• 농업과학연구
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• 제47권4호
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• pp.753-767
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• 2020

The commercial value of strawberries is affected by various factors such as their shape, size and color. Among them, size determined by weight is one of the main factors determining the quality grade of strawberries. In this study, image technology was developed to predict the weight of strawberries using the shape characteristics of strawberry cultivars. For realtime weight measurements of strawberries in transport, an image measurement system was developed for weight prediction with a charge coupled device (CCD) color camera and a conveyor belt. A strawberry weight prediction algorithm was developed for three cultivars, Maehyang, Sulhyang, and Ssanta, using the number of pixels in the pulp portion that measured the strawberry weight. The discrimination accuracy (R2) of the weight prediction models of the Maeyang, Sulhyang and Santa cultivars was 0.9531, 0.951 and 0.9432, respectively. The discriminative accuracy (R2) and measurement error (RMSE) of the integrated weight prediction model of the three cultivars were 0.958 and 1.454 g, respectively. These results show that the 2D imaging technology considering the shape characteristics of strawberries has the potential to predict the weight of strawberries.

• 비파괴검사학회지
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• 제27권5호
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• pp.383-392
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• 2007

본 논문에서는 신경회로망을 이용하여 원자력 발전소 증기발생기 세관의 결함 깊이와 폭을 예측하는 연구를 수행한다. 결함 크기 추정을 위하여 우선, I-In 형태, I-Out 형태, V-In 형태, V-Out 형태의 4가지 결함형상에 대한 와전류탐상시험(ECT) 신호를 생성한다. 특히, 유한요소법에 기반한 수치해석 기법을 이용하여 여러 가지 폭과 깊이를 갖는 결함 400개의 ECT 신호를 생성한다. 이와 같이 생성된 ECT 신호로부터, 결함 크기와 폭을 예측하기 위한 새로운 특징벡터를 추출하는데, 이 특징벡터에는 최대 임피던스 값을 갖는 점과 최대 임피던스값의 1/2의 값을 갖는 점 사이의 위상각이 포함된다. 추출된 특징벡터를 이용하여 결함의 크기를 예측하기 위해서 하나의 은닉층을 갖는 다층퍼셉트론을 이용하였다. 컴퓨터 모의실험 연구를 통하여 제안된 방법이 우수한 예측성능을 갖는다는 것을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.1976-1995
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• 2023

The anchor-free object detector CenterNet regards the object as a center point and predicts it based on the Gaussian circle region. For each object's center point, CenterNet directly regresses the width and height of the objects and finally gets the boundary range of the objects. However, the critical range of the object's center point can not be accurately limited by using the Gaussian circle region to constrain the prediction region, resulting in many low-quality centers' predicted values. In addition, because of the large difference between the width and height of different objects, directly regressing the width and height will make the model difficult to converge and lose the intrinsic relationship between them, thereby reducing the stability and consistency of accuracy. For these problems, we proposed a center point prediction method based on the Gaussian elliptic region and a size component regression method based on the small solution space. First, we constructed a Gaussian ellipse region that can accurately predict the object's center point. Second, we recode the width and height of the objects, which significantly reduces the regression solution space and improves the convergence speed of the model. Finally, we jointly decode the predicted components, enhancing the internal relationship between the size components and improving the accuracy consistency. Experiments show that when using CenterNet as the improved baseline and Hourglass-104 as the backbone, on the MS COCO dataset, our improved model achieved 44.7%, which is 2.6% higher than the baseline.

• 전자통신동향분석
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• 제35권1호
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• 한국멀티미디어학회논문지
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• 제12권6호
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• pp.876-881
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• 2009

Online game servers usually has been using the static thread pool system. But this system is not fit for huge online game server because the overhead is always up-and-down. Therefore, in this paper, we suggest the new algorithm for huge online game server. This algorithm is based on the prediction-based dynamic thread pool system. But it was developed for web servers and every 0.1 seconds the system prediction the needed numbers of threads and determine the thread pool size. Some experimental results show that the check time of 0.4 seconds is the best one for online game server and if the number of worker threads do not excess or lack to the given threshold then we do not predict and keep the current state. Otherwise we apply the prediction algorithm and change the number of threads. Some experimental results shows that this proposed algorithm reduce the overhead massively and make the performance of huge online game server improved in comparison to the static thread pool system.

• 한국분무공학회지
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• 제10권2호
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• pp.32-40
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• 2005

This paper presents the analytical approaches to predict cutting fluid aerosol formation characteristics in machining process. The prediction model which is based on the rotary atomization theory analyzes aerosol behaviors in terms of size and concentration. Experiments were tarried out to verify the aerosol formation prediction model under various operational conditions. The experimental results which are obtained by Dual-PDA measurement show resonable agreement with prediction results of aerosol concentration. This study can be provided as a basis to estimate and control the hazardous cutting fluid aerosol in machining process in view of environmental consciousness.

• 한국소음진동공학회논문집
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• 제24권9호
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• pp.695-705
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• 2014

In this paper, two types of techniques for the prediction of radiated sound pressure due to vibration of a structure are investigated. The prediction performance using wave-number sensing technique is compared to that of conventional prediction method, such as Rayleigh's integral method, for the prediction of far-field radiated sound pressure. For a coupled plate, wave-number components are predicted by the vibration response of plate and the prediction performance of far-field sound is verified. In addition, the applicability of distributed sensors that are not allowable to Rayleigh's integral method is considered and these can replace point sensors. Experimental implementation verified the prediction accuracy of far-field sound radiation by the wave-number sensing technique. Prediction results from the technique are as good as those of Rayleigh's integral method and with distributed sensors, more reduced computation time is expected. To predict the radiated sound by the efficient configuration of structural sensors, composed(synthesized) mode considering sound power contribution is determined and from this size and location of sensors are chosen. Four types of sensor configuration are suggested, simulated and compared.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Journal of Biosystems Engineering
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• 제26권5호
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• pp.475-480
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• 2001

Sensing soil organic matter is crucial for precision farming and environment friendly agriculture. Near infra-red(NIR) was utilized to measure the soil organic matter. Multivariate calibration methods, including stepwise multiple linear regression(MLR), principal components recession(PCR) and partial least squares regression(PLS), were applied to soil spectral reflectance data to predict the organic matter content. The effect of soil particle size and water content was studied. The range of soil organic matter contents was from 0.5 to 11%. Near infrared (NIR) region from 700 to 2,500nm was applied. For uniform soil particle size, result had good correlation (R$\^$2/ = 0.984, standard error of prediction= 0.596). The effect of soil particle size could be eliminated with 1st order derivative of the NIR signal. However. moist soil had a little lower correlation. R$\^$2/ was 0.95 and standard error of prediction was 0.94% using the PLS method. The results showed the possibility of soil organic matter measurement using NIR reflectance on the field.

• 한국정밀공학회지
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• 제33권12호
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• pp.1021-1030
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• 2016

The runner system of the injection mould and the injection volume of the injection molding process greatly affect the quality of the produced part. The goal of this paper is the design of the runner system and the prediction of the injection volume for the injection moulding of a housing part of small-size air cleaner to improve the formability through the three-dimensional injection moulding analysis. The effects of the runner system of the mould on the injection moulding characteristics are investigated. From the results of the investigation, a proper design of the runner system with uniform filling characteristics and the minimized defect formation is obtained. In addition, the influence of the moving distance of the screw on filling characteristics, weldline formation and deformation characteristics is examined. From the results of the examination, an appropriate moving distance of the screw for the housing part of small-size air cleaner is estimated.