• Progress in Medical Physics
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• v.29 no.4
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• pp.106-114
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• 2018

This study aimed to compare the performance of previous optimization algorithms against new a photon optimizer (PO) algorithm for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans for prostate cancer. Eighteen patients with prostate cancer were retrospectively selected and planned to receive 78 Gy in 39 fractions of the planning target volume (PTV). All plans for each patient optimized with the dose volume optimizer (DVO) and progressive resolution optimizer (PRO) algorithms for IMRT and VMAT were compared against plans optimized with the PO within Eclipse version 13.7. No interactive action was performed during optimization. Dosimetric and radiobiological indices for the PTV and organs at risk were analyzed. The monitor units (MU) per plan were recorded. Based on the plan quality for the target coverage, prostate IMRT and VMAT plans using the PO showed an improvement over DVO and PRO. In addition, the PO generally showed improvement in the tumor control probability for the PTV and normal tissue control probability for the rectum. From a technical perspective, the PO generated IMRT treatment plans with fewer MUs than DVO, whereas it produced slightly more MUs in the VMAT plan, compared with PRO. The PO showed over potentiality of DVO and PRO whenever available, although it led to more MUs in VMAT than PRO. Therefore, the PO has become the preferred choice for planning prostate IMRT and VMAT at our institution.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.467-473
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• 2007

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1247-1256
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• 2006

VM(Virtual Machine) can be considered as a software processor which interprets the abstract machine code. Also, it is considered as a conceptional computer that consists of logical system configuration. But, the execution speed of VM system is much slower than that of a real processor system. So, it is very important to optimize the code for virtual machine to enhance the execution time. In this paper, we designed and implemented the optimizer for the virtual(or abstract) machine code(VMC) which is actually SIL(Standard Intermediate Language) that is an intermediate code of EVM(Embedded Virtual Machine). The optimizer uses the pattern matching optimization techniques reflecting the characteristics of the VMC as well as adopting the existing optimization methodology. Also, we tried a benchmark test for the VMC optimizer and obtained reasonable results.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.184-185
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• 2019

PV 계통연계형 시스템은 N대의 PV 스트링 Power optimizer와 1대의 인버터로 구성되며 초기 운전 시 인버터 보조전원에 전력을 공급하기 위한 Power optimizer의 DC링크 전압제어 운전 모드가 요구된다. 본 논문에서는 DC링크의 전압제어가 가능한 스트링 Power Optimizer의 start-up 제어기법을 제안한다. 제안하는 제어기법은 추가회로 없이 초기구동 시에 DC-Link의 전압을 서서히 원하는 전압으로 제어할 수 있는 장점을 갖는다. 또한 제안하는 start-up 시퀀스로 제안하는 제어기법을 검증하기 위한 6.25kW 시작품의 시험결과로 타당성을 검증하였다.

• Proceedings of the KIPE Conference
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• 2011.11a
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• pp.277-278
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• 2011

본 논문은 태양광발전의 주변 환경 변화에 종속적인 태양광 모듈 출력을 개선하기 위해 사용되는 두 개의 다른 형태의 태양광발전 시스템용 DC Optimizer에 대해 기술한다. 일사량 가변 및 외부 음영 조건 발생 시 서로 다른 두 유형의 DC Optimizer 동작 특성을 비교 분석하고 이를 시뮬레이션을 통해 검증한다.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.32-37
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• 2019

In this paper, we have designed a solar photovoltaic system to attach solar photovoltaic modules to each module and develop the best efficiency in each module. The efficiency of the designed solar panel optimizer was more than 99.27% and MPPT efficiency of 99.66%. In addition, the monitoring of power generation and abnormal operation phenomenon in each optimum period and tracking for failure location of specific photovoltaic module have improved the utilization rate of photovoltaic power generation. Wired and wireless communication methods has been proposed to monitor the power generation and operation status of the solar panel optimizer. For this purpose, the RS485 communication was used for wire communication and Zigbee communication was used for wireless communication to monitor the status of each module in real time. It is shown that communication redundancy can be achieved through the proposed method, and the possibility of commercialization is suggested.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.100-106
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• 2021

Over the past few decades, natural language processing research has not made much. However, the widespread use of deep learning and neural networks attracted attention for the application of neural networks in natural language processing. Sentiment analysis is one of the challenges of natural language processing. Emotions are things that a person thinks and feels. Therefore, sentiment analysis should be able to analyze the person's attitude, opinions, and inclinations in text or actual text. In the case of emotion analysis, it is a priority to simply classify two emotions: positive and negative. In this paper we propose the deep learning based sentimental analysis system according to various optimizer that is SGD, ADAM and RMSProp. Through experimental result RMSprop optimizer shows the best performance compared to others on IMDB data set. Future work is to find more best hyper parameter for sentimental analysis system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.133-141
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• 2023

This work take deeper analysis of Adaptive Moment Estimation (Adam) and Adam with Weight Decay (AdamW) implementation in real world text classification problem (DGA Malware Detection). AdamW is introduced by decoupling weight decay from L2 regularization and implemented as improved optimizer. This work introduces a novel implementation of AdamW variant as AdamW+ by further simplifying weight decay implementation in AdamW. DGA malware detection LSTM models results for Adam, AdamW and AdamW+ are evaluated on various DGA families/ groups as multiclass text classification. Proposed AdamW+ optimizer results has shown improvement in all standard performance metrics over Adam and AdamW. Analysis of outcome has shown that novel optimizer has outperformed both Adam and AdamW text classification based problems.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.202-209
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• 2019

In this study, an acoustic full-waveform inversion using Adam optimizer was proposed. The steepest descent method, which is commonly used for the optimization of seismic waveform inversion, is fast and easy to apply, but the inverse problem does not converge correctly. Various optimization methods suggested as alternative solutions require large calculation time though they were much more accurate than the steepest descent method. The Adam optimizer is widely used in deep learning for the optimization of learning model. It is considered as one of the most effective optimization method for diverse models. Thus, we proposed seismic full-waveform inversion algorithm using the Adam optimizer for fast and accurate convergence. To prove the performance of the suggested inversion algorithm, we compared the updated P-wave velocity model obtained using the Adam optimizer with the inversion results from the steepest descent method. As a result, we confirmed that the proposed algorithm can provide fast error convergence and precise inversion results.