• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2882-2903
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• 2022

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on. This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement. We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse. Also we proposed a hybrid loss function based on L₁ error and L_∞ error to achieve better L_∞ error performance. The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling. Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L_∞ performance is improved by 3.64%. It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network. The L_∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function. That is to say, the L_∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L_∞ error sensitive tasks.

• 한국정보전자통신기술학회논문지
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• 제11권3호
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• pp.270-276
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• 2018

This paper presents a low area 256-point pipelined FFT architecture, especially for IEEE 802.16a WiMAX systems. Radix-24 algorithm and single-path delay feedback (SDF) architecture are adopted in the design to reduce the complexity of twiddle factor multiplication. A new cascade canonical signed digit (CSD) complex multipliers are proposed for twiddle factor multiplication, which has lower area and less power consumption than conventional complex multipliers composed of 4 multipliers and 2 adders. Also, the proposed cascade CSD multipliers can remove look-up table for storing coefficient of twiddle factors. In hardware implementation with Cyclone 10LP FPGA, it is shown that the proposed FFT design method achieves about 62% reduction in gate count and 64% memory reduction compared with the previous schemes.

• 한국해양공학회지
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• 제33권2호
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• pp.139-145
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• 2019

It is important that an MGO Chiller System, which is one of the sulfur oxide emission control technologies, is designed to meet the fuel temperature requirements, even with sudden engine load changes. Three different control algorithms (PI, Cascade, and MPC) were applied to an indirect MGO chiller system to compare and analyze the outlet temperature dynamic characteristics of the system through a case study. The results showed that the MPC control method had the best temperature following characteristics in the case study, and the temperature deviation range was reduced by approximately 5% compared to the PI control method.

• 한국전자통신학회논문지
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• 제19권1호
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• pp.301-307
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• 2024

본 연구는 다양한 스케일 조건에서 열화상 이미지를 향상시키기 위한 새로운 캐스케이드 융합 구조를 제안한다. 특정 스케일에 맞춰 설계된 방법들은 다중 스케일에서 열화상 이미지 처리에 한계가 있었다. 이를 극복하기 위해 본 논문에서는 다중 스케일 표현을 활용하는 캐스케이드 특징 융합 기법에 기반한 통합 프레임워크를 제시한다. 서로 다른 스케일의 신뢰도 맵을 순차적으로 융합함으로써 스케일에 제약받지 않는 학습이 가능해진다. 제안된 구조는 상호 스케일 의존성을 강화하기 위해 엔드 투 엔드 방식으로 훈련된 합성곱 신경망으로 구성되어 있다. 실험 결과, 제안된 방법은 기존의 다중 스케일 열화상 이미지 향상 방법들보다 우수한 성능을 보인다는 것을 확인할 수 있었다. 또한, 실험 데이터셋에 대한 성능 분석 결과 이미지 품질 지표가 일관되게 개선되었으며, 이는 캐스케이드 융합 설계가 스케일 간 견고한 일반화를 가능하게 하고 교차 스케일 표현 학습을 더 효율적으로 수행하는 데 기여하는 것을 보여준다.

• 한국해양공학회지
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• 제18권3호
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• pp.26-31
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• 2004

This paper presents a process planning system that will generate and verify assembly sequences of block assemblies. It consists of a CAD interface system and an assembly sequence planning system. In developing this system, we used an open architecture CAD kernel for the CAD interface system, for visualizing the CAD model and the assembly sequences, and an expert system shell for the assembly sequence planning system. This paper also proposes a framework for the integration of all the steps required to automate the procedures, from design to production. The process planning system is demonstrated by a simple example.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2003년도 춘계학술대회 논문집
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• pp.17-21
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• 2003

This paper presents a CAD interface system that imports CAD model data and exports input information to a CAPP(Compute Aided Process Planning) system to generate a sequence for block assembly operations. In developing this system we use an open architecture CAD kernel, OpenCASCASE. The functions of the system developed are visualization of the product, definition of relations between parts, and generation of relation graph and input file for CAPP. The functions are demonstrated with a simple example.

• 지능정보연구
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• 제10권2호
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• pp.53-63
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• 2004

순환 케스케이드 코릴레이션(Recurrent Cascade Correlation(RCC))은 감독에 의하여 학습하는 알고리즘이고 네트워크의 크기와 형태는 자동으로 이루어진다. RCC는 새로운 은닉뉴런들이 한 충에 하나씩 순서대로 네트워크에 삽입되기 때문에 다층구조를 형성하고 2계(Second Order) RCC는 새로운 은닉뉴런들이 한 층에만 순서대로 생성되어 나열되므로 2층 구조를 형성한다. 따라서 이러한 은닉뉴런들끼리는 서로 연결하지 않는다. 이 논문에서는 RCC와 2계 RCC의 조합을 통한 RCC 네트워크의 일반화를 소개한다. 새로운 RCC 알고리즘은 은닉뉴런이 네트워크에 첨가될 때마다 네트워크가 수직성장 또는 수평성장을 해야 하는지를 스스로 결정한다. 또한 뉴런의 활성화를 위한 새로운 활성화함수를 소개하고 기존의 sigmoid, tanh 함수와 함께 사용하여 모스 벤치마크 문제에 관하여 실험하였다. 이러한 활성화 함수들을 사용한 RCC 네트워크의 일반화 실험에서 은닉뉴런의 숫자가 감소하였음을 알 수 있다.

• 한국조경학회지
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• 제31권1호
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• pp.23-33
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• 2003

The purpose of this study is to analyse six different factors of water space that influence interior landscape architecture of officers and commercial spaces. Six different factors of water space that influence interior landscape architecture are the height of the water space, the design form, the blending with the location environment, location, general satisfaction with the water space, and general satisfaction with whole space. The results of this study are as follows: 1. Water space of commercial space was bigger than office space. The satisfaction is the highest in 0∼0.6M(B2) of office space and 3.5M(C6) of commercial space. The cascade form in the office space and large water volume design in commercial areas, which supplies abundant sound and water volume, were the highest. 2. In the analysis of preferences, the design form and the general design of the office space influences satisfaction levels. The height of the water space, and a combination of other factors influence general satisfaction. Water space influences commercial areas in higher degrees. 3. The items were most desired or satisfactory for interior landscape architecture were a cascade, water fall, and small water fountain. 4. A place introduced with water space had higher satisfaction levels. Water space produces higher than general satisfaction and indicates water, space alone cannot make general interior space satisfactory, although it can make interior landscape architecture space satisfactory. 5. There is more general satisfaction in commercial space than in office space. 6. The design of water space influences overall satisfaction: a rest area of office space needs an impressive and aggressive approach, while the office space should harmonize with its surroundings, as a commercial space.

• 전기전자학회논문지
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• 제21권4호
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• pp.388-396
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• 2017

얼굴 검출에는 다양한 포즈, 빛의 세기, 얼굴이 가려지는 현상 등의 많은 변수가 존재하므로, 높은 성능의 검출 시스템이 요구된다. 이에 영상 분류에 뛰어난 Convolutional Neural Network (CNN)이 적절하나, CNN의 많은 연산은 고성능 하드웨어 자원을 필요로한다. 그러나 얼굴 검출을 위한 소형, 모바일 시스템의 개발에는 저가의 저전력 환경이 필수적이고, 이를 위해 본 논문에서는 소형의 FPGA를 타겟으로, 얼굴 검출에 적절한 3-Stage Cascade CNN 구조를 기반으로하는 CPU-FPGA 통합 시스템을 설계 구현한다. 가속을 위해 알고리즘 단계에서 Adaptive Region of Interest (ROI)를 적용했으며, Adaptive ROI는 이전 프레임에 검출된 얼굴 영역 정보를 활용하여 CNN이 동작해야 할 횟수를 줄인다. CNN 연산 자체를 가속하기 위해서는 FPGA Accelerator를 이용한다. 가속기는 Bottleneck에 해당하는 Convolution 연산의 가속을 위해 FPGA 상에 다수의 FeatureMap을 한번에 읽어오고, Multiply-Accumulate (MAC) 연산을 병렬로 수행한다. 본 시스템은 Terasic사의 DE1-SoC 보드에서 ARM Cortex A-9와 Cyclone V FPGA를 이용하여 구현되었으며, HD ($1280{\times}720$)급 입력영상에 대해 30FPS로 실시간 동작하였다. CPU-FPGA 통합 시스템은 CPU만을 이용한 시스템 대비 8.5배의 전력 효율성을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.1098_1099
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• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.