• Proceedings of the Korean Society of Marine Engineers Conference
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• 2012.06a
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• pp.127-127
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• 2012

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1137-1140
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• 2014

Faulty electric motors onboard vessels with anomalies in windings or poor insulation are usually repaired at land based workshops and reinstalled in place by crew hands after receiving the repaired motors. Especially for 3 phase induction motors which need Y-${\delta}$ starters with 6 lead wires, it would happen that the polarities of stator windings cannot be well distinguished if the original tags of these wires are erased or not visible clearly, resulting in subsequent damage to the repaired motor due to extreme current flow when the power is given to the motor the stator windings of which are wrongly connected in the polarity. This study proposes an easy way to make correct connection in winding polarities without failures based on the electro-magnetically induced voltages on windings when a slight DC current is supplied to a winding coil by using an analog multi-tester. The proposed method is applied to actual motors and delves into the applicability for polarity discrimination through a few measurements onboard vessels.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1146-1149
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• 2014

To discriminate polarities of stator windings for 3 phase induction motors terminal tags of which are not readable, it is possible to utilize the residual magnetic flux present at their rotors as well as to use the way based on external exciting current. The induced voltages are basically decided by parameters such as the quantity of residual flux, the rotator speed by hand force and the phase properties between stator windings. To adopt induced voltages by residual flux for polarity discrimination at sites, the measured voltages by multi-testers need to be readable in magnitude enough to discriminate winding condition with reasonable phase characteristics. This study focuses on the analysis of various connection cases in the expectation that the summing voltages induced by residual flux shall show zero in case of normal connections while the sum becomes greater indication if the connection is in wrong condition. The proposed method is applied to actual motors to disclose how effective it is for polarity discrimination at sites through comparison of output signals between normal and fault connections.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1141-1145
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• 2014

When the stator windings of 3 phase induction motors are in wrong condition, the mutual inductive responses between windings can be utilized for the purpose of diagnosing motors in that fault windings affect even the responses by DC excitation. Three phase induction motors are supposed to generate consistent inductive voltages at the remaining windings when exciting DC current is given to one of 3 windings, while the inconsistence of their voltages indicates the existence of disorder at electric motors. This study describes how the exciting current to one of three windings cause the other windings to create induced voltages, analyzing responses by transfer functions, and discloses whether or not the balance relation at two windings is normal in the way of measuring the differential voltage of their outputs. For experiment, common analog multi-testers is used for applying exciting current and measuring the output signal to confirm whether the proposed method is useful enough to be able to discriminate wrong polarities of windings onboard vessels including also the case of exciting current by AC.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.