• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.114-123
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• 2019

Tracking ICT applications on construction business processes is critical to the success of ICT-applied construction projects. Existing IDEF0 is a representative modeling method for visualizing and analyzing business processes. It defines a construction production process into a visual information model, hence, encouraging the project participant to understand the activities, their deliverable, and control flow of the process. However, IDEF0 dose not lend itself to ICT-applied construction processes, because it does not provide a mean to define how, in what order, by which each and every activity that ICT applied implements. This paper presents a new business modeling method that improves the traceability of ICT application (IAMB: ICT Application tracking Model for Business process) for construction management. The IAMB contributes to handle the sophisticated features of construction management processes to which ICT are applied. The method categorizes the process into three types: management, construction, and information exchange. The validity of IAMB was confirmed by analyzing the performance when it is used for tracking each modeling step of lift reservation process which making use of ICT. The test case provides an admissible evidence that the method encourage to define who, what, how, which order, and by which ICT tools the construction process exchanges production information.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of Navigation and Port Research
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• v.31 no.8
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• pp.733-739
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• 2007

Demand for Sea & Air intermodal transport has increased between north-China costal cities and Incheon since China's international airline network was not established completely. It will be big opportunity for Incheon to be logistics hub of Sea & Air intermodal transport in the north-east Asia, musing large sea-air cargoes to be transferred at the port of Incheon. Therefore, this study aims to propose competitive strategy on Sea & Air intermodal transport system of Incheon. In this analysis results, this paper shows that sea & air cargoes rather from china to U.S. than from China to Europe is very significant, considering geographically for Incheon and is also devote to not only providing high quality services but also activating RFS(Road Feeder Service) system, enlarging toward Shanghai, Weihai, and Yantai.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.299-304
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• 2018

Brand Extension is a leading marketing strategy that makes use of brand assets, and it aims to see the success of a new product of a brand by utilizing the level of consumers' brand awareness, loyalty, resemblance, and image. However, the review of previous studies revealed that there were cases of fashion brands extending into coffee brands, but not vice versa. The study aims to suggest a new proposal to extend the coffee brand, the most preferred brand by consumers, to an effective office space service. It proposes making a napping and working area for the working Starbucks lovers who often go business trips and like to work in a private space. The researcher expects that the study will inform about the efficient manner to use office spaces including shared service and area.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.33-47
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• 2007

The requirements have been changed during development progresses, since it is impossible to define all of software requirements. These requirements change leads to mistakes because the developers cannot completely understand the software's structure and behavior, or they cannot discover all parts affected by a change. Requirement changes have to be managed and assessed to ensure that they are feasible, make economic sense and contribute to the business needs of the customer organization. We propose a feature-oriented requirements change management method to manage requirements change with value analysis and feature-oriented traceability links including intermediate catalysis using features. Our approach offers two contributions to the study of requirements change: (1) We define requirements change tree to make user requirements change request generalize by feature level. (2) We provide overall process such as change request normalization, change impact analysis, solution dealing with change request, change request implementation, change request evaluation. In addition, we especially present the results of a case study which is carried out in asset management portal system in details.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.53-58
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• 2024

This study is about the technology convergence analysis of e-commerce related patents containing Artificial Intelligence applied for in Korea. The relationships between core technologies were analyzed and visualized using social network analysis. As a result of social network analysis, the core IPC codes that make up the mutual technology network in e-commerce related patents containing Artificial Intelligence were found to be G06Q, G06F, G06N, G16H, G10L, H04N, G06T, and A61B. In particular, it can be confirmed that there is an important convergence of data processing-related technologies such as [G06Q-G06F], [G06Q-G06N], and voice and image signals such as [G06Q-G10L], [G06Q-H04N], and [G06Q-G06T]. Using this research method, it is possible to identify future technology trends in e-commerce related patents and create new Business Models.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.417-428
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• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.203-211
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• 2011

Recently, information communication technologies such as smartphone or mobile app greatly affect various application fields including geo-spatial domain. And development scheme of mobile web app or hybrid app regards as the most important computing technology which is combined each advantage of mobile app and mobile web. Despite these trends, it is general case that satellite images are used for the background image for other contents services. With this motivation, hybrid app for geo-metadata as the base for dissemination and service is designed and implemented as the prototype, in this study. At the design stage, HTML5, which is the core technology on an international standardization process for hybrid app, is applied. In the implementation, PhoneGap and Sencha Touch as mobile SDK(Software Development Kit) supporting HTML5 on cross-platform in open sources are used. In prototype, some KOMPSAT-2 images covering small area and mandatory elements in geo-metafata standard are tested. As mobile industry applications and business service models based on satellite images on mobile platform are progressing and diversifying, it is expected that this approach and implemented prototype are considered as an important reference.