• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.3-14
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• 2023

Recently, the effect of using social media on psychological well-being has been highlighted. However, studies exploring factors that may predict the quality of social media relationships are relatively rare. The present study investigated whether social media activity and psychological states, such as loneliness and depression, can predict the quality of social media relationships during the COVID-19 quarantine period using a machine learning technique. Ninety-five participants completed a self-report survey on loneliness, Instagram activity, quality of social media relationships, and depression at different time points (during the self-isolation and after the release of self-isolation). Similarity analyses, including multidimensional scaling (MDS), representational similarity analysis (RSA), and classification analyses, were conducted separately at each point in time. The results of MDS revealed that time spent on social media and depression were distinguished from others in the first dimension, and loneliness and passive use were distinguished from others in the second dimension. We divided the data into two groups based on the quality of social media relationships (high and low), and we conducted RSA on each group. Findings indicated an interaction between the quality of the social media relationships and the situation. Specifically, the effect of self-isolation on the high-quality social media relationship group is more pronounced than that on the low-quality group. The classification results also revealed that the predictors of social media relationships depend on whether or not they are isolated. Overall, the results of this study imply that social media relationship could be well predicted when people are not in isolated situations.

• Journal of Korean Physical Therapy Science
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• v.31 no.1
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• pp.16-32
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• 2024

Background: Exercise equipments and assistive devices for the disabled are being developed, but improvements for usability are still needed. The purpose of this study was to improve and utilize the developed exercise equipment and assistance devices by conducting usability test for people with spinal cord injury. Design: Cross-sectional Study. Methods: Scenarios and usability indicators were derived by conducting a preliminary usability test, 5 non-disabled men and women aged 19 or older. In the scenario, a total of 9 tasks were sequentially performed, including 2 tasks of entry and exit, 5 tasks of assistance devices and weight stack adjustment, and 2 tasks of pre exercise and exercise. The usability indicators were task success (success or fail), execution time (sec), safety, and convenience. For safety, 7 questions (Likert scale, 1~5 point) related to safety, stability and hazard were derived, and for convenience, the system usability scale (SUS score) was used (range: 0~100, 50 percentile rank is 68 point). Results: As a result of the usability test of people with spinal cord injury, there was a large variation among subjects in the task of adjusting the position of the pulley and support in the execution time (11.64~25.44 seconds), and one person failed to adjust the pulley. The safety level showed a lower score (score = 3 points) than other items in the item of entrapment or skin pressure, and in the case of SUS, the average score was 64.5 points, which was close to the acceptable level. Conclusion: Through the usability test, it was confirmed that exercise equipment for the disabled needs improvement in operability, pinching, and pressure, and that it is necessary to develop an assistive device that provides unrestrained posture information (biofeedback) to maintain correct posture during exercise.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.2
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• pp.39-44
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• 2020

Recently, numerous researches are being conducted in flexible substrate to apply to wearable devices. Particularly, Conductive substrate researches that can implement the wearable devices on clothing are massive. In this study, we formed fiber substrate spraying CNT and Pd mixed solution on it and plated metal layer with electroless plating. Used SEM equipment and EDS analysis to analysis structure of the plated fiber substrate and discovered Ni layer was created. For check electrical properties, mapping was performed to check surface resistance and distribution of resistance of electroless plated fiber substrate with 4-point probe. It was confirmed that conductivity was improved as the duration of electroless plating was increased, and it was found that distribution of resistance by surface location was uniform. Changes in resistance due to mechanical stress were measured through tensile, bending, and twisting tests. As a result, it was confirmed that resistance change of flexible substrate gradually disappeared as plating time increased. Using UTM (Universal testing machine), it was analyzed mechanical properties of the electroless plated substrate with respect to changes in plating time were improved. In the case of conductive fiber substrate in which electroless plating was performed for 2 hours, tensile strength was increased by 16 MPa than fiber substrate. Based on these results, we found that Ni-CNT-Fabric flexible substrate is adequate for clothing-intergrated conductive substrate and we positively expect that this experiment shows flexible substrate can adapt to and develop not only a wearable device technology but also other fields needing flexibility such as battery, catalyst and solar cell.

• Journal of Korean Philosophical Society
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• v.147
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• pp.117-146
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• 2018

This paper discusses, whether religion is possible even in the age of artificial intelligence, and whether humans alone are the subject of religious faith or ultra intelligent machines with human minds can be also subjects of faith. In order for ultra intelligent machines to be subjects of faith in the same conditions as humans, they must be able to have unique characteristics such as emotion, will, and self-consciousness. With the advent of ultra intelligent machines with the same level of cognitive and emotional abilities as human beings, the religious actions of artificial intelligence will be inevitable. The ultra intelligent machines after 'singularity' will go beyond the subject of religious belief and reign as God who can rule humans, nature and the world. This is also the common view of Morabeck, Kurzweil and Harari. Leonhart also reminds us that technological advances should make us used to the fact that we are now 'gods'. But we fear we may face distopia despite the general affluence of the 'Star Trec' economy. For this reason, even if a man says he has learned the religious truth, one can't help but wonder if it is true. Kant and Bloch are thinkers who critically reflected on our religious ideals and highest concept in different world-view premises. Kant's concept of God as 'idea of pure reason' and 'postulate of practical reason', can seem like a 'god of gap' as Jesse Bering said earlier. Kant recognized the need for religious faith only on a strict basis of moral necessity. The subjects of religious faith should always strive to do the moral good, but such efforts themselves were not enough to reach perfection and so postulated immortality of the soul. But if an ultra intelligent machines that has emerged above a singularity is given a new status in an intellectual explosion, it can reach its morality by blocking evil tendencies and by the infinite evolution of super intelligence. So it will no longer need Kant's 'Postulate for continuous progress towards greater goodness', 'Postulate for divine grace' and 'Postulate for infinite expansion of the kingdom of God on earth.' Artificial intelligence robots would not necessarily consider religious performance in the Kant's meaning, and therefore religion will also have to be abolished. Ernst Bloch transforms Kant's postulate to be Persian dualism. Therefore, in Bloch, even though the ultra intelligent machines is a divine being, one must critically ask whether it is a wicked or a good God. Artificial intelligence experts warn that ultra intellectual machine as Pandora's gift will bring disaster to mankind. In the Kant's Matrix, a ultra intelligent machines, which is the completion of morality and God itself, may fall into a bad god in Bloch's Matrix. Therefore, despite the myth of singularity, we still believe that ultra intelligent machines, whether as God leads us to the completion of one of our only religious beliefs, or as bad god to the collapse of mankind through complete denial of existence.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.215-222
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• 2021

Duchamp chose readymades mainly for his denial of retinal painting and his desire and fluid thinking for changes inside him. His thinking is well presented in his readymade works. One of his early readymades, Bicycle Wheel, expresses correlations between beings and capital. Its essence is capital and the desire of beings. It is connected to Desire or Desire-Machine proposed as a social reform and creation model by Gilles Deleuze and Félix Guattari. Bicycle Wheel does not simply end in desire relations between capital and beings, though. When the desire of beings is converted into positive desire, it can keep inventing and creating something that is repetitive, fluid, and new. Duchamp tried to convey this positive desire through his readymade Fountain. Duchamp created a ready-made article toilet into a work of art in Fountain, being driven by desire flowing inside him. This is connected to the attributes of nomads that refused to settle down and continue to flow in search of new landscapes. This way of his thinking is clearly reflected in his Box in a Valise. He was motivated to create this work by his fluid thinking. In this work, various situations can unfold often according to the directions of two elements, place and arrangement. Here, place guarantees fluidity, and arrangement variability or desire. According to Gilles Deleuze and Félix Guattari, fluidity represents nomads with the characteristics of flow and variation, and desire represents productivity or production or creation. Box in a Valise represents nomads themselves. The nomads defined by Gilles Deleuze and Félix Guattari keep flowing and create a new world. They are in line with the thinking of Duchamp that refused to settle down at one place and escape from the old order to create a new artistic world. This type of nomads can be called revolutionists or creators that acknowledge differences and create. Nomads also contain the concept of resistance to the old order. Deleuze and Guattari proposed a war machine as a model of this resistance. Duchamp and his readymades can thus be called war machines.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.