• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.6
• /
• pp.792-797
• /
• 2011

This paper discusses the application of a hybrid discretiziation method for the discretization procedure that needs to be included in discrete particle swarm optimization (DPSO) for the problem of allocating PV (photovoltaic) systems onto distribution power systems. For this purpose, this paper proposes a rule-based expert system considering the objective function value and its optimizing speed as the input parameters and applied it to the PV allocation problem including discrete decision variables. For multi-level discretization, this paper adopts a hybrid method combined with a simple rounding and sigmoid funtion based 3-step and 5-step quantization methods, and the application of the rule based expert system proposing the adequate discretization method at each PSO iteration so that the DPSO with the hybrid discretization can provide better performance than the previous DPSO.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.648-653
• /
• 2007

Researches on artificial emotion which generates emotion artificially from various external excitations imitating human emotion has been initiated in recent years. But the conventional studies in which the emotion state is changed exponentially or linearly by external emotion excitation have a drawback that the variation of emotion state is changed rapidly and abruptly. In this paper, we propose an artificial emotion generation model which reflects not only strength and frequency of external emotion excitations but also period of it in the emotion state and represents the emotion state with a sigmoid curve w.r.t. time. And we propose an artificial emotion system which generates emotion at the situation of no external emotional excitations through recollection of past emotional excitations, and show its effectiveness through computer simulation results.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.2
• /
• pp.287-292
• /
• 2021

In this paper, we propose an efficient CNN application method by analyzing the effect of activation function on the failure diagnosis of the inductive motor stator. Generally, the main purpose of the inductive motor stator failure diagnosis is to prevent the failure by rapidly diagnosing the minute turn short. In the application of activation function, experiments show that the Sigmoid function is 23.23% more useful in accuracy of diagnosis than the ReLu function, although it is shown that ReLu has superiority in overall fixer failure in utilizing the activation function.

• Journal of Korea Society of Industrial Information Systems
• /
• v.24 no.4
• /
• pp.1-8
• /
• 2019

Plant phenomics is a technique for observing and analyzing morphological features in order to select plant varieties of excellent traits. The conventional methods is difficult to apply to the phenomics system. because the color threshold value must be manually changed according to the detection target. In this paper, we propose the convolution neural network (CNN) structure that can automatically segment plants from the background for the phenomics system. The LeafNet consists of nine convolution layers and a sigmoid activation function for determining the presence of plants. As a result of the learning using the LeafNet, we obtained a precision of 98.0% and a recall rate of 90.3% for the plant seedlings images. This confirms the applicability of the phenomics system.

• Journal of Broadcast Engineering
• /
• v.24 no.2
• /
• pp.234-242
• /
• 2019

In this paper, we propose a deep neural network which extracts SIFT feature points by determining whether the center pixel of a cropped image is a SIFT feature point. The data set of this network consists of a DIV2K dataset cut into $33{\times}33$ size and uses RGB image unlike SIFT which uses black and white image. The ground truth consists of the RobHess SIFT features extracted by setting the octave (scale) to 0, the sigma to 1.6, and the intervals to 3. Based on the VGG-16, we construct an increasingly deep network of 13 to 23 and 33 convolution layers, and experiment with changing the method of increasing the image scale. The result of using the sigmoid function as the activation function of the output layer is compared with the result using the softmax function. Experimental results show that the proposed network not only has more than 99% extraction accuracy but also has high extraction repeatability for distorted images.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.6
• /
• pp.809-817
• /
• 2020

In the design and development process of Small-Sat power distribution and transmission module, the stability of dynamic resources was evaluated by a deep learning algorithm. The requirements for the stability evaluation consisted of the power distribution function of the power distribution module and demand module to the SAR radar in Small-Sat. To verify the performance of the switching power components constituting the power module PDM, the reliability was verified using a dynamic neural network. The adoption material of deep learning for reliability verification is the power distribution function of the payload to the power supplied from the small satellite main body. Modeling targets for verifying the performance of this function are output voltage (slew rate control), voltage error, and load power characteristics. First, to this end, the Coefficient Structure area was defined by modeling, and PCB modules were fabricated to compare stability and reliability. Second, Levenberg-Marquare based Two-Way NARX neural network Sigmoid Transfer was used as a deep learning algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.2
• /
• pp.51-62
• /
• 2022

This study suggests a design of predictive modeling for a hospital fall risk based on inpatients' posture. Inpatient's profile, medical history, and body measurement data along with basic information about a bed they use, were used to predict a fall risk and suggest an algorithm to determine the level of risk. Fall risk prediction is largely divided into two parts: a real-time fall risk evaluation and a qualitative fall risk exposure assessment, which is mostly based on the inpatient's profile. The former is carried out by recognizing an inpatient's posture in bed and extracting rule-based information to measure fall risk while the latter is conducted by medical staff who examines an inpatient's health status related to hospital fall risk and assesses the level of risk exposure. The inpatient fall risk is determined using a sigmoid function with recognized inpatient posture information, body measurement data and qualitative risk assessment results combined. The procedure and prediction model suggested in this study is expected to significantly contribute to tailored services for inpatients and help ensure hospital fall prevention and inpatient safety.

• Journal of Broadcast Engineering
• /
• v.26 no.6
• /
• pp.809-812
• /
• 2021

Air bubbles which may be generated during the PCB coating process can be a major cause of malfunction. so it is necessary to detect the bubbles in advance. In previous studies, candidates for bubbles were extracted using the brightness characteristics of bubbles, and the candidates were verified using CNN(Convolutional Neural Networks). In this paper, we propose a bubble detection method using a transfer learning-based CNN model. The VGGNet is adopted and sigmoid is used as a classification layer, and the last convolutional layer and classification layer are trained together when transfer learning is applied. The performance of the proposed method is F1-score 0.9044, which shows an improvement of about 0.17 compared to the previous study.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.4
• /
• pp.213-221
• /
• 2021

In this paper, we suggest the Deep Neural Network Model System for predicting results of the match of 'League of Legends (LOL).' The model utilized approximately 26,000 matches of the LOL game and Keras of Tensorflow. It performed an accuracy of 93.75% without overfitting disadvantage in predicting the '2020 League of Legends Worlds Championship' utilizing the real data in the middle of the game. It employed functions of Sigmoid, Relu and Logcosh, for better performance. The experiments found that the four variables largely affected the accuracy of predicting the match --- 'Dragon Gap', 'Level Gap', 'Blue Rift Heralds', and 'Tower Kills Gap,' and ordinary users can also use the model to help develop game strategies by focusing on four elements. Furthermore, the model can be applied to predicting the match of E-sports professional leagues around the world and to the useful training indicators for professional teams, contributing to vitalization of E-sports.

• Nuclear Engineering and Technology
• /
• v.54 no.4
• /
• pp.1414-1420
• /
• 2022

High dose rate (HDR) brachytherapy treatment planning usually involves optimization methods to deliver uniform dose to the target volume and minimize dose to the healthy tissues. Four optimizations were used to evaluate the high-risk clinical target volume (HRCTV) coverage and organ at risk (OAR). Dose-volume histogram (DVH) and dosimetric parameters were analyzed and evaluated. Better coverage was achieved with PGO (mean CI = 0.95), but there were no significant mean CI differences than GrO (p = 0.03322). Mean EQD₂ doses to HRCTV (D₉₀) were also superior for PGO with no significant mean EQD₂ doses than GrO (p = 0.9410). The mean EQD₂ doses to bladder, rectum, and sigmoid were significantly higher for NO plan than PO, GrO, and PGO. PO significantly reduced the mean EQD₂ doses to bladder, rectum, and sigmoid but compromising the conformity index to HRCTV. PGO was superior in conformity index (CI) and mean EQD₂ doses to HRCTV compared with the GrO plan but not statistically significant. The mean EQD₂ doses to the rectum by PGO plan slightly exceeded the limit from ABS recommendation (mean EQD₂ dose = 78.08 Gy EQD₂). However, PGO can shorten the treatment planning process without compromising the CI and keeping the OARs dose below the tolerance limit.