• Journal of Wetlands Research
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• v.20 no.1
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• pp.43-53
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• 2018

Laguna Lake is the largest inland fresh water body in the Philippines. It primarily serves as a site for aquaculture, hydropower, transportation, and water supply industries. Due to Laguna Lake's diverse functionalities, competition among water users became prominent. Water quality began to deteriorate due to various pollutant contributions in this process, thereby affecting the soundness of the aquatic ecosystem. This study was conducted to evaluate the current water quality management policy from the viewpoint of ecological environment through the evaluation of the water quality of Laguna Lake. Concentrations of water pollutants such as ammonia ($NH_3$), biochemical oxygen demand (BOD), chloride ($Cl^-$), pH, and total suspended solids (TSS) exceeded the water quality standards of the Philippines' Department of Environment and Natural Resources (DENR). The water quality of the lake was also affected by the pollutant load due to agriculture and urban stormwater runoff in the watershed. The salinity and contaminated water from Pasig River also affected the water quality of Laguna Lake. Long-term water quality analysis showed that the water quality of Laguna Lake is also influenced by rainfall-related seasonal variations. The results of the water quality analysis of Laguna Lake indicated that the environmental management techniques of the Philippines should be changed from the conventional water management into an integrated watershed management scheme in the future. It is therefore necessary to study and introduce advanced watershed management measures in the Philippines based from the policies of other developed countries.

• Composites Research
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• v.31 no.6
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• pp.332-339
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• 2018

Fiber metal hybrid laminate (FML) can be used as an economic material with superior mechanical properties and light weight than conventional metal by bonding of metal and FRP. However, there are disadvantages that it is difficult to predict fracture behavior because of the large difference in properties depending on the type of fiber and lamination conditions. In this paper, we study the failure behavior of hybrid materials with laminated glass fiber reinforced plastics (GFRP, GEP118, woven type) in Al6061-T6 alloy. The Al alloys were coated with GFRP 1, 3, and 5 layers, and fracture behavior was analyzed by using a static test and a low cycle fatigue test. In the low cycle fatigue test, strain - life analysis and the total strain energy density method were used to analyze and predict the fatigue life. The Al alloy did not have tensile properties strengthening effect due to the GFRP coating. The fatigue hysteresis geometry followed the behavior of the Al alloy, the base material, regardless of the GFRP coating and number of coatings. As a result of the low cycle fatigue test, the fatigue strength was increased by the coating of GFRP, but it did not increase proportionally with the number of GFRP layers.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.234-252
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• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.81-92
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• 2013

The ultimate flexural strength of HSB I-girders, considering the effect of local bucking, was investigated through a series of nonlinear finite element analysis. The girders were selected such that the inelastic local flange buckling or the plastic yielding of compression flanges governs the flexural strength. Both homogeneous sections fabricated from HSB600 or HSB800 steel and hybrid sections with HSB800 flanges and SM570-TMC web were considered. In the FE analysis, the flanges and web were modeled using thin shell elements and initial imperfections and residual stresses were imposed on the FE model. An elasto-plastic strain hardening material was used for steels. After establishing the validity of present FE analysis by comparing FE results with test results published in the literature, the effects of initial imperfection and residual stress on the inelastic flange local buckling behavior were assessed. The ultimate flexural strengths of 60 I-girders with various compression flange slenderness were obtained by FE analysis and compared with those calculated from the KHBDC, AASHTO LRFD and Eurocode 3 provisions. Based on the comparison, the applicability of design equations in these specifications for the flexural strength of I-girder considering flange local buckling was evaluated.

• Applied Chemistry for Engineering
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• v.29 no.3
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• pp.330-335
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• 2018

A new approach for the fabrication of organic-organic conducting composite thin films using simultaneous co-vaporization vapor phase polymerization (SC-VPP) of two or more monomers that have different polymerization mechanisms (i.e., oxidation-coupling polymerization and radical polymerization) was reported for the first time. In this study, a PEDOT-PSMA composite thin film consisting of poly(3,4-ethylenedioxythiophene)(PEDOT) and poly(styrene-co-maleic anhydride)(PSMA) was prepared by SC-VPP process. The preparation of organic-organic conductive composite thin films was confirmed through FT-IR and $^1H-NMR$ analyses. The surface morphology analysis showed that the surface of PEDOT-PSMA thin film was rougher than that of PEDOT thin film. Therefore, PEDOT-PSMA exhibited lower electrical conductivity than that of PEDOT. But the conductivity can be improved by adding 2-ethyl-4-methyl imidazole as a weak base. The contact angle of PEDOT-PSMA was about $50^{\circ}$, as compared to $62^{\circ}$ for PEDOT. The demonstrated methodology for preparing an organic-organic conductive hybrid thin film is expected to be useful for adjusting intrinsic conductive polymer (ICP)'s surface properties such as mechanical, optical, and roughness properties.