• Journal of KIISE:Computer Systems and Theory
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• v.36 no.1
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• pp.26-32
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• 2009

The multi-level storage architecture has been widely adopted in servers and data centers. However, while prefetching has been shown as a crucial technique to exploit sequentiality in accesses common for such systems and hide the increasing relative cost of disk I/O, existing multi-level storage studies have focused mostly on cache replacement strategies. In this paper, we show that prefetching algorithms designed for single-level systems may have their limitations magnified when applied to multi-level systems. Overly conservative prefetching will not be able to effectively use the lower-level cache space, while overly aggressive prefetching will be compounded across levels and generate large amounts of wasted prefetch. We design and implement a hierarchy-aware lower-level prefetching strategy called PMS(Prefetching strategy for Multi-level Storage system) that applicable to any upper level prefetching algorithms. PMS does not require any application hints, a priori knowledge from the application or modification to the va interface. Instead, it monitors the upper-level access patterns as well as the lower-level cache status, and dynamically adjusts the aggressiveness of the lower-level prefetching activities. We evaluated the PMS through extensive simulation studies using a verified multi-level storage simulator, an accurate disk simulator, and access traces with different access patterns. Our results indicate that PMS dynamically controls aggressiveness of lower-level prefetching in reaction to multiple system and workload parameters, improving the overall system performance in all 32 test cases. Working with four well-known existing prefetching algorithms adopted in real systems, PMS obtains an improvement of up to 35% for the average request response time, with an average improvement of 16.56% over all cases.

• Journal of KIISE:Information Networking
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• v.27 no.3
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• pp.348-359
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• 2000

Most of the important real time multimedia applications require multipoint transmissions. To support these applications in ATM based Intermet environments, it is important to provide efficient IP multicast transportations over ATM networks. IETF proposed MARS(Multicast Address Resolution Server) as the service architecture to transport connectionless IP multicast flows over connection oriented ATM VCs. MARS assumes UNI3.0/3.1 signalling. Since UNI3.0/3.1 does not provide any means for receivers to request a join for a multicast ATM VC, MARS provides overlay service to relay join request from IP multicast group members to the sources of the multicast group. Later on, ATM Forum standardized UNI4.0 signalling which is provisioned with a new signalling mechanism called LIJ(Leaf Initiated Join). LIJ enables receivers to directly signal the source of an ATM VC to join. In this paper, we propose a new service architecture providing IP multicast flow transportation over ATM networks deploying UNI4.0 signalling. The proposed architecture is named UNI4MARS. It comprises service components same as those of the MARS. The main functionality provided by the UNI4MARS is to provide source information to the receivers so that the receivers may exploit LIJ to join multicast ATM VCs dynamically. The implementation overhead of UNI4MARS and that of MARS are compared by a course of simulations. The simulation results show that the UNI4MARS supports the dynamic IP multicast group changes more efficiently with respect to processing, memory and bandwidth overhead.

• Journal of Korean Philosophical Society
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• v.146
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• pp.115-135
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• 2018

This article attempts to deal with the problem that science and technology function as a mechanism to oppress and exploit humans rather than to release humans from labor. To explore this subject, it is necessary to consider the difference between the theory of labor value and the theory of 'technology value'. In addition, it is also important to refer to Marx's critical view of the 'capitalist' use of technology. Above all, Marx' concepts of relative surplus value and special surplus value, and his analysis of organic composition of capital are still valid in explaining that development of technology tightens control over workers and intensity of labor, and worsens instability of employment. Reflection of the limitations of capitalist development of technology is also important for realization of its usefulness. Industry 4.0 in Germany therefore deserves to be noticed as a good example because it shows a different way from extreme capitalist exploitation. The model suggests also some points that shouldn't be overlooked, when we try to actualize the tremendous slogan of the current fourth industrial revolution as real innovation and progress in human life. In this matter, the most important point is the possibility of technological development that doesn't oppose workers' interests.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.79-84
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• 2018

Cloud services are readily available through a variety of media, attracting a lot of attention from users. However, there are various security damages that abuse the privacy of users who use cloud services, so there is not enough technology to prevent them. In this paper, we propose a protection model to safeguard user's privacy in a cloud environment so as not to illegally exploit user's privacy. The proposed model randomly manages the user's signature to strengthen the role of the middle manager and the cloud server. In the proposed model, the user's privacy information is provided illegally by the cloud server to the user through the security function and the user signature. Also, the signature of the user can be safely used by bundling the random number of the multiplication group and the one-way hash function into the hash chain to protect the user's privacy. As a result of the performance evaluation, the proposed model achieved an average improvement of data processing time of 24.5% compared to the existing model and the efficiency of the proposed model was improved by 13.7% than the existing model because the user's privacy information was group managed.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1965-1974
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• 2021

Satellite remote sensing approach can be actively used for forest monitoring. Especially, it is much meaningful to utilize Korea multi-purpose satellites, an independently operated satellite in Korea, for forest monitoring of Korea, Recently, several studies have been performed to exploit meaningful information from satellite remote sensed data via machine learning approaches. The forest information produced through machine learning approaches can be used to support the efficiency of traditional forest monitoring methods, such as in-situ survey or qualitative analysis of aerial image. The performance of machine learning approaches is greatly depending on the characteristics of study area and data. Thus, it is very important to survey the best model among the various machine learning models. In this study, the performance of deep neural network to classify artificial or natural forests was analyzed in Samcheok, Korea. As a result, the pixel accuracy was about 0.857. F1 scores for natural and artificial forests were about 0.917 and 0.433 respectively. The F1 score of artificial forest was low. However, we can find that the artificial and natural forest classification performance improvement of about 0.06 and 0.10 in F1 scores, compared to the results from single layered sigmoid artificial neural network. Based on these results, it is necessary to find a more appropriate model for the forest type classification by applying additional models based on a convolutional neural network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.15-22
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• 2019

Awireless sensor network utilizes small sensors with a low cost and low power being deployed over a wide area. They monitor the surrounding environment and gather the associated information to transmit it to a base station via multi-hop transmission. Most of the research has mainly focused on static sensors that are located in a fixed position. Unlike a wireless sensor network based on static sensors, we can exploit drone-based technologies for more efficient wireless networks in terms of coverage and energy. In this paper, we introduce a transmission power model and a video encoding power model to design the network environment. We also explain a priority mapping scheme, and deploy drones oriented for network coverage and energy consumption. Through our simulations, this research shows coverage and energy improvements in adrone-based wireless sensor network with fewer sensors, compared to astatic sensor-based wireless sensor network. Concretely, coverage increases by 30% for thedrone-based wireless sensor network with the same number of sensors. Moreover, we save an average of 25% with respect to the total energy consumption of the network while maintaining the coverage required.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Journal of Microbiology and Biotechnology
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• v.28 no.12
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• pp.2095-2105
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• 2018

In our previous studies, we have identified several in vivo-induced antigens and evaluated their potential as subunit vaccine candidates in a murine model, in which the recombinant protein GalT showed the most potent immunogenicity and immunoprotective efficacy against Actinobacillus pleuropneumoniae. To exploit a more efficient way of delivering GalT proteins, in this study, we employed the widely studied E. coli outer membrane vesicles (OMVs) as a platform to deliver GalT protein and performed the vaccine trial using the recombinant GalT-OMVs in the murine model. Results revealed that GalT-OMVs could elicit a highly-specific, IgG antibody titer that was comparable with the adjuvant GalT group. Significantly higher lymphocyte proliferation and cytokines secretion levels were observed in the GalT-OMVs group. 87.5% and 50% of mice were protected from a lethal dose challenge using A. pleuropneumoniae in active or passive immunization, respectively. Histopathologic and immunohistochemical analyses showed remarkably reduced pathological changes and infiltration of neutrophils in the lungs of mice immunized with GalT-OMVs after the challenge. Taken together, these findings confirm that OMVs can be used as a platform to deliver GalT protein and enhance its immunogenicity to induce both humoral and cellular immune responses in mice.

• The Korean Journal of Archival Studies
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• no.31
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• pp.43-93
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• 2012

The quality of human resources is critical element which promote efficiency in the community management. Each country all of the world is developing national competency standards to exploit them on human resources management. In response to this trend, the field of records management need to develop competency standards and to build human resources. Although each agency is placing qualified record manager and operating archives organization. growth and development is needed on this field. Under these circumstances, examining requirement for records management competency and using them is a task of great significance on the side of strengthening specialist and normalizing archives operation. This study suggests procedures and methodology competency standards enable to be exploited in entire fields of records management including private sectors as well as public sectors. Furthermore, as for procedures of suggesting measures for developments, cases of procedures of stand designs are shown, effectively verifying for feasibility, validity, and usability of development of competency standards in the field of records management. In this process, models for competency standard and elements based on competency units in the field of records management are suggested. This study will be ultimately expected to contribute to an adoption of manpower development system and a systematic method via procedures of approving expertise of records management being recently highlighted as an issue from revision process of records management act.

• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.