• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.165-182
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• 2014

An RFID systems employ efficient Anti-Collision Algorithms (ACAs) to enhance the performance in various applications. The EPC-Global G2 RFID system utilizes Frame Slotted Aloha (FSA) as its ACA. One of the common approaches used to maximize the system performance (tag identification efficiency) of FSA-based RFID systems involves finding the optimal value of the frame length relative to the contending population size of the RFID tags. Several analytical models for finding the optimal frame length have been developed; however, they are not perfectly optimized because they lack precise characterization for the timing details of the underlying ACA. In this paper, we investigate this promising direction by precisely characterizing the timing details of the EPC-Global G2 protocol and use it to derive a precise-optimal frame length model. The main objective of the model is to determine the optimal frame length value for the estimated number of tags that maximizes the performance of an RFID system. However, because precise estimation of the contending tags is difficult, we utilize a parametric-heuristic approach to maximize the system performance and propose two simple schemes based on the obtained optimal frame length-namely, Improved Dynamic-Frame Slotted Aloha (ID-FSA) and Exponential Random Partitioning-Frame Slotted Aloha (ERP-FSA). The ID-FSA scheme is based on the tag set estimation and frame size update mechanisms, whereas the ERP-FSA scheme adjusts the contending tag population in such a way that the applied frame size becomes optimal. The results of simulations conducted indicate that the ID-FSA scheme performs better than several well-known schemes in various conditions, while the ERP-FSA scheme performs well when the frame size is small.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.6
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• pp.537-544
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• 1998

We selected Spathiyhyllum patinii and Pachira aqkatica, since the former has high O3 absorption while the latter low absorption, and analyzed physiological factors such as diffusive coefficient, transpiration rate, photosynthetic rate, and CO2 absorption rate, which affected O3 absorption capacity There was significant relationship between gas absorption capacity and the other factors; photosynthetic rate, diffusive resistance, stomatal resistance and CO2 absorption rate. Therefore model formula for estimation of O3 absorption rate in plant was formulated by making use of these factors. There was difference for the estimation of O3 absorption rate according to plant species. In case of Spathiphyllum patinii, photosynthetic rate is an optimal factor for estimation of O3 absorption capacity. On the other hand, stomatal resistance and diffusive resistance are optimal factors of Pachira aquatica among various physiological ones. And we knew that CO2 absorption rate is a potential factor to evaluate gas absorption capacity regardless of plant species. But considering efficiency and practicality, diffusive resistance was the most effective factor for the estimation of O3 gas absorption.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.8
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• pp.1-7
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• 2004

The effect of the sub-optimal beam-forming and the fading channel correlation on the closed loop transmit antenna diversity(CTD) system is investigated in frequency flat Rayleigh fading channels. The fast channel fading prevents the perfect channel estimation at a mobile station, hence the imperfect weight is applied to the antenna branch of transmitter. The weight causes sub-optimalbeam-forming and aggravates the performance of CTD system. The fading correlation or a wireless channel also is one of the factors decreasing the diversity gain. A bit error rate expression for the CTD system is analytically derived as a function of the channel estimation error, the channel correlation coefficient the feedback delay, and fading index. It is shown that the channel estimation error gives more severe effect to the system performance than the channel correlation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.11
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• pp.599-609
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. First, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The control input for the system in the HFC combined PID controller with fuzzy controller is a convex combination of the FLC's output and PID's output by a fuzzy variable, namely, membership function of weighting coefficient. Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed to show applicability and superiority with the and of three representative processes.

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.616-623
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• 2003

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However if we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Journal of Korea Water Resources Association
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• v.37 no.12
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• pp.1009-1018
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• 2004

A systematic method using an optimal estimation algorithm is presented for simultaneous estimation of diffuse pollution distributed along a stream reach and model parameters for a stream water quality model. It was applied with the QVAL2E model to the South Han River for optimal estimation of kinetic constants and diffuse loads along the river. Initial calibration results for kinetic constants selected from a sensitivity analysis reveal that diffuse source inputs for nitrogen and phosphorus are essential to satisfy the system mass balance. Diffuse loads for total nitrogen and total phosphorus were estimated solving the expanded inverse problem. Comparison of kinetic constants estimated simultaneously with diffuse sources to those estimated without diffuse loads, suggests that diffuse sources must be included in the optimization not only for its own estimation but also for adequate estimation of the model parameters. Application of optimization method to river water quality modeling is discussed in terms of the sensitivity coefficient matrix structure.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1492-1501
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• 2011

A wide-area protection intelligent technique has been used to improve a reliability in power systems and to prevent a blackout. Nowadays, voltage and current phasor estimation has been executed by GPS-based synchronized PMU, which has become an important way of wide-area blackout protection for the prevention of expending faults in power systems. As this technique has the difficulties in collecting and sharing of information, there have been used a FNET method for the wide-area intelligent protection. This technique is very useful for the prediction of the inception fault and for the prevention of fault propagation with accurate monitoring frequency and frequency deviation. It consists of FDRs and IMS. It is well known that FNET can detect the dynamic behavior of system and obtain the real-time frequency information. Therefore, FDRs must adopt a optimal frequency estimation method that is robust to noise and fault. In this paper, we present comparative studies for the frequency estimation method using IRDWT(improved recursive discrete wavelet transform), for the frequency estimation method using FRDWT(fast recursive discrete wavelet transform). we used the Republic of Korea 345kV power system modeling data by EMTP-RV. The user-defined arbitrary waveforms were used in order to evaluate the performance of the proposed two kinds of RDWT. Also, the frequency variation data in various range, both large range and small range, were used for simulation. The simulation results showed that the proposed frequency estimation technique using FRDWT can be the optimal frequency measurement method applied to FDRs.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.55-76
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• 2000

We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

• Journal of the Korean Society of Safety
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• v.35 no.5
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• pp.39-48
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• 2020

This study proposes a fast estimation method of dynamic reliability indices or failure probability for SDOF structure subjected to earthquake excitations. The proposed estimation method attempts to derive coefficient function for correcting dynamic effects from static reliability analysis in order to estimate the dynamic reliability analysis results. For this purpose, a total of 60 cases of structures with various characteristics of natural frequency and damping ratio under various allowable limits were taken into account, and various types of approximation coefficient functions were considered as potential candidate models for dynamic effect correction. Each reliability index was computed by directly performing static and dynamic reliability analyses for the given 60 cases, and nonlinear curve fittings for potential candidate models were performed from the computed reliability index data. Then, the optimal estimation model was determined by evaluating the accuracy of the dynamic reliability analysis results estimated from each candidate model. Additional static and dynamic reliability analyses were performed for new models with different characteristics of natural frequency, damping ratio and allowable limit. From these results, the accuracy and numerical efficiency of the optimal estimation model were compared with the dynamic reliability analysis results. As a result, it was confirmed that the proposed model can be a very efficient tool of the dynamic reliability estimation for seismically excited SDOF structure since it can provide very fast and accurate reliability analysis results.