• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.3
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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.

• Journal of Ocean Engineering and Technology
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• v.33 no.2
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.

• Journal of Ocean Engineering and Technology
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• v.18 no.3
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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.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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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.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.53-63
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• 2004

Recurrent-Cascade-Correlation(RCC) is a supervised teaming algorithm that automatically determines the size and topology of the network. RCC adds new hidden neurons one by one and creates a multi-layer structure in which each hidden layer has only one neuron. By second order RCC, new hidden neurons are added to only one hidden layer. These created neurons are not connected to each other. We present a generalization of the RCC Architecture by combining the standard RCC Architecture and the second order RCC Architecture. Whenever a hidden neuron has to be added, the new RCC teaming algorithm automatically determines whether the network topology grows vertically or horizontally. This new algorithm using sigmoid, tanh and new activation functions was tested with the morse-benchmark-problem. Therefore we recognized that the number of hidden neurons was decreased by the experiments of the RCC network generalization which used the activation functions.

• Journal of the Korean Institute of Landscape Architecture
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• v.31 no.1
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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.

• Journal of IKEEE
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• v.21 no.4
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• pp.388-396
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• 2017

Since there are many variables such as various poses, illuminations and occlusions in a face detection problem, a high performance detection system is required. Although CNN is excellent in image classification, CNN operatioin requires high-performance hardware resources. But low cost low power environments are essential for small and mobile systems. So in this paper, the CPU-FPGA integrated system is designed based on 3-stage cascade CNN architecture using small size FPGA. Adaptive Region of Interest (ROI) is applied to reduce the number of CNN operations using face information of the previous frame. We use a Field Programmable Gate Array(FPGA) to accelerate the CNN computations. The accelerator reads multiple featuremap at once on the FPGA and performs a Multiply-Accumulate (MAC) operation in parallel for convolution operation. The system is implemented on Altera Cyclone V FPGA in which ARM Cortex A-9 and on-chip SRAM are embedded. The system runs at 30FPS with HD resolution input images. The CPU-FPGA integrated system showed 8.5 times of the power efficiency compared to systems using CPU only.

• Proceedings of the KIEE Conference
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• 2009.07a
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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.