• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.66-76
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• 2009

The motion estimation in video encoding system occupies the biggest part. So, we require the motion estimator with efficient structure for real-time operation. And for motion estimator's implementation, it is desired to design hardware module of an exclusive use that perform the encoding process at high speed. This paper proposes motion estimation detection block(MED), 41 SADs(Sum of Absolute Difference) calculation block, minimum SAD calculation and motion vector generation block based on parallel processing. The parallel processing can reduce effectively the amount of the operation. The minimum SAD calculation and MED block uses the pre-computation technique for reducing switching activity of the input signal. It results in high-speed operation. The MED and 41 SADs calculation blocks are composed of adder tree which causes the problem of critical path. So, the structure of adder tree has changed the most commonly used ripple carry adder(RCA) with carry skip adder(CSA). It enables adder tree to operate at high speed. In addition, as we enabled to easily control key variables such as control signal of search range from the outside, the efficiency of hardware structure increased. Simulation and FPGA verification results show that the delay of MED block generating the critical path at the motion estimator is reduced about 19.89% than the conventional strukcture.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.309-311
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• 2014

Despite increasing demands for big data application based on the analysis of scattered unstructured data, few relevant studies have been reported. Accordingly, the present study suggests a technique enabling a sentence-based semantic analysis by extracting objects from collected web information and automatically analyzing the relationships between such objects with collective intelligence and language processing technology. To be specific, collected information is stored in DBMS in a structured form, and then morpheme and feature information is analyzed. Obtained morphemes are classified into objects of interest, marginal objects and objects of non-interest. Then, with an inter-object attribute recognition technique, the relationships between objects are analyzed in terms of the degree, scope and nature of such relationships. As a result, the analysis of relevance between the information was based on certain keywords and used an inter-object relationship extraction technique that can determine positivity and negativity. Also, the present study suggested a method to design a system fit for real-time large-capacity processing and applicable to high value-added services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.2
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• pp.21-34
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• 2002

In this paper we propos a new hardware architecture of modular exponentiation using a division chain method which has been proposed in (2). Modular exponentiation using the division chain is performed by receding an exponent E as a mixed form of multiplication and addition with divisors d=2 or $d=2^I +1$ and respective remainders r. This calculates the modular exponentiation in about $1.4log_2$E multiplications on average which is much less iterations than $2log_2$E of conventional Binary Method. We designed a linear systolic array multiplier with pipelining and used a horizontal projection on its data dependence graph. So, for k-bit key, two k-bit data frames can be inputted simultaneously and two modular multipliers, each consisting of k/2+3 PE(Processing Element)s, can operate in parallel to accomplish 100% throughput. We propose a new encoding scheme to represent divisors and remainders of the division chain to keep regularity of the data path. When it is synthesized to ASIC using Samsung 0.5 um CMOS standard cell library, the critical path delay is 4.24ns, and resulting performance is estimated to be abort 140 Kbps for a 1024-bit data frame at 200Mhz clock In decryption process, the speed can be enhanced to 560kbps by using CRT(Chinese Remainder Theorem). Futhermore, to satisfy real time requirements we can choose small public exponent E, such as 3,17 or $2^{16} +1$, in encryption and verification process. in which case the performance can reach 7.3Mbps.

• Smart Media Journal
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• v.11 no.3
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• pp.39-53
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• 2022

Cloud of Things (CoT) can provide IoT applications with unlimited storage functions and processing power supported by cloud services. However, in a centralized cloud of things, it can create a single point of failure that can lead to bottleneck problems, outages of the CoT network. In this paper, to solve the problem of centralized cloud of things and interoperate between different service domains, we propose an IoT service interoperability system using distributed fog computing and blockchain technology. Distributed fog is used to provide real-time data processing and services in fog systems located at a geographically close distance to IoT devices, and to enable service interoperability between each fog using smart contracts and distributed ledgers of the blockchain. The proposed system provides services within a region close to the distributed fog entrusted with the service from the cloud, and it is possible to access the services of other fogs without going through the cloud even between fogs. In addition, by sharing a service right token issuance information between the cloud and fog nodes using a blockchain network, the integrity of the token can be guaranteed and reliable service interoperability between fog nodes can be performed.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.103-110
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• 2023

With the spread of the Internet, anonymous communities emerged along with the activation of communities for communication between people, and many users are doing harm to others, such as posting aggressive posts and leaving comments using anonymity. In the past, administrators directly checked posts and comments, then deleted and blocked them, but as the number of community users increased, they reached a level that managers could not continue to monitor. Initially, word filtering techniques were used to prevent malicious writing from being posted in a form that could not post or comment if a specific word was included, but they avoided filtering in a bypassed form, such as using similar words. As a way to solve this problem, deep learning was used to monitor posts posted by users in real-time, but recently, the community uses words that can only be understood by the community or from a human perspective, not from a general Korean word. There are various types and forms of characters, making it difficult to learn everything in the artificial intelligence model. Therefore, in this paper, we proposes a preprocessing technique in which each character of a sentence is imaged using a CNN model that learns the consonants, vowel and spacing images of Korean word and converts characters that can only be understood from a human perspective into characters predicted by the CNN model. As a result of the experiment, it was confirmed that the performance of the LSTM, BiLSTM and CNN-BiLSTM models increased by 3.2%, 3.3%, and 4.88%, respectively, through the proposed preprocessing technique.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.63-70
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• 2018

Biomedical signal measurement technology using images has been developed, and researches on respiration signal measurement technology for maintaining life have been continuously carried out. The existing technology measured respiratory signals through a thermal imaging camera that measures heat emitted from a person's body. In addition, research was conducted to measure respiration rate by analyzing human chest movement in real time. However, the image processing using the infrared thermal image may be difficult to detect the respiratory organ due to the external environmental factors (temperature change, noise, etc.), and thus the accuracy of the measurement of the respiration rate is low.In this study, the images were acquired using visible light and infrared thermal camera to enhance the area of the respiratory tract. Then, based on the two images, features of the respiratory tract region are extracted through processes such as face recognition and image matching. The pattern of the respiratory signal is classified through the k-nearest neighbor classifier, which is one of the statistical classification methods. The respiration rate was calculated according to the characteristics of the classified patterns and the possibility of breathing rate measurement was verified by analyzing the measured respiration rate with the actual respiration rate.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1001-1006
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• 2023

Globally, rising energy costs and increased energy demand are important issues for the food processing and manufacturing industries, which consume significant amounts of energy throughout the supply chain. Accordingly, there is a need for the development of a real-time energy monitoring and analysis system that can optimize energy use. In this study, a food factory energy monitoring system was proposed based on IoT installed in a food factory, including monitoring of each facility, energy supply and usage monitoring for the heat treatment process, and search functions. The system is based on the IoT sensor of the food processing plant and consists of PLC, database server, OPC-UA server, UI server, API server, and CIMON's HMI. The proposed system builds big data for food factories and provides facility-specific monitoring through collection functions, as well as energy supply and usage monitoring and search service functions for the heat treatment process. This data collection-based energy monitoring system will serve as a guide for the development of a small and medium-sized factory energy monitoring and management system for energy savings. In the future, this system can be used to identify and analyze energy usage to create quantitative energy saving measures that optimize process work.

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.83-94
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• 2006

This paper describes ATM cell encipherment method using Rijndael Algorithm adopted as an AES(Advanced Encryption Standard) by NIST in 2001. ISO 9160 describes the requirement of physical layer data processing in encryption/decryption. For the description of ATM cell encipherment method, we implemented ATM data encipherment equipment which satisfies the requirements of ISO 9160, and verified the encipherment/decipherment processing at ATM STM-1 rate(155.52Mbps). The DES algorithm can process data in the block size of 64 bits and its key length is 64 bits, but the Rijndael algorithm can process data in the block size of 128 bits and the key length of 128, 192, or 256 bits selectively. So it is more flexible in high bit rate data processing and stronger in encription strength than DES. For tile real time encryption of high bit rate data stream. Rijndael algorithm was implemented in FPGA in this experiment. The boundary of serial UNI cell was detected by the CRC method, and in the case of user data cell the payload of 48 octets (384 bits) is converted in parallel and transferred to 3 Rijndael encipherment module in the block size of 128 bits individually. After completion of encryption, the header stored in buffer is attached to the enciphered payload and retransmitted in the format of cell. At the receiving end, the boundary of ceil is detected by the CRC method and the payload type is decided. n the payload type is the user data cell, the payload of the cell is transferred to the 3-Rijndael decryption module in the block sire of 128 bits for decryption of data. And in the case of maintenance cell, the payload is extracted without decryption processing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.9-20
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• 2011

Future warfare paradigm is changing to network-centric warfare and effects-based operations. In order to find first and strike the enemy in the battlefield, friendly unit requires real-time target acquisition, intelligence collection, accurate situation assessment, and timely decision. The rapid development in advanced sensor technology and wireless networks requires a significant change in operational concepts of the battlefield surveillance. In particular, the introduction of a battlefield surveillance sensor network system is a big challenge to the ground forces which have lack of automated information collection assets. Therefore this paper proposes an ontology-based context-aware framework for the battlefield surveillance sensor network system which is needed for early finding the enemy and visualizing the battlefield in the ground force operations. Compared with the performance of existing systems, the one of the proposed framework has shown highly positive results by applying the context systems evaluation method. The framework has also proven to be satisfactory by the structured evaluation method using device collaboration. Since the proposed ontology-based context-aware framework has a lot of advantages in terms of scalability and reusability, the ground force's reconnaissance and surveillance system can be widely applied to expand in the future. And, ontology-based model has some weak points such as ontology data size, processing time, and limitation of network bandwidth. However, these problems can be resolved by customizing properly to fit the mission and characteristics of the unit. Moreover, development of the next-generation communication infrastructure can expedite the intelligent surveillance and reconnaissance service and may be expected to contribute greatly to expanding the information capacity.