• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.17-24
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• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• The KIPS Transactions:PartC
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• v.11C no.7 s.96
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• pp.1009-1022
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• 2004

Smoothing is a transmission plan where variable rate video data is converted to a constant bit rate stream. Among them are CHA, MCBA. MVBA, e-PCRTT and others. E-PCRTT algorithm, which was improved from PCRTT, restricts the number of rate changes with fixed-size run. This causes unnecessary rate changes and may not guarantee QoS when buffer size is small. In this paper. a smoothing algorithm is pro-posed, where the number of rate changes are not limited and the size of run can be flexible, in order to overcome the shortcomings of e-PCRTT. Experiments demonstrated that the proposed algorithm such as e-PCRTT. In order to show the performance, var-ious evaluation factors were used such as the nember of rate changes, buffer size to maintain QoS, and so on.

• Journal of Internet of Things and Convergence
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• v.7 no.2
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• pp.47-53
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• 2021

In order to transmit video data stored in servers with limited bandwidth in IoT environments to many clients, a transmission plan must be established by considering factors such as the number of transmission rate changes, peak transmission rate, and transmission rate changes. This transmission plan is called smoothing, and includes CBA that minimizes the number of transmission rate increases, MCBA that minimizes the number of transmission rate changes, and MVBA to minimize the transmission rate changes. In this work, to evaluate the performance of the proposed algorithm[16], we compare the proposed algorithm with the existing smoothing algorithms with the peak rate, the number of transmission rate changes, the rate increase, the peak rate utilization, and the average transmission rate with various video data and buffer sizes. The evaluation results show that the proposed algorithm helps to efficiently use the server's finite network resources by establishing a transport plan with the lowest average transfer rate.

• Current Optics and Photonics
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• v.2 no.1
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• pp.7-14
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• 2018

As a sophisticated detection technology, LIDAR has been widely employed to probe low-altitude windshear. Due to the drawbacks of the traditional ramp algorithm, the alarm accuracy of the LIDAR has not been satisfactory. Aiming at settling this matter, a novel method is proposed on the basis of improved signal smoothing and second windshear detection, which essentially acts as a combination of ramp algorithm and segmentation approach, involving the human factor as well as signal fluctuations. Experiments on the real and artificial signals verify our approach.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.809-815
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• 2004

Researches on mobile robots have been mainly focused on the autonomous navigation and a lot of interesting results have been published so far. Most of applications are, however, fancy, unpractical, and very expensive to be used for 'UN-expensive' purpose. Well-known soccer robot may be an example of unpractical application. Un-autonomous mobile robot has, however, potential for a lot of practical applications. Especially, tele-operation of the un-autonomous mobile robot may the central issue of research. Major research topics for the tele-operated un-autonomous mobile robot include development of a force reflecting joystick for tele-operation and development of a sophisticated algorithm for smooth tele-operation. A new concept named fuzzy command smoothing algorithm is proposed in this paper in order to provide smooth motion to a tele-operated mobile robot. It gives smooth motion command to the mobile robot from possibly abrupt quick turn motion command of the joystick using fuzzy logic. Simulation results verify the usefulness of the proposed algorithm.

• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1027-1038
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• 2002

Multimedia data services have been widely developed with the recent progression of the Internet technology. Multimedia services, especially, must guarantee Quality of Service with restricted environment resources. The abrupt increase of transmission quanta, so called burst, and the number of client that access multimedia server, with limited network bandwidth will demage the quality of service and the utilization of network resource. Smoothing techniques can reduce the burst of a variable bit rate stream by transmitting data at a series of fixed rates. In this paper, we present a new smoothing algorithm using MPEG's characteristics in transmitting stored video data with VBR. The smoothing algorithm we present will be verified with assorted existing techniques in diverse environments.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.311-319
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• 2001

Statistical reconstruction methods in the context of a Bayesian framework have played an important role in emission tomography since they allow to incorporate a priori information into the reconstruction algorithm. Given the ill-posed nature of tomographic inversion and the poor quality of projection data, the Bayesian approach uses regularizers to stabilize solutions by incorporating suitable prior models. In this work we show that, while the quantitative performance of the standard filtered backprojection (FBP) algorithm is not as good as that of Bayesian methods, the application of spline-regularized smoothing to the sinogram space can make the FBP algorithm improve its performance by inheriting the advantages of using the spline priors in Bayesian methods. We first show how to implement the spline-regularized smoothing filter by deriving mathematical relationship between the regularization and the lowpass filtering. We then compare quantitative performance of our new FBP algorithms using the quantitation of bias/variance and the total squared error (TSE) measured over noise trials. Our numerical results show that the second-order spline filter applied to FBP yields the best results in terms of TSE among the three different spline orders considered in our experiments.

• Korean Management Science Review
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• v.21 no.2
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• pp.79-91
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• 2004

In industrial production settings, scheduling problems for detailed day-to-day operations are often ordeals to production practitioners. For those who have scheduling experiences with the Gantt Chart, the job oriented heuristic scheduling has illustrated its merits in solving practically large scale scheduling problems. It schedules all operations of a job within a finite capacity before considering the next job. In this paper, we Introduce the LSB (load smoothing backward) scheduling algorithm for the job oriented heuristic scheduling. Through a computer experiment in a hypothetical setting, we make a performance comparison of LSB scheduling algorithm with existing algorithms and also suggest a guideline for selecting the suitable algorithm for certain industrial settings.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.3
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• pp.128-134
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• 2014

A real-time, continuous-scale image interpolation method is proposed based on a bilinear interpolation with directionally adaptive low-pass filtering. The proposed algorithm was optimized for hardware implementation. The ordinary bi-linear interpolation method has blocking artifacts. The proposed algorithm solves this problem using directionally adaptive low-pass filtering. The algorithm can also solve the severe blurring problem by selectively choosing low-pass filter coefficients. Therefore, the proposed interpolation algorithm can realize a high-quality image scaler for a range of imaging systems, such as digital cameras, CCTV and digital flat panel displays.