• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3C
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• pp.293-298
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• 2008

Recently, with the development of mobile techniques and the consideration to conveniency of using, the research on Mobile RFID Reader technique is getting more and more attentions. Until now, all security authentication algorithms of RFID are algorithms about range between Tag and Reader. The range between Reader and backend DB is composed by wired networks, so it's supposed to be secure range. But it must be taken account of the problem of information security and privacy in wireless range during the design of Mobile RFID Reader. In this paper we design an blind signature scheme based on weil-paring finite group's ECC encryption scheme, and by using this blind signature we propose the anonymous authorization scheme to Mobile RFID Reader's users.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1296-1299
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• 2004

MIM capacitor has been investigated for the next generation DRAM. Conventional poly-Si bottom electrode cannot satisfy the requirement of electrical properties and comparability to the high k materials. New bottom electrode material such as ruthenium has been suggested in the fabrication of MIM structure capacitor. However, the ruthenium has to be planarized due to the backend scalability. For the planarization CMP has been widely used in the manufacture of integrated circuit. In this research, ruthenium thin film was Polished by CMP with cerium ammonium nitrate (CAN)base slurry. HNO3 was added on the CAN solution as an additive. In the various concentration of chemical and alumina abrasive, ruthenium surface was etched and polished. After static etching and polishing, etching and removal rate was investigated. Also microroughness of surface was observed by AFM. The etching and removal rate depended on the concentration of CAN, and HNO3 accelerated the etching and polishing of ruthenium. The reasonable removal rate and microroughness of surface was achieved in the 1wt% alumina slurry.

• Smart Structures and Systems
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• v.10 no.3
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• pp.209-228
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• 2012

Structural health monitoring with wireless sensor networks has received much attention in recent years due to the ease of sensor installation and low deployment and maintenance costs. However, sensor network technology needs to solve numerous challenges in order to substitute conventional systems: large amounts of data, remote configuration of measurement parameters, on-site calibration of sensors and robust networking functionality for long-term deployments. We present a structural health monitoring network that addresses these challenges and is used in several deployments for monitoring of bridges and buildings. Our system supports a diverse set of sensors, a library of highly optimized processing algorithms and a lightweight solution to support a wide range of network runtime configurations. This allows flexible partitioning of the application between the sensor network and the backend software. We present an analysis of this partitioning and evaluate the performance of our system in three experimental network deployments on civil structures.

• ETRI Journal
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• v.33 no.5
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• pp.731-740
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• 2011

Reconfigurable computing using a field-programmable gate-array (FPGA) device has become a promising solution in system design because of its power efficiency and design flexibility. To bring the benefit of FPGA to many application programmers, there has been intensive research about automatic translation from high-level programming languages (HLL) such as C and C++ into hardware. However, the large gap of syntaxes and semantics between hardware and software programming makes the translation challenging. In this paper, we introduce a new approach for the translation by using the widely used GCC compiler. By simply adding a hardware description language (HDL) backend to the existing state-of- the-art compiler, we could minimize an effort to implement the translator while supporting full features of HLL in the HLL-to-HDL translation and providing high performance. Our translator, called GCC2Verilog, was implemented as the GCC's cross compiler targeting at FPGAs instead of microprocessor architectures. Our experiment shows that we could achieve a speedup of up to 34 times and 17 times on average with 4-port memory over PICO microprocessor execution in selected EEMBC benchmarks.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.6-9
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• 2021

The 3D industry is drawing attention for its applications in various markets, including architecture, media, VR/AR, metaverse, imperial broadcast, and etc.. The current feature of the architecture we are introducing is to make 3D models more easily created and modified than conventional ones. Existing methods for generating 3D models mainly obtain values using specialized equipment such as RGB-D cameras and Lidar cameras, through which 3D models are constructed and used. This requires the purchase of equipment and allows the generated 3D model to be verified by the computer. However, our framework allows users to collect data in an easier and cheaper manner using cell phone cameras instead of specialized equipment, and uses 2D data to proceed with 3D modeling on the server and output it to cell phone application screens. This gives users a more accessible environment. In addition, in the 3D modeling process, object classification is attempted through deep learning without user intervention, and mesh and texture suitable for the object can be applied to obtain a lively 3D model. It also allows users to modify mesh and texture through requests, allowing them to obtain sophisticated 3D models.

• Journal of The Korean Astronomical Society
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• v.55 no.6
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• pp.207-213
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• 2022

The Seoul Radio Astronomy Observatory (SRAO) operates a 6.1-meter radio telescope on the Gwanak campus of Seoul National University. We present the efforts to reform SRAO to a Very Long Baseline Interferometry (VLBI) station, motivated by recent achievements by millimeter interferometer networks such as Event Horizon Telescope, East Asia VLBI Network, and Korean VLBI Network (KVN). For this goal, we installed a receiver that had been used in the Combined Array for Research in Millimeter-wave Astronomy and a digital backend, including an H-maser clock. The existing hardware and software were also revised, which had been dedicated only to single-dish operations. After several years of preparations and test observations in 1 and 3-millimeter bands, a fringe was successfully detected toward 3C 84 in 86 GHz in June 2022 for a baseline between SRAO and KVN Ulsan station separated by 300 km. Thanks to the dual frequency operation of the receiver, the VLBI observations will soon be extended to the 1 mm band and verify the frequency phase referencing technique between 1 and 3-millimeter bands.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.5
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• pp.418-428
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• 2016

In this study, a Vessel Shore Report Management System (VSRMS) is developed for the International Maritime Organization (IMO), Maritime Service Portfolio (MSP) Number 8, which comprises vessel shore reporting. Several documents have to be completed before the arrival/departure of a vessel at a port, as each national port has its own reporting format and data. The present vessel reporting system is inefficient, time-consuming, and involves excessive paperwork, which results in duplications and errors. To solve this problem, in this study, the vessel reporting formats and data contents of various national ports are investigated, as at present, the reporting documents required by the current IMO standard includes insufficient information which is requested by national ports. Initially, the vessel reporting information of various national ports are collected and analyzed. Subsequently, a database structure for managing vessel reporting data for ports worldwide is devised. To make the transfer of data and the exchange of information of vessel reports much more reliable, efficient, and paper-free, VSRMS, which is a software application for the simplification and facilitation of vessel report formalities, is developed. This application is developed using the latest Microsoft C#.Net Programming Language in the Microsoft Visual Studio framework 4.5. It provides a user interface and a backend MySQL server used for database management. SAP Crystal Reports 2013 is used for designing and generating vessel reports in the original report formats. The VSRMS can facilitate vessel reporting and improve data accuracy through the reduction of input data, efficient data exchange, and reduction of the cost of communication. Adoption of the VSRMS will allow the vessel shore reporting system to be automated, resulting in enhanced work efficiency for shipping companies. Based on this information system and architecture, the consensus of various international organizations, such as the IMO, the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA), the Federation of National Associations of Ship Brokers and Agents (FONASBA), and the Baltic and International Maritime Council (BIMCO), is required so that vessel reporting is standardized internationally.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1035-1045
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• 2015

Recent studies in Big Data Analysis are showing promising results, utilizing the main memory for rapid data processing. In-memory computing technology can be highly advantageous when used with high-performing servers having tens of gigabytes of RAM with multi-core processors. The constraint in network in these infrastructure can be lessen by combining in-memory technology with distributed parallel processing. This paper discusses the research in the aforementioned concept applying to a test taxi hailing application without disregard to its underlying RDBMS structure. The application of IMDG technology in the application's backend API without restructuring the database schema yields 6 to 9 times increase in performance in data processing and throughput. Specifically, the change in throughput is very small even with increase in data load processing.