• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2196-2203
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• 2000

As processor's operational frequency increases and processors execute multiple instructions per cycle, the processor performance becomes more dependent on the load operand referencing latency and the data dependency. To reduce the operand fetch latency and to increase ILP by breaking the data dependency, we propose a value-address hybrid predictor using a reasonable size prediction buffer and analyse the performance improvement by the proposed predictor. Through the extensive simulation of 5 benchmark programs, the proposed hybrid prediction scheme accurately predicts 62.72% of all loads which are 12.64% higher than the value prediction scheme and show its cost-effectiveness compared to the address predition scheme. In addition, we analyse the performance improvement achieved by the stride management and the history of previous predictions.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.1
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• pp.66-70
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• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.16-20
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• 2018

The development of modern information technology has increased the amount of big data about patients' information and diseases. In this study, we developed a prediction model of diabetes using the health examination data provided by the public data portal in 2016. In addition, we graphically visualized diabetes incidence by sex, age, residence area, and income level. As a result, the incidence of diabetes was different in each residence area and income level, and the probability of accurately predicting male and female was about 65%. In addition, it can be confirmed that the influence of X on male and Y on female is highly to affect diabetes. This predictive model can be used to predict the high-risk patients and low-risk patients of diabetes and to alarm the serious patients, thereby dramatically improving the re-admission rate. Ultimately it will be possible to contribute to improve public health and reduce chronic disease management cost by continuous target selection and management.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2001.11a
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• pp.14-21
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• 2001

Recently, with the increasing of construction works, large construction equipment are used to reduce the term of work and labor cost in construction field. Therefore, construction equipment noise has caused much annoyance for a number of dweller in nearby construction field and it has become a very serious problem in our living environment. Nevertheless, in our country, adequite guidelines for the construction equipment noise are very deficiency because of the lack of basic data and insufficient research works. From this point of view, this study attempts to survey the characteristics and prediction of SIP piling noise. On the basis of measurement value, we analysed about prediction possibility of SIP piling noise by computer simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.102-107
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• 2001

Recently, with the increasing of construction works, large construction equipment are used to reduce the term of work and labor cost in construction field. Therefore, construction equipment noise has caused much annoyance for a number of dweller in nearby construction field and it has become a very serious problem in our living environment. Neverthless, in our country, adequite guidelines for the construction equipment noise are very deficiency because of the lack of basic data and insufficient research works. From this point of view, this study attempts to survey the characteristics and prediction of piling noise in oil pressure method. On the basis of measurement value, we analysed about prediction possibility of piling noise in oil pressure method.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.121-128
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• 2018

Obstacle avoidance is one of the most important parts of autonomous mobile robot. In this study, we proposed safe and efficient local path planning of robot for obstacle avoidance. The proposed method detects and tracks obstacles using the 3D depth information of an RGB-D sensor for path prediction. Based on the tracked information of obstacles, the paths of the obstacles are predicted with probability circle-based spatial search (PCSS) method and Gaussian modeling is performed to reduce uncertainty and to create the cost function of caution. The possibility of collision with the robot is considered through the predicted path of the obstacles, and a local path is generated. This enables safe and efficient navigation of the robot. The results in various experiments show that the proposed method enables robots to navigate safely and effectively.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.103-109
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• 2015

In this paper, a novel method for the classification of term and preterm birth is proposed based on time-frequency analysis of electrohysterogram (EHG) using multivariate empirical mode decomposition (MEMD). EHG is a promising study for preterm birth prediction, because it is low-cost and accurate compared to other preterm birth prediction methods, such as tocodynamometry (TOCO). Previous studies on preterm birth prediction applied prefilterings based on Fourier analysis of an EHG, followed by feature extraction and classification, even though Fourier analysis is suboptimal to biomedical signals, such as EHG, because of its nonlinearity and nonstationarity. Therefore, the proposed method applies prefiltering based on MEMD instead of Fourier-based prefilters before extracting the sample entropy feature and classifying the term and preterm birth groups. For the evaluation, the Physionet term-preterm EHG database was used where the proposed method and Fourier prefiltering-based method were adopted for comparative study. The result showed that the area under curve (AUC) of the receiver operating characteristic (ROC) was increased by 0.0351 when MEMD was used instead of the Fourier-based prefilter.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.345-350
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• 2014

DC electrolytic capacitor is widely used in the power converter including PWM inverter, switching power supply and PFC Boost converter system because of its large capacitance, small size and low cost. In this paper, basic characteristics of DC electrolytic capacitor vs. frequency is presented and the real-time estimation scheme of ESR and capacitance based on the bandpass filtering is adopted to the single phase boost converter of uninterruptible power supply to diagnose its split dc-link capacitors. The feasibility of this real-time failure prediction monitoring system is verified by the computer simulation of the 5[kW] singe phase PFC half-bridge boost converter.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.294-299
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• 2023

Micro ribonucleic acids (miRNAs) can regulate the protein expression levels of genes in the human body and have recently been reported to be closely related to the cause of disease. Determining the genes related to miRNAs will aid in understanding the mechanisms underlying complex miRNAs. However, the identification of miRNA-related genes through wet experiments (in vivo, traditional methods are time- and cost-consuming). To overcome these problems, recent studies have investigated the prediction of miRNA relevance using deep learning models. This study presents a method for predicting the relationships between miRNAs and genes. First, we reconstruct a negative dataset using the proposed method. We then extracted the feature using an autoencoder, after which the feature vector was concatenated with the original data. Thereafter, the concatenated data were used to train a long short-term memory model. Our model exhibited an area under the curve of 0.9609, outperforming previously reported models trained using the same dataset.