• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.2
• /
• pp.71-78
• /
• 2021

In order to classify a target image in a cluster of images, the difference in brightness between the object and the background is mainly concerned, which is not easy to classify if the shape of the object is blurred and the sharpness is low. However, there are a few studies attempted to solve these problems, and there is still the problem of not properly distinguishing between wrong pattern and right pattern images when applied to actual data analysis. In this paper, we propose an algorithm that classifies 3D films into sharp and blurry using the width of the pixel values histogram. This algorithm determines the width of the right and wrong images based on the width of the pixel distributions. The larger the width histogram, the sharp the image, while the shorter the width histogram the blurry the image. Experiments show that the proposed algorithm reflects that the characteristics of these histograms allows classification of all wrong images and right images. To determine the reliability and validity of the proposed algorithm, we compare the results with the other obtained from preprocessed 3D films. We then trained the 3D films using few-shot learning algorithm for accurate classification. The experiments verify that the proposed algorithm can perform higher without complicated computations.

• Journal of Platform Technology
• /
• v.11 no.3
• /
• pp.45-55
• /
• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.13 no.4
• /
• pp.395-401
• /
• 2013

In-situ optical emission spectroscopy (OES) is employed for leak detection in plasma etching system. A misprocessing is reported for significantly reduced silicon etch rate with chlorine gas, and OES is used as a supplementary sensor to analyze the gas phase species that reside in the process chamber. Potential cause of misprocessing reaches to chamber O-ring wear out, MFC leaks, and/or leak at gas delivery line, and experiments are performed to funnel down the potential of the cause. While monitoring the plasma chemistry of the process chamber using OES, the emission trace for nitrogen species is observed at the chlorine gas supply. No trace of nitrogen species is found in other than chlorine gas supply, and we found that the amount of chlorine gas is slightly fluctuating. We successfully found the root cause of the reported misprocessing which may jeopardize the quality of thin film processing. Based on a quantitative analysis of the amount of nitrogen observed in the chamber, we conclude that the source of the leak is the fitting of the chlorine mass flow controller with the amount of around 2-5 sccm.

• The Journal of Society for e-Business Studies
• /
• v.25 no.2
• /
• pp.49-63
• /
• 2020

The accurate prediction of box office in the early stage is crucial for film industry to make better managerial decision. With aims to improve the prediction performance, the purpose of this paper is to evaluate the use of machine learning methods. We tested both classification and regression based methods including k-NN, SVM and Random Forest. We first evaluate input variables, which show that reputation-related information generated during the first two-week period after release is significant. Prediction test results show that regression based methods provides lower prediction error, and Random Forest particularly outperforms other machine learning methods. Regression based method has better prediction power when films have small box office earnings. On the other hand, classification based method works better for predicting large box office earnings.

• Journal of Sensor Science and Technology
• /
• v.11 no.5
• /
• pp.279-285
• /
• 2002

Two $SnO_2$ based sensing films(pure $SnO_2$ and $SnO_2$/Pt) and a Pt thin film for temperature sensor on an alumina substrate were designed and fabricated for classifying the indoor environmental gases. By controlling the heating power in the shape of trapezoid, unique four sensing response curves created from both $SnO_2$ film and $SnO_2$/Pt film. Then, various parameters were extracted from sensing response curves and carried out principal component analysis(PCA). The results confirm that a sensor array with the proposed operating mode was extremely effective in classifying indoor environmental gases such as $CO_2$, $C_3H_8$, $C_4H_{10}$.

• Journal of Biosystems Engineering
• /
• v.33 no.1
• /
• pp.30-37
• /
• 2008

Dynamic excitation and response analysis is an acceptable method to determine some of physical properties of agricultural product for quality evaluation. There is a difference in the internal viscoelasticity between sound and defective fruits due to the difference of geometric structures, thereby showing different vibration characteristics. This study was carried out to develop a portable piezoelectric film-based glove sensor system that can separate internally damaged watermelons from sound ones using an acoustic impulse response technique. Two piezoelectric sensors based on polyvinylidene fluoride (PVDF) films to measure an impact force and vibration response were separately mounted on each glove. Various signal parameters including number of peaks, energy ratio, standard deviation of peak to peak distance, zero-crossing rate, and integral value of peaks were examined to develop a regression-estimated model. When using SMLR (Stepwise Multiple Linear Regression) analysis in SAS, three parameters, i.e., zeros value, number of peaks, and standard deviation of peaks were selected as usable factors with a coefficient of determination ($r^2$) of 0.92 and a standard error of calibration (SEC) of 0.15. In the validation tests using twenty watermelon samples (sound 9, defective 11), the developed model provided good capability showing a classification accuracy of 95%.

• Journal of the Society of Naval Architects of Korea
• /
• v.43 no.4 s.148
• /
• pp.512-520
• /
• 2006

The effects of flexibilities of supporting structures on shaft alignment are growing as ship sizes are Increasing mainly for container carrier and LNG carrier. But, most of classification societies not only do not suggest any quantitative guidelines about the flexibilities but also do not have shaft alignment design program considering the flexibility of supporting structures. A newly developed program, which is based on innovative shaft alignment technologies including nonlinear elastic multi-support bearing concept and hull deflection database approach, has S basic modules : 1)fully automated finite element generation module, 2) hull deflection database and it's mapping module on bearings, 3) squeezing and oil film pressure calculation module, 4) optimization module and 5) gap & sag calculation module. First module can generate finite element model including shafts, bearings, bearing seats, hull and engine housing without any misalignment of nodes. Hull deflection database module has built-in absolute deflection data for various ship types, sizes and loading conditions and imposes the transformed relative deflection data on shafting system. The squeezing of lining material and oil film pressures, which are relatively solved by Hertz contact theory and built-in hydrodynamic engine, can be calculated and visualized by pressure calculation module. One of the most representative capabilities is an optimization module based on both DOE and Hooke-Jeeves algorithm.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.7 no.2 s.17
• /
• pp.81-87
• /
• 2004

Semiconductor thick film gas sensors based on tin oxide are fabricated and their gas response characteristics are examined for four simulant gases of chemical warfare agent (CWA)s. The sensing materials are prepared in three different sets. 1) The Pt or Pd $(1,\;2,\;3\;wt.\%)$ as catalyst is impregnated in the base material of $SnO_2$ by impregnation method.2) $Al_2O_3\;(0,\;4,\;12,\;20\;wt.\%),\;In_2O_3\;(1,\;2,\;3\;wt.\%),\;WO_3\;(1,\;2,\;3\;wt.\%),\;TiO_2\;(3,\;5,\;10\;wt.\%)$ or $SiO_2\;(3,\;5,\;10\;wt.\%)$ is added to $SnO_2$ by physical ball milling process. 3) ZnO $(1,\;2,\;3,\;4,\;5\;wt.\%)$ or $ZrO_2\;(1,\;3,\;5\;wt.\%)$ is added to $SnO_2$ by co-precipitation method. Surface morphology, particle size, and specific surface area of fabricated sensing films are performed by the SEM, XRD and BET respectively. Response characteristics are examined for simulant gases with temperature in the range 200 to $400^{\circ}C$, with different gas concentrations. These sensors have high sensitivities more than $50\%$ at 500ppb concentration for test gases and also have shown good repetition tests. Four sensing materials are selected with good sensitivity and stability and are fabricated as a sensor array A sensor array Identities among the four simulant gases through the principal component analysis (PCA). High sensitivity is acquired by using the semiconductor thick film gas sensors and four CWA gases are classified by using a sensor array through PCA.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.4
• /
• pp.161-166
• /
• 2021

Errors in the semiconductor process are generated by a change in the state of the equipment, and errors usually arise when the state of the equipment changes or when parts that make up the equipment have flaws. In this investigation, we anticipated that aging of the mass flow controller in the plasma enhanced chemical vapor deposition SiO2 thin film deposition method caused a minute flow rate shift. In seven cases, fourier transformation infrared film quality analysis of the deposited thin film was used to characterize normal and pathological processes. The plasma condition was monitored using optical emission spectrometry data as the flow rate changed during the procedure. Preprocessing was used to apply the collected OES data to the artificial immune system algorithm, which was then used to process diagnosis. Through comparisons between datasets, the learning algorithm compared classification accuracy and improved the method. It has been confirmed that data characterized as a normal process and abnormal processes with differing flow rates may be discriminated by themselves using the artificial immune system data mining method.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.1970-1971
• /
• 2011

정서인식분야에서 현재 활발히 연구되고 있는 방법은 다양한 생체신호를 통해 인간의 감정을 인식하는 것이다. 생리심리학적 연구에서 인간의 감정상태와 생체반응은 강한 상관이 있다고 알려져 있다. 생체신호는 센서 등으로 비교적 간단하게 획득할 수 있으며, 이를 이용한 감정인식은 사회적, 문화적인 차이에 덜 민감하므로 최근에 주목 받고 있다. 본 연구에서는 audio-visual film clips 자극으로 기쁨, 분노, 놀람, 스트레스 4종류의 정서를 유발하고 그에 대한 반응으로써 생체신호를 측정하였다. 그리고 생체신호로부터 feature를 추출하였고, 주성분분석(PCA)로 특징 축소를 수행하였다. 4가지 정서를 분류 한 결과, 9명의 가우시안 프로세스 분류기에 의한 평균 정서 판별율은 64.85 % (57.14~70.0)의 결과를 얻었다.