• Membrane and Water Treatment
• /
• v.2 no.2
• /
• pp.105-119
• /
• 2011

This study presents the tests of an Immersed Membrane BioReactor (IMBR) equipped with a draft tube and focuses on the influence of hydrodynamic conditions on membrane fouling in a pilot-scale using a hollow fiber membrane module of ZW-10 under ambient conditions. In this system, the cross-flow velocities across the membrane surface were induced by a cylindrical draft-tube. The relationship between cross-flow velocity and aeration strength and the influence of the cross-flow on fouling rate (under various hydrodynamic conditions) were investigated using Multi-Dimension Scaling (MDS) analysis. MDS technique is especially suitable for samples with many variables and has relatively few observations, as the data about Membrane Bio-Reactor (MBR) often is. Observations and variables are analyzed simultaneously. According to the results, a specialized form of MDS, CoPlot enables presentation of the results in a two dimensional space and when plotting variables ratio (output/input) rather than original data the efficient units can be visualized clearly. The results indicate that: (i) aeration plays an important role in IMBR performance; (ii) implementing the MDS approach with reference to the variables ratio is consequently useful to characterize performance changes for data classification.

• Journal of Ship and Ocean Technology
• /
• v.11 no.1
• /
• pp.36-46
• /
• 2007

The maintenance of a ship is essential for safe navigation and hence regular surveys are prescribed according to the rule of classification societies. A hull inspection is generally performed by professional divers, but it takes a long time and the efficiency is low in terms of time and cost. In this research, a ROV(Remotely Operated Vehicle) named as SNU-ROV(Seoul National University-ROV) is developed to replace the conventional inspection method. In this system, the ROV is intended to be used for inspecting ship and harbor because harbor inspection is merging as a safety measure against any possible terror actions. In order to increase the efficiency of inspection, the ROV must be able to measure the exact position of damages. SNU-ROV has a positioning system based on LBL(Long Base Line). In shallow water such as harbor, however, LBL has bad DOP(Dilution of Precision) in the depth direction due to the limited depth. Thus LBL only can not locate the exact depth position. To solve the DOP problem, a pressure sensor is introduced to LBL and a complementary filter is attached by using indirect feedback Kalman filter. Thus developed positioning system is verified by simulation and experiment in towing tank.

• Ocean and Polar Research
• /
• v.25 no.3
• /
• pp.331-345
• /
• 2003

The fishing ground surrounding Wangdol-cho is not only overexploited by the littering of dilapidated fishing net and equipment, but also by fishermen's overfishing, surpassing optimum fisheries resources. In addition, increasing fishing efforts (number of fishing vessel and fishing net, etc) contribute to the deterioration of fishing ground, and it is urgently required that schemes to tackle the problems should be taken. To effectively address the problems as such, this paper aims to propose sustainable utilization and management scheme of fishing ground through classification of fishing ground surrounding Wangdol-cho as one area which is less than 50m deep, measuring $13.66km^2$ and the other, permission fishing area of Gill Net fishery, measuring $347.23km^2$. The analysis shows that, for the water area less than 50m deep, implementation from a short-term perspective includes autonomous management fishery by gill net and trap fishery. For the permission fishing area of Gill Net fishery, implementation includes limit on fishing period, real name system of fishing equipment and limit on fishing equipment. Implementation from a medium and long-term perspective includes limit on scuba diving, designation of underwater sightseeing zone, sea farming, facilities of surveillance, adoption of approval system for the permission fishing area of Gill Net fishery and introduction of report system for fishing.

• Journal of Korean Society on Water Environment
• /
• v.31 no.2
• /
• pp.120-133
• /
• 2015

Due to increasing impervious surfaces resulting from urbanization and industrialization which are directly linked to urban inundation and non-point pollutants runoff, there is a need to manage them systematically. A management over urban impervious surfaces calls for pervious/impervious maps, which enable viewing the distribution of impervious surfaces. Nevertheless, relevant data are absent as now. In this respect, despite the diversity of proposed methods, pilot implementation and accuracy verification have never been conducted. Therefore, this study is aimed to produce a pilot pervious/impervious map based on previously proposed methods and to elucidate its pros and cons with a view to proposing a method for producing a GIS-based optimal pervious/impervious map. Following previously proposed methods, a pervious/impervious map of Bupyeong-gu, Incheon was produced. Then, a method of producing optimal pervious/impervious maps applicable to urban areas was proposed through the comparison of pros and cons of relevant spatial data. As a result, the map had been confirmed 99.2% of classification accuracy. Based on the present findings, future studies should establish a standardized method for producing. Also, this method should be used to produce pervious/impervious maps of other regions so that it can be applied to managing impervious surfaces in major urban areas nationwide.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.60 no.6
• /
• pp.43-54
• /
• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.4
• /
• pp.235-243
• /
• 2021

River surveying is conducted to acquire basic geographic data for river master plans and various river maintenance, and it is also used to predict changes after river maintenance construction. ABL (Airborne Bathymetric LiDAR) system is a cutting-edge surveying technology that can simultaneously observe the water surface and river bed using a green laser, and has many advantages in river surveying. In order to use the ABL data for river surveying, it is prerequisite step to segment and extract the water surface and river bed points from the original point cloud data. In this study, point cloud segmentation was performed by applying the ground filtering technique, ATIN (Adaptive Triangular Irregular Network) to the ABL data and then, the water surface and riverbed point clouds were extracted sequentially. In the Gokgyocheon river area, Chungcheongnam-do, the experiment was conducted with the dataset obtained using the Leica Chiroptera 4X sensor. As a result of the study, the overall classification accuracy for the water surface and riverbed was 88.8%, and the Kappa coefficient was 0.825, confirming that the ABL data can be effectively used for river surveying.

• Journal of Korea Water Resources Association
• /
• v.56 no.4
• /
• pp.261-272
• /
• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.26 no.5
• /
• pp.514-522
• /
• 2020

To understand the ecotoxicological differences between representative Skeletonema sp. and Dunaliella tertiolecta, both producers as international standard test species for marine ecotoxicity testing, we compared each standard test method, and comparatively analyzed the suitability of the species for environmental assessment and their sensitivity to various test substances. Although most of the test conditions were the same in each method, there were differences in limitation of pH changing and the initial inoculation density in the validation criteria, which is supposed to originate from the low growth rate of D. tertiolecta. In terms of suitability, both species showed consistency in test performance by repeatedly meeting the validation criteria required by the standard test methods. The salinity ranges available for testing were 20 and 10 psu for Skeletonema sp. and D. tertioelecta, respectively. Finally, regarding sensitivity, the toxicity sensitivity of Skeletonema sp. was relatively higher than that of D. tertiolecta for the reference toxicant, actual polluted water discharged (ballast water), and other chemicals. This implies that using at least two species of microalgae from different classification groups could help increase the reliability and objectivity of test results in the performance of marine ecotoxicity tests using producers.

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

• Korean Journal of Remote Sensing
• /
• v.32 no.2
• /
• pp.119-131
• /
• 2016

Descriptions are provided of the automated aerosol-type classification and mass concentration calculation algorithm for real-time data processing and aerosol products in Korea Aerosol Lidar Observation Network (KALION, http://www.kalion.kr). The KALION algorithm provides aerosol-cloud classification and three aerosol types (clean continental, dust, and polluted continental/urban pollution aerosols). It also generates vertically resolved distributions of aerosol extinction coefficient and mass concentration. An extinction-to-backscatter ratio (lidar ratio) of 63.31 sr and aerosol mass extinction efficiency of $3.36m^2g^{-1}$ ($1.39m^2g^{-1}$ for dust), determined from co-located sky radiometer and $PM_{10}$ mass concentration measurements in Seoul from June 2006 to December 2015, are deployed in the algorithm. To assess the robustness of the algorithm, we investigate the pollution and dust events in Seoul on 28-30 March, 2015. The aerosol-type identification, especially for dust particles, is agreed with the official Asian dust report by Korean Meteorological Administration. The lidar-derived mass concentrations also well match with $PM_{10}$ mass concentrations. Mean bias difference between $PM_{10}$ and lidar-derived mass concentrations estimated from June 2006 to December 2015 in Seoul is about $3{\mu}g\;m^{-3}$. Lidar ratio and aerosol mass extinction efficiency for each aerosol types will be developed and implemented into the KALION algorithm. More products, such as ice and water-droplet cloud discrimination, cloud base height, and boundary layer height will be produced by the KALION algorithm.