• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1517-1522
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• 2023

This study evaluates the impact of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) expected error (EE) on the accuracy of surface reflectance (SR) derived from the KOMPSAT-3A satellite, utilizing the Second Simulation of the Satellite Signal in the Solar Spectrum Vector radiative transfer model. By considering a range of ground-based AOD and the resultant MODIS AOD EE, the research identifies significant influences on SR accuracy, particularly under high solar zenith angles(SZA) and shorter wavelengths. The study's simulations reveal that SR errors increase with shorter wavelengths and higher SZAs, highlighting the necessity for further research to improve atmospheric correction algorithms by incorporating wavelength and SZA considerations. Additionally, the study provides foundational data for better understanding the use of AOD data from other satellites in atmospheric correction processes and contributes to advancing atmospheric correction technologies.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.1-21
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• 2023

This study is related to the performance of open innovation collaboration between startups and large corporations and financial institutions. In the life cycle of a typical company, the growth of a startup is difficult to predict. Startups that possess innovative technology but have only recently been established seek to verify their technology and capabilities by participating in open innovation with large corporations and financial institutions, and further strive to lay the foundation for corporate growth. However, if you approach it only as a theoretical coexistence plan, it will be viewed as a vague attempt from the startup's perspective. The purpose of this study is to differentiately verify the benefits of open innovation by analyzing the difference in sales growth of startups for the purpose of sales performance based on the open innovation participation of large companies and small and medium-sized companies(startups). In verifying this, the analysis was based on the sales results of the actual open innovation collaboration B2C model, and the difference was confirmed by comparing before and after collaboration. Here, the differentiation of the study was added by reflecting the corporate growth stage theory, a growth theory. When the corporate growth stage theory was excluded, it was confirmed that sales growth due to open innovation of startups was applied from the third month, and sales growth depending on participation was confirmed to be significant. On the other hand, when the corporate growth stage theory was applied, sales growth was not significant, but the difference in growth could be confirmed from the fourth month, and it was also confirmed in sales growth depending on participation. As a result, this study objectively confirms the effects that can be gained when startups participate in Open-innovation, and it is expected that Open-innovation led by large corporations, financial institutions, and government agencies will develop into a high-quality program environment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.116-118
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• 2023

In recent years, the need for economical and sustainable ship routing has emerged due to the enforced regulations on environmental issues. Despite the development of weather forecasting technology, maritime accidents by rough waves have continued to occur due to incorrect weather forecasts. In this study, onboard measurements are conducted to observe the acutal situation on merchant ships in operation encountering rough waves. The types of measured data include information related to navigation (Ship's position, speed, bearing, rudder angle) and engine (engine revolutions, power, shaft thrust, fuel consumption), weather conditions (wind, waves), and ship motions (roll, pitch, and yaw). These ship experiments was conducted to 28,000 DWT bulk carrier, 63,000 DWT bulk carrier, 20,000 TEU container ship, and 12,000 TEU container ship. The actual ship experiment of each ship is intended to acquire various types of data and utilize them for multi-objective studies related to ship operation. Additionally, in order to confirm the sea conditions, the directional wave spectrum was reproduced using a wave simulation model. Through data collection from ship experiments and wave simulations, various studies could be proceeding such as the measurement for accurate wave information by marine radar and analysis for cargo collapse accidents. In addition, it is expected to be utilized in various themes from the perspective of safety and efficiency in ship operation.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.42-50
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• 2023

The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

• Information Systems Review
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• v.24 no.1
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• pp.125-155
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• 2022

Platform contrasts with traditional industry in terms of innovativeness as it is rapidly developing with information technology. To redeem preceding researches which have separately focused on either platform acceptance based on technology acceptance model or resistance factors against platform's innovation, this study applied dual factor theory to check the simultaneous influence of enablers and inhibitors on resistance. This study chose purchasers of online used trading platform as a subject of study since it contrasts with other platforms in many characteristics. Based on preceding studies, the moderating effects of their past purchase numbers on the relations between resistance and use intention were also checked. The findings reveal that economic benefit as an enabler had significant negative influence on the resistance, but social influence didn't have expected influence. In case of inhibitors, both perceived complexity and perceived risk had significant positive influence on the resistance. Though resistance had significant negative influence on the use intention, its influence was moderated into the positive direction as users' purchase number increased. Lastly, resistance had mediation effect between antecedent factors (economic benefit and perceived complexity) and use intention.

• Information Systems Review
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• v.24 no.2
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• pp.41-66
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• 2022

With the development of IT, mobile apps and the expansion of contactless services due to COVID-19, "smart orders" have recently been activated in the food and beverage service. Even in recent years, when sales have declined, the number of orders made by smart orders has been steadily increasing, and this ordering method can accumulate customer data, enabling effective customized services in the future. In the present study, satisfaction with smart orders and continuous use intention were studied based on the technology acceptance model (TAM). And it focused on whether there is a difference in personality when using smart orders. For this purpose, a survey was conducted on 317 smart order users, and the hypothesis was verified by structural equation model analysis. Perceived benefits had a significant effect on satisfaction; also, satisfaction had a significant effect on continuous use intention. There is a significant disparity between introvert and extrovert type. As a consequence, the introverted type has a greater intention to perceive usefulness of smart orders and continuously use them. These results suggest that the customer's personality type should be considered in future customer customization strategies.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.158-177
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• 2024

This paper proposes and tests a theoretical model of the relational link between a novel form of customer-perceived fairness for a reward design (distributive peer justice climate) and C2C helping intention via community identification and online C2C interaction (friend-, neighboring customer-, audience-interaction) qualities in a collective consumption context (MMORPG). To test hypotheses, we amassed survey data within a collective consumption context (massively multiplayer online role-playing games, MMORPGs). We used structural equation modeling in analyzing the survey data. The results reveal that user-perceived distributive peer justice climate for a reward design enhances their C2C helping intention via community identification and C2C interactions in MMORPG contexts. Collective consumption-type service managers should focus on promoting the user-perceived distributive peer justice climate for their reward system to enhance users' present C2C co-creation experience (community identification, C2C interaction) and future C2C co-creation behavior (helping intention). By adopting an intra-unit level distributive justice concept (customer-perceived distributive peer justice climate) to a reward design in a collective consumption context (MMORPGs), this study informed collective consumption-type service managers of the importance of its management.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.39-50
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• 2024

Color is an essential visual element that has a significant impact on the formation of a city's image and people's perceptions. Quantitative analysis of color in urban environments is a complex process that has been difficult to implement in the past. However, with recent rapid advances in Machine Learning, it has become possible to analyze city colors using photos shared by tourists. This study selected Dali City, a popular tourist destination in China, as a case study. Photos of Dali City shared by tourists were collected, and a method to measure large-scale city colors was explored by combining machine learning techniques. Specifically, the DeepLabv3+ model was first applied to perform a semantic segmentation of tourist sharing photos based on the ADE20k dataset, thereby separating artificial elements in the photos. Next, the K-means clustering algorithm was used to extract colors from the artificial elements in Dali City, and an adjacency matrix was constructed to analyze the correlations between the dominant colors. The research results indicate that the main color of the artificial elements in Dali City has the highest percentage of orange-grey. Furthermore, gray tones are often used in combination with other colors. The results indicated that local ethnic and Buddhist cultures influence the color characteristics of artificial elements in Dali City. This research provides a new method of color analysis, and the results not only help Dali City to shape an urban color image that meets the expectations of tourists but also provide reference materials for future urban color planning in Dali City.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.250-258
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• 2007

Current speech recognition technology s achieved high performance with the development of hardware devices, however it is insufficient for some applications where high reliability is required, such as voice control of powered wheelchairs for disabled persons. For the system which aims to operate powered wheelchairs safely by voice in real environment, we need to consider that non-voice commands such as user s coughing, breathing, and spark-like mechanical noise should be rejected and the wheelchair system need to recognize the speech commands affected by disability, which contains specific pronunciation speed and frequency. In this paper, we propose non-voice rejection method to perform voice/non-voice classification using both YIN based fundamental frequency(F0) extraction and reliability in preprocessing. We adopted a multi-template dictionary and acoustic modeling based speaker adaptation to cope with the pronunciation variation of inarticulately uttered speech. From the recognition tests conducted with the data collected in real environment, proposed YIN based fundamental extraction showed recall-precision rate of 95.1% better than that of 62% by cepstrum based method. Recognition test by a new system applied with multi-template dictionary and MAP adaptation also showed much higher accuracy of 99.5% than that of 78.6% by baseline system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.1-11
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• 2024

In the midst of the current turbulent global economy, traditional investment metrics are undergoing a metamorphosis, signaling the onset of what's often referred to as an "Investment cold season". Early-stage startups, despite their boundless potential, grapple with immediate revenue constraints, intensifying their pursuit of critical investments. While financial indicators once took center stage in investment evaluations, a notable paradigm shift is underway. Organizational culture, once relegated to the sidelines, has now emerged as a linchpin in forecasting a startup's resilience and enduring trajectory. Our comprehensive research, integrating insights from CVF and OCAI, unveils the intricate relationship between organizational culture and its magnetic appeal to investors. The results indicate that startups with a pronounced external focus, expertly balanced with flexibility and stability, hold particular allure for investment consideration. Furthermore, the study underscores the pivotal role of adhocracy and market-driven mindsets in shaping investment desirability. A significant observation emerges from the study: startups, whether they secured investment or failed to do so, consistently display strong clan culture, highlighting the widespread importance of nurturing a positive employee environment. Leadership deeply anchored in market culture, combined with an unwavering commitment to innovation and harmonious organizational practices, emerges as a potent recipe for attracting investor attention. Our model, with an impressive 88.3% predictive accuracy, serves as a guiding light for startups and astute investors, illuminating the intricate interplay of culture and investment success in today's economic landscape.