• Structural Engineering and Mechanics
• /
• v.86 no.1
• /
• pp.119-137
• /
• 2023

The purpose of this research is to assess the performance of CBN and ceramic tools during the dry turning of gray cast iron EN GJL-350. During the turning operation, the variable machining parameters are cutting speed, feed rate, depth of cut and type of the cutting material. This contribution consists of two sections, the first one deals with the performance evaluation of four materials in terms of evolution of flank wear, surface roughness (2D and 3D) and cutting forces. The focus of the second section is on statistical analysis, followed by modeling and optimization. The experiments are conducted according to the Taguchi design L₃₂ and based on ANOVA approach to quantify the impact of input factors on the output parameters, namely, the surface roughness (Ra), the cutting force (Fz), the cutting power (Pc), specific cutting energy (Ecs). The RSM method was used to create prediction models of several technical factors (Ra, Fz, Pc, Ecs and MRR). Subsequently, the desirability function approach was used to achieve a multi-objective optimization that encompasses the output parameters simultaneously. The aim is to obtain optimal cutting regimes, following several cases of optimization often encountered in industry. The results found show that the CBN tool is the most efficient cutting material compared to the three ceramics. The optimal combination for the first case where the importance is the same for the different outputs is Vc=660 m/min, f=0.116 mm/rev, ap=0.232 mm and the material CBN. The optimization results have been verified by carrying out confirmation tests.

• Journal of Advanced Navigation Technology
• /
• v.27 no.5
• /
• pp.510-518
• /
• 2023

This paper introduces a MATLAB graphical user interface (GUI) based software for performance analysis of navigation message and wide area differential correction of regional navigation satellite system (RNSS). This software was developed to analyze satellite orbit/clock-related performance of navigation message and wide area differential correction simulating RNSS for regions near Korea based on different distributions of monitor and reference stations. As a result of software operation, navigation message and wide area differential correction are given as output in MATLAB file format. From the analysis of output, it was confirmed that valid navigation message and wide area differential correction could be generated from the results about statistical feature of orbit and clock prediction errors, cm-level fitting errors for navigation message parameters, and 81.9% enhancement in range error for wide area differential correction.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.2
• /
• pp.154-159
• /
• 2007

Wake distribution data of stem flow fields have been accumulated systematically by model tests. If the correlation between geometrical hull information and wake distribution is grasped through the accumulated data, this correlation can be helpful to designing similar ships. In this paper, Neuro-Fuzzy system that is emerging as a new knowledge over a wide range of fields nowadays is tried to estimate the wake distribution on the propeller plan. Neuro-Fuzzy system is well known as one of prospective and representative analysis method for prediction, classification, diagnosis of real complicated world problem, and it is widely applied even in the engineering fields. For this study three-dimensional stern hull forms and nominal wake values from a model test ate structured as processing elements of input and output layer, respectively. The proposed method is proved as an useful technique in ship design by comparing measured wake distribution with predicted wake distribution.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.789-799
• /
• 2018

This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.37 no.5
• /
• pp.76-83
• /
• 2000

This paper proposes a new color halftorning method using color correction by vector error diffusion to reduce color difference, necessarily appears on cross-media color reproduction In order to predict output colors on each device, a neural system IS applied and mean prediction errors in device characterization for monitor and printer are defined to calculate the thresholds for color correction Thus, color difference between monitor and printer is compared per each pixel If color difference is larger than the predetermined mean prediction errors, the halftoned dots to the current pixel are rearranged by vector error diffusion The proposed method can reduce the smear artifact by selective vector error diffusion and decrease color difference on cross- media color reproduction by color correction.

• Smart Structures and Systems
• /
• v.25 no.2
• /
• pp.183-195
• /
• 2020

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

• Journal of Computational Design and Engineering
• /
• v.3 no.4
• /
• pp.337-348
• /
• 2016

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.26 no.10
• /
• pp.907-913
• /
• 2015

This work presents a method to predict the near magnetic field distribution on the digital switching circuit mounted on PCB using co-simulation of circuit and electromagnetic fields. The proposed method first obtains the normalized near field distribution by exciting the signal and power ports of the switching circuit using sinusoidal sources. Then the real near magnetic field distribution is determined by weighting the normalized field distribution using the current spectrum of the switching circuit. To confirm the proposed method, a switching IC with a ring oscillator and a output buffer is fabricated and measured in the form of chip-on-board. The surface magnetic field distribution is measured using a magnetic probe above the PCB and compared with the simulation results. Experimental results show the correspondence between simulation and measurement results within 10 dB up to fifth harmonics.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.12
• /
• pp.3821-3828
• /
• 2000

Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.

• The KIPS Transactions:PartB
• /
• v.11B no.6
• /
• pp.625-632
• /
• 2004

Since the H.264 encoding adopts both forward prediction and hi-direction prediction modes and exploits Variable Length Coding(VLC), the amount of data generated from video encoder varies as Flaying time goes by. The fixed bit rate encoding system which has limited transmission channel capacity uses a buffer to control output bitstream It's necessary to control the bitstream to maintain within manageable range so as to protect buffer from overflow or underflow. With existing bit amount control algorithms, the $\lambda_{MODE}$ which is relationship between distortion value and quantization parameter often excesses normal value to end up with video error. This paper proposes an algorithm to protect buffer from overflow or underflow by introducing a new quantization parameter against distortion value of H.264 video data. The test results of 6 exemplary data show that the proposed algorithm has the same PSNR as and up to 8% reduced bit rate against existing algorithms.