• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.77-85
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• 2017

Thin crystalline silicon (C-Si) solar cell is expected to be a low price energy source by decreasing the consumption of Si. However, thin c-Si solar cell entails the bowing and crack issues in high temperature manufacturing process. Thus, the conventional tabbing process, based on high temperature soldering (> $250^{\circ}C$), has difficulties for applying to thin c-Si solar cell modules. In this paper, a conductive paste (CP) based interconnection process has been proposed to fabricate thin c-Si solar cell modules with high production yield, instead of existing soldering materials. To optimize the process condition for CP based interconnection, we compared the performance and stability of modules fabricated under various lamination temperature (120, 150, and $175^{\circ}C$). The power from CP based module is similar to that with conventional tabbing process, as modules are fabricated. However, the output of CP based module laminated at $120^{\circ}C$ decreases significantly (14.1% for Damp heat and 6.1% for thermal cycle) in harsh condition, while the output drops only in 3% in the samples process at $150^{\circ}C$, $175^{\circ}C$. The peel test indicates that the unstable performance of sample laminated at $120^{\circ}C$ is attributed to weak adhesion strength (1.7 N) between cell and ribbon compared to other cases (2.7 N). As a result, optimized lamination temperature for CP based module process is $150^{\circ}C$, considering stability and energy consumption during the fabrication.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.48-59
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• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.37-44
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• 2002

Automatic text classification is a task of assigning predefined categories to free text documents. Its importance is increased to organize and manage a huge amount of text data. There have been some researches on automatic text classification based on machine learning techniques. While most of them was focused on proposal of a new machine learning methods and cross evaluation between other systems, a through evaluation or optimization of a method has been rarely been done. In this paper, we propose an improving method of kNN-based Korean text classification system using heuristic informations about decision function, the number of nearest neighbor, and feature selection method. Experimental results showed that the system with similarity-weighted decision function, global method in considering neighbors, and DF/ICF feature selection was more accurate than simple kNN-based classifier. Also, we found out that the performance of the local method with well chosen k value was as high as that of the global method with much computational costs.

• Journal of Fashion Business
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• v.20 no.3
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• pp.17-29
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• 2016

To develop baseball catcher leg guards, 3-dimensional (3D) methodologies, which are 3D human body data, reverse engineering, modeling, and printing, optimized guard design for representative positions. Optimization was based on analysis of 3D body surface data and subjective evaluation using 3D printing products. Reverse engineering was used for analysis and modeling based on data in three postures: standing, $90^{\circ}$ knee flexion, and $120^{\circ}$ knee flexion. During knee flexion, vertical skin length increased, with the thigh and knee larger in anterior area compared to the horizontal dimension. Moreover, $120^{\circ}$ knee flexion posture had a high radius of curvature in knee movement. Therefore, guard designs were based on increasing rates of skin deformation and numerical values of radius of curvature. Guards were designed with 3-part zoning at the thigh, knee, and shin. Guards 1 and 2 had thigh and knee boundaries allowing vertical skin length deformation because the shape of thigh and knee significantly affects to its performance. Guard 2 was designed with a narrower thigh and wider knee area than guard 1. The guards were manufactured as full-scale products on a 3D printer. Both guards fit better in sitting than standing position, and guard 2 received better evaluations than guard 1. Additional modifications were made and an optimized version (guard 3) was tested. Guard 3 showed the best fit. A design approach based on 3D data effectively determines best fitting leg guards, and 3D printing technology can customize guard design through immediate feedback from a customer.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.972-980
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• 2010

In this study, we introduce the design methodology of an optimized type-2 fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. The type-2 fuzzy cascade controller scheme consists of the outer controller and the inner controller as two cascaded fuzzy controllers. In type-2 fuzzy logic controller(FLC) as the expanded type of type-1 fuzzy logic controller(FLC), we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. The control parameters(scaling factors) of each fuzzy controller using HFCGA which is a kind of parallel genetic algorithms(PGAs). HFCGA helps alleviate the premature convergence being generated in conventional genetic algorithms(GAs). We estimated controller characteristic parameters of optimized type-2 fuzzy cascade controller applied ball & beam system such as maximum overshoot, delay time, rise time, settling time and steady-state error. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.123-132
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• 2011

Recently, DVC (Distributed Video Coding) is receiving a lot of attention as one of the low complexity video encoding techniques suitable for various applications with computation-limited and/or power-limited environment, and is being actively studied for improving the coding efficiency. This paper proposes a rate-distortion based selective block encoding scheme. First, the motion information is obtained in the process of generating side information at decoder and received through the feedback channel, and then, based on this information, the proposed method performs a selective block encoding based on rate-distortion optimization. Experimental results show that the performance of the proposed scheme reaches up to 2.2 dB PSNR gain over the existing scheme. Moreover, it is shown that the complexity can be reduced by encoding parts of region considering rate-distortion cost.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7B
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• pp.745-759
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• 2011

In this paper, we propose the global handover mechanism that is able to provide the unlimited range of next-generation multimedia mobile services in an integrated environment. This mechanism consists of a multicast source based forwarding scheme and a global session management scheme. Global session management scheme provides LMA session information management, global mobility and route optimization. Multicast source based forwarding scheme delivers data between previously attached LMA and newly attached LMA without packet loss. In addition, this scheme removes the redundancy of buffered data. We present a performance evaluation and features analysis by the simulations using the ns-2. Global session management scheme has a less handover latency, propagation delay and signaling cost than the conventional methods. Multicast source based forwarding scheme can deliver buffer data without loss and it has less buffer size than conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.348-352
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• 2022

Hydrogen gas (H₂) which is odorless, colorless is attracting attention as a renewable energy source in varions applications but its leakage can lead to disastrous disasters, such as inflammable, explosive, and narcotic disasters at high concentrations. Therefore, it is necessary to develop H₂ gas sensor with high performance. In this paper, we confirmed that H₂ gas detection ability of SnO₂ based H₂ gas sensor along with thermal treatment effect of SnO₂. Proposed SnO₂ based H₂ gas sensor is fabricated by MEMS technologies such as photolithgraphy, sputtering and lift-off process, etc. Deposited SnO₂ thin films are thermally treated in various thermal treatement temperature in range of 500-900 ℃ and their H₂ gas detection ability is estimatied by measuring output current of H₂ gas sensor. Based on experimental results, fabricated H₂ gas sensor with SnO₂ thin film which is thermally treated at 700 ℃ has a superior H₂ gas detection ability, and it can be expected to utilize at the practical applications.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.