• Journal of Contemporary Eastern Asia
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• v.19 no.2
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• pp.84-103
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• 2020

Spurred by the ongoing globalization, an increase in mobility has diversified migrant categories and strengthened intercultural rapport. Alongside the "traditional" migrants, "White" (Caucasian) individuals are coming into greater focus of migration studies as "lifestyle migrants". Although White migrations are not a new phenomenon, the deep-seated idea of White supremacy continues to play an important role in contemporary intercultural communication, awarding Whites across communities a "cosmopolitan" status of highly educated cultural elites. As such, the focus of this research is on highly skilled White European migrants, on their subjective experiences of integration in Japan, and whether they perceive Whiteness as an obstacle or an advantage in this process, if integration is desired at all. To discern the connection between race and integration, this research investigates the non-White majority society of Japan as it has established racial hierarchies according to the Western models, consequently influencing the status of its contemporary White immigrants. Privileged, yet singled out as racial and cultural role models, White Europeans' integration seemingly becomes nearly impossible. The data obtained in fifteen semi-structured interviews confirms that Whiteness grants advantages when entering the Japanese job market, but remains an obstacle in everyday community integration. European professionals do not feel accepted and abandon efforts to integrate, if such were made, retreating into "cosmopolitan islets" wherein they renegotiate their White European identities.

• East Asian Economic Review
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• v.17 no.1
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• pp.55-79
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• 2013

The objective of this article is to explore the economic relationship between China and the surrounding dynamic Asian economies. It delves into China's influence over the Asian economies and whether this relationship is a market-led or de facto symbiosis. The three principal channels of regional integration analyzed in this article are trade, FDI and vertically integrated production networks. They are essentially based on the activities of the private-sector in these economies. China methodically expanded and deepened its economic ties with the regional neighbors. At the present juncture, China's integration with the surrounding Asia is deep. Another issue that this article explores is the so-called China "threat" or "fear" in Asia. It implies that China is crowding out exports of the other Asian economies in the world market place. Also, as China has become the most attractive FDI destination among the developing countries, it is apprehended that China is receiving FDI at the expense of the Asian economies. These concerns were examined by several empirical studies, and the inference is that they are exaggerated. This article concludes that the private-sector business activities in China and other rapidly growing Asian economies were (and are) instrumental in bringing together the production structures and real economies. The result is both convergence and integration among the dynamic Asian economies. Over the years China and its Asian neighbors has developed a close and symbiotic economic relationship and a de facto regional integration.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.275-284
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• 2023

Land monitoring involves systematically understanding changes in land use, leveraging spatial information such as satellite imagery and aerial photographs. Recently, the integration of deep learning technologies, notably object detection and semantic segmentation, into land monitoring has spurred active research. This study developed a web service to facilitate such integrations, allowing users to analyze aerial and drone images using CNN models. The web service architecture comprises AI, WEB/WAS, and DB servers and employs three primary deep learning models: DeepLab V3, YOLO, and Rotated Mask R-CNN. Specifically, YOLO offers rapid detection capabilities, Rotated Mask R-CNN excels in detecting rotated objects, while DeepLab V3 provides pixel-wise image classification. The performance of these models fluctuates depending on the quantity and quality of the training data. Anticipated to be integrated into the LX Corporation's operational network and the Land-XI system, this service is expected to enhance the accuracy and efficiency of land monitoring.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.49-56
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• 2017

In this paper we propose the approach for big data integration so as to analyze, visualize and predict the future of the trend of the market, and that is to get the integration data model using the R language which is the future of the statistics and the Hadoop which is a parallel processing for the data. As four approaching methods using R and Hadoop, ff package in R, R and Streaming as Hadoop utility, and Rhipe and RHadoop as R and Hadoop interface packages are used, and the strength and weakness of four methods are described and analyzed, so Rhipe and RHadoop are proposed as a complete set of data integration model. The integration of R, which is popular for processing statistical algorithm and Hadoop contains Distributed File System and resource management platform and can implement the MapReduce programming model gives us a new environment where in R code can be written and deployed in Hadoop without any data movement. This model allows us to predictive analysis with high performance and deep understand over the big data.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.97-105
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• 2022

Web services show a rapid evolution and integration to meet the increased users' requirements. Thus, web services undergo updates and may have performance degradation due to undetected faults in the updated versions. Due to these faults, many performances and regression anomalies in web services may occur in real-world scenarios. This paper proposed applying the deep learning model and innovative explainable framework to detect performance and regression anomalies in web services. This study indicated that upper bound and lower bound values in performance metrics provide us with the simple means to detect the performance and regression anomalies in updated versions of web services. The explainable deep learning method enabled us to decide the precise use of deep learning to detect performance and anomalies in web services. The evaluation results of the proposed approach showed us the detection of unusual behavior of web service. The proposed approach is efficient and straightforward in detecting regression anomalies in web services compared with the existing approaches.

• ETRI Journal
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• v.45 no.5
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• pp.735-745
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• 2023

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.

• 한국노년학
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• v.36 no.3
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• pp.827-846
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• 2016

The presented study was based on the notions that 1)the age-separated perspective would not be functional for post modern society which characterized by a diversity of life styles and 2)effects of wage peak system have been investigated mainly through managemental efficiency with quantitative analysis. In those notions, this study aimed to investigate effects of wage peak system based on age integration perspective with a qualitative method. Deep case study were executed with four workers who fully understand regarding wage peak system. Results showed that three sub categories were drawn in the meaning focused on issues of wage peak system; uneasy attention on workers who applied for wage peak system; pro and con of wage peak system for aged workers; achieving age integrated environment through mutual understanding.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.381-388
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• 2022

In instance segmentation, Mask-RCNN is mostly used as a base model. Increasing the performance of Mask-RCNN is meaningful because it affects the performance of the derived model. Mask-RCNN has a transform module for unifying size of input images. In this paper, to improve the Mask-RCNN, we apply deep-learning-based ASSR to the resizing part in the transform module and inject calculated scale information into the model using IM(Integration Module). The proposed IM improves instance segmentation performance by 2.5 AP higher than Mask-RCNN in the COCO dataset, and in the periment for optimizing the IM location, the best performance was shown when it was located in the 'Top' before FPN and backbone were combined. Therefore, the proposed method can improve the performance of models using Mask-RCNN as a base model.