• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.3856-3868
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• 2011

Until recently, research trend in real estate has been focused on real estate market and the market analysis. But the studies on real estate training program development for real estate agents to improve their job performance are relatively short in numbers. Thus, this study shows empirical analysis of the needs for the training programs for real estate agents in Cheonan to improve their job performance. The results are as follows. First, in the survey of asking what educational contents they need in order to improve real estate agents' job performance, most of the respondents show their needs for the analysis of house's value, legal knowledge, real estate management, accounting, real estate marketing, and understanding of the real estate policy. This is because they are well aware that the best way of responding to the changing clients' needs comes from training programs. Secondly, asked about real estate marketing strategies, most of respondents showed their awareness of new strategies to meet the needs of clients. This is because new forms of marketing strategies including internet ads are needed in the field as the paradigm including Information Technology changes. Thirdly, asked about the need for real estate-related training programs, 92% of the respondents answered they need real estate education programs run by the continuing education centers of the universities. In addition, the survey showed their needs for retraining programs that utilize the resources in the local universities. Other than this, to have effective and efficient training programs, they demanded running a training system by utilizing the human resources of the universities under the name of the department of 'Real Estate Contract' for real estate agents' job performance. Fourthly, the survey revealed real estate management(44.2%) and real estate marketing(42.3%) is the most chosen contents they want to take in the regular course for improving real estate agents' job performance. This shows their will to understand clients' needs through the mind of real estate management and real estate marketing. The survey showed they prefer the training programs as an irregular course to those in the regular one. Despite the above results, this study chose subjects only in Cheanan and thus it needs to research more diverse areas. The needs of programs to improve real estate agents job performance should be analyzed empirically targeting the real estate agents not just in Cheonan but also cities like Pyeongchon, Ilsan and Bundang in which real estate business is booming, as well as undergraduate and graduate students whose major is real estate studies. These studies will be able to provide information to help develop the customized training programs by evaluating elements that real estate agents need in order to meet clients satisfaction and improve their job performance. Many variables of the program development learned through these studies can be incorporated in the curriculum of the real estate studies and used very practically as information for the development of the real estate studies in this fast changing era.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• The Korean Journal of Petroleum Geology
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• v.8 no.1_2 s.9
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• pp.1-43
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• 2000

A comparison study for understanding a stratigraphic response to tectonic evolution of sedimentary basins in the Yellow Sea and adjacent areas was carried out by using an integrated stratigraphic technology. As an interim result, we propose a stratigraphic framework that allows temporal and spatial correlation of the sedimentary successions in the basins. This stratigraphic framework will use as a new stratigraphic paradigm for hydrocarbon exploration in the Yellow Sea and adjacent areas. Integrated stratigraphic analysis in conjunction with sequence-keyed biostratigraphy allows us to define nine stratigraphic units in the basins: Cambro-Ordovician, Carboniferous-Triassic, early to middle Jurassic, late Jurassic-early Cretaceous, late Cretaceous, Paleocene-Eocene, Oligocene, early Miocene, and middle Miocene-Pliocene. They are tectono-stratigraphic units that provide time-sliced information on basin-forming tectonics, sedimentation, and basin-modifying tectonics of sedimentary basins in the Yellow Sea and adjacent area. In the Paleozoic, the South Yellow Sea basin was initiated as a marginal sag basin in the northern margin of the South China Block. Siliciclastic and carbonate sediments were deposited in the basin, showing cyclic fashions due to relative sea-level fluctuations. During the Devonian, however, the basin was once uplifted and deformed due to the Caledonian Orogeny, which resulted in an unconformity between the Cambro-Ordovician and the Carboniferous-Triassic units. The second orogenic event, Indosinian Orogeny, occurred in the late Permian-late Triassic, when the North China block began to collide with the South China block. Collision of the North and South China blocks produced the Qinling-Dabie-Sulu-Imjin foldbelts and led to the uplift and deformation of the Paleozoic strata. Subsequent rapid subsidence of the foreland parallel to the foldbelts formed the Bohai and the West Korean Bay basins where infilled with the early to middle Jurassic molasse sediments. Also Piggyback basins locally developed along the thrust. The later intensive Yanshanian (first) Orogeny modified these foreland and Piggyback basins in the late Jurassic. The South Yellow Sea basin, however, was likely to be a continental interior sag basin during the early to middle Jurassic. The early to middle Jurassic unit in the South Yellow Sea basin is characterized by fluvial to lacustrine sandstone and shale with a thick basal quartz conglomerate that contains well-sorted and well-rounded gravels. Meanwhile, the Tan-Lu fault system underwent a sinistrai strike-slip wrench movement in the late Triassic and continued into the Jurassic and Cretaceous until the early Tertiary. In the late Jurassic, development of second- or third-order wrench faults along the Tan-Lu fault system probably initiated a series of small-scale strike-slip extensional basins. Continued sinistral movement of the Tan-Lu fault until the late Eocene caused a megashear in the South Yellow Sea basin, forming a large-scale pull-apart basin. However, the Bohai basin was uplifted and severely modified during this period. h pronounced Yanshanian Orogeny (second and third) was marked by the unconformity between the early Cretaceous and late Eocene in the Bohai basin. In the late Eocene, the Indian Plate began to collide with the Eurasian Plate, forming a megasuture zone. This orogenic event, namely the Himalayan Orogeny, was probably responsible for the change of motion of the Tan-Lu fault system from left-lateral to right-lateral. The right-lateral strike-slip movement of the Tan-Lu fault caused the tectonic inversion of the South Yellow Sea basin and the pull-apart opening of the Bohai basin. Thus, the Oligocene was the main period of sedimentation in the Bohai basin as well as severe tectonic modification of the South Yellow Sea basin. After the Oligocene, the Yellow Sea and Bohai basins have maintained thermal subsidence up to the present with short periods of marine transgressions extending into the land part of the present basins.