• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1516-1539
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• 2022

This article shows a set of physical information fusion IoT systems that we designed for smart buildings. Its essence is a computer system that combines physical quantities in buildings with quantitative analysis and control. In the part of the Internet of Things, its mechanism is controlled by a monitoring system based on sensor networks and computer-based algorithms. Based on the design idea of the agent, we have realized human-machine interaction (HMI) and machine-machine interaction (MMI). Among them, HMI is realized through human-machine interaction, while MMI is realized through embedded computing, sensors, controllers, and execution. Device and wireless communication network. This article mainly focuses on the function of wireless sensor networks and MMI in environmental monitoring. This function plays a fundamental role in building security, environmental control, HVAC, and other smart building control systems. The article not only discusses various network applications and their implementation based on agent design but also demonstrates our collaborative information fusion strategy. This strategy can provide a stable incentive method for the system through collaborative information fusion when the sensor system is unstable in the physical measurements, thereby preventing system jitter and unstable response caused by uncertain disturbances and environmental factors. This article also gives the results of the system test. The results show that through the CPS interaction of HMI and MMI, the intelligent building IoT system can achieve comprehensive monitoring, thereby providing support and expansion for advanced automation management.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.713-719
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• 2010

Collaborative spectrum sensing has been found to be an effective means for detecting the activity of primary users in a fading environment. Most previous works on collaborative spectrum sensing are based on the assumption that the local spectrum sensing decisions of secondary users are statistically independent. However, it may not hold in some practical situations. In this paper, we consider a cognitive radio network where the local spectrum sensing decisions of secondary users are statistically correlated with the same level of correlation if they are next to each other in location and statistically independent, otherwise. Then, for the system, we analyzed the performance of the collaborative spectrum sensing with the AND and the OR fusion rules and found that the scheme with the AND fusion rule performs better than the one with OR fusion rule when the degree of correlation is significant.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.642-650
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• 2010

Collaborative spectrum sensing allows secondary users scattered in location to work together to detect the activity of primary users and has been shown to significantly reduce the performance degradation due to fading phenomenon. Most previous works on collaborative spectrum sensing are based on the assumption that local spectrum sensing decisions of secondary users are statistically independent. However, it may not hold in some practical situations with shadowing effect. In this paper, we consider the case that the secondary users are evenly spaced in the form of a linear array and only adjacent secondary users are statistically correlated, and analyze the effect of the statistical correlation on the performance of collaborative spectrum sensing and the throughput of a cognitive radio network. Here we assumed the AND and OR fusion rules for combining the local decisions of secondary users. The analysis showed that the AND fusion rule achieves higher throughput than the OR fusion rule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.229-240
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• 2012

Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8A
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• pp.633-639
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• 2009

For operation of IEEE 802.22 WRAN system, spectrum sensing is a essential function. However, due to strict sensing requirement of WRAN system, spectrum sensing process of CR nodes require long quiet period. In addition, CR nodes sometimes fail to detect licensed users due to shadowing effect of wireless communication environment. To overcome this problem, CR nodes collaborate with each other for increasing the sensing reliability or mitigating the sensitivity requirement. A general approach for decision fusion, the "k out of N" rule is often taken as the decision fusion rule for its simplicity. However, since k out of N rules can not achieve better performance than the highest SNR node when SNR is largely different among CR nodes, the local SNR of each node should be considered to achieve better performance. In this paper, we propose two novel data fusion methods by utilizing confidence vector which represents the confidence level of individual sensing result. The simulation results show that the proposed schemes improve the signal detection performance than the conventional data fusion algorithms.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.22-25
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• 2005

The 10 Year Implementation Plan for a Global Earth Observation System of Systems (GEOSS), which was endorsed at the Third Earth Observation Summit in Brussels in February, 2005, emphasizes the importance of data management facilities for diverse and large-volume Earth Observation data from inhomogeneous information sources. A three year research plan for addressing this key target of GEOSS has just approved as the first step by the Japanese government. The goals of this research are, (1) to develop a data management core system consisting of data integration and information fusion functions and interoperability and information service functions; (2) to establish data and information flows between data providers and users; (3) to promote application studies of data integration and information fusion, especially in the fields of weather forecasting, flood forecasting, agricultural management, and climate variability and changes. The research group involves leading scientists on information science and technology, who have been developing giant data archive servers, storage area networks, metadata models, ontology for the earth observations. They are closely cooperating with scientists on earth sciences, water resources management, and agriculture, and establishing an effective collaborative research framework.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.462-472
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• 2021

With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2801-2816
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• 2016

Owing to their low scalability, weak support on big data, insufficient data collaborative analysis and inadequate situational awareness, the traditional methods fail to meet the needs of the security data analysis. This paper proposes visualization methods to fuse the multi-source security data and grasp the network situation. Firstly, data sources are classified at their collection positions, with the objects of security data taken from three different layers. Secondly, the Heatmap is adopted to show host status; the Treemap is used to visualize Netflow logs; and the radial Node-link diagram is employed to express IPS logs. Finally, the Labeled Treemap is invented to make a fusion at data-level and the Time-series features are extracted to fuse data at feature-level. The comparative analyses with the prize-winning works prove this method enjoying substantial advantages for network analysts to facilitate data feature fusion, better understand network security situation with a unified, convenient and accurate mode.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.457-466
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• 2004

Recommendation is to offer information which fits user's interests and tastes to provide better services and to reduce information overload. It recently draws attention upon Internet users and information providers. The collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users' preferences for the target item or the target user's preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate integration of diverse information to solve the sparsity problem and selecting the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.131-153
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• 2010

The fusion of learning, information, and technology presents dynamic challenges for all librarians, educators and students in 21st century libraries and schools. At the heart of this fusion is the growth of a pervasive, integrated information environment characterized by vast quantities of digital content, open choice, collaborative and participatory digital spaces, and the transition of the web environments from consumption of information to creation of information. This environment heralds important opportunities for librarians and teachers to rethink, re-imagine and recreate a dynamic approaches to information literacy instruction. Drawing on an extensive body of research undertaken through the Center for International Scholarship in School Libraries (CISSL), and published research on both information literacy and constructivist learning, this paper provides a critical examination of the current status of information literacy: its multiple conceptualizations, competing models, viewpoints, and its operationalizations in educational and library environments. The paper will challenge information literacy practices which center on simplistic, reductionist approaches to information literacy development, and the separation of information process and knowledge content. In particular it will address apparent contradictions in espoused conceptions of information literacy which revolve around "knowledge": knowledge construction, critical thinking, problem solving and the development of knowledgeable people; and information literacy practices which revolve around "information": a predominant focus on skills of access and evaluation of resources and with less attention given to engaging with found information to develop deep knowledge and understanding. The paper will present a series of challenges for moving forward with information literacy agendas in libraries and schools.