• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1417-1426
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• 2011

Business still relies heavily on pen and paper for legal reasons or convenience. The handwritten document is to be converted into digitalized document for IT system to manage and process in real time. Because the previous document digitalization systems convert the handwritten documents into digitalized documents by scanning and post-processing the documents, it is difficult to seamlessly proceed the work process. This paper proposes the LiveForm, a framework for digitalizing handwritten document using digital pen and handwriting recognition technology. To prove the applicability of the proposed LiveForm, we also implement a LiveForm based service in industrial gas distribution process and analyze effects of the system. The LiveForm generates the same digital image as the handwritten document by writing up the paper with absolute coordinates by digital pen and converts the handwriting data to digital text to insert the information into back-end system. The LiveForm based system eliminates scanning for document digitalization and data input with keyboard into back-end system in paper-based information gathering. Therefore, it is possible for the LiveForm to improve work process in various business areas.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.11a
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• pp.147-153
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• 2002

Due to the growth of population and industrial development at the coastal cities, there has been much increase in necessity to effective control of the wastes into the coastal water and river. The amount of disposal at those waters has been increased rapidly and it is necessary for us to track of it in order to keep the water clean. The investigation and research related to the water quality in this region have been conducted continuously but the systematic survey of the disposed wastes at the bottom was neglected and/or minor. In this study we surveyed the status of disposed waste distribution at the bottom coastal water and river from the scanned images. The intensity of sound received by the side scan sonar tow vehicle from the sea floor provides information as to the general distribution and characteristics of the superficial wastes. The port and starboard side scanned images produced from a transducer borne on a tow fish connected by tow cable to a tug boat have the area with width of 22m∼112m, and band of 44m∼224m. All data are displayed in real-time on a high-resolution color display (1280 ${\times}$ 1024 pixels) together with position information by DGPS. From the field measurement and analysis of the recorded images, we could draw the location and distribution of bottom disposals. Furthermore, we made a database system which might be fundamental for planning the waste reception and process control system.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.319-326
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• 2004

To help network intrusion detection systems(NIDSs) keep up with the demands of today's networks, that we the increasing network throughput and amount of attacks, a radical new approach in hardware and software system architecture is required. In this paper, we propose a Network Processor(NP) based In-Line mode NIDS that supports the packet payload inspection detecting the malicious behaviors, as well as the packet filtering and the traffic metering. In particular, we separate the filtering and metering functions from the deep packet inspection function using two-level searching scheme, thus the complicated and time-consuming operation of the deep packet inspection function does not hinder or flop the basic operations of the In-line mode system. From a proto-type NP-based NIDS implemented at a PC platform with an x86 processor running Linux, two Gigabit Ethernet ports, and 2.5Gbps Agere PayloadPlus(APP) NP solution, the experiment results show that our proposed scheme can reliably filter and meter the full traffic of two gigabit ports at the first level even though it can inspect the packet payload up to 320 Mbps in real-time at the second level, which can be compared to the performance of general-purpose processor based Inspection. However, the simulation results show that the deep packet searching is also possible up to 2Gbps in wire speed when we adopt 10Gbps APP solution.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.173-176
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• 2001

In this thesis, a new measurement system is implemented for depth data extraction based on the camera calibration of the known pattern. The relation between 3D world coordinate and 2D image coordinate is analyzed. A new camera calibration algorithm is established from the analysis and then, the internal variables and external variables of the CCD camera are obtained. Suppose that the measurement plane is horizontal plane, from the 2D plane equation and coordinate transformation equation the approximation values corresponding minimum values using Newton-Rabbson method is obtained and they are stored into the look-up table for real time processing . A slit laser light is projected onto the object, and a 2D image obtained on the x-z plane in the measurement system. A 3D shape image can be obtained as the 2D (x-z)images are continuously acquired, during the object is moving to the y direction. The 3D shape images are displayed on computer monitor by use of OpenGL software. In a measuremental result, we found that the resolution of pixels have $\pm$ 1% of error in depth data. It seems that the error components are due to the vibration of mechanic and optical system. We expect that the measurement system need some of mechanic stability and precision optical system in order to improve the system.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.16-23
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• 2019

Fine particulate matter known as PM 2.5 refers to the atmospheric particulate matter that has a diameter less than 2.5 micrometer identified as dangerous element for human health and its concentration can provide us a clear picture about air dust concentration. Humans stay indoor almost 90% of their life time and also there is no official indoor dust concentration data, so our study is focused on measuring the indoor air quality. Indoor dust data monitoring is very important in hospital environments beside that other places can also be considered for monitoring like classrooms, cements factories, computer server rooms, petrochemical storage etc. In this paper, visible light communication system is proposed by Manchester encoding technique for electromagnetic interference (EMI)-free indoor dust monitoring. Important indoor environment information like dust concentration is transferred by visible light channel in wide range. An average voltage-tracking technique is utilized for robust light detection to eliminate ambient light and low-frequency noise. The incoming light is recognized by a photo diode and are simultaneously processed by a receiver micro-controller. We can monitor indoor air quality in real-time and can take necessary action according to the result.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2874-2890
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• 1997

This paper describes a methodology for translation of concurrent software expressed in operating system (OS) calls to Ada. Concurrency is expressed in some legacy software by OS calls that perform concurrent process/task control. Examples considered in this paper are calls in programs in C to Unix and calls in programs in CMS-2 to the Executive Service Routines of ATES or SDEX-20 other software re/reverse engineering research has focused on translating the OS calls in a legacy software to calls to another OS. In this approach, the understanding of software has required knowledge of the underlying OS, which is usually very complicated and informally documented. The research in this paper has focused on translating the OS calls in a legacy software into the equivalent protocols using the Ada facilities. In translation to Ada, these calls are represented by Ada equivalent code that follow the scheme of a message-based kernel oriented architecture. To facilitate translation, it utilizes templates placed in library for data structures, tasks, procedures, and messages. This methodology is a new approach to modeling OS in Ada in software re/reverse engineering. There is no need of knowledge of the underlying OS for software understanding in this approach, since the dependency on the OS in the legacy software is removed. It is portable and interoperable on Ada run-time environments. This approach can handle the OS calls in different legacy software systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.755-758
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• 2014

In this paper, we propose effective Deblocking Filter hardware architecture for High Efficiency Video Coding encoder. we propose Deblocking Filter hardware architecture with less processing time, filter ordering for low area design, effective memory architecture and four-pipeline for a high performance HEVC(High Efficiency Video Coding) encoder. Proposed filter ordering can be used to reduce delay according to preprocessing. It can be used for realtime single-port SRAM read and write. it can be used in parallel processing by using two filters. Using 10 memory is effective for solving the hazard caused by a single-port SRAM. Also the proposed filter can be used in low-voltage design by using clock gating architecture in 4-pipeline. The proposed Deblocking Filter encoder architecture is designed by Verilog HDL, and implemented by 100k logic gates in TSMC $0.18{\mu}m$ process. At 150MHz, the proposed Deblocking Filter encoder can support 4K Ultra HD video encoding at 30fps, and can be operated at a maximum speed of 200MHz.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.39-55
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• 2024

This study aimed to examine research trends related to Natural Language Processing (NLP) in science education by analyzing 37 domestic and international documents that utilized NLP techniques in the field of science education from 2011 to September 2023. In particular, the study systematically analyzed the content, focusing on the main application areas of NLP techniques in science education, the role of teachers when utilizing NLP techniques, and a comparison of domestic and international perspectives. The analysis results are as follows: Firstly, it was confirmed that NLP techniques are significantly utilized in formative assessment, automatic scoring, literature review and classification, and pattern extraction in science education. Utilizing NLP in formative assessment allows for real-time analysis of students' learning processes and comprehension, reducing the burden on teachers' lessons and providing accurate, effective feedback to students. In automatic scoring, it contributes to the rapid and precise evaluation of students' responses. In literature review and classification using NLP, it helps to effectively analyze the topics and trends of research related to science education and student reports. It also helps to set future research directions. Utilizing NLP techniques in pattern extraction allows for effective analysis of commonalities or patterns in students' thoughts and responses. Secondly, the introduction of NLP techniques in science education has expanded the role of teachers from mere transmitters of knowledge to leaders who support and facilitate students' learning, requiring teachers to continuously develop their expertise. Thirdly, as domestic research on NLP is focused on literature review and classification, it is necessary to create an environment conducive to the easy collection of text data to diversify NLP research in Korea. Based on these analysis results, the study discussed ways to utilize NLP techniques in science education.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.1-8
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• 2005

In this paper, we propose a two-stage head tracking algorithm adequate for real-time active camera system having pan-tilt-zoom functions. In the color convergence stage, we first assume that the shape of a head is an ellipse and its model color histogram is acquired in advance. Then, the min-shift method is applied to roughly estimate a target position by examining the histogram similarity of the model and a candidate ellipse. To reflect the temporal change of object color and enhance the reliability of mean-shift based tracking, the target histogram obtained in the previous frame is considered to update the model histogram. In the updating process, to alleviate error-accumulation due to outliers in the target ellipse of the previous frame, the target histogram in the previous frame is obtained within an ellipse adaptively shrunken on the basis of the model histogram. In addition, to enhance tracking reliability further, we set the initial position closer to the true position by compensating the global motion, which is rapidly estimated on the basis of two 1-D projection datasets. In the subsequent stage, we refine the position and size of the ellipse obtained in the first stage by using shape information. Here, we define a robust shape-similarity function based on the gradient direction. Extensive experimental results proved that the proposed algorithm performs head hacking well, even when a person moves fast, the head size changes drastically, or the background has many clusters and distracting colors. Also, the propose algorithm can perform tracking with the processing speed of about 30 fps on a standard PC.