• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4191-4211
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• 2019

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.240-245
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• 1998

Fuzzy information retrieval which uses the concept of fuzzy relation is able to retrieve documents in the way based on not morphology but semantics, dissimilar to traditional information retrieval theories. Fuzzy information retrieval logically consists of three sets : the set of documents, the set of terms and the set of queries. It maintains a fuzzy relational matrix which describes the relationship between documents and terms and creates a thesaurus with fuzzy relational product. It also provides the user with documents which are relevant to his query. However, there are some problems on building a thesaurus with fuzzy relational product such that it has big time complexity and it uses fuzzy values to be processed with flating-point. Actually, fuzzy values have to be expressed and processed with floating-point. However, floating-point operations have complex logics and make the system be slow. If it is possible to exchange fuzzy values with binary values, we could expect sp eding up building the thesaurus. In addition, binary value expressions require just a bit of memory space, but floating -point expression needs couple of bytes. In this study, we suggest a new method of building a thesaurus, which accelerates the operation of the system by pre-applying an ${\alpha}$-cut. The experiments show the improvement of performance and reliability of the system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.12
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• pp.38-44
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• 2019

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.

• Environmental Engineering Research
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• v.24 no.1
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• pp.1-16
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• 2019

N-nitrosodimethylamine (NDMA) is a typical nitrogen disinfection by-product, which has posed a potential threat to human health during drinking water disinfection. Because of the well-known effects of mutagenesis, carcinogenesis and teratogenesis, the high detection rate in water engineering systems (such as coagulation, membrane filtration and biological systems), and difficulty to remove, it has received wide concern in the field of water engineering systems. The NDMA is a low molecular weight hydrophilic organic substance, which is difficult to remove. Also, the mechanism for NDMA formation is also recognized to be complex, and many steps still needed to be further evaluated. Therefore, the mechanistic knowledge on NDMA formation potential and their removal processes is of particularly interest. Few papers summarize the occurrence and control of NDMA in water engineering systems. It is for this reason that the content of this paper is particularly important for us to understand and control the amount of NDMA thus reducing the threat of disinfection by-products to drinking water. Four parts including the mechanisms for the NDMA formation potential, the factors affecting the NDMA formation potential, the technologies for removal of NDMA are summarized. Finally, some definite suggestions are given.

• Bulletin of the Korean Chemical Society
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• v.28 no.12
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• pp.2139-2152
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• 2007

Ruthenium complex-catalyzed reactions often require highly qualified tuning of reaction conditions with substrates to attain high yield and selectivity of the products. In this review, our strategies for achieving characteristic ruthenium complex-catalyzed co-oligomerization of different alkenes are disclosed: 1) The codimerization of 2-norbornenes with acrylic compounds by new ruthenium catalyst systems of RuCl3(tpy)/Zn [tpy = 2,2':6',2''-terpyridine] or [RuCl2(η6-C6H6)]2/Zn in alcohols, 2) A novel synthesis of 2-alkylidenetetrahydrofurans from dihydrofurans and acrylates by zerovalent ruthenium catalysts, such as Ru(η4-cod)(η6-cot) [cod = 1,5-cyclooctadiene, cot = 1,3,5-cyclooctatriene] and Ru(η6-cot)(η2-dmfm)2 [dmfm = dimethyl fumarate], 3) Regio- and stereoselective synthesis of enamides by Ru(η6-cot)(η2-dmfm)2-catalyzed codimerization of N-vinylamides with alkenes, and 4) Unusual head-to-head dimerization of styrenes and linear codimerization of styrenes with ethylene by Ru(η6-cot)(η2-dmfm)2 catalyst in the presence of primary alcohols.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.37-46
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• 2012

The scheduling problem of large products like ships, airplanes, space shuttles, assembled constructions, and automobiles is very complex in nature. To reduce inherent computational complexity, we often design scheduling systems that the original problem is decomposed into small sub-problems, which are scheduled independently and integrated into the original one. Moreover, the steep growth of communication technology and logistics makes it possible to produce a lot of multi-nation corporation by which products are produced across more than one plant. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. In this research, we suggest an agent-based coordinating mechanism for multi-level scheduling systems in supply chain. For design of a general coordination mechanism, at first, we propose a grammar to define individual scheduling agents which are responsible to their own plants, and a meta-level coordination agent which is engaged to supervise individual scheduling agents. Second, we suggest scheduling agent communication protocols for each scheduling agent topology which is classified according to the system architecture, existence of coordinator, and direction of coordination. We also suggest a scheduling agent communication language which consists of three layers : Agent Communication Layer, Scheduling Coordination Layer, Industry-specific Layer. Finally, in order to improve the efficiency of communication among scheduling agents we suggest a rough capacity coordination model which supports to monitor participating agents and analyze the status of them. With this coordination mechanism, we can easily model coordination processes of multiple scheduling systems. In the future, we will apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly-plant-scheduling agents, and a meta-level coordination agent. A series of experiment using the real-world data will be performed to examine this mechanism.

• Biodesign
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• v.5 no.3
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• pp.122-125
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• 2017

Receptor for advanced glycation end products (RAGE) is one of the single transmembrane domain containing receptors and causes various inflammatory diseases including diabetes and atherosclerosis. RAGE extracellular domain has three consecutive IgG-like domains (V-C₁-C₂ domain) which interact with various soluble ligands including heparan sulfate or HMGB1. Studies have shown that each ligand induces different oligomeric forms of RAGE which results in a ligand-specific signal transduction. The structure of mouse RAGE bound to heparan sulfate has been previously determined but the electron density map of heparan sulfate was too ambiguous that the exact position of heparin sulfate could not be defined. Furthermore, the complex structure of human RAGE and heparin sulfate still remains elusive. Therefore, to determine the structure, human RAGE was overexpressed using bacterial expression system and crystallized using the sitting drop method in the condition of 0.1 M sodium acetate trihydrate pH 4.6, 8 % (w/v) polyethylene glycol 4,000 at 290 K. The crystal diffracted to 3.6 Å resolution and the space group is C121 with unit cell parameters a= 206.04 Å, b= 68.64 Å, c= 98.73 Å, α= 90.00°, β= 90.62°, γ= 90.00°.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4212-4226
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• 2019

In this paper, a design and implementation for direct deal distribution platform is proposed to bypass the complex traditional distribution structure of agricultural market, as one of the fields where distribution patterns have changed. In the case of domestic agricultural distribution, demand and supply are unstable since the sales market is excessively concentrated in the designated wholesale market. Besides sales must go through multiple stages of distribution leading to problems in freshness and stability of agricultural products and downward pressure on profit margins for producers. To solve the above mentioned issues, we propose a cloud service convergence direct deal distribution platform based on asynchronous-driven Node.js. The proposed platform can facilitate a variety of direct trading functions and also access to visualization information related to agricultural products, which may increase user confidence at an intermediary-free direct transactions platform. First, we describe the requirements of intermediary-free direct transactions of agricultural products and transaction entities. Next the database structure and transaction functions are designed and then implemented according to those requirements. Finally, an API based cloud convergence service structure is designed to provide the analyzed information to ensure a trustworthy system.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.3
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• pp.56-65
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• 2006

It is well known and widely accepted that the conceptual design is the most influential step in the design process of a product or a system and that about 75% of the life cycle cost is committed as the results of this stage. The purpose of this paper is to present and demonstrate the step of ICDM(Integrated, Customer Driven, Conceptual Design Method) for the development of a ground launcher system, TEL(Transporter, Erector and Launcher). The results of the study show the effectiveness of the method during the conceptual design phase of new complex systems or high-tech products.