaged to avoid any periodic flooding for sink localization, while the mobile sink adaptively moves towards the event region for data collection. Furthermore, a role switching mechanism is applied to the protocol in order to send the nearest sink to the recent event area, hence shorten the path. This algorithm could be employed in event-driven and multi-hop scenarios. Analytical model and extensive simulation results for EEDARS demonstrate a significant improvement on the network metrics especially the lifetime, the load and the end-to-end delay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5271-5289
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• 2016

On large-scale data analysis platforms deployed on cloud infrastructures over the Internet, the instability of the data transfer time and the dynamics of the processing rate require a more sophisticated data distribution scheme which maximizes parallel efficiency by achieving the balanced load among participated computing elements and by eliminating the idle time of each computing element. In particular, under the constraints that have the real-time and limited data buffer (in-memory storage) are given, it needs more controllable mechanism to prevent both the overflow and the underflow of the finite buffer. In this paper, we propose an auto regulated data provisioning model based on receiver-driven data pull model. On this model, we provide a synchronized data replenishment mechanism that implicitly avoids the data buffer overflow as well as explicitly regulates the data buffer underflow by adequately adjusting the buffer resilience. To estimate the optimal size of buffer resilience, we exploits an adaptive buffer resilience control scheme that minimizes both data buffer space and idle time of the processing elements based on directly measured sample path analysis. The simulation results show that the proposed scheme provides allowable approximation compared to the numerical results. Also, it is suitably efficient to apply for such a dynamic environment that cannot postulate the stochastic characteristic for the data transfer time, the data processing rate, or even an environment where the fluctuation of the both is presented.

• Journal of Digital Convergence
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• 제18권6호
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• pp.17-32
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• 2020

This study analyzes the regulatory issues of stakeholders, the firm, the government, and the individual, in the data industry using the principal-agent theory. While the importance of data driven economy is increasing rapidly, policy regulations and restrictions to use data impede the growth of data industry. We applied descriptive case analysis methodology using principal-agent theory. From our analysis, we found several meaningful results. First, key policy actors in data industry are data firms and the government among stakeholders. Second, two major concerns are that firms frequently invade personal privacy and the global companies obtain monopolistic power in data industry. This paper finally suggests policy and strategy in response to regulatory issues. The government should activate the domestic agent system for the supervision of global companies and increase data protection. Companies need to address discriminatory regulatory environments and expand legal data usage standards. Finally, individuals must embody an active behavior of consent.

• Magazine of the Korean Society of Agricultural Engineers
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• 제39권1호
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• pp.41-47
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• 1997

A geographical resource analysis support system (GRASS) was incorporated to an input and output processor for the agricultural nonpoint source pollution (AGNPS) model. The resulting interface system, GIS-AGNPS was a user-friendly, menu-driven system. GIS-AGNPS was developed to automatically process the input and output data from GIS-based data using GRASS and Motif routines. GIS-AGNPS was consisted of GISAGIN which was an input processor for the AGNPS model, GISAGOUT a output processor for the AGNPS and management submodel. The system defines an input data set for AGNPS from attributes of basic and thematic maps. It also provides with editing modes so that users can adjust and detail the values for selected input parameters, if needed. The post-processor at the system displays graphically the outputs from AGNPS, which may he used to identify areas significantly contributing nonpoint source pollution loads.