• Journal of the Institute of Convergence Signal Processing
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• 제12권2호
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• pp.113-118
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• 2011

In this paper, we develop a speaker gender classification technique using collaborative sensor fusion for use in a wireless sensor network. The distributed sensor nodes remove the unwanted input data using the BER(Band Energy Ration) based voice activity detection, process only the relevant data, and transmit the hard labeled decisions to the fusion center where a global decision fusion is carried out. This takes advantages of power consumption and network resource management. The Bayesian sensor fusion and the global weighting decision fusion methods are proposed to achieve the gender classification. As the number of the sensor nodes varies, the Bayesian sensor fusion yields the best classification accuracy using the optimal operating points of the ROC(Receiver Operating Characteristic) curves_ For the weights used in the global decision fusion, the BER and MCL(Mutual Confidence Level) are employed to effectively combined at the fusion center. The simulation results show that as the number of the sensor nodes increases, the classification accuracy was even more improved in the low SNR(Signal to Noise Ration) condition.

• Convergence Security Journal
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• 제17권5호
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• pp.57-61
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• 2017

The National research networks exist to support advanced science and technology in each country. The national research network must meet the requirements of science and technology in each field and continuously develop around the continuously changing environment. In recent years, demand for data - oriented science and technology research forms has been increasing. To cope with this demand, advanced national research networks are establishing mid - to long - term strategies. In this paper, the status of the advanced research network and trends and requirements of the national research network are analyzed from the viewpoint of the change of the research network environment, the change of science / technology, and the change of industry and life. In order to respond to the analyzed requirements, we propose mid - to long - term development directions and plans for establishing future network backbone, providing user - centered collaborative research environment, providing global collaborative network service, and providing high - tech science and technology research data information protection service.

• Journal of Digital Convergence
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• 제10권6호
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• pp.189-196
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• 2012

In this paper, we propose a Scorm-based Sequencing & Navigation Model for Collaborative Learning. It is an e-Learning process control model that is used to efficiently and graphically defining Scorm's content aggregation model and its sequencing prerequistites through a formal approach. To define a process based model uses the expanded ICN(Information Control Net) model. which is called SCOSNCN(SCO Sequencing & Navigation Control Net). We strongly believe that the process-driven model delivers a way of much more convenient content aggregating work and system, in terms of not only defining the intended sequence and ordering of learning activities, but also building the runtime environment for sequencing and navigation of learning activities and experiences.

• The Journal of the Convergence on Culture Technology
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• 제4권3호
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• pp.63-74
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• 2018

This study suggested cooperative governance cases and improvement strategies between local welfare centers and urban regeneration support centers, centered on urban revitalization areas around Seoul Station, by applying FGI. Based on the analysis of the contents of FGI participating in the cooperative governance process, it was proved that the common role and identity between the local welfare center and the urban regeneration support center were regional based activities. And the advantage of constructing collaborative governance was analyzed by four ways. In addition, this study presented improvement strategies from a microscopic and macro perspective. The results of this study suggest that the change in the curriculum of social workers' education and the expansion of the scope of the practitioner in the Urban Regeneration Business Act propose.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제20권8호
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• pp.1407-1414
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• 2016

In this study, we develop that Location Recommending System using personal emotion information based on Collaborative Filtering. Previous Location Recommending System recommended a place visited by the user of the rating or the pattern of location for the user place. These systems are not high user satisfaction because that dose not consider the user status or have not objectively the information. Using user's personal emotion information to recommend a high-affinity users who have visited the place felt similar emotions objectively can improve user satisfaction with the place. In this study, a user using a mobile application directly register the recognized emotion information using the current position and bio-signal, and using the registered information measuring the similarity of user with a similarity emotion, predicts a preference for the place it is recommended to emotional place. The system consists of a user interface, a database, a recommendation module.

• Journal of Digital Convergence
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• 제9권5호
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• pp.175-191
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• 2011

To improve cooperative performance, organizations are increasingly developing its technology capabilities to maintain performance and satisfaction with its collative partners. The data for 143 collaboration experiences from domestic manufacturing firms were used to test hierarchical regression model with trust moderating between technology capabilities and collaborative performance and satisfaction. The result suggests that technological human resources, technological organization, technological leadership are the most important factors to affect positively collaborative performance and satisfaction, and there are interaction effects of trust on technological assets and technological organization with collaborative performance and satisfaction.

• Journal of Digital Convergence
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• 제14권8호
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• pp.313-318
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• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• The Journal of the Convergence on Culture Technology
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• 제4권1호
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• pp.187-192
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• 2018

Mind map is a very useful technique for brainstorming. Therefore, it can be effectively used for collaboration as a creative group conception. However, existing mind map software is not possible to collaborate because it is difficult to share mind map or discuss among members. In this study, we developed a mind map system that can support collaborative work as well as individual authoring and editing. This can incorporate individual mind maps of members into mind map of team, share and discuss them, and modify the mind map. This is convenient because it does not require a separate program installation, and verifies usability to support the process of collaborative process through usability evaluation.

• Journal of the Korea Society of Computer and Information
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• 제26권12호
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• pp.85-97
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• 2021

In this paper, in order to support rapid and effective decision-making and response in disaster situations, we identified the author's organization of academic research papers and conducted a collaborative relationship analysis study based on this. For this purpose, 2,308 papers in 69 Korean academic journals classified by disaster and safety type were selected for analysis and experimental data were constructed based on the Korea Science Citation Database (KSCD) and institutional identification data provided by KISTI. Collaborative relationship analysis was conducted for each of the four units (Institution, Institution type, Institution region and University department type). First, statistical status such as frequency of appearance was compared, and basic properties and main centrality index of each co-occurrence network were calculated and analyzed using Social Network Analysis Method. In addition, a visualization map was created and presented for each network so that the collaborative relationship could be viewed and understood as a whole. The results of this study are expected to contribute to the search activities of institutions and cooperative groups that support effective disaster response and to lay the foundation for the information service system.

• Journal of Internet of Things and Convergence
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• 제9권6호
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.