• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 하계종합학술대회 논문집(2)
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• pp.399-402
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• 2004

This paper presents a system-on-chip prototype implementing a full integration for a portable digital audio system. The chip is composed of a audio processor block to implements audio decoding and voice compression or decompression software, a system control block including 8-bit MCU core and Memory Management Unit (MMU) a low power 16-bit ${\Sigma}{\Delta}$ CODEC, two DC-to-BC converter, and a flash memory controller. In order to support other audio algorithms except Mask ROM type's fixed codes, a novel 16-bit fixed-point DSP core with the program-download architecture is proposed. Funker, an efficient power management technique such as task-based clock management is implemented to reduce power consumption for portable application. The proposed chip has been fabricated with a 4 metal 0.25um CMOS technology and the chip area is about 7.1 mm ${\times}$ 7.1mm with 100mW power dissipation at 2.5V power supply.

• JSTS:Journal of Semiconductor Technology and Science
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• 제17권3호
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• pp.333-340
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• 2017

In this paper, we describe HW/SW co-optimizations for reconfigurable application specific instruction-set processors (ASIPs). Based on our previous very long instruction word (VLIW) ASIP, the proposed framework realizes various forward error-correction (FEC) algorithms for wireless communication systems. In order to enhance the energy efficiency, we newly introduce several design methodologies including high-radix algorithms, task-level out-of-order executions, and intensive resource allocations with loop-level rescheduling. The case study on the radix-4 turbo decoding shows that the proposed techniques improve the energy efficiency by 3.7 times compared to the previous architecture.

• Smart Structures and Systems
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• 제23권6호
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• pp.553-564
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• 2019

Modeling of magnetorheological (MR) dampers for cable vibration control to facilitate the design of even more effective and economical systems is still a challenging task. In this study, a parameter-adaptive three-element model is first established for a full-scale MR damper based on laboratory tests. The parameters of the model are represented by a set of empirical formulae in terms of displacement amplitude, voltage input, and excitation frequency. The model is then incorporated into the governing equation of cable-damper system for investigation of open-loop vibration control of stay cables in a cable-stayed bridge. The concept of optimal voltage/current input achieving the maximum damping for the system is put forward and verified. Multi-mode suboptimal and Single-mode optimal open-loop control method is then developed. Important conclusions are drawn on application issues and unique characteristics of open-loop cable vibration control using MR dampers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.21-22
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• 2021

Early academic warning is considered as an inherent problem in education data mining. Early and timely concern and guidance can save a student's university career. It is widely assumed as a multi-class classification system in view of machine learning. Therefore, An accurate and precise methodical solution is a complicated task to accomplish. For this issue, we present a hybrid model employing rough set theory with a back-propagation neural network to ameliorate the predictive capability of the system with an illustrative example. The experimental results show that it is an effective early academic warning model with an escalating improvement in predictive accuracy.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.124-128
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• 2024

Cloud computing is a technology for delivering information in which resources are retrieved from the internet through a web-based tools and applications, rather than a direct connection with the server. It is a new emerging computing based technology in which any individual or organization can remotely store or access the information. The structure of cloud computing allows to store and access various information as long as an electronic device has access to the web. Even though various merits are provided by the cloud from the cloud provides to cloud users, it suffers from various flaws in security. Due to these flaws, data integrity and confidentiality has become a challenging task for both the storage and retrieval process. This paper proposes a novel approach for data protection by an improved auditing based methodology in cloud computing especially in the process of cloud storage. The proposed methodology is proved to be more efficient in auditing the threats while storing data in the cloud computing architecture.

• 건축역사연구
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• 제26권1호
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• pp.7-16
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• 2017

One of the essential characteristics of Lewis Mumford's architectural criticism is the coherent emphasis on symbolism. Such emphasis stems from his understanding of city and humanity in the context of civilization: first, that the architecture symbolizes institutions of urban civilization; second, that the technical aspect of human nature should be balanced with its artistic aspects. Mumford believed that each architectural type requires an appropriate symbolic expression corresponding to its purpose and that a new symbolic expression, in a new cultural context, should replace the conventional expression. He took symbolism for an intuitional expression, and read multi-layered meanings of architecture: 'practical function' by way of rational reason and 'symbolic function' by way of intuition. He pursued a balance between practicality and beauty to rectify the situation of modern civilization, in which symbolism, the expression of its intuitional aspect, is in crisis. Ultimately, for Mumford, the essential task of architectural critic is of the interpretation of symbolism, aiming at the correspondence and communication between the architect(artist)'s intuition and critic(interpreter)'s via the media of symbol. The critic can play some privileged role of interpreting even symbols unintended by the architect. The ideal architectural critic, after all, would be the one who is able to understand the city, technology and human beings in the perspective of civilization and to interpret the architect's artistic expression in its highest form through intuition. Mumford established himself as such a critic and evaluated the status of aesthetic accomplishment of his contemporary architecture and technological civilization, giving emphasis on the artistic practice in architecture as a solution.

• 한국산학기술학회논문지
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• 제12권4호
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• pp.1786-1795
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• 2011

사람이 생활하는 환경에서 일반적인 휠베이스 이동(Mobility) 방식의 로봇은 장애물에 둘러싸여 로봇의 움직임에 있어 자유로운 주행 제약을 받게 된다. 장애물을 신속하게 회피하려면 회전과정 없이 단순히 좌우 이동만 하면 되는 홀로노믹(Holonomic) 시스템 특성의 이동로봇이 필요하다. 본 논문에서는 세 개의 옴니휠(Omni-Wheels)을 사용한 홀로노믹 이동로봇의 추적제어기를 개발한다. 옴니휠을 이용한 이동로봇은 시스템 파라미터의 불확실성(uncertainty)으로 인하여 선형 제어기로는 추적제어가 매우 어려운 상황이다. 그러므로 강인성이 탁월한 퍼지 제어기를 이용한 퍼지 적응 제어 기법을 설계하여 옴니휠 이동 로봇의 추적제어(tracking control) 성능을 높인다. 본 논문에서 제어 대상 시스템의 매개 변수의 불확실성에 강인한 퍼지 제어기를 병렬로 설계하고 시스템 인식(system identification)을 이용하여 대상 시스템이 특성 변화에 적절히 대처할 수 있는 적합한 퍼지 제어기를 선택한 후 피드백 제어를 실행하는 퍼지 다층 제어기(Fuzzy Multi-Layered Controller) 시스템을 이용한 적응 제어기법을 제시한다. 고전 적응 제어기와 기존 퍼지 적응 제어기의 문제점을 극복한 퍼지 적응 제어기를 도입하여 강인 제어기를 병렬로 설계하고 시스템 인식을 이용하여 대상 시스템의 특성 변화에 적절히 대처할 수 있는 적합한 퍼지 제어기를 선택한 후 피드백 제어를 실행하는 퍼지 다층 제어기(FMLC)를 제시한다.

• 한국인터넷방송통신학회논문지
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• 제19권6호
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• pp.227-233
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• 2019

최근 딥러닝 기술의 발달과 함께 신경 네트워크는 컴퓨터 비전에서도 성공을 거두고 있다. 컨볼루션 신경망은 단순한 영상 분류 작업뿐만 아니라 객체 분할 및 검출 등 난이도가 높은 작업에서도 탁월한 성능을 보였다. 그러나 그러한 많은 심층 학습 모델은 지도학습에 기초하고 있으며, 이는 이미지 라벨보다 주석 라벨이 더 많이 필요하다. 특히 semantic segmentation 모델은 훈련을 위해 픽셀 수준의 주석을 필요로 하는데, 이는 매우 중요하다. 이 논문은 이러한 문제를 해결하기 위한 네트워크 훈련을 위해 영상 수준 라벨만 필요한 약지도 semantic segmentation 방법을 제안한다. 기존의 약지도학습 방법은 대상의 특정 영역만 탐지하는 데 한계가 있다. 반면에, 본 논문에서는 우리의 모델이 사물의 더 다른 부분을 인식하도 multi-classifier 심층 학습 아키텍처를 사용한다. 제안된 방법은 VOC 2012 검증 데이터 세트를 사용하여 평가한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.