• Journal of Distribution Science
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• v.21 no.3
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• pp.113-121
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• 2023

Purpose: Online distribution channel alludes to the many different digital channels utilized in marketing and distributing goods and services to end users. The present research aims to explore and provide various retail strategy for the success of the art market within online distribution channel. Research design, data and methodology: The current author has conducted and investigate the qualitative textual methodology to take a look at carefully the current and prior literature dataset to achieve the purpose of the present research so that the present author could obtain total 27 relevant prior studies. Results: According to the comprehensive literature investigation, this research has found that there are six kinds of retail strategy for the prosperity of the art market within online distribution channel as follows: (1) Blockchain Technology, (2) Artificial Intelligence (AI), (3) Virtual Reality (VR), (4) Online Market Places, (5) Social Media, and (6) Regulations. Conclusions: The results of this analysis of the relevant literature show that the art market industry needs to adjust to keep up with the quickly shifting landscape of the digital world. In addition, although these technologies can be helpful in addressing difficulties linked to authenticity and transparency, they cannot eliminate the hazards of fraud and misrepresentation.

• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.169-206
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• 2019

This article seeks to explore new relationships and ethics of human and technology by analyzing a cultural imaginary produced by artificial intelligence. Drawing on theoretical reflections of the Feminist Scientific and Technological Studies which understand science and technology as the matter of care(Puig de la Bellacas, 2011), this paper focuses on the fact that artificial intelligence and robots materialize cultural imaginary such as autonomy. This autonomy, defined as the capacity to adapt to a new environment through self-learning, is accepted as a way to conceptualize an authentic human or an ideal subject. However, this article argues that artificial intelligence is mediated by and dependent on invisible human labor and complex material devices, suggesting that such autonomy is close to fiction. The recent growth of the so-called 'assistant technology' shows that it is differentially visualizing the care work of both machines and humans. Technology and its cultural imaginary hide the care work of human workers and actively visualize the one of the machine. And they make autonomy and agency ideal humanness, leaving disabled bodies and dependency as unworthy. Artificial intelligence and its cultural imaginary negate the value of disabled bodies while idealizing abled-bodies, and result in eliminating the real relationship between man and technology as mutually dependent beings. In conclusion, the author argues that the technology we need is not the one to exclude the non-typical bodies and care work of others, but the one to include them as they are. This technology responsibly empathizes marginalized beings and encourages solidarity between fragile beings. Inspired by an art performance of artist Sue Austin, the author finally comes up with and suggests 'artificial intelligence in wheelchair' as an alternative figuration for the currently dominant 'autonomous artificial intelligence'.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.507-515
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• 2019

The positive effects of 'robot actor' born in the age of artificial intelligence on the characteristics of theater (comprehensive, liveness, duality, planning) is due to the collaboration with 'robot' engineers, which increases the comprehensive. It is possible to respond to it, so that various reaction are maintained in every performance, and enhanced illusion can be provided in 'robot' material works in which 'robot actor' plays the role of 'robot'. However, the power focused on the director can reduce the comprehensiceness, the synthesis is reduced, and the 'robot actor' cannot perform the sweat or breath of 'human actor'. In itself, duality is incomplete. In addition, there is a high risk that the improvisation within the scope of planning is likely to occur as a sudden reaction, which may limit the postponement of the 'human actor'. Based on these findings, 'philosophy', 'science' and 'art' can predict the development of artificial intelligence side by side. It is considered necessary to study to redefine the direction and identity of arts and theater that should be moved forward.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• Steel and Composite Structures
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• v.52 no.6
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• pp.621-626
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• 2024

The work researched the application of artificial intelligence to the design and analysis of advanced nanoplates, with a particular emphasis on size and surface effects. Employing an integrated theoretical framework, this study developed a more accurate model of complex nanoplate behavior. The following analysis considers nanoplates embedded in a Pasternak viscoelastic fractional foundation and represents the important step in understanding how nanoscale structures may respond under dynamic loads. Surface effects, significant for nanoscale, are included through the Gurtin-Murdoch theory in order to better describe the influence of surface stresses on the overall behavior of nanoplates. In the present analysis, the modified couple stress theory is utilized to capture the size-dependent behavior of nanoplates, while the Kelvin-Voigt model has been incorporated to realistically simulate the structural damping and energy dissipation. This paper will take a holistic approach in using sinusoidal shear deformation theory for the accurate replication of complex interactions within the nano-structure system. Addressing different aspectsof the dynamic behavior by considering the length scale parameter of the material, this work aims at establishing which one of the factors imposes the most influence on the nanostructure response. Besides, the surface stresses that become increasingly critical in nanoscale dimensions are considered in depth. AI algorithms subsequently improve the prediction of the mechanical response by incorporating other phenomena, including surface energy, material inhomogeneity, and size-dependent properties. In these AI- enhanced solutions, the improvement of precision becomes considerable compared to the classical solution methods and hence offers new insights into the mechanical performance of nanoplates when applied in nanotechnology and materials science.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.1-8
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• 2019

This paper, we argue that sector in terms of the creative industries the need to the institutional settle of the rent-a-driver business; the industry could create more than 200,000 jobs, the effect is to bring out the about 4 trillion underground economy and Can be interpreted industry, which contributes to reduce social costs, ranging from 1.7 trillion won to 5.7 trillion per year. Through institutionalization of policy should support. Observation and in-depth interviews were conducted with the law and the president of the rent-a-driver business company. The operating system should be improved. the rent-a-driver business, for the formulation of the work ethic and education is desperately needed. The effect socio-economic contribution effect of the rent-a-driver business industry can be summarized as follows. First, it is an industry that has an operating system utilizing state-of-the-art technology and equipment, and the convergence of creative industries to comply with the market. Second, the effect appears as an industry that creates jobs for the populace to replace the social security system, social safety net is considered as an industry. Third, this is an industry that can be self-reliant in the short term at least political support, the industry is considered in the industry to maximize the effectiveness and efficiency of the support policy.

• ETRI Journal
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• v.41 no.6
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• pp.797-810
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• 2019

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high-contrast background, but they have no effect on the extraction of a salient object from images with complex low-contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics-like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low-contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low-contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state-of-the-art approaches, the proposed approach performs well.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.880-889
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• 2021

In everyday life, recognizing people's emotions from their frames is essential and is a popular research domain in the area of computer vision. Visual emotion has a severe class imbalance in which most of the data are distributed in specific categories. The existing methods do not consider class imbalance and used accuracy as the performance metric, which is not suitable for evaluating the performance of the imbalanced dataset. Therefore, we proposed a method for recognizing visual emotion using balanced data augmentation to address the class imbalance. The proposed method generates a balanced dataset by adopting the random over-sampling and image transformation methods. Also, the proposed method uses the Focal loss as a loss function, which can mitigate the class imbalance by down weighting the well-classified samples. EfficientNet, which is the state-of-the-art method for image classification is used to recognize visual emotion. We compare the performance of the proposed method with that of conventional methods by using a public dataset. The experimental results show that the proposed method increases the F1 score by 40% compared with the method without data augmentation, mitigating class imbalance without loss of classification accuracy.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.15-19
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• 2022

This study is to understand the relationship between turnover and various conditions. Turnover refers to workers moving from one company to another, which exists in various ways and forms. Currently, a large number of workers are considering many turnover rates to satisfy their income levels, distance between work and residence, and age. In addition, they consider changing jobs a lot depending on the type of work, the decision-making ability of workers, and the level of education. The company needs to accept the conditions required by workers so that competent workers can work for a long time and predict what measures should be taken to convert them into long-term workers. The study was conducted because it was necessary to predict what conditions workers must meet in order to become long-term workers by comparing various conditions and turnover using regression and decision trees. It used Microsoft Azure machines to produce results, and it found that among the various conditions, it looked for different items for long-term work. Various methods were attempted in conducting the research, and among them, suitable algorithms adopted algorithms that classify various kinds of algorithms and derive results, and among them, two decision tree algorithms were used to derive results.