• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2017.11a
• /
• pp.33-33
• /
• 2017

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.9
• /
• pp.1-9
• /
• 2016

Recent embedded processors employ set-associative L1 instruction cache to improve the performance. The energy consumption in the set-associative L1 instruction cache accounts for considerable portion in the embedded processor. When an instruction is required from the processor, all ways in the set-associative instruction cache are accessed in parallel. In this paper, we propose the technique to reduce the energy consumption in the set-associative L1 instruction cache effectively by accessing only one way. Gshare branch predictor is employed to predict the instruction flow and determine the way to fetch the instruction. When the branch prediction is untaken, next instruction in a sequential order can be fetched from the instruction cache by accessing only one way. According to our simulations with SPEC2006 benchmarks, the proposed technique requires negligible hardware overhead and shows 20% energy reduction on average in 4-way L1 instruction cache.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1996.10a
• /
• pp.122-130
• /
• 1996

During the cold forming, due to high working pressure acting on the die surface, failure mechanics must be considered before die design. One of the main reasons of die failure in industrial application of metal forming technologies is wear. Die wear affects the tolerances of formed parts, metal flow and costs of process etc. The only way to control these failures is to develop methods which allow prediction of die wear and costs of process etc. The only way to control these failures is to develop methods which allow prediction of die wear and which are suited to be used in the design state in order to optimize the process. In this paper, the wear experiments to abtain the wear coefficients and the upsetting processes was accomplished to observe the wear phenomenon during the cold forming process. The analysis of upsetting processes was accomplished to observe the wear phenomenon during the cold forming process. The analysis of upsetting processes was accomplished by the rigid-plastic finite element method. The result from the deformation analysis was used to analyse the die wear during the processes and the predicted die wear profiles were compared with the measured die wear profiles.

• Journal of Information Technology Services
• /
• v.15 no.4
• /
• pp.1-24
• /
• 2016

With the innovation of information technology, non-face-to-face robo advisor with high accessibility and convenience is spreading. The current robot advisor recommends appropriate investment products after understanding the investment propensity based on the structured data entered directly or indirectly by individuals. However, it is an inconvenient and obtrusive way for financial consumers to inquire or input their own subjective propensity to invest. Hence, this study proposes a way to deduce the propensity to invest in unstructured data that customers voluntarily exposed during consultation or online. Since prediction performance based on unstructured document differs according to the characteristics of text, in this study, classification algorithm optimized for the characteristic of text left by financial consumers is selected by performing prediction performance evaluation of various learning discrimination algorithms and proposed an intelligent method that automatically recommends investment products. User tests were given to MBA students. After showing the recommended investment and list of investment products, satisfaction was asked. Financial consumers' satisfaction was measured by dividing them into investment propensity and recommendation goods. The results suggest that the users high satisfaction with investment products recommended by the method proposed in this paper. The results showed that it can be applies to non-face-to-face robo advisor.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.6B
• /
• pp.375-381
• /
• 2007

This paper focuses on the prediction algorithm among the context-awareness technologies. With a representative algorithm, Bayesian Networks, it is difficult to realize a context-aware as well as to decrease process time in real-time environment. Moreover, it is also hard to be sure about the accuracy and reliability of prediction. One of the simplest algorithms is the sequential matching algorithm. We use it by adding the proposed Cache Scheme. It is adequate for a context-aware service adapting user's habit and reducing the processing time by average 48.7% in this paper. Thus, we propose a design method of user location prediction algorithm that uses sequential matching with the cache scheme by taking user's habit or behavior into consideration. The novel approach will be dealt in a different way compared to the conventional prediction algorithm.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.332-340
• /
• 2022

Recently, with the development of deep learning and artificial neural network technologies, research on the application of neural network has been actively conducted in the field of video coding. In particular, deep learning-based intra prediction is being studied as a way to overcome the performance limitations of the existing intra prediction techniques. This paper presents a method of context-adaptive neural network-based intra prediction model training and its coding performance analysis. In other words, in this paper, we implement and train a known intra prediction model based on convolutional neural network (CNN) that predicts a current block using contextual information from reference blocks. Then, we integrate the trained model into HM16.19 as an additional intra prediction mode and evaluate the coding performance of the trained model. Experimental results show that the trained model gives 0.28% BD-rate bit saving over HEVC in All Intra (AI) coding mode. In addition, the coding performance change of training considering block partition is also presented.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.4
• /
• pp.100-108
• /
• 1995

This study is aimed to show that revised prediction method in road traffic noise after comparing prediction method of National Institute of Environmental Research with that of the Acoustical Society of Japan. For this purpose, prediction equation and caluation procedure of each case was programmized and data predicted by each method were compared with those measured in the 14 fields. The result show that data predicted by 14 fields. The result show that data predicted by the method proposed in this study were closer to the data measured in the fields than those predicted by National Institute of Environmental Research and the Acoustical Society of Japan in the surroundings of urban express way (include highway) and general traffic road where the vehicle speed is higher than 40km/h.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.10b
• /
• pp.22-27
• /
• 2007

Phosphorylation is one of the most important post translational modifications which regulate the activity of proteins. The problem of predicting phosphorylation sites is the first step of understanding various biological processes that initiate the actual function of proteins in each signaling pathway. Although many prediction methods using single or multiple features extracted from protein sequences have been proposed, systematic data integration approach has not been applied in order to improve the accuracy of predicting general phosphorylation sites. In this paper, we propose an optimal way of integrating multiple features in the framework of multiple kernel learning. We optimally combine seven kernels extracted from sequence, physico-chemical properties, pairwise alignment, and structural information. Using the data set of Phospho. ELM, the accuracy evaluated by 5-fold cross-validation reaches 85% for serine, 85% for threonine, and 81% for tyrosine. Our computational experiments show significant improvement in the performance of prediction relative to a single feature, or to the combined feature with equal weights. Moreover, our systematic integration method significantly improves the prediction preformance compared with the previous well-known methods.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.229-234
• /
• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.330-334
• /
• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.