• ETRI Journal
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• v.36 no.5
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.189-207
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• 1997

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

• Asian Journal of Innovation and Policy
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• v.5 no.3
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• pp.225-250
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• 2016

This paper aims to explore the institutional difference between Tsinghua University Science Park (TusPark) in Beijing, and business incubator of Research Institute of Tsinghua University in Shenzhen (RITS), and to examine how the difference leads to different new product performance for tenants. In doing so, we use survey methodology to investigate the innovation sources, university linkages, and innovation outputs of tenants in TusPark and RITS. We found that tenants in RITS reply more on "market-driven" knowledge sources for innovation: including knowledge from customers, suppliers, and competitors. The empirical findings suggest that the technology support provided by RITS and the high dependency on "market-driven" knowledge sources jointly contribute to the better new product performance for tenants in RITS.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.683-690
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• 2004

Garment modeling and simulation is now one of the important elements in broad range of digital contents. Though there have been recent products on garment simulation, general users do not know well enough how to design a virtual costume that meets some requirements about its specific clothing pattern. In particular, Hanbok - the Korean traditional costume - has many different characteristics against western ones in the aspect of its pattern design and of draping. This paper presents a knowledge-driven approach for virtual Hanbok modeling without knowing how to make real Hanbok. First, parameterized knowledge for several fabric types art solicited using visual similarity assessment from simulated and real cloth. Secondly, based on the analysis of designer's knowledge, we defined multi-level adjustment processes of Hanbok measurements with regard to body shape features for different virtual actors. An experimental system is developed as the form of a Maya plug-in and the result shows the applicability of the proposed method.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.33-56
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• 2010

The current business environment has been characterized as less munificent, highly uncertain and constantly evolving. In this environment, the company with dynamic capability is reported to be more successful than others in building competitive advantage. Dynamic capability focuses on the link between a dynamically changing environment, strategic agility, architectural reconfiguration, and value creation. Being characterized to be flexible and adaptive to market circumstance changes, an organization with dynamic capability is described to have high resource fluidity, which represents business process, resource allocation, human resource management and incentives that make business transformation faster and easier. Successful redeployment of the resources for dynamic adaptation requires organizational forms and reward systems to be well aligned with firm's technological infrastructures and business process. The alignment is considered to be an executive level commitment. Building dynamic capability is knowledge driven; relying on new knowledge to reconfigure firm's resources. Past studies established the link between the effective execution of a knowledge-focused strategy and relevant setting of architectural elements such as human resources, structure, process and information systems. They do not, however, describe in detail the underlying processes by which architectural elements are adjusted in coordinated manners to build knowledge-driven dynamic capability. In fact, understandings of these processes are one of the top issues in IT management. This study analyzed how a Korean corporation with a knowledge-focused strategy aligned its architectural elements to develop the dynamic capability and thus create value in the dynamically changing markets. When the Korean economy was in crisis, the company implemented a knowledge-focused strategy, restructured the organization's architecture by which human and knowledge resources are identified, structured, integrated and coordinated to identify and seize market opportunity. Specifically, the following architectural elements were reconfigured: human resource, decision rights, reward and evaluation systems, process, and IT infrastructure. As indicated by sales growth, the reconfiguration helped the company create value under an extremely turbulent environment. According to Ancona et al. (2001), depending on the types of lenses the organization uses, different types of architecture will emerge. For example, if an organization uses political lenses focusing on power, influence, and conflict. the architecture that leverage power and negotiate across multiple interest groups would emerge. Similarly, if an organization uses economic lenses focusing on the rational behavior of organizational actors making choices based on the costs and benefits of action, organizational architecture should be designed to motivate and provide incentives for the actors (Smith, 2001). Compared to this view, information processing perspectives consider architecture to be designed to maximize the capacity of information processing by the actors. Using knowledge lenses, the company studied in this research established architectural elements in a manner that allows the firm to effectively structure knowledge resources to form dynamic capability. This study is revelatory single case with a historic perspective. As a result of this study, a set of propositions and a framework are derived, which can be used for architectural alignment.

• Information Systems Review
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• v.21 no.4
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• pp.197-220
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• 2019

This study aims to identify consumer motivations of open innovation project participation from digital transformation perspective. By extending a traditional intrinsic/extrinsic motivation framework, we propose a three-dimensional perspective of the self-driven, firm-driven, and sociality-driven motivations. This reveals the significance of the social effects of open innovation projects as an example of digital transformation by categorizing the motivations based on the 'influencer' of the motivation building and by highlighting the importance of sociality as an influencer. As a result, self-efficacy is identified as a key motivation when the influencer exists internally. Economic incentive and firm reputation are identified when the influencer exists externally. Finally, competition, peer evaluation and social contributions are identified when the influencer exists socially. The role of knowledge type sought through innovation projects is further introduced to explain its moderating effects on motivations. The study is validated in two steps. First, we investigate four cases of open innovation projects and examine what motivations are highlighted in each context. Second, we collect survey data from 203 online game users and ask them on their motivations. The results confirm most of our hypotheses and highlight the significance of sociality in the knowledge-seeking process in open innovation projects. This study largely contributes to digital transformation literature by extending the view of motivation and examining the moderating role of knowledge involved in the projects.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.133-141
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• 2012

Virtual project organization may be recognized as one of the promising business models in which many knowledge sources externalize through cross boundaries of knowledge-based organizations. This paper proposes a knowledge competence evaluation of virtual project organization based on the following perspectives: 1) Individual knowledge perspective, 2) Activity-oriented knowledge perspective, and 3) Knowledge-driven social network perspective. In the framework, individual knowledge competence having experienced or learned from knowledge-based activities and virtual networks in the project, should be evaluated according to the assumption that knowledge and collaboration competence depends on the activities and networks acquired proportionally by the past participation to projects. An illustrative SI example is given in order to validate the proposed evaluation and computing procedure.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes the hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes th hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.