• Journal of IKEEE
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• v.23 no.4
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• pp.1360-1365
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• 2019

In this paper, we describe a sound source localization(SSL) system which can be applied to mobile robot and automatic control systems. Usually the SSL method finds the Interaural Time Difference, the Interaural Level Difference, and uses the geometrical principle of microphone array. But here we proposed another approach based on the deep neural network to obtain the horizontal directional angle(azimuth) of the sound source. We pick up the sound source signals from the two microphones attached symmetrically on both sides of the robot to imitate the human ears. Here, we use difference of spectral distributions of sounds obtained from two microphones to train the network. We train the network with the data obtained at the multiples of 10 degrees and test with several data obtained at the random degrees. The result shows quite promising validity of our approach.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.187-196
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• 2014

In this paper, Minimize driving energy for operation within a defined distance yeokgan fixed time-resolved and determine the nature of the train is traveling, and to model mathematically. Urban rail car cruise in general by the PID controller is used instead of automatically tracking a target value while traveling in energy consumption to be minimized by using optimal control model railroad charyangreul was designed under real operating conditions the same. The actual track conditions apply to the minimum value or a separate listing of cars around the track facility without a driving energy of the automatic operation and to reduce the driving energy. Therefore, actual route chosen straight line 8 / gradient segment / curve for the measured data analysis, such as sections within the city-minute drive each section and presented how the trains to save energy, depending on the pattern of the train station in the region.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.3081-3083
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• 2005

한국형 고속전철차량의 자동제어 구현을 위해서 우선 다양한 종류의 장치들로부터 상태정보(Line Voltage-열차가선전압, Bogie Hunting, Preset Speed, PWM, Train Velocity, Brake Pressure, Reservoir Pressure)를 취득해야하며, Main Process Unit(MPU)에서의 고속 Data 처리를 위해서 취득한 Analog Data를 신속하게 Digital Data로 변환해야 한다. 또한 열차내의 특수한 조건(Noise, Vibration)에서도 안정적인 데이터의 취득을 만족시켜야한다. 이와 같은 상황을 고려한 독자적이 통합형 데이터 취득 장치 -Integrated Data Acquisition Board(IDAB)-의 설계방안을 제시하였다.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.130-136
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• 2017

The three-wheel pitching machine is a device that throws balls automatically instead of a pitcher and is used chiefly to train baseball players. The machine is abundantly used by people in indoor baseball grounds for baseball games. However, in Korea, foreign products are more popular because the efficiency of domestic products is poor as compared to that of the foreign ones. Therefore, a miniature pitching machine was manufactured to analyze and solve the problems of the existing machine. We added a feeder device to insert the balls in the machine and developed a smart phone application. The machine is easily controlled by a smart phone with bluetooth. While manufacturing the miniature, the existing problems were mitigated and the machine was redesigned for mass production. This study attempted to render the pitching machine more convenient and safer as a substitute for foreign pitching machines.

• Journal of the Ergonomics Society of Korea
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• v.36 no.5
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• pp.525-533
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• 2017

Objective: The aim of this study is to investigate maritime officers' strategies to avoid the ship collision in crossing situations. Background: In a situation where there is a risk of collision between two ships, maritime officers can change the direction and speed of the own-ship to avoid the collision. They have four options to select; adjusting the speed only, the direction only, both the speed and direction at the same time and no action. Research questions were whether the strategy they are using differs according to the shipboard experience of maritime officers and the representation method of ARPA (automatic radar plotting aid) - radar graphic information. Method: Participants were 12. Six of them had more than 3 years of onboard experience, while the others were 4th grade students at Korea Maritime and Ocean University. For each participant, 32 ship encounter situations were provided with ARPA-radar information. 16 situations were presented by the north-up display and 16 situations were presented by the track-up display. Participants were asked to decide how to move the own-ship to avoid the ship collision for each case. Results: Most participants attempted to avoid the collision by adjusting the direction of the ship, representing an average of 22.4 times in 32 judgment trials (about 70%). Participants who did not have experience on board were more likely to control speed and direction at the same time than participants with onboard experience. Participants with onboard experience were more likely to control the direction of the ship only. On the other hand, although the same ARPA Information was provided to the participants, the participants in many cases made different judgments depending on the method of information representation; track-up display and north-up display. It was only 25% that the participants made the same judgment under the same collision situations. Participants with onboard experience did make the same judgment more than participants with no onboard experience. Conclusion: In marine collision situations, maritime officers tend to avoid collisions by adjusting only the direction of their ships, and this tendency is more pronounced among maritime officers with onboard experience. The effect of the method of information representation on their judgment was not significant. Application: The results of this research might help to train maritime officers for safe navigation and to design a collision avoidance support system.