• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.129-134
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• 2003

In this paper, we determine optimal reduction in the lead time and setup cost for some stochastic inventory models. And we propose more general model that allow the backorder rate as a control variable. We first assume that the lead time demand follows a normal distribution. And we assume that the backorder rate is dependent on the length of lead time through the amount of shortages. The stochastic models analyzed in this paper are the classical continuous and periodic review policy models with a mixture of backorders and lost sales. For each of these models, we provide a sufficient conditions for the uniqueness of the optimal operating policy. We also develop algorithms for solving these models and provide illustrative numerical examples.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• Korean Journal of Mathematics
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• v.29 no.2
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• pp.371-386
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• 2021

This paper investigates two discrete time queueing inventory models with positive service time and lead time. Customers arrive according to a Bernoulli process and service time and lead time follow geometric distributions. The first model under discussion based on replenishment of order upto S policy where as the second model is based on order placement by a fixed quantity Q, where Q = S - s, whenever the inventory level falls to s. We analyse this queueing systems using the matrix geometric method and derive an explicit expression for the stability condition. We obtain the steady-state behaviour of these systems and several system performance measures. The influence of various parameters on the systems performance measures and comparison on the cost analysis are also discussed through numerical example.

• Journal of Environmental Science International
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• v.12 no.12
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• pp.1269-1276
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• 2003

In order to know the effect of atuoclaving on the heavy metal removal using chitosan, lead removal capacities and removal rates by various chitosans in aqueous solution were compared according to the various autoclaving time. The lead removal efficiencies and removal rates by the autoclaved chitosan were found to be on the order of 15 min(98％) ＞ 10 min(95％) ＞ 30 min(83％) ＞ 5 min(53％) ＞ 60 min(47％) ＞ 0 min(22％) chitosan. The molecular weight of chitosan was decreased by the increase of autoclaving time. Therefore, the heavy metal removal capacity was not well correlated to the molecular weight. Langmuir isotherm was well fitted to experimental results of equilibrium adsorption on chitosan. In order to examine the process of lead removal by the autoclaved chitosan, TEMs, SEMs and FT-IR analyses were used. The surface of autoclaved chitosan was much more porous and the lead removal was mainly occurred on the surface of chitosan. The structure of autoclaved chitosan was same as that of controlled chitosan.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.305-312
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• 2016

Modern embedded systems are typically operated by the rechargeable batteries in our daily life. Since charge of batteries is considered as an time consuming task, there have been extensive efforts to manage the charge time from the perspective of materials, circuits, and systems. Estimation of battery charge time is one of the essential information to design the charge circuitry. A compact macro model for the constant-current and constant-voltage charge protocol was recently introduced, which gives us a quick estimation of charge time with similar shape to the famous Peukert's law for discharge time estimation. The CC-CV charging protocol is widely used for Lithium-based batteries and Lead-acid batteries. In this paper, we characterize the lead-acid battery by measurement to extract the model coefficients, which was not covered by the previous studies. By our proposed model, the key coefficient Kcc results in 1.18-1.31, which is little bit higher than that of Lithium batteries. The accuracy of our model is within the range of ${\pm}10%$ error, which is compatible with the other studies such as Peukert's law.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.720-725
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• 1991

In this thesis, the lead-lag compensator is designed to improve output characteristics of flyback zero voltage switching quasi-resonant converters. The switch and the diode are assumed ideally. And the SMPS is modelled by state equations with four operation modes. And the model for controller design is also achived by using a state space averaging method, which is continuous time average of state variables every period. The lag, the lead and the lead-lag compensator is designed the SMPS respectively. The time domain analysis and the frequency domain analysis are done for each compensated circuit. It is possible increasing the phase margin and improving the transient response by the compensators. The phase lag compensator has small overshoot comparatively. But the bandwidth is narrower than the others, so it has longest settling time. For the phase lead compensator, the response come to steady-state within short period. But the overshoot is the largest due to its large peak gain. Finally, the phase lead-lag compensator has medium characteristics in the overshoot and the settling time.

• Journal of Environmental Health Sciences
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• v.37 no.4
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• pp.258-266
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• 2011

Objectives: The aim of this study is to examine the possible changes over the past ten years in the estimated daily dietary lead intake (Pb-D) stemming from the variation of daily intake amounts for each food group. The following factors were considered; 1. time trends in Pb-D as the estimated values, 2. the time trend in Pb-D by food groups 3. the most influential food groups for dietary Pb intake. Methods: Estimated Pb-D was drawn from food consumption according to food groups reported in the Korean National Health and Nutrition Survey and the lead contents of each food group as reported in 23 prior publications. Results: The estimated Pb-D in a 2009 survey was 40.8 ${\mu}g/day$, of which 22.5 ${\mu}g/day$ (55.1%) was of plant origin and 18.3 ${\mu}g/day$ (44.9%) was of animal origin. Meats and poultry, fish and shellfish among foods of animal origin and beverages of plant origin had the largest contribution in Pb-D among the food groups. Conclusion: Over past ten years, daily lead intakes have slightly increased among men. Otherwise, no clear variation is apparent among women.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.508-521
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• 2020

We compared a novel encoding Lempel-Ziv complexity (ELZC) with three common complexity algorithms i.e., approximate entropy (ApEn), sample entropy (SampEn), and classic Lempel-Ziv complexity (CLZC) so as to determine a satisfied complexity and its corresponding quality indices for assessing quality of multi-lead electrocardiogram (ECG). First, we calculated the aforementioned algorithms on six artificial time series in order to compare their performance in terms of discerning randomness and the inherent irregularity within time series. Then, for analyzing sensitivity of the algorithms to content level of different noises within the ECG, we investigated their change trend in five artificial synthetic noisy ECGs containing different noises at several signal noise ratios. Finally, three quality indices based on the ELZC of the multi-lead ECG were proposed to assess the quality of 862 real 12-lead ECGs from the MIT databases. The results showed the ELZC could discern randomness and the inherent irregularity within six artificial time series, and also reflect content level of different noises within five artificial synthetic ECGs. The results indicated the AUCs of three quality indices of the ELZC had statistical significance (>0.500). The ELZC and its corresponding three indices were more suitable for multi-lead ECG quality assessment than the other three algorithms.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1207-1216
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• 2018

Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.93-113
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• 2007

In the previous research, we considered a logistics brokerage problem with the objective of minimizing total transportation lead time of freights in a logistics e-marketplace, in which a logistics brokerage agent intermediates empty vehicles and freights registered by car owners and shippers [7]. However, in the logistics e-marketplace, transportation due date tardiness is more important than the transportation lead time, since transportation service level is critically determined by whether the due date is met or not. Therefore, in this paper, we deal with the logistics brokerage problem with the objective of minimizing total tardiness of freights. Hungarian method based matching algorithms, real time matching(RTM), periodic matching(PM), and fixed matching(FM), are used for solving the problem considered in this paper. In order to test performance of the proposed algorithms, we perform computational experiments on a various problem instances. The results show that the waiting-and-matching algorithms, PM and FM, also give better performance than real time matching strategy, RTM, for the total tardiness minimization problem as the algorithms did for the total lead time minimization problem.