• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.33 no.3
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• pp.86-98
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• 2020

Objectives : The purpose of this study is to analyze the Korean medicine treatment on Tinnitus to identify the trend and the effectiveness. Methods : The domestic journal databases(OASIS, NDSL, KISS, KTKP) were used to search for case studies related to the herbal medicine treatment of tinnitus. There was no restriction on the time of publication. A total of 14 papers were selected. Results : Out of 127 articles searched, 14 papers were selected. All of those were case-study or case-series studies. Features of the papers, characteristics of patients studied, herbal medicines, acupuncture, other treatments, evaluation methods and the results were analyzed. Conclusions : This study shows that various medical treatment methods for tinnitus are effectively used in Korean medicine. We hope that this study would give helpful information for tinnitus treatment.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.568-576
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• 2001

Most of clustering algorithms data to degenerate rapidly on high dimensional spaces. Moreover, high dimensional data often contain a significant a significant of noise. which causes additional ineffectiveness of algorithms. Therefore it is necessary to develop algorithms adapted to the structure and characteristics of the high dimensional data. In this paper, we propose a clustering algorithms CLIP using the projection The CLIP is designed to overcome efficiency and/or effectiveness problems on high dimensional clustering and it is the is based on clustering on each one dimensional subspace but we use the incremental projection to recover high dimensional cluster and to reduce the computational cost significantly at time To evaluate the performance of CLIP we demonstrate is efficiency and effectiveness through a series of experiments on synthetic data sets.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.21-30
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• 2021

This study examines the role of the manufacturing sector in stimulating economic growth in the Saudi economy. Even though the economic literature shows how the manufacturing sector stimulates economic growth, it does not clearly show the role of the manufacturing sector in economic growth. The study employed annual time-series data spanning the 1980-2018 period from the databases of the Saudi Arabian Monetary Authority. Moreover, the cointegration and VECM approaches were employed to examine the short- and long-run relationship causality between variables. The results show a two-way causal relationship exists between the manufacturing sector and economic growth. Furthermore, the results indicate that a unidirectional causal relationship exists, running from the manufacturing sector to the services sector. The study recommends that the determinants of the growth of the Saudi manufacturing sector must be investigated. Moreover, the most productive Saudi manufacturing industries must be identified, and the productivity of other sectors must be increased in a way that contributes to economic plans and policies. Thus, adopting economic policies that stimulate investment in the manufacturing sector contributes to increasing non-oil exports to diversify sources of income to achieve vision 2030 of the Kingdom of Saudi Arabia.

• The korean journal of orthodontics
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• v.50 no.5
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• pp.314-323
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• 2020

Objective: To identify the available evidence on the effects of rapid maxillary expansion (RME) with three-dimensional imaging and provide meta-analytic data from studies assessing the outcomes using computed tomography. Methods: Eleven electronic databases were searched, and prospective case series were selected. Two authors screened all titles and abstracts and assessed full texts of the remaining articles. Seventeen case series were included in the quantitative synthesis. Seven outcomes were investigated: nasal cavity width, maxillary basal bone width, alveolar buccal crest width, alveolar palatal crest width, inter-molar crown width, inter-molar root apex width, and buccopalatal molar inclination. The outcomes were investigated at two-time points: post-expansion (2-6 weeks) and post-retention (4-8 months). Mean differences and 95% confidence intervals were used to summarize and combine the data. Results: All the investigated outcomes showed significant differences post-expansion (maxillary basal bone width, +2.46 mm; nasal cavity width, +1.95 mm; alveolar buccal crest width, +3.90 mm; alveolar palatal crest width, +3.09 mm; intermolar crown width, +5.69 mm; inter-molar root apex width, +2.85 mm; and dental tipping, +3.75°) and post-retention (maxillary basal bone width, +2.21 mm; nasal cavity width, +1.55 mm; alveolar buccal crest width, +3.57 mm; alveolar palatal crest width, +3.32 mm; inter-molar crown width, +5.43 mm; inter-molar root apex width, +4.75 mm; and dental tipping, 2.22°) compared to pre-expansion. Conclusions: After RME, skeletal expansion of the nasomaxillary complex was greater in most caudal structures. Maxillary basal bone showed 10% post-retention relapse. During retention period, uprighting of maxillary molars occurred.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.119-132
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• 2007

In molecular biology, approximate subsequence search is one of the most important operations. In this paper, we propose an accurate and efficient method for approximate subsequence search in large DNA databases. The proposed method basically adopts a binary trie as its primary structure and stores all the window subsequences extracted from a DNA sequence. For approximate subsequence search, it traverses the binary trie in a breadth-first fashion and retrieves all the matched subsequences from the traversed path within the trie by a dynamic programming technique. However, the proposed method stores only window subsequences of the pre-determined length, and thus suffers from large post-processing time in case of long query sequences. To overcome this problem, we divide a query sequence into shorter pieces, perform searching for those subsequences, and then merge their results. To verify the superiority of the proposed method, we conducted performance evaluation via a series of experiments. The results reveal that the proposed method, which requires smaller storage space, achieves 4 to 17 times improvement in performance over the suffix tree based method. Even when the length of a query sequence is large, our method is more than an order of magnitude faster than the suffix tree based method and the Smith-Waterman algorithm.

• Journal of People, Plants, and Environment
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• v.23 no.3
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• pp.347-362
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• 2020

Background and objective: It is difficult to conclusively determine the exact cause of tree defects since multiple causes are involved such as climate change, plantation, tree quality and planting time, construction, planting base, drainage, sunshine conditions, maintenance, and microclimate. The data related to landscaping construction defects are scattered or fragmented by companies and years, but not managed systematically by the defect information management system. Most of the earlier studies associated with tree defects in apartment complexes suggested defect rates after examining tree defects in the completed construction site and proposed fragmentary and subjective conclusions about the causes of defects observed in trees with high defect rates. It is proposed to continue to conduct studies on the establishment and analysis of systematic databases to identify the exact causes of tree defects and measures to improve, and the need to accumulate systematic data in the construction process where many defects arises. This study was conducted to reduce the defects of trees planted in apartment complexes. Methods: Main factors related to tree defects were subdivided based on the results of literature review and a defect investigation at the completion site, and tree history management items were selected and subdivided during the construction stage. Results: The criteria for the preparation of subdivided items were obtained, and the tree history management checklist was written for the site under actual construction and a systematic database was established. Items that are categorized based to the causes of defects include the location of nurseries, date, tree quality, site conditions, planting techniques, microclimates, and maintenance. Conclusion: This study suggested tree history management items based on the tree defects that can be identified at the construction stage and applied them to the selected study site, which differentiates this study from earlier studies. It will be necessary to conduct a comprehensive and objective time series analysis on tree defects that occur over time by continuously monitoring and collecting data after construction.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.42-55
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• 2006

We propose a single Index approach for subsequence matching that supports moving average transform of arbitrary order in time-series databases. Using the single index approach, we can reduce both storage space overhead and index maintenance overhead. Moving average transform is known to reduce the effect of noise and has been used in many areas such as econometrics since it is useful in finding overall trends. However, the previous research results have a problem of occurring index overhead both in storage space and in update maintenance since tile methods build several indexes to support arbitrary orders. In this paper, we first propose the concept of poly-order moving average transform, which uses a set of order values rather than one order value, by extending the original definition of moving average transform. That is, the poly-order transform makes a set of transformed windows from each original window since it transforms each window not for just one order value but for a set of order values. We then present theorems to formally prove the correctness of the poly-order transform based subsequence matching methods. Moreover, we propose two different subsequence matching methods supporting moving average transform of arbitrary order by applying the poly-order transform to the previous subsequence matching methods. Experimental results show that, for all the cases, the proposed methods improve performance significantly over the sequential scan. For real stock data, the proposed methods improve average performance by 22.4${\~}$33.8 times over the sequential scan. And, when comparing with the cases of building each index for all moving average orders, the proposed methods reduce the storage space required for indexes significantly by sacrificing only a little performance degradation(when we use 7 orders, the methods reduce the space by up to 1/7.0 while the performance degradation is only $9\%{\~}42\%$ on the average). In addition to the superiority in performance, index space, and index maintenance, the proposed methods have an advantage of being generalized to many sorts of other transforms including moving average transform. Therefore, we believe that our work can be widely and practically used in many sort of transform based subsequence matching methods.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.357-372
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• 2001

In this paper, we present the concept of generalization in constructing windows for subsequence matching and propose a new subsequence matching method. GeneralMatch, based on the generalization. The earlier work of Faloutsos et al.(FRM in short) causes a lot of false alarms due to lack of the point-filtering effect. DualMatch, which has been proposed by the authors, improves performance significantly over FRM by exploiting the point filtering effect, but it has the problem of having a smaller maximum window size (half that FRM) given the minimum query length. GeneralMatch, an improvement of DualMatch, offers advantages of both methods: it can use large windows like FRM and, at the same time, can exploit the point-filtering effect like DualMatch. GeneralMatch divides data sequences into J-sliding windows (generalized sliding windows) and the query sequence into J-disjoint windows (generalized disjoint windows). We formally prove that our GeneralMatch is correct, i.e., it incurs no false dismissal. We also prove that, given the minimum query length, there is a maximum bound of the window size to guarantee correctness of GeneralMatch. We then propose a method of determining the value of J that minimizes the number of page accesses, Experimental results for real stock data show that, for low selectivities ($10^{-6}~10^{-4}$), GeneralMatch improves performance by 114% over DualMatch and by 998% iver FRM on the average; for high selectivities ($10^{-6}~10^{-4}$), by 46% over DualMatch and by 65% over FRM on the average.