• International Journal of Knowledge Content Development & Technology
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• v.14 no.4
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• pp.65-84
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• 2024

The study was conducted on the awareness and application of internet of things in universities libraries in Kwara State, Nigeria. the study formulated used four research questions and used eighty five (85) samples as the population using total enumerative sampling techniques. A survey method was used in undertaking the study, in which answers were sought on the level of awareness of the internet of things in universities libraries in Kwara State, the extent of application of the internet of things in universities libraries in Kwara State, the benefit of internet of things in universities libraries in Kwara State, the challenges faced in the application of internet of things in universities libraries in Kwara State. The data collected from the study were analyzed using frequency tables and percentage. The study discovered that there the students are aware of the internet of things in universities libraries in Kwara State and the benefit of internet of a things include: Device in the IoT platforms are heterogeneous and are based on different hardware platforms and networks, It gives the high level of interoperability and interconnectivity, IoT platform has sensors which detect or measure any changes in the environment to generate data that can report on their status or even interact with the environment, IoT comes with the combination of algorithms and computation, software & hardware that makes it smart and Anything can be interconnected with the global information and communication infrastructure and the study identified data interpretation problem, Lack of skilled and specialized workers, Cost and Challenges in online security as well as Software complexity are major challenges faced in the application of internet of things in universities libraries in Kwara State. In conclusion the study made some recommendations which include that: Future libraries should be equipped with new technologies and networking devices as soon as possible. As this will be essential for users and librarians to have sufficient knowledge about IOT technologies.

• Spatial Information Research
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• v.18 no.5
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• pp.93-105
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• 2010

Spatial neighborhoods are spaces which are relate to target space. A 3D spatial query which is a function for searching spatial neighborhoods is a significant function in spatial analysis. Various methodologies have been proposed in related these studies, this study suggests an adjacent based methodology. The methodology of this paper implements topological data for represent a adjacency via using network based topological data model, then apply modifiable Dijkstra's algorithm to each topological data. Results of ordering analysis about an adjacent space from a target space were visualized and considered ways to take advantage of. Object of this paper is to implement a 3D spatial query for searching a target space with a adjacent relationship in 3D space. And purposes of this study are to 1)generate adjacency based 3D network data via network based topological data model and to 2)implement a 3D spatial query for searching spatial neighborhoods by applying Dijkstra's algorithms to these data.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.21-29
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• 2008

The High Level Architecture(HLA) specifies a framework for interoperation between heterogeneous simulators, and Run-Time Infrastructure(RTI) is a implementation of the HLA Interface Specification. The Data Distribution Management(DDM) services, one category of IEEE 1516 HLA/RTI management services, control filters for data transmission and reception of data volume among simulators. In this paper, we propose design concept of DDM and show its implementation for light-weighted RTI. The design concept of DDM is to minimize total amount of message that each federate and a federation process generate using the rate of RTI service execution. The design of our proposed DDM follows that a data transfer mechanism is differently applied as the rate of RTI service execution. A federate usually publishes or subscribes data when it starts. The federate constantly updates the data and modifies associated regions while it continues to advance its simulation time. Therefore, the proposed DDM design provides fast update or region modification in exchange of complex publish and subscribe services. We describe how to process the proposed DDM in IEEE 1516 HLA/RTI and experiment variable scenarios while modifying region, changing overlap ratio, and increasing data volume.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.226-241
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• 2014

LOD(Linked Open Data) is composed of RDF triples which are based on ontologies. They are identified, linked, and accessed under the principles of linked data. Publications of LOD data sets lead to the extension of LOD cloud and ultimately progress to the web of data. However, if ontologically the same things in different LOD data sets are identified by different URIs, it is difficult to figure out their sameness and to provide trustworthy links among them. To solve this problem, we suggest a Trustworthiness Improving Link Evaluation, TILE for short, technique. TILE evaluates links in 4 steps. Step 1 is to consider the inference property of syntactic elements in LOD data set and then generate RDF triples which have existed implicitly. In Step 2, TILE appoints predicates, compares their objects in triples, and then evaluates links between the subjects in the triples. In Step 3, TILE evaluates the predicates' syntactic property at the standpoints of subject description and vocabulary definition and compensates the evaluation results of Step 2. The syntactic elements considered by TILE contain RDFS, OWL, OWL2 which are recommended by W3C. Finally, TILE makes the publisher of LOD data set review the evaluation results and then decide whether to re-evaluate or finalize the links. This leads the publishers' responsibility to be reflected in the trustworthiness of links among the data published.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.55-63
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• 2020

Earthwork automation has emerged as a promising technology in the construction industry, and the application of earthwork automation technology is starting from the acquisition and processing of point cloud data of the site. Point cloud data has more than a million data due to vast extent of the construction site, and the processing time of the original point cloud data is critical because it takes tens or hundreds of hours to generate a Digital Terrain Model (DTM), and enhancement of the processing time can largely impact on the efficiency of the modeling. Currently, a benchmark program (BP) is actively used for the purpose of both point cloud data processing and BIM design as an integrated program in Korea, however, there are some aspects to be modified and refined. This study modified the BP, and developed an updated program by adopting a compile-based development environment, newly designed UI/UX, and OpenGL while maintaining existing PCD processing functions, and expended compatibility of the PCD file formats. We conducted a comparative test in terms of loading speed with different number of point cloud data, and the results showed that 92 to 99% performance increase was found in the developed program. This program can be used as a foundation for the development of a program that reduces the gap between design and construction by integrating PCD and earthwork BIM functions in the future.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.239-249
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• 2010

The Geostationary Ocean Color Imager (GOCI) data-processing system (GDPS), which is a software system for satellite data processing and analysis of the first geostationary ocean color observation satellite, has been developed concurrently with the development of th satellite. The GDPS has functions to generate level 2 and 3 oceanographic analytical data, from level 1B data that comprise the total radiance information, by programming a specialized atmospheric algorithm and oceanic analytical algorithms to the software module. The GDPS will be a multiversion system not only as a standard Korea Ocean Satellite Center(KOSC) operational system, but also as a basic GOCI data-processing system for researchers and other users. Additionally, the GDPS will be used to make the GOCI images available for distribution by satellite network, to calculate the lookup table for radiometric calibration coefficients, to divide/mosaic several region images, to analyze time-series satellite data. the developed GDPS system has satisfied the user requirement to complete data production within 30 minutes. This system is expected to be able to be an excellent tool for monitoring both long-term and short-term changes of ocean environmental characteristics.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used