• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.22-28
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• 2008

In this paper, we propose a DCT coefficient domain watermarking scheme, which makes use of the sinusoidal patterns created by the watermark embedding. The embedded watermark can be detected in the spatial domain by computing the correlation. Also, the proposed algorithm can detect the spatial synchronization without additional sync bit embedding. Experimental results show that the proposed algorithm is robust to various StirMark attacks.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.113-116
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• 2004

No specific guidelines are for computing the shear strength of steel coupling beam connections embedded in the reinforced concrete shear wall. In this paper, a theoretical study of the strength of hybrid coupled shear wall connections is achieved. The bearing stress at failure in the concrete below the steel coupling beam section is related to the concrete compressive strength and the ratio of the width of the steel coupling beam section to the thickness of the hybrid coupled shear wall. To revise factor affecting shear transfer strength across connections between coupled shear walls and steel coupling beam, experimental studies are achieved. The main test variables were auxiliary details of stud bolts. In this studies, these proposed equations are shown to be in good agreement with the test results reported in the paper and with other test data in the literature.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.578-584
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• 2004

The CSVM(Current Support Vector Machine) that is a digital architecture performing all phases of recognition process including kernel computing, learning, and recall of SVM(Support Vector Machine) on a chip is proposed. Concurrent operation by parallel architecture of elements generates high speed and throughput. The classification problems of bio data having high dimension are solved fast and easily using the CSVM. Quadratic programming in original SVM learning algorithm is not suitable for hardware implementation, due to its complexity and large memory consumption. Hardware-friendly SVM learning algorithms, kernel adatron and kernel perceptron, are embedded on a chip. Experiments on fixed-point algorithm having quantization error are performed and their results are compared with floating-point algorithm. CSVM implemented on FPGA chip generates fast and accurate results on high dimensional cancer data.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.459-462
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• 2003

본 논문에서는 임베디드 자바 시스템을 위한 핵심 클래스 파일에서 상수풀 (constant pool) 의 각 항목들에 대해 통계를 내고 분석해 보았다. 분석 대상 클래스 파일들은 썬 마이크로 시스템사의 J2ME/CLDC 클래스 파일들과, RTJ Computing 사의 simpleRTJ 시스템의 클래스 파일들이다. 이들 파일들에 대한 분석 결과 임베디드 자바 시스템을 위한 핵심 클래스 파일에서 상수풀은 전체 파일 크기의 거의 절반에 해당되는 46%를 차지하고 있음을 알 수 있었다. 또한 상수풀에는 평균 44개의 상수들이 있으며, 이들 중 실제 바이트코드 실행에 사용되는 상수들은 단지 6퍼센트에 불과한 3개에 지나지 않았다. 나머지 78퍼센트의 상수들은 단지 형식 확인과 클래스 링크 목적으로만 사용되는 것들이었다. 이 결과는 실행 시간시 동적인 형식 확인과 클래스 렁크를 하지 않는 환경이라면 매우 큰 메모리 절감을 이룰 수 있음을 보여주고 있는 것이다. 본 연구의 결과는 클래스 파일이 ROM 등에 탑재되어 있는 임베디드 시스템 환경에 적용될 수 있다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.1 no.1
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• pp.35-53
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• 1997

This paper analyzes the time complexity of Genetic Algorithm based Task Scheduling (GATS) which is designed for the scheduling of parallel programs with diverse embedded parallelism types in a heterogeneous network systems. The analysis of time complexity is performed based on two representation methods (REIA, REIS) which are proposed in this paper to encode the scheduling information. And the heterogeneous network systems consist of a set of loosely coupled parallel and vector machines connected via a high-speed network. The objective of heterogeneous network computing is to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. Therefore, when scheduling in heterogeneous network systems, the matching of the parallelism characteristics between tasks and parallel machines should be carefully handled in order to obtain more speedup. This paper shows how the parallelism type matching affects the time complexity of GATS.

• Structural Engineering and Mechanics
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• v.70 no.1
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• pp.1-11
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• 2019

This study deals with a method based on the modified bivariate gamma function for reconstructions of dynamic behavior of delaminated composite plates subjected to impact loads. The proposed bivariate gamma function is associated with micro-genetic algorithms, which is capable of solving inverse problems to determine the stiffness reduction associated with delamination. From computing the unknown parameters, it is possible for the entire dynamic response data to develop a prediction model of the dynamic response through a regression analysis based on the measurement data. The validity of the proposed method was verified by comparing with results employing a higher-order finite element model. Parametric results revealed that the proposed method can reconstruct dynamic responses and the stiffness reduction of delaminated composite plates can be investigated for different measurements and loading locations.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.9-17
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• 2022

In this paper, we propose a secure smart home network model and a novel cryptographic protocol called the Smart Home Security Protocol (SHSP). Authentication, key distribution, and encryption functions are properly supported in order to make a smart home secure, and a residential gateway (RG) plays a central role in performing these functions. According to the characteristics of networks and attached devices, we classify smart homes into three different types of sub-networks and these networks are interconnected with one another by the RG. Depending on a sub-network, we use different types of secure schemes to reduce the burden of the process and the delay in devices while it provides proper security functions. The proposed secure smart home model is implemented and verified by using a variety of embedded system environments.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.12-25
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• 2022

In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct highresolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.113-124
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• 2022

News in the form of web data generates increasingly large amounts of information as unstructured text. The capability of understanding the meaning of news is limited to humans; thus, it causes information overload. This hinders the effective use of embedded knowledge in such texts. Therefore, Automatic Knowledge Extraction (AKE) has now become an integral part of Semantic web and Natural Language Processing (NLP). Although recent literature shows that AKE has progressed, the results are still behind the expectations. This study proposes a method to auto-extract surface knowledge from English news into a machine-interpretable semantic format (triple). The proposed technique was designed using the grammatical structure of the sentence, and 11 original rules were discovered. The initial experiment extracted triples from the Sri Lankan news corpus, of which 83.5% were meaningful. The experiment was extended to the British Broadcasting Corporation (BBC) news dataset to prove its generic nature. This demonstrated a higher meaningful triple extraction rate of 92.6%. These results were validated using the inter-rater agreement method, which guaranteed the high reliability.