• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.17-30
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• 2022

It has been verified in several studies that dynamic capabilities has a very important effect on the competitive advantage of technology startups. And the network has an important influence on this dynamic capability. This is even more important for start-ups that lack the resources and knowledge. Networks that directly and significantly affect dynamic capabilities have been studied mainly the value chain internal. However, network activities of start-ups are conducted not only with the value chain internal networks but also with the value chain external networks. Therefore, it is necessary to study the effect of the value chain internal and external network activity of start-ups on the dynamic capabilities, but prior studies are lacked. In this study, We make a model that encompass the value chain internal and external network for technology startups, and a study was conducted to demonstrate the effect on dynamic capabilities and competitive advantage. As a result of the study, value chain internal network activity directly and significantly affected dynamic capabilities, and value chain external network activity did not directly significantly affect dynamic capacity. And dynamic capabilities had a significant effect on competitive advantage. As confirmed through additional research, value chain external network activity affects value chain internal network activity, and through this, dynamic capabilities are strengthened, and positively affect competitive advantage.. The intensity of value chain external network activity was not significant to dynamic capabilities and the diversity of value chain external network activity had a significant effect on the competitive advantage by double mediating the value chain internal network activity and dynamic capability. Through this study, it is confirmed that the value chain internal networks is important in order for startups to strengthen their dynamic capabilities and increase their competitive advantage, and that both strong and diversified the value chain internal networks positively affects competitive advantage by enhancing dynamic capabilities.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.26-30
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• 2019

This paper proposes a new voice activity detection (VAD) method which is based on SNR and non-intrusive speech intelligibility estimation. In the conventional SNR-based VAD methods, voice activity probability is obtained by estimating frame-wise SNR at each spectral component. However these methods lack performance in various noisy environments. We devise a hybrid VAD method that uses non-intrusive speech intelligibility estimation as well as SNR estimation, where the speech intelligibility score is estimated based on deep neural network. In order to train model parameters of deep neural network, we use MFCC vector and the intrusive speech intelligibility score, STOI (Short-Time Objective Intelligent Measure), as input and output, respectively. We developed speech presence measure to classify each noisy frame as voice or non-voice by calculating the weighted average of the estimated STOI value and the conventional SNR-based VAD value at each frame. Experimental results show that the proposed method has better performance than the conventional VAD method in various noisy environments, especially when the SNR is very low.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4176-4197
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• 2020

Botnet is a type of dangerous malware. Botnet attack with a collection of bots attacking a similar target and activity pattern is called bot group activities. The detection of bot group activities using intrusion detection models can only detect single bot activities but cannot detect bots' behavioral relation on bot group attack. Detection of bot group activities could help network administrators isolate an activity or access a bot group attacks and determine the relations between bots that can measure the correlation. This paper proposed a new model to measure the similarity between bot activities using the intersections-probability concept to define bot group activities called as B-Corr Model. The B-Corr model consisted of several stages, such as extraction feature from bot activity flows, measurement of intersections between bots, and similarity value production. B-Corr model categorizes similar bots with a similar target to specify bot group activities. To achieve a more comprehensive view, the B-Corr model visualizes the similarity values between bots in the form of a similar bot graph. Furthermore, extensive experiments have been conducted using real botnet datasets with high detection accuracy in various scenarios.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.819-822
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• 2012

This study is about social network services for tourism. In detail, the relationship among relation oriented activities, relation value, commitment and loyalty is analyzed in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.12C
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• pp.1660-1668
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• 2004

This paper proposes a Voice Activity Detection (VAD) algorithm based on Radial Basis Function (RBF) network using dual threshold. The k-means clustering and Least Mean Square (LMS) algorithm are used to upade the RBF network to the underlying speech condition. The inputs for RBF are the three parameters in a Code Exited Linear Prediction (CELP) coder, which works stably under various background noise levels. Dual hangover threshold applies in BRF-VAD for reducing error, because threshold value has trade off effect in VAD decision. The experimental result show that the proposed VAD algorithm achieves better performance than G.729 Annex B at any noise level.

• Information Systems Review
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• v.10 no.1
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• pp.213-235
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• 2008

Recent reports about economic structure and changes show that global economy changes to rely on service rather than manufacturing. This phenomenon can be explained in two ways: the growth of service industry itself and the increasing reliance on service by other industry areas. The importance of service is indicated by the fact that the contribution of research and development, marketing, and finance to the revenue generation of companies is getting greater than that of manufacturing. This change in the economic structure calls for researchers' attention on the importance of service and the service-based economy. In the service-based economy, firms are considered value proposition providers and consumers actual value creators so that the concept of co-value creation becomes a key thesis to study. However, there are a variety of definitions of service and diversified measurements of service anchored in the supplier-oriented understanding of service yet. This lack of understanding of the service and the economic paradigm change causes a lot of problems in the areas of service productivity, service quality, and service innovation. Even though a new movement called service science started and rigorously pursues the way of enhancing the understanding of the economic change and service productivity, there is no framework that provides a unified view about the new role of service, service productivity, service quality, and thus service innovation. This study proposes a unified framework to provide a structured view about service provision and to facilitate the research on service. The framework is built on the concepts from previous studies about service marketing and service science: service life cycle and service networks including value activity network, resource integrator network, and capability network. We hope this study can be used as the basis for future studies on service science.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.351-365
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• 2011

The purpose of this study is to investigate how regional resource-based industries affect regional economic space. To achieve this goal, a new framework has been proposed, which integrates traditional firm network models in economic geography with the concept of value chain. Firms' networks are elaborated and classified by firm activities along with global value chain. Firm-activities are composed of primary activities and supportive activities. Primary activities include, production, sales, and marketing. Support activities are conceptualized as networks of R&D and of firm support services. On the basis of this framework, fermented soy product industry, representative industry of regional resource-based industries in Korea, is analyzed. Finally, this study shows the dynamics of regional resource-based industry.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.38-44
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• 2017

Currently, wired gables have been installed or portable storage devices have been installed for data acquisition of flying drone. In this paper, we propose a technology to transmit data wirelessly by sensing information such as battery discharge value, acceleration, and temperature by attaching RF sensor to a drone. The purpose of this paper is to design and develop the monitoring technology of agriculture drone battery usage in real time using RF sensor. In this paper, we propose a monitoring system that can check real time data of battery changed value, temperature, and acceleration during pesticide control activity of agricultural drone.