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The pressure on water infrastructure has increased due to an increase in the number of aging water pipes. Aging pipes are prone to failure, causing significant financial losses and service disruptions. The increasing number of aged pipes and limited budget for pipe rehabilitation or replacement necessitates water infrastructure asset management to ensure sustainable water supply services. In this study, contingent valuation was used to estimate the willingness to pay (WTP) and value improved water supply services through the implementation of asset management. To estimate the WTP at the individual and county levels, we performed a nationwide survey including eight provinces and 24 counties/cities with distinct water supply service performances. At the individual level, the median WTP estimated using the double-bounded dichotomous choice model was 249.50 KRW/month (0.22 USD/month). The results showed that high-level satisfaction of customers with water supply services and positive price perception of water bills resulted in a high WTP. At the county level, decreasing water supply service performances were associated with a low WTP, indicating that proper interventions by local utilities are required to achieve sustainable water supply services. Our results provide a quantitative basis for decision-making in implementation of water infrastructure asset management.

Peak load rate(i.e., maximum daily flow/average daily flow) has not been considered for industrial water demand planning in Korea to date, while area unit method based on average daily flow has been applied to decide capacity of industrial water treatment plants(WTPs). Designers of industrial WTPs has assumed that peak load would not exist if operation rate of factories in industrial sites were close to 100%. However, peak load rates were calculated as 1.10~2.53 based on daily water flow from 2009 to 2014 for 9 industrial WTPs which have been operated more than 9 years(9-38 years). Furthermore, average operation rates of 9 industrial WTPs was less than 70% which means current area unit method has tendency to overestimate water demand. Therefore, it is not reasonable to consider peak load for the calculation of water demand under current area unit method application to prevent overestimation. However, for the precise future industrial water demand calculation more precise data gathering for average daily flow and consideration of peak load rate are recommended.

A variety of methods for detecting the location of an underground water pipeline are being used across the world; the current main methods used in South Korea, however, have the problems of low precision and efficiency and the limitations in actual application. On this, this study developed locator capable of detecting the location of a water pipe by the use of an IMU sensor, and technology for using the extended karman filter to correct error in location detection and to plot the location on the coordinate system. This study carried out a tract test and a road test as basic experiments to measure the performance of the developed technology and equipment. As a result of the straight line, circular and ellipse track tests, the 1750 IMU sensor showed the average error of 0.08-0.11%; and thus it was found that the developed locator can detect a location precisely. As a result of the 859.6-m road test, it was found that the error was 0.31 m in case the moving rate of the sensor was 0.3-0.6 m/s; and thus it was judged that the equipment developed by this study can be applied to long-distance water pipes of over 1 km sufficiently. It is planned to evaluate its field applicability in the future through an actual pipe network pilot test, and it is expected that locator capable of detecting the location of a water pipe more precisely will be developed through research for the enhancement of accuracy in the algorithm of location detection.

Pipe Deterioration Prediction (PDP) and Pipe Failure Risk Prediction (PFRP) models were developed in an attempt to predict the deterioration and failure risk in water mains using fuzzy technique and the markov process. These two models were used to determine the priority in repair and replacement, by predicting the deterioration degree, deterioration rate, failure possibility and remaining life in a study sample comprising 32 water mains. From an analysis approach based on conservative risk with a medium policy risk, the remaining life for 30 of the 32 water mains was less than 5 years for 2 mains (7%), 5-10 years for 8 (27%), 10-15 years for 7 (23%), 15-20 years for 5 (17%), 20-25 years for 5 (17%), and 25 years or more for 2 (7%).

Owing to time and cost constraints, new methods that would make it possible to evaluate the safety of the water supply pipeline in a less time- and cost-consuming manner are urgently needed. In response to this exigency, the present study developed a new statistical model to assess the safety of the water supply pipeline using the quantification theory type II. In this research, the safety of the water supply pipeline was defined as `a possibility of the pipeline failure`. Quantification analysis was conducted on the qualitative data, such as pipe material, coating, and buried condition. The results of analyses demonstrate that the hit ratio of the quantification function amounted to 77.8% of hit ratio, which was a fair value. In addition, all variables that were included in the quantification function were logically valid and demonstrated statistically significant. According to the results derived from the application of the safety evaluation model, the coefficient of determination () between K-region`s water supply pipeline safety and the safety inspection amounted to 0.80. Therefore, these findings provide meaningful insight for the measured values in real applications of the model. The results of the present study can also be meaningfully used in further research on safety evaluation of pipelines, establishing of renewal prioritization, as well as asset management planning of the water supply infrastructure.

In a pipeline system, aging of the pipeline due to a variety of internal and external factors reduces its functionality as a water supply system and increases the risk of pipe failure. Failure of aging pipelines leads to greater social and economic damage. Thus, through proper rehabilitation and replacement, the pipeline systems must be managed to ensure safe water quality and structural performance. This paper shows the use of the genetic algorithm technique to find a near optimal rehabilitation schedule of water pipeline systems. The goal is to minimize the present value of pipe replacement, renovation and repairs costs over a defined analysis period while requirements for water standards are fulfilled. A case study shows that the rehabilitation model proposed in this study can be a powerful tool to assist in planning the asset management strategies for water pipeline systems.

In the Republic of Korea, multi-regional water supply pipelines were intensively laid during the rapid economic growth period; however, these pipelines are now aging. The cost of renewal of the aged pipelines would be enormous. Therefore, limited resources need to be distributed effectively in accordance with the priority of renewal. In this study, the risks involved in the multi-regional water supply pipeline of region K were calculated and assessed for setting the priority of renewal. The probability of failure of the pipeline was calculated through a quantification theory, and the effects of failure on the water supply pipeline were calculated by interpreting a pressure driven analysis. The risk considering the probability and consequence of failure comprehensively was found to be the largest at section A12. Three alternatives were considered for risk reduction, and the effect of each alternative on the risk reduction was analyzed. As a result, the construction of double piping by laying a new pipeline was found to be the most effective measure for risk reduction. The results of this study can be utilized as the basic data for establishing an investment plan for the renewal of the water supply pipeline and asset management plan.

This research carried out an analysis on input cost and leakage reduction effect by leakage reduction method, focusing on the project for establishing an optimal water pipe network management system in the Taebaek region, which has been executed annually since 2009. Based on the result, optimal cost-benefit analysis models for water distribution network rehabilitation project were developed using DEA(data envelopment analysis) and multiple regression analysis, which have been widely utilized for efficiency analysis in public and other projects. DEA and multiple regression analysis were carried out by applying 4 analytical methods involving different ratios and costs. The result showed that the models involving the analytical methods 2 and 4 were of low significance (which therefore were excluded), and only the models involving the analytical methods 1 and 3 were suitable. From the result it was judged that the leakage management method to be executed with the highest priority for the improvement of revenue water ratio was installation of pressure reduction valve, followed by replacement of water distribution pipe, replacement of water supply pipe, and then leakage detection and repair; and that the execution of leakage management methods in this order would be most economical. In addition, replacement of water meter was also shown to be necessary in case there were a large number of defective water meters.

District Metered Area (DMA) construction is one of the most cost effective alternatives for management of water loss (i.e., water leakage) and energy consumption (i.e., water pressure) in water distribution systems. Therefore, it`s being implemented to numerous new and existing water distribution systems worldwide. However, due to the complexity of water distribution systems, especially large-scale and highly looped systems, it is still very difficult to define the optimal boundary of DMAs considering all the aspects of water distribution system management requirements. In this study, a DMA design methodology (or a DMA design model) was developed with Geographic Information Systems (GIS) and hydraulic distribution system model to determine the optimal DMA boundary.

Because pipeline systems represent more than 80% of the total asset value of water-distribution systems, their management is an important issue for water utilities. A pipeline deteriorates over time after installation and, along with the deterioration, pipe bursts can occur as various types, and the choice of a maintenance and repair strategy will depend on the burst types. It is therefore important to forecast the occurrence probability of each burst type. This paper addresses a competing deterioration-hazard model that allows modelling of deterioration by multiple types of failure and focuses on the bursts which occur in the pipe body or connection. The Weibull hazard model is used to address the lifetime of each pipeline, measured from when it was buried, and the model takes into account the competing nature of various types of failure by using a competing hazard model. The competing deterioration-hazard model allows us to determine the probability of deterioration in the pipe body and connection. The model is estimated by Bayesian inference using a Markov chain Monte Carlo method. The applicability of the method to data for an existing pipeline system is examined.

Water pipes corrode as the endurance period elapses, or become deteriorated according to the buried environment, sometimes causing functional failure and problems with stable water supply. Stable and continuous water supply for consumers requires the risk assessment of water pipes. The safety factor has been used in many fields as a direct evaluation index that can express the state of a facility and is also used as a reference to judge the necessity of pipe renewal. In this study, the safety factor was calculated in order to establish the Probability of Failure model, by using result data such as the pipe property and ratio of residual stress from the technological inspection of waterworks.

This study was carried out to analyze water suspension in the water supply system through fault tree analysis. And quantitative factors was evaluated to minimize water suspension. Consequently the aim of this study is to build optimal planning by analyzing scenarios for water suspension. Accordingly the fault tree model makes it possible to estimate risks for water suspension, current risks is . The result of scenario analysis by pipe replacement, risks for water suspension was reduced when replacing WD4 pipe. As a result of scenario analysis by water district connections, the amount of risk reduction is maximized when it is connecting to network pipe of D Zone. Therefore, connecting to network pipe for D Zone would be optimal to reduce risk for water suspension.

Pumping regimes for water distribution systems have been established to reduce pump operation costs. Optimization of water reservoir networks has been studied extensively for operating pumps efficiently. According to research, optimization reduces pump operation costs by approximately 10–20%. However, comparing economic benefits per country would be too complex because the effects of optimization differ depending on the power cost estimation system of each country and the existing operations. This study presents an optimization algorithm to minimize the operating costs of an entire system via the application of dynamic programming. Case studies were conducted to apply the algorithm to real values in four scenarios. By setting cost minimization as an objective, an optimum water pump operation plan was derived that encompasses operating conditions, variations in water demand, and energy costs. Results showed that operating a standby pump in an existing system can increase the system's efficiency. In addition, even if hourly water consumption remains constant, each case requires its own operating method, because each case has different operational costs. By considering various conditions such as those mentioned in this study, future pump operation schedules can be optimized and stabilized to maintain economic water supply.

Indexes of safety, restoration, damage impact, and management reliability were developed to assess reliability of drinking water distribution networks (DWDNs) management. The developed indexes were applied to evaluate the reliability of the pipeline management stage during unexpected mechanical and hydraulic accidents of components. The results were used to support the decision-making process in effective management and maintenance by enhancing the administrator`s system understanding and by helping to create appropriate maintenance and management policies. The results of this study indicated that application of a management reliability index to assess DWDNs reliability may help create a more effective plan for establishing DWDNs management and maintenance.

The water pipe is an important component of the tap water facility, and plays a role as critical as that of the blood vessel in the body. However, accurate diagnosis of its condition is very difficult because it is buried under the ground, even as pipe aging frequently leads to phenomena that threaten water quality such as pipe damage and water leakage. In order to maintain and improve the performance of the existing pipe network, planned examination, operation, and maintenance, as well as technologies for proper renewal, are required. Accordingly, the present research proposes a method that can be used for the maintenance of the pipe network in each water distribution block (WDB). For this purpose, the weighted performance indicator (PI) determined by means of PI and the analytic hierarchy process technique which allows evaluation for individual water WDBs, was applied to the ELECTRE technique which is a multi-factor decision-making method. This result may be used as a reference for water leakage...

This study examines a plan for efficient leakage investigation to decide the number of survey-subjected water meters in the Option A method, which requires considerable manpower and expense, along with possible civil complaints, among the possible methods for determining the minimum night flow. The Bayesian approach was adopted, and the prior distribution of night use and water supply pipe night flow losses were proposed in order to estimate the most efficient survey rate in Option A test. From the results, the prior distribution was decided as the gamma distribution by surveying the minimum night flow for all the water meters for district metered area A. The estimated value of the posterior distribution, i.e., the minimum night flow, was determined by using the Bayesian inference by changing the survey rate of water meters. From the results, the survey rate for maximizing the survey efficiency was calculated. The most effective survey rate was 58% for household water meters and 78% for non-household water meters.

A water supply risk (WSR) assessment model was developed for a water distribution network and applied in a targeted area for determining the pipe burst probability (ProbPB), the impact of pipe burst (ImpPB), and the WSR calculated as the product of these two values. ImpPB was separately calculated for the leakage duration time and the repair work time when water service is cut-off or reduced. The WSR for the block in the study area was calculated at 1.507 m3/year. To verify this WSR, pipe replacement was performed based on ProbPB, which is a management indicator for the water provider, ImpPB, which is a management indicator for the water consumer, and the WSR that considers both of these, by analyzing the WSR reduction effect of each. The pipe replacement cost, which is a restrictive condition, was set at 5% of the full replacement cost (5.3 billion won) for the entire pipe network in the targeted area. Pipe replacement was performed based on ProbPB, ImpPB and WSR. The block WSR reduction efficien...

Water pipelines deliver water of daily-life demand to customers. However, pipelines tend to be damaged after years of use. Thus, many waterworks are now faced with the major task of water pipeline rehabilitation. However, budgets for pipeline replacement are limited. The present study aims to propose a long-term plan for water pipeline rehabilitation under limited rehabilitation budgets that utilizes a damage occurrence model. Especially, this study attempts long-term rehabilitation planning by efficiently allocating a budget for pipeline rehabilitation. First, a simulation model is used to obtain the failure rate curve which uses reliability theory and data analysis for pipeline leakage accidents, and Monte Carlo simulation is applied to the simulation model. Secondly, in order to set the best planning, several cases with changed annual replacement rate are applied to this study. Finally, the simulation model is applied to key pipelines. The key pipelines are vital pipelines located in central districts. Thus, the replacement of key pipelines is considered to be the priority. From these steps, this study is expected to provide desirable alternative plans for water pipelines when the budget for replacement is limited.

Water distribution pipes installed underground have potential risks of pipe failure and burst. After years of use, pipe walls tend to be corroded due to aggressive soil environments where they are located. The present study aims to assess the degree of external corrosion of a distribution pipe network. In situ data obtained through test pit excavation and direct sampling are carefully collated and assessed. A statistical approach is useful to predict severity of pipe corrosion at present and in future. First, criteria functions defined by discriminant function analysis are formulated to judge whether the pipes are seriously corroded. Data utilized in the analyses are those related to soil property, i.e., soil resistivity, pH, water content, and chloride ion. Secondly, corrosion factors that significantly affect pipe wall pitting (vertical) and spread (horizontal) on the pipe surface are identified with a view to quantifying a degree of the pipe corrosion. Finally, a most reliable model represented in the form of a multiple regression equation is developed for this purpose. From these analyses, it can be concluded that our proposed model is effective to predict the severity and rate of pipe corrosion utilizing selected factors that reflect the fuzzy soil environment.

The water supply pipes are buried across wide range of areas, so it is hard to spot them using excavation and takes a large amount of expense. Thus, there is a high risk for direct research and application, accompanying many difficulties in implementation of them. Therefore, it is more economical and convenient to use indirect evaluation variables than direct evaluation of the buried pipes in assessing the degree of pipe deterioration. To assess the degree of pipe deterioration using the indirect evaluation variables, it should be done first to identify how and to what extent they affect the degree of deterioration. This study measured the evaluation variables for pipe deterioration using the pipe endoscope and analyzed the measurement results and the degree of impact on the pipes. In addition, this study attempted to evaluate the adequateness of the pipe deterioration evaluation using the indirect variables based on the analysis results. The evaluation variables measured through the pipe endoscope were the thickness of sediments, size of scale, degree of desquamation and condition of connections. For the indirect evaluation variables, the data such as the property data from GIS pipe network map as well as the material, diameter, age and pipe lining material of the pipe, road type, leakage frequency, average water velocity and water pressure using the leakage repair records was collected. Using the collected data, this study comparatively analyzed the indirect evaluation variables for the degree of pipe deterioration and the results from the pipe endoscope to choose appropriate variables for pipe deterioration evaluation and calculated the weights of the indirect variables on the degree of deterioration. The results showed that the order of the impact of indirect variables on deterioration was pipe age > pipe lining material > road type > leakage frequency > average water velocity with their weights of 0.45, 0.20, 0.15, 0.10, and 0.10, respectively. Conclusively, the results suggest that the measures of sediment thickness, scale size, degree of desquamation and condition of connections are appropriate for the evaluation of pipe deterioration and sufficient for the analysis of the impact of the indirect variables on deterioration.

A nonlinear power distribution network model for characterizing the immunity of integrated circuits (ICs) to electrical fast transients (EFTs) is proposed and validated. The model includes electrostatic discharge (ESD) protection diodes and passive impedances between power domains. Model parameters are based on external measurements using a vector network analyzer and curve tracer. Impedance is measured between pins while the IC is biased and operating, and is used to determine individual elements of the network model. Inclusion of active power-clamp circuitry is also explored. The model is able to successfully predict pin currents and voltages during EFTs on the power pin when the IC is operating or turned off and when the ESD power clamp is either activated or not activated. This model might be used to evaluate the immunity of the IC in a variety of systems and to better understand why failures occur within the IC and how to fix them.

Pipelines are a very important component of water supply systems. Specially, the pipe bursts and leaks are very useful indicators to show the condition of the network. To keep and improve the performance of the system, much accumulated know-how for inspections, operation, maintenance, and suitable rehabilitation to achieve the best performance is needed, as well as a logical method that can estimate the optimal time and range of replacement/rehabilitation work with an understanding of deterioration factors of pipe networks. Therefore, in this study, a statistical probability model for pipe burst risk was developed with various data from leak-repairing records and local characteristics of the circumstance on the real-scale distribution system in Seoul in order to utilize this method for management and operation of the water pipe network, including prioritization of pipe replacement/ rehabilitation work.

This study proposes a method using genetic algorithms (GA) to optimize selection of appropriate pipe diameter during pipeline replacement planning for water distribution networks. Mathematical programming problems were first formulated to minimize cost of replacement while considering hydraulic constraints such as flow velocity for each pipe and water pressure at each node. In addition to the economic perspective, stability of flow velocity in pipes was considered as another objective function of the multipurpose programming problem. After this, a GA model combined with hydraulic pipe network analysis was created: the HGA model. Finally, a case study was conducted to show the validity of the proposed model. Results reveal that this multipurpose HGA model is useful for optimization of pipeline replacement planning.

Some system-level electrostatic discharge (ESD) tests repeat badly if different ESD generators are used. For improving repeatability, ESD generator specifications have been changed, and modified generators have been compared in a worldwide round robin test. The test showed up to 1 : 3 variations of failure levels. Multiple parameters that characterize ESD generators have been measured. This paper correlates the parameters to test result variations trying to distinguish between important and nonrelevant parameters. The transient fields show large variations among different ESD generators. A correlation has been observed in many equipment under tests (EUTs) between failure levels and the spectral content of the voltage induced in a semicircular loop. EUT resonance enhances the field coupling, and is the dominate failure mechanism. The regulation on the transient field is expected to improve the test repeatability.

Susceptibility scanning is an increasingly adopted method for root cause analysis of system-level immunity sensitivities. It allows localizing affected nets and integrated circuits (ICs). Further, it can be used to compare the immunity of functionally identical or similar ICs or circuit boards. This paper explains the methodology as applied to electrostatic discharge and provides examples of scan maps and signals probed during immunity scanning. Limitations of present immunity analysis methods are discussed.

A method for analyzing electrostatic discharge (ESD) generators and coupling to equipment under test in the frequency domain is proposed. In ESD generators, the pulses are excited by the voltage collapse across relay contacts. The voltage collapse is replaced by one port of a vector network analyer (VNA). All the discrete and structural elements that form the ESD current pulse and the transient fields are excited by the VNA as if they were excited by the voltage collapse. In such a way, the method allows analyzing the current and field-driven linear coupling without having to discharge an ESD generator, eliminating the risk to the circuit and allowing the use of the wider dynamic range of a network analyzer relative to a real-time oscilloscope. The method is applicable to other voltage-collapse-driven tests, such as electrical fast transient, ultrawideband susceptibility testing but requires a linear coupling path.

Formulations of the transient behaviour in series pipe systems are derived using impulse response analysis. The impact of leakage is incorporated into the transfer functions of the complex head and discharge. The impedance transfer functions along a series pipeline are derived to simulate impact of pressure and discharge about a maneuver of valve located at the end of pipe system. An algorithm is proposed to predict leak quantity and location for a series pipeline systems. Genetic Algorithm (GA) is integrated into the impulse response method. The objective functions for the leakage detection can be made using the pressure-head response at the valve, or the pressure-head or the flow response at a certain point of the pipeline located upstream from the valve. Frequency dependent-friction is used to consider the impact of unsteady friction in the transient flow. Simulation and calibration results show that the proposed method of leak detection has significant potential for improved leak management for series pipe systems.

The relationships between pipe breaks of service pipes and mains and several factors were examined. Historical pipe breaks, and water and soil temperatures were also modeled by an artificial neural network to predict pipe breaks for efficient management and maintenance of the pipe networks. It was observed that the breaks of pipes increased after the temperatures of water and soil crossed in spring and fall. The pipe breaks were closely related to water and soil temperature, especially mains were affected more than service pipes. The fittings and valves were susceptible to the temperatures and needed to take measures for preventing breaks. The prediction of the pipe breaks by the ANN model built had a good performance except that the sensitivity was not good when the pipe breaks rapidly increased or decreased. The ANN model gave a good performance and was to be useful to predict the patterns of pipe breaks on a seasonal basis.

In this paper, downlink performance of multicarrier CDMA (MC-CDMA) systems under correlated fading channels is analytically investigated. Under code hopping, a signal to interference plus noise ratio (SINR) and bit error rate (BER) performance are derived in multi-cell environments. In addition, specific conditions to validate the commonly used Gaussian approximation of an interference plus noise distribution in MC-CDMA systems are discussed. It is proved that the approximation is adequate in case of low correlation between subcarriers and a large spreading factor (SF). The proposition is confirmed through comparison between analytical and simulation results.

In Seoul, the multiple regression models were used to estimate future water demand and to verify the ability of the water supply to cope with regional development. A regional development project extending over two districts was planned to stimulate the regional economy of Seoul in October 2003, and multiple regression models for each district were developed to verify the capacity of water facilities and the retention time of reservoirs. Two variables, the population and the area of the commercial district, were used to express domestic and commercial water usage. Coefficients for variables of models should be positive values; however, the coefficient for population was negative in Jung-gu. The prediction of water demand with one regression formula for each district may not be sufficient to characterize the water use pattern of a district. So, by characterizing each sub-district of the two districts, applying principal component and cluster analysis, they were divided into residential and commercial groups. Then, multiple regression models with the same variables were developed for each group. As a result, the models not only had positive coefficients for all variables, but also could provide reasonable sensitivity for the variables. For each group, the commercial area had nearly same sensitivity, but the population in the commercial area showed more sensitivity than in the residential area, because people living in the commercial area do not have to go to another district to work or sleep. Future water demands were estimated, depending on three scenarios of regional development, using the existing and newly developed models. The water demands estimated by the newly developed model are 3,416-11,372 ton/day less than those by existing model. Therefore, the model developed gave the correct water demand and prevented a wrong decision from being made.

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

Recently, Seoul city has tried to modify and upgrade the existing facilities and utilities and to improve the established water treatment plants, instead of application of a new treatment process. These efforts have finally lowered the turbidity of finished water below 0.1NTU. Small lab-scale and pilot-scale experiments have been conducted and they have provided optimum parameters for the design and operation of drinking water treatment plants. In addition, quantitative and/or trace analysis technologies developed for monitoring water quality of effluent from unit processes and automization of facilities, have contributed to the improvement of turbidity in drinking water. The Kueui water treatment plant, one of the drinking water treatment plants in Seoul, produces finished water with 0.08 NTU. It results from the operators' continuous endeavor to lower the turbidity in a scale of 0.01 NTU. The data for 12 months indicated that turbidity of settled water was less than 1.16 NTU and that of filtered water was less than 0.12 NTU for 95% of the period. Sedimentation basins and sand filters satisfy the recommended turbidity criteria, 2 NTU and 0.3 NTU, respectively. Also Kueui water treatment plant has focused on the control of organic matters to decrease in DBPs and on the removal of microorganisms.

To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.

In this paper, we propose a time series model for hourly fluctuation of supplied water consumption. This model gives us basic information about water distribution control. First, after analysis of the significant characteristics of the hourly fluctuation, we find that it has two component parts. One of them is the cyclic component, which regularly makes typical fluctuated patterns with a l-day cycle. The other is the deviational component, which deviates complexly and randomly from the typical patterns of the cyclic component. Second, we make an hourly fluctuation model, which is based on the two components mentioned above. We make the cyclic and deviational component models by applying the finite Fourier series and multiple ARIMA models respectively, and combine them to form an hourly fluctuation model. Third, we show a case study in Seoul City, and use this proposed model to estimate the supplied water consumption. From the results of this case study, we can obtain an effective model to describe the hourly fluctuation of supplied water consumption. Our proposed model is useful for water distribution control planning.

The simultaneous switching noise (SSN) from single-ended signaling I/O interfaces is significant when there are multiple channels transmitting data in parallel. The memory interfaces use the single-ended signaling scheme where the power delivery noise spectrum may coincide with some critical radio bands or resonance frequencies within the platform depending on the bit pattern being transmitted from the driver buffers. This will adversely affect the timing margin and can be one of the electromagnetic interference (EMI) sources as well. While it is good to use transistor buffer models in SSN estimation, it may significantly increase the complexity resulting in too long simulation time or convergence problems. The suggested method takes the transient supply currents when the transistor buffer transits its state from 0 to 1 and from 1 to 0, and synthesizes the full supply current for an arbitrary bit-pattern and data rate. This allows high accuracy in the supply current profiles while minimizing the power integrity simulation complexity. The method is extendable to tri-state buffers and different channel termination schemes.

An algorithm for generating a reduced circuit model of a multi-port power delivery network (PDN) is proposed. Compared to a macromodeling method, this algorithm creates a reduced model which is much simpler and uses only a small portion of CPU time during transient analysis for system power delivery. The algorithm uses an expanded Pi-network to easily visualize the internal branch connection impedances within the PDN. The reduced models efficiently replace the existing macromodels for the motherboard and package in a system analysis where the decoupling capacitors complement the band-limited nature of the reduced model.

Accurate modeling of chip and chip-package is critical for EMI (Electromagnetic Interference) and RFI (RF Interference) analysis and prediction. In this paper, a model based on an array of dipoles from near-field measurement is proposed. A simple active circuit is simulated in a 3-D full-wave simulation tool, and the dipole model is calculated from the near-field data in the simulation using inverse method with regularization technique. This model has clear physical meaning, and it is validated using field at other place.