• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.51-61
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• 2022

Gas separation via hollow fibre membrane modules (HFMM) is deemed to be a promising technology for natural gas sweetening, particularly for lowering the level of carbon dioxide (CO₂) in natural gas, which can cause various problems during transportation and process operation. Separation performance via HFMM is affected by membrane properties, module specifications and operating conditions. In this study, a mathematical model for HFMM is developed, which can be used to assess the effects of the aforementioned variables on separation performance. Appropriate boundary conditions are imposed to resolve steady-state values of permeate variables and incorporated in the model equations via an iterative numerical procedure. The developed model is proven to be reliable via model validation against experimental data in the literature. Also, the model is capable of capturing axial variations of process variables as well as predicting key performance indicators. It can be extended to simulate a large-scale plant and identify an optimal process design and operating conditions for improved separation efficiency and reduced cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.653-669
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• 2024

Dynamic scene deblurring is a complex computer vision problem owing to its difficulty to model mathematically. In this paper, we present a novel approach for image deblurring with the help of the sharp reference image, which utilizes the reference image for high-quality and high-frequency detail results. To better utilize the clear reference image, we develop an encoder-decoder network and two novel modules are designed to guide the network for better image restoration. The proposed Reference Extraction and Aggregation Module can effectively establish the correspondence between blurry image and reference image and explore the most relevant features for better blur removal and the proposed Spatial Feature Fusion Module enables the encoder to perceive blur information at different spatial scales. In the final, the multi-scale feature maps from the encoder and cascaded Reference Extraction and Aggregation Modules are integrated into the decoder for a global fusion and representation. Extensive quantitative and qualitative experimental results from the different benchmarks show the effectiveness of our proposed method.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.224-237
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• 2015

In this paper, we introduce the fast decoding method with the SIMD (Single Instruction Multiple Data) instructions for HEVC RExt (High Efficiency Video Coding Range Extensions). Several tools of HEVC RExt such as intra prediction, interpolation, inverse-quantization, inverse-transform, and clipping modules can be classified as the proper modules for applying the SIMD instructions. In consideration of bit-depth increasement of RExt, intra prediction, interpolation, inverse-quantization, inverse-transform, and clipping modules are accelerated by SSE (Streaming SIMD Extension) instructions. In addition, we propose effective implementations for interpolation filter, inverse-quantization, and clipping modules by utilizing a set of AVX2 (Advanced Vector eXtension 2) instructions that can use 256 bits register. The evaluation of the proposed methods were performed on the private HEVC RExt decoder developed based on HM 16.0. The experimental results show that the developed RExt decoder reduces 12% average decoding time, compared with the conventional sequential method.

• Transactions on Electrical and Electronic Materials
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• v.15 no.2
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• pp.103-111
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• 2014

This paper investigates the output powers of PV modules by predicting three unknown parameters: reverse saturation current, and series and shunt resistances. A theoretical model using the non-uniform physical parameters of solar cells, including the temperature coefficients, voltage, current, series and shunt resistances, is proposed to obtain the I-V characteristics of PV modules. The solar irradiation effect is included in the model to improve the accuracy of the output power. Analytical and Newton methods are implemented in MATLAB to calculate a module output. Experimental data of the non-uniform solar cells for both serial and parallel connections are used to extend the implementation of the model based on the I-V equation of the equivalent circuit of the cells and to extend the application of the model to m by n modules configuration. Moreover, the theoretical model incorporates, for the first time, the variations of series and shunt resistances, reverse saturation current and irradiation for easy implementation in real power generation. Finally, this model can be useful in predicting the degradation of a PV system because of evaluating the variations of series and shunt resistances, which are critical in the reliability analysis of PV power generation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2231-2236
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• 2005

CP (Cellular Phone) is currently one of the most attractive technologies and RT (Robot Technology) is also considered as one of the most promising next generation technology. We present a new technological concept named RCP (Robotic Cellular Phone), which combines RT and CP. RCP consists of 3 sub-modules, $RCP^{Mobility}$, $RCP^{Interaction}$, and $RCP^{Integration}$. $RCP^{Interaction}$ is the main focus of this paper. It is an interactive emotion system which provides CP with multi-emotional signal receiving functionalities. $RCP^{Interaction}$ is linked with communication functions of CP in order to interface between CP and user through a variety of emotional models. It is divided into a tactile, an olfactory and a visual mode. The tactile signal receiving module is designed by patterns and beat frequencies which are made by mechanical-vibration conversion of the musical melody, rhythm and harmony. The olfactory signal receiving module is designed by switching control of perfume-injection nozzles which are able to give the signal receiving to the CP-called user through a special kind of smell according to the CP-calling user. The visual signal receiving module is made by motion control of DC-motored wheel-based system which can inform the CP-called user of the signal receiving through a desired motion according to the CP-calling user. In this paper, a prototype system is developed for multi-emotional signal receiving modes of CP. We describe an overall structure of the system and provide experimental results of the functional modules.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.139-147
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• 2014

The ballast water may be discharged into another sea area with marine organisms, it caused problems to disturb the marine ecosystem. So, in order to remove these environmental risk factors, the IMO has mandated the installation of BWTS to the all ships. Our monitoring system diagnose and predict a failure of BWTS by analyzing the sensor information of BWTS collected from which the ships scattered in the ocean of several. This paper presents the design and implementation of communication modules for BWTS remote monitoring considering the satellite communication charge fee. In the our study, we implemented the safety and cost-saving communication modules by LabVIEW program. The collected sensor informations is encrypted and compressed by LabVIEW modules running on RIO. Then they will be transfer to the land server and will be decrypt to enable monitoring in the land server. For the verification, we build the test modules which can verify from collecting the sensor data to consuming them in the monitoring server. We carried out 20 times for the data pattern in all of case. So, we verified the excellent functionality and reliability through the experimental result.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.33-40
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• 2006

We present a new technological concept named RCP (Robotic Cellular Phone), which combines RT and CP. That is an ubiquitous robot. RCP consists of 3 sub-modules, RCP Mobility, RCP interaction, and RCP Integration. RCP Interaction is the main focus of this paper. It is an interactive emotion system which provides CP with multi-emotional signal receiving functionalities. RCP Interaction is linked with communication functions of CP in order to interface between CP and user through a variety of emotional models. It is divided into a tactile, an olfactory and a visual mode. The tactile signal receiving module is designed by patterns and beat frequencies which are made by mechanical-vibration conversion of the musical melody, rhythm and harmony. The olfactory signal receiving module is designed by switching control of perfume-injection nozzles which are able to give the signal receiving to the CP-called user through a special kind of smell according to the CP-calling user. The visual signal receiving module is made by motion control of DC-motored wheel-based system which can inform the CP-called user of the signal receiving through a desired motion according to the CP-calling user. In this paper, a prototype system is developed far multi-emotional signal receiving modes of CP. We describe an overall structure of the system and provide experimental results of the functional modules.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.93-100
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• 2021

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

• Smart Structures and Systems
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• v.32 no.3
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.