• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.526-529
• /
• 2015

as the smartphone industry developed, the smartphone's internal hardware devices have become high-end devices and it requires more power consumption than the previous one. therefore a battery of high capacity needed, but there is a limit in order to equip a large battery on account of smartphone minimization. The Linux Kernel provides the DVFS Mechanism to compensate for these limitations by software techniques. DVFS is dynamically adjust the frequency of the CPU to reduce the power consumption of the CPU. ondemand governor, the default policy in DVFS, apply the maximum frequency of the CPU whenever exceeding the up_threshold. so it result in a waste of CPU resources. by paying attention to this point, this paper propose the mechanism that maintain a high CPU utilization in proportion to the current frequency of the cpu to prevent the waste of CPU resources and conserve energy.

• Journal of Service Research and Studies
• /
• v.5 no.2
• /
• pp.25-33
• /
• 2015

As Data mining is a method of extracting the information based on the large data, the technique has been used in many application areas to deal with data in particular. However, the status of the algorithm that can deal with the healthcare data are not fully developed. In this paper, One of clustering algorithm, the EM and DBSCAN are used for performance comparison. It could be analyzed using by the same data. To do this, EM and DBSACN algorithm are changing performance according to the variables in Health expenditure database. Based on the results of the experimental data, We analyze more precise and accurate results using by Kernel Filtering. In this study, we tried comparison of the performance for the algorithm as well as attempt to improve the performance. Through this work, we were analyzed the comparison result of the application of the experimental data and of performance change according to expansion algorithm. Especially, Collects data from the various cluster using the medical record, it could be recommended the effective spending on medical services.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.6
• /
• pp.1499-1506
• /
• 2017

The use of smartphones and the launch of various apps have increased exponentially, and malicious apps have also increased. Existing app recommendation systems have been limited to operate based on static information analysis such as ratings, comments, and popularity categories of other users who are online. In this paper, we first propose a robust app recommendation system that realistically uses dynamic information of apps actually used in smartphone and considers static information and dynamic information at the same time. In other words, this paper proposes a robust Android app recommendation system by partially reflecting the time of the app, the frequency of use of the app, the interaction between the app and the app, and the number of contact with the Android kernel. As a result of the performance evaluation, the proposed method proved to be a robust and efficient app recommendation system.

• Journal of KIISE:Information Networking
• /
• v.33 no.1
• /
• pp.59-75
• /
• 2006

In this paper, we propose a congestion control scheme to control the congestion due to the mobility of ubiquitous devices on ubiquitous computing environment. Especially, this congestion control scheme provides a reverse congestion avoidance state which can classify between packet error by features of wireless network and packet dropping by congestion. Also, it provides a slow stop state which can minimize bandwidth waste due to congestion control. The proposed congestion control scheme controls more adaptive than existing congestion control schemes. The proposed congestion control scheme is designed based on DCCP(Datagram Congestion Control Protocol) being proposed by IETF(Internet Engineering Task Force) and implemented on the Linux kernel. In simulation results, the proposed congestion control scheme provides good bandwidth throughput in wireless network as well as in wired network.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.2
• /
• pp.31-42
• /
• 2014

Debugger systems are necessary to apply dynamic program analysis when evaluating security properties of embedded system software. It may be possible to make the use of software-based debugger and/or DBI framework if target devices support general purpose operating systems, however, constraints on applicability as well as environmental transparency might be incurred thereby hindering overall analyzability. Analysis with JTAG (IEEE 1149.1) debugging devices can overcome these difficulties in that no change would be involved in terms of internal software environment. In that sense, JTAG API can facilitate to practically perform dynamic program analysis for evaluating security properties of target device software. In this paper, we introduce an implementation of JTAG API to enable analysis of ARM core based embedded systems. The API function set includes the categories of debugger and target device controls: debugging environment and operation. To verify API applicability, we also provide example analysis tool implementations: our JTAG API could be used to build kernel function fuzzing and live memory forensics modules.

• The KIPS Transactions:PartA
• /
• v.16A no.6
• /
• pp.463-472
• /
• 2009

Real-time operating systems must have satisfying various conditions such as effective scheduling policies, minimized interrupt delay, resolved priority inversion problems, and its applications to be completed within desired deadline. The real-time operating systems, therefore, should be designed and developed to be optimal for these requirements. MicroC/OS-II, a kind of Real-time operating systems, uses the basic priority inheritance with a mutex to solve priority inversion problems. For the implementation of mutex, the kernel in an operating system should provide supports for numerous tasks with same priority. However, MicroC/OS-II does not provide this support for the numerous tasks of same priority. To solve this problem, MicroC/OS-II cannot but using priority reservation, which leads to the waste of unnecessary resources. In this study, we have dealt with new design a protocol, so called TPSP(Temporary Priority Swap Protocol), by an effective solution for above-mentioned problem, eventually enabling embedded systems with constrained resources environments to run applications.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_1
• /
• pp.959-971
• /
• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.1
• /
• pp.112-125
• /
• 2021

Marine spatial planning(MSP) designates the marine as nine kinds of use zones for the systematic and rational management of marine spaces. One of them is the fishery protection zone, which is necessary for the sustainable production of fishery products, including the protection and fosterage of fishing activities. This study intends to quantitatively identify the fishing activity space, one of the elements necessary for the designation of fisheries protection zones, by mapping of fishery activities using V-Pass data and deriving the fishery activity concentrated zone. To this end, pre-processing of V-Pass data was performed, such as constructing a dataset that combines static and dynamic information, calculating the speed of fishing vessels, extracting fishing activity points, and removing data in non-fishing activity zone. Finally, using the selected V-Pass point data, a fishery activity map was made by kernel density estimation, and the concentrated space of fishery activity was analyzed. In addition, it was confirmed that there is a difference in the spatial distribution of fishing activities according to the type of fishing vessel and the season. The pre-processing technique of large volume V-Pass data and the mapping method of fishing activities performed through this study are expected to contribute to the study of spatial characteristics evaluation of fishing activities in the future.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.5
• /
• pp.133-138
• /
• 2022

Recently, as GPU performance has improved in HPC and artificial intelligence, its use is becoming more common, but GPU programming is still a big obstacle in terms of productivity. In particular, due to the difficulty of managing host memory and GPU memory separately, research is being actively conducted in terms of convenience and performance, and various CPU-GPU memory transfer programming methods are suggested. Meanwhile, recently many SoC (System on a Chip) products such as Apple M1 and NVIDIA Tegra that bundle CPU, GPU, and integrated memory into one large silicon package are emerging. In this study, data between CPU and GPU devices are used in such an integrated memory device and performance-related research is conducted during transmission. It shows different characteristics from the existing environment in which the host memory and GPU memory in the CPU are separated. Here, we want to compare performance by CPU-GPU data transmission method in NVIDIA SoC chips, which are integrated memory devices, and NVIDIA SMX-based V100 GPU devices. For the experimental workload for performance comparison, a two-dimensional matrix transposition example frequently used in HPC applications was used. We analyzed the following performance factors: the difference in GPU kernel performance according to the CPU-GPU memory transfer method for each GPU device, the transfer performance difference between page-locked memory and pageable memory, overall performance comparison, and performance comparison by workload size. Through this experiment, it was confirmed that the NVIDIA Xavier can maximize the benefits of integrated memory in the SoC chip by supporting I/O cache consistency.

• Korean Chemical Engineering Research
• /
• v.46 no.2
• /
• pp.233-247
• /
• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.