• Composites Research
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• v.30 no.1
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• pp.35-40
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• 2017

In this study, natural fiber composites including flax fiber reinforcement was manufactured. It was tried to find optimum design of drill and machining factor for minimizing the damage during hole machining in natural fiber composites. Taguchi optimization was used for minimizing the number of experiments and evaluation of the effect of machining factor during hole machining in natural fiber composites. The experimental results indicate that the newly designed drill distributes cutting resistance well and minimizes surface roughness and produces fine surfaces. Developed new drill has been dispersed in the cutting resistance during processing, it was possible to obtain the smooth hole surface. Also, it was found that optimal rotational speed and feed rate of drill for hole machining.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.43-50
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• 2016

NAND flash based storage devices adopts multi-channel and multi-way architecture to improve performance using parallel operation of multiple NAND devices. However, multiple NAND devices consume higher current and peak power overlap problem influences on the system stability and data reliability. In this paper, current waveform is measured for erase, program and read operations, peak current and model is defined by profiling method, and estimated probability of peak current overlap among NAND devices. Also, system level TLM simulator is developed to analyze peak overlap phenomenon depending on various simulation scenario. In order to remove peak overlapping, token-ring based simple power management method is applied in the simulation experiments. The optimal peak overlap ratio is proposed to minimize performance degradation based on relationship between peak current overlapping and system performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.2
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• pp.267-276
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• 2009

Nowadays, numerous of valuable internet services are available not only for personal computer but also for mobile appliances in wireless network environment. Therefore, as the amount of contents is increased for those services, the storage limitation on mobile devices has became a significant issue. In this paper, we present a new block-level storage network protocol, iATA (Internet Advanced Technology Attachment) as a solution to the above problem. iATA is designed to transport ATA block-level data and command over the ubiquitous TCP/IP network. With iATA, a mobile appliance is able to access and control the ATA storage devices natively through network from anywhere and at anytime as if the storage devices is attached locally. We describe the concepts, design and diverse consideration of iATA protocol. Based on the benchmark experiments and application exploitation, we strongly believe that iATA as a light-weight protocol is efficient and cost-effective to be used as a storage network protocol on a resource limited device that utilizes common-off-the-shelf storage hardware and existing IP infrastructure.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.440-445
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• 2017

Due to the advance of e-commerce systems, the number of people using online shopping and products has significantly increased. Therefore, the need for an accurate recommendation system is becoming increasingly more important. Recurrent neural network is a deep-learning algorithm that utilizes sequential information in training. In this paper, an evaluation is performed on the application of recurrent neural networks to recommendation systems. We evaluated three recurrent algorithms (RNN, LSTM and GRU) and three optimal algorithms(Adagrad, RMSProp and Adam) which are commonly used. In the experiments, we used the TensorFlow open source library produced by Google and e-commerce session data from RecSys Challenge 2015. The results using the optimal hyperparameters found in this study are compared with those of RecSys Challenge 2015 participants.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.303-308
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• 2010

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.355-364
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• 2005

With fast advances in computing power and network technologies, online streaming services of continuous media (CM) have been popularly implemented on the Web. To implement such services, a variety of CM streams need to be processed efficiently, so that the Sweep scheme was proposed. This scheme has several advantages such as hiccup-free playbacks and seek-time optimization. In this scheme, however, the entire CM streams are scheduled with a single scheduling period, called a cycle. Since only one scheduling period is allowed in this scheme, a significant amount of disk time is usually wasted because of its inflexible disk schedules. To solve this, we propose a new dynamic Sweep scheme. For this, we devise an algorithm that is able to expand scheduling periods of serviced CM streams and propose a new admission control mechanism guaranteeing hiccup-free playbacks. To show performance gains, we execute various simulation experiments. From the experimental results, we can see that the proposed scheme outperforms the Sweep scheme in terms of disk utilization and scheduling flexibility.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.544-554
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• 2008

Histograms are popularly used for selectivity estimation in database systems. In conventional histogram methods, buckets return the approximated results based on the assumption that all objects in a bucket are uniformly distributed. However, the objects within the region of a query are not likely to be uniformly distributed. That is, there can be some skews (i.e., clusters) in the buckets, which may significantly degrade the accuracy of the histogram. The aim of this work is to enhance the accuracy of histograms. For this purpose, we propose a new two-dimensional histogram method considering clusters. The proposed method detects dense regions and exploits them for organizing buckets. Since the proposed method effectively reduces accuracy degradation caused by clusters, it can provide improved, robust accuracy against skewed data distributions. Through experiments, we show that the proposed method provides up to 74% improved performance compared with the conventional histogram.

• Journal of KIISE
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• v.45 no.1
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• pp.15-21
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• 2018

Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.624-635
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• 2006

This paper applies simulated annealing algorithm to the problem of generating a plan for intra-block remarshalling with multiple transfer cranes. Intra-block remarshalling refers to the task of rearranging containers scattered around within a block into certain designated target areas of the block so that they can be efficiently loaded onto a ship. In generating a remarshalling plan, the predetermined container loading sequence should be considered carefully to avoid re-handlings that may delay the loading operations. In addition, the required time for the remarshalling operation itself should be minimized. A candidate solution in our search space specifies target locations of the containers to be rearranged. A candidate solution is evaluated by deriving a container moving plan and estimating the time needed to execute the plan using two cranes with minimum interference. Simulation experiments have shown that our method can generate efficient remarshalling plans in various situations.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.483-488
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• 2016

In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data with hyperspheres, and a hypersphere must cover the data from the same class. The radius of a hypersphere is computed by the mid point of the two distances to the farthest same class point and the nearest other class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that cover all the training data. The proposed prototype selection method is designed by a greedy algorithm and applicable to process a large-scale training set in parallel. The prediction rule is the nearest-neighbor rule and the new training data is the set of prototypes. In experiments, the generalization performance of the proposed method is superior to existing methods.