• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.296-301
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• 2007

본 논문은 낸드 플래쉬 디바이스의 고질적인 문제인 마운트 지연시간을 바이트 접근성을 가지는 비휘발성 저장소자를 이용하여 해결하는 기법을 다룬다. 낸드 플래쉬 디바이스를 사용하기 위해서는, 마운트시에 낸드 플래쉬 디바이스의 전 영역에 걸쳐 분산되어 저장되어 있는 메타 데이터를 스캔하여, 해당 파일 시스템 파티션의 사용-구성정보 자료를 주기억장치에 생성해야 한다. 이러한 과정은 대용량 낸드 플래쉬 디바이스를 사용하는 경우 매우 긴 시간을 필요로 하게 되어 실제 환경에서는 낸드 플래쉬 디바이스를 채용하기가 어렵다. 본 논문에서는 차세대 비휘발성 저장장치의 바이트 단위의 접근 가능성을 활용한다. 낸드 플래쉬 디바이스 마운트시에 생성되는 최종 자료구조를 직접 NVRAM에 저장함으로써 낸드 플래실 디바이스의 메타 데이터를 스캔 하는 절차를 완전히 제거하였다. 즉, 낸드 플래처 디바이스의 마운트에 필요한 메타 데이터의 In-memory Data Structure를 NVRAM상에 저장하여 두면 이 후 NVRAM상에는 그 정보가 계속 유지되어 있기 때문에 낸드 플래쉬 디바이스의 마운트 동작은 단순히 Memory Pointer Mapping 정도의 간단하고 빠른 동작만으로도 충분하다. 본 논문에서는 비휘발성 메모리 소자가 블록 디바이스와 메모리 영역에 동시에 사상되어 있는 융합 파일 시스템을 성공적으로 개발하였다. 마운트 시간의 측정결과 효율적인 기존의 낸드 플래쉬 파일 시스템인 YAFFS에 비해 파티션의 크기나 파티션내 File의 개수에 관계없이 그 값이 매우 작고 고정적인 수치를 갖는다는 것을 확인하였다.

• Biomolecules & Therapeutics
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• v.27 no.2
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• pp.231-239
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• 2019

Suppressor of Variegation 3-9 Homolog 2 (SUV39H2) methylates the lysine 9 residue of histone H3 and induces heterochromatin formation, resulting in transcriptional repression or silencing of target genes. SUV39H1 and SUV39H2 have a role in embryonic development, and SUV39H1 was shown to suppress cell cycle progression associated with Rb. However, the function of human SUV39H2 has not been extensively studied. We observed that forced expression of SUV39H2 decreased cell proliferation by inducing $G_1$ cell cycle arrest. In addition, SUV39H2 was degraded through the ubiquitin-proteasomal pathway. Using yeast two-hybrid screening to address the degradation mechanism and function of SUV39H2, we identified translationally controlled tumor protein (TCTP) as an SUV39H2-interacting molecule. Mapping of the interacting regions indicated that the N-terminal 60 amino acids (aa) of full-length SUV39H2 and the C-terminus of TCTP (120-172 aa) were critical for binding. The interaction of SUV39H2 and TCTP was further confirmed by co-immunoprecipitation and immunofluorescence staining for colocalization. Moreover, depletion of TCTP by RNAi led to up-regulation of SUV39H2 protein, while TCTP overexpression reduced SUV39H2 protein level. The half-life of SUV39H2 protein was significantly extended upon TCTP depletion. These results clearly indicate that TCTP negatively regulates the expression of SUV39H2 post-translationally. Furthermore, SUV39H2 induced apoptotic cell death in TCTP-knockdown cells. Taken together, we identified SUV39H2, as a novel target protein of TCTP and demonstrated that SUV39H2 regulates cell proliferation of lung cancer cells.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.66-82
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• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.697-703
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• 2023

Intertidal zones, akin to tidal flats, are among the potential areas where underwater cultural heritage might be submerged. However, the shallow depths in these regions present challenges for conventional vessel-based survey methods. Moreover, during low tides, intertidal zones transform into tidal flats, limiting the efficiency of survey efforts due to restricted access and potential risks. As a result, proper underwater cultural heritage surveys encounter difficulties in these environments. In recent times, extensive research is underway to address these issues by investigating underwater cultural heritage surveys in intertidal zones, encompassing diverse fields, including equipment-based investigations. This study aimed to explore the feasibility of utilizing unmanned aerial vehicles (UAVs) to conduct intertidal cultural heritage surveys, employing aerial photography and 3D mapping to create detailed orthoimages and 3D models. The study focused on assessing the potential application of these techniques for cultural heritage surveying within intertidal zones. Notably, the survey conducted in Jindo's Naesan-ri demonstrated high-resolution capabilities, enabling the distinction of actual pottery fragments mixed within gravel fields. Similarly, in the survey of Jindo's Byeokpa-hang, it was found that a wooden pillar structure existed in a section about 200m long. The integration of various sensors, including LiDAR, with UAVs allows for diverse investigation possibilities, including bathymetric measurements, and is expected to facilitate the acquisition of varied datasets for further research and assessment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2768-2787
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• 2023

As an important research direction of the application of computer science in the medical field, the automatic generation technology of radiology report has attracted wide attention in the academic community. Because the proportion of normal regions in radiology images is much larger than that of abnormal regions, words describing diseases are often masked by other words, resulting in significant feature loss during the calculation process, which affects the quality of generated reports. In addition, the huge difference between visual features and semantic features causes traditional multi-modal fusion method to fail to generate long narrative structures consisting of multiple sentences, which are required for medical reports. To address these challenges, we propose a multi-feature optimization Transformer (MFOT) for generating radiology reports. In detail, a multi-dimensional mapping attention (MDMA) module is designed to encode the visual grid features from different dimensions to reduce the loss of primary features in the encoding process; a feature pre-fusion (FP) module is constructed to enhance the interaction ability between multi-modal features, so as to generate a reasonably structured radiology report; a detail enhanced attention (DEA) module is proposed to enhance the extraction and utilization of key features and reduce the loss of key features. In conclusion, we evaluate the performance of our proposed model against prevailing mainstream models by utilizing widely-recognized radiology report datasets, namely IU X-Ray and MIMIC-CXR. The experimental outcomes demonstrate that our model achieves SOTA performance on both datasets, compared with the base model, the average improvement of six key indicators is 19.9% and 18.0% respectively. These findings substantiate the efficacy of our model in the domain of automated radiology report generation.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.601-606
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• 2024

Today, governments are evolving into digital governments that actively leverage digital technologies to promote national development. Government websites play a crucial role as key elements in reshaping the interaction between individuals and the government. Within this context, government website chatbots play an important role in facilitating citizens' easy access to information. However, the chatbot ND on the National R&D Knowledge Information Portal (NTIS) exhibits low usage rates. This study proposes a framework based on user journey mapping to address the usability issues of chatbot ND. By delineating the user journey into pre-usage, in-usage, and post-usage stages, the study aims to identify points of inconvenience experienced by users at each stage and provide enhanced user experiences.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.293-310
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• 2024

In this paper, we propose an agent model for Language-Driven Zero-Shot Object Navigation (L-ZSON) tasks, which takes in a freeform language description of an unseen target object and navigates to find out the target object in an inexperienced environment. In general, an L-ZSON agent should able to visually ground the target object by understanding the freeform language description of it and recognizing the corresponding visual object in camera images. Moreover, the L-ZSON agent should be also able to build a rich spatial context map over the unknown environment and decide efficient exploration actions based on the map until the target object is present in the field of view. To address these challenging issues, we proposes AML (Agent Model for L-ZSON), a novel L-ZSON agent model to make effective use of AI foundation models such as Large Language Model (LLM) and Vision-Language model (VLM). In order to tackle the visual grounding issue of the target object description, our agent model employs GLEE, a VLM pretrained for locating and identifying arbitrary objects in images and videos in the open world scenario. To meet the exploration policy issue, the proposed agent model leverages the commonsense knowledge of LLM to make sequential navigational decisions. By conducting various quantitative and qualitative experiments with RoboTHOR, the 3D simulation platform and PASTURE, the L-ZSON benchmark dataset, we show the superior performance of the proposed agent model.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.25-41
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• 2014

Recently, the number of social media users has increased rapidly because of the prevalence of smart devices. As a result, the amount of real-time data has been increasing exponentially, which, in turn, is generating more interest in using such data to create added value. For instance, several attempts are being made to analyze the relevant search keywords that are frequently used on new portal sites and the words that are regularly mentioned on various social media in order to identify social issues. The technique of "topic analysis" is employed in order to identify topics and themes from a large amount of text documents. As one of the most prevalent applications of topic analysis, the technique of issue tracking investigates changes in the social issues that are identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has two limitations. First, when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. This creates practical limitations in the form of significant time and cost burdens. Therefore, this traditional approach is difficult to apply in most applications that need to perform an analysis on the additional period. Second, the issue is not only generated and terminated constantly, but also one issue can sometimes be distributed into several issues or multiple issues can be integrated into one single issue. In other words, each issue is characterized by a life cycle that consists of the stages of creation, transition (merging and segmentation), and termination. The existing issue tracking methods do not address the connection and effect relationship between these issues. The purpose of this study is to overcome the two limitations of the existing issue tracking method, one being the limitation regarding the analysis method and the other being the limitation involving the lack of consideration of the changeability of the issues. Let us assume that we perform multiple topic analysis for each multiple period. Then it is essential to map issues of different periods in order to trace trend of issues. However, it is not easy to discover connection between issues of different periods because the issues derived for each period mutually contain heterogeneity. In this study, to overcome these limitations without having to analyze the entire period's documents simultaneously, the analysis can be performed independently for each period. In addition, we performed issue mapping to link the identified issues of each period. An integrated approach on each details period was presented, and the issue flow of the entire integrated period was depicted in this study. Thus, as the entire process of the issue life cycle, including the stages of creation, transition (merging and segmentation), and extinction, is identified and examined systematically, the changeability of the issues was analyzed in this study. The proposed methodology is highly efficient in terms of time and cost, as it sufficiently considered the changeability of the issues. Further, the results of this study can be used to adapt the methodology to a practical situation. By applying the proposed methodology to actual Internet news, the potential practical applications of the proposed methodology are analyzed. Consequently, the proposed methodology was able to extend the period of the analysis and it could follow the course of progress of each issue's life cycle. Further, this methodology can facilitate a clearer understanding of complex social phenomena using topic analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.7
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• pp.508-519
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• 2007

Flash memory based storage has been used in various mobile systems and now is to be used in Laptop computers in the name of Solid State Disk. The Flash memory has not only merits in terms of weight, shock resistance, and power consumption but also limitations like erase-before-write property. To overcome these limitations, Flash memory based storage requires special address mapping software called FTL(Flash-memory Translation Layer), which often performs merge operation for block recycling. In order to reduce block recycling cost in NAND Flash memory based storage, we introduce another block recycling scheme which we call migration. As a result, the FTL can select either merge or migration depending on their costs for each block recycling. Experimental results with Postmark benchmark and embedded system workload show that this cost-based selection of migration/merge operation improves the performance of Flash memory based storage. Also, we present a solution of macroscopic optimal migration/merge sequence that minimizes a block recycling cost for each migration/merge combination period. Experimental results show that the performance of Flash memory based storage can be more improved by the macroscopic optimization than the simple cost-based selection.