• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.523-529
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• 2021

In this paper, we designed and developed an Emotional Speech Synthesis Markup Language (SSML) processor. Multi-speaker emotional speech synthesis technology that can express multiple voice colors and emotional expressions have been developed, and we designed Emotional SSML by extending SSML for multiple voice colors and emotional expressions. The Emotional SSML processor has a graphic user interface and consists of following four components. First, a multi-speaker emotional text editor that can easily mark specific voice colors and emotions on desired positions. Second, an Emotional SSML document generator that creates an Emotional SSML document automatically from the result of the multi-speaker emotional text editor. Third, an Emotional SSML parser that parses the Emotional SSML document. Last, a sequencer to control a multi-speaker and emotional Text-to-Speech (TTS) engine based on the result of the Emotional SSML parser. Based on SSML which is a programming language and platform independent open standard, the Emotional SSML processor can easily integrate with various speech synthesis engines and facilitates the development of multi-speaker emotional text-to-speech applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.43-50
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• 2019

Block-chain enjoys technical advantages such as "robust security," owing to the structural characteristic that forgery is impossible, decentralization through sharing the ledger between participants, and the hyper-connectivity connecting Internet of Things, robots, and Artificial Intelligence. As a result, public organizations have highly positive attitudes toward the adoption of technology using block-chain, and the design of university information services is no exception. Universities are also considering the application of block-chain technology to foundations that implement various information services within a university. Through case studies of block-chain applications across various industries, this study designs an empirical model of an integrated information service platform that integrates information systems in a university. A basic road map of university information services is constructed based on block-chain technology, from planning to the actual service design stage. Furthermore, an actual empirical model of an integrated information service in a university is designed based on block-chain by applying this framework.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.243-250
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• 2019

Because of a moving UAV has a lot of potential/kinetic energy, if the UAV falls to the ground, it may have a lot of impact. Because this can lead to human casualities, in this paper, the population density area on the UAV flight path is defined as a dangerous area. The conventional UAV path flight was a passive form in which a UAV moved in accordance with a path preset by a user before the flight. Some UAVs include safety features such as a obstacle avoidance system during flight. Still, it is difficult to respond to changes in the real-time flight environment. Using public Big Data for UAV path flight can improve response to real-time flight environment changes by enabling detection of dangerous areas and avoidance of the areas. Therefore, in this paper, we propose a method to detect and avoid dangerous areas for UAVs by utilizing the Big Data collected in real-time. If the routh is designated according to the destination by the proposed method, the dangerous area is determined in real-time and the flight is made to the optimal bypass path. In further research, we will study ways to increase the quality satisfaction of the images acquired by flying under the avoidance flight plan.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.847-857
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• 2021

Recently, domestic schools are increasingly interested in environmental education related to COVID-19 and the severe climate crisis, as well as artificial intelligence education related to the 4th industrial revolution that is rapidly approaching us. In particular, AI education is highly likely to be applied to 5th to 6th graders of elementary school, so measures related to connection with 1st to 4th graders are needed. There are many students who are not proficient in computers in the lower grades of elementary school, so there may be many restrictions in using the currently used teaching aids. Therefore, this study tried to develop an artificial intelligence education program for the lower grades of elementary school to secure the linkage of artificial intelligence education. The theme of the program was developed based on the topic of environmental education, which has recently increased in interest. As for the educational method, considering the developmental stage of the lower grades of elementary school, the STEAM education method was used, which was fused with various subjects and unplugged using play and games without a computer. of the program. For validity verification, Lawshe (1975)'s content validity ratio (CVR) calculation formula was used. The verification results were analyzed to be suitable for the purpose of development of all programs. In the future, it is necessary to measure the degree of effectiveness by applying the program proposed in this study to the lower grades of elementary school.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.123-131
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• 2021

The Fourth Industrial Revolution was triggered by data-driven digital technologies such as AI and big data. There is a rapid movement to expand the scope of data utilization to the privacy area, which was considered only a protected area. Through the revision of the Data 3 Act, laws and systems were established that allow personal information to be freely transferred and utilized under their consent. But, it will be necessary to support the platform that encompasses the entire process from collecting personal information to managing and utilizing it. In this paper, we propose a platform model that can be applied to building mydata ecosystem using personal information. It describes the six essential functional requirements for building MyData platforms and the procedures and methods for implementing them. The six proposed essential features describe consent, sharing/downloading/ receipt of data, data collection and utilization, user authentication, API gateway, and platform services. We also illustrate the case of applying the MyData platform model to real-world, underprivileged mobility support services.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.857-862
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• 2021

Recently, rapid climate change has had a significant impact on the bee ecosystem. The decrease in the number of bees and the change in the flowering period have a huge impact on the harvesting of beekeepers. Accordingly, attention is focused on smart beekeeping, which introduces IoT technology to beekeeping. According to the characteristics of beekeeping, it is impossible to continuously observe the beehive in the hive with the naked eye, and the condition of the hive is mostly dependent on knowledge from experience. Although a system that can measure partly through sensors such as temperature/humidity change inside the hive and measurement of the amount of CO2 is applied, there is no research on measuring the movement path and amount of movement of bees inside the beehive. Part of the migration of honeybees inside the hive can provide basic information to predict the most important cleavage time in beekeeping. In this study, we propose a device that detects the movement path of bees and measures and records data entering and exiting the hive in real time. The device proposed in this study was developed according to the honeycomb standard of the existing beehive so that beekeeping farms could use it. The development method used a photodetector that can detect the movement of bees to configure 16 movement paths and to detect the movement of bees in real time. If the measured honeybee movement status is utilized, the problem of directly observing the colony with the naked eye in order not to miss the swarming time can be solved.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.873-879
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• 2022

In the future, AI and the metaverse are becoming so powerful that their application areas and influences are swallowing up the world. The advertising field is no exception, and it is becoming more important to predict, analyze, and strategize these future changes. In order to study the future change of advertising according to the development of artificial intelligence and metaverse, literature research related to the development of artificial intelligence and metaverse technology and the resulting change in the advertising environment, in-depth interviews with future and advertising experts, and Delphi technique research method I want to study change. First, through this study, we would like to examine the opinions of experts through in-depth interviews on the development of artificial intelligence and metaverse technology and the changes in the advertising sector in the post-coronavirus era of civilizational transformation. In addition, the Delphi technique is used to determine how important the change is by future advertising technology area, future advertising media area, future advertising form area, future advertising effect area, future advertising application area, and future advertising process area, and at what point in the future it will change. In addition, we want to study how the future advertising form will change in detail. Also, based on this, we would like to propose a countermeasure for the advertising industry.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1074-1081
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• 2021

Nitrous oxide (N₂O) is one of the six major greenhouse gases and is known to produce a greenhouse ef ect by absorbing infrared radiation in the atmosphere. In particular, its global warming potential (GWP) is 310 times higher than that of CO₂, making N₂O a global concern. Accordingly, strong environmental regulations are being proposed. N₂O reduction technology can be classified into concentration recovery, catalytic decomposition, and pyrolysis according to physical methods. This study intends to provide information on temperature conditions and reaction time required to reduce nitrogen oxides with cost. The high-temperature ranges selected for pyrolysis conditions were calculated at intervals of 100 K from 1073 K to 1373 K. Under temperatures of 1073 K and 1173 K, the N₂O reduction rate and nitrogen monoxide concentration were observed to be proportional to the residence time, and for 1273 K, the N₂O reduction rate decreased due to generation of the reverse reaction as the residence time increased. Particularly for 1373 K, the positive and reverse reactions for all residence times reached chemical equilibrium, resulting in a rather reduced reaction progression to N₂O reduction.