• Smart Media Journal
• /
• v.13 no.3
• /
• pp.18-26
• /
• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

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

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.31-38
• /
• 2024

The digital phase-locked loop(DPLL) is one of the circuits composed of a digital detector, digital loop filter, voltage-controlled oscillator, and divider as a fundamental circuit, widely used in many fields such as electrical and circuit fields. A state estimator using various mathematical algorithms is used to improve the performance of a digital phase-locked loop. Traditional state estimators have utilized Kalman filters of infinite impulse response state estimators, and digital phase-locked loops based on infinite impulse response state estimators can cause rapid performance degradation in unexpected situations such as inaccuracies in initial values, model errors, and various disturbances. In this paper, we propose a two-layer Frobenius norm-based finite impulse state estimator to design a new digital phase-locked loop. The proposed state estimator uses the estimated state of the first layer to estimate the state of the first layer with the accumulated measurement value. To verify the robust performance of the new finite impulse response state estimator-based digital phase locked-loop, simulations were performed by comparing it with the infinite impulse response state estimator in situations where noise covariance information was inaccurate.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.52 no.2
• /
• pp.39-50
• /
• 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
• /
• v.29 no.5
• /
• pp.314-324
• /
• 2010

In waveguide structures, waves may be partially reflected by local non-uniformities. The effects of local non-uniformities has been previously investigated by means of a combined spectral element and finite element (SE/FE) method at relatively low frequencies. However, since the SE is formulated based on a beam theory, the SE/FE method is not appropriated for analysis at higher frequencies where complex deformation of the waveguide occurs. So it is necessary to extend this approach for high frequencies. For the wave propagation at higher frequencies, a combined spectral super element and finite element (SSE/FE) method is introduced in this paper. As an example of the application of this method, wave reflection and transmission due to a local defect in a rail are simulated at frequencies between 20kHz and 30kHz. Also numerical errors are evaluated by means of the conservation of the incident power.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.2
• /
• pp.112-118
• /
• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Information Systems Review
• /
• v.24 no.4
• /
• pp.23-40
• /
• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• Information Systems Review
• /
• v.17 no.2
• /
• pp.77-100
• /
• 2015

This study examined the factors that influence the smartphone user's decision to accept the personalized mobile advertisement. As a theoretical basis, we applied the privacy calculus model (PCM) that illustrates how consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. In particular, we investigated how smartphone users make a risk-benefit assessment under which personalized service as benefit-side factor and information privacy risks as a risk-side factor accompanying their acceptance of advertisements. Further, we extend the current PCM by considering advertisement fatigue as a new factor that may influence the user's acceptance. The research model with five (5) hypotheses was tested using data gathered from 215 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a mobile advertisement service was provided. The results showed that three (3) out of five (5) hypotheses were supported. First, we found that the intention to accept advertisements is positively and significantly influenced by the perceived value of personalization. Second, perceived advertisement fatigue was also found to be a strong predictor of the intention to accept advertisements. However, we did not find any evidence of direct influence of privacy risks. Finally, we found that the significant moderating effect between the perceived value of personalization and advertisement fatigue. This suggests that the firms should provide effective tailored advertisement that can increase the perceived value of personalization to mitigate the negative impacts of advertisement fatigue.

• Cartoon and Animation Studies
• /
• s.33
• /
• pp.99-126
• /
• 2013

This study is meant to be seeing how fun of animation works by reviewing it theoretically and coordinating it to suggest the structure which integrates fun of animation and validates the proposed fun model. After reviewing fun theoretically, the fun of animation could be able to coordinate that fun of animation is consist of perceptive fun, cognitive fun, and psychological fun. Perceptive fun is induced by visual, auditory and other sensory information and it is directly affected the image, sound, and movement. Cognitive fun can be obtained by reasoning and interpretation to mobilize their knowledge with sensuously perceived stimulation and it is directly affected the story. Psychological fun occurs when the audience see the animation. The psychological fun is the psychological emotional state when the audience watches animation by relieving psychological congestion. It consists of fun of unfamiliarity or identification. By suggesting research model and validating it how the perceptive fun, cognitive fun, and psychological fun affects each other, perceptive fun enhances cognitive fun and psychological fun. Although cognitive fun enhances psychological fun, cognitive fun enhances psychological fun twice than perceptive fun. Also when perceptive fun affects psychological fun, cognitive fun shows the indirect effect as a parameter. In conclusion, perceptive fun affects psychological fun directly and be enhanced through cognitive fun. Fun of animation can be experienced when perceptive fun caused by accepting sensory information of animation instantly, cognitive fun caused by interpretation and understanding sensory information of animation, and psychological fun caused by relieving psychological identity through recognition fuses and acts as one. An animation emphasized a certain element is difficult to be loved by the audience. In this reason, an harmonical combination among the elements of story, image, sound and movement are important to combinate harmoniously for a successful animation to make the audiences fun by arising funny emotions.

• Analytical Science and Technology
• /
• v.31 no.5
• /
• pp.208-218
• /
• 2018

Autophagy is a cellular process whereby cytosolic materials or organelles are taken up in a double-membrane vesicle structure known as an autophagosome and transported into a lysosome for degradation. Although autophagy has been studied at the genetic, cellular, or biochemical level, systematic ultrastructural quantitative analysis of autophagosomes during the autophagy process by using transmission electron microscopy (TEM) has not yet been reported. In this study, we performed ultrastructural analysis of autophagosomes in wild-type (WT) mouse embryonic fibroblasts (MEFs) and autophagy essential gene (atg5) knockout (KO) MEFs. First, we performed ultrastructural analysis of autophagosomes in WT MEFs compared to atg5 KO MEFs in basal autophagy or starvation-induced autophagy. Although we observed phagopore, early, late autophagosomes, or autolysosomes in WT MEFs, atg5 KO MEFs had immature autophagosomes that showed incomplete closure. Upon starvation, late autophagosomes accumulated in WT MEFs while the number of immature autophagosomes significantly increased in atg5 KO MEF indicating that atg5 plays an important role in the maturation of autophagosomes. Next, we examined autophagosomes in the cell model expressing polyQ-expanded N-terminal fragment of huntingtin. Our TEM analysis indicates that the number of late autophagosomes was significantly increased in the cells expressing the mutant huntingtin, indicating that improving the fusion of autophagosome with lysosome may be effective to enhance autophagy for the treatment of Huntington's disease. Taken together, the results of our study indicate that ultrastructural and quantitative analysis of autophagosomes using TEM can be applied to various human cellular disease models, and that they will provide an important insight for cellular pathogenesis of human diseases associated with autophagy.