• Journal of the Korean Society for information Management
• /
• v.25 no.4
• /
• pp.43-66
• /
• 2008

Currently be proceeded a lot of researchers for 'user information demand description' for interface of an information retrieval system or Web search engines, but user information demand description for a natural language form is a difficult situation. These reasons are as they cannot provide the semantic similarity that an information retrieval model can be completely satisfied with variety regarding an information demand expression and semantic relevance for user information description. Therefore, this study using the description logic that is a knowledge representation base of OWL and a vector model-based weight between concept, and to be able to satisfy variety regarding an information demand expression and semantic relevance proposes a decision way for perfect assistances of user information demand description. The experiment results by proposed method, semantic similarity of a polyseme and a synonym showed with excellent performance in decision.

• The Journal of Bigdata
• /
• v.8 no.1
• /
• pp.99-109
• /
• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• Journal of KIISE:Databases
• /
• v.35 no.2
• /
• pp.155-168
• /
• 2008

This paper proposes a relational security model-based RDF Web ontology access control model. The Semantic Web is recognized as a next generation Web and RDF is a Web ontology description language to realize the Semantic Web. Much effort has been on the RDF and most research has been focused on the editor, storage, and inference engine. However, little attention has been given to the security issue, which is one of the most important requirements for information systems. Even though several researches on the RDF ontology security have been proposed, they have overhead to load all relevant data to memory and neglect the situation that most ontology storages are being developed based on relational database. This paper proposes a novel RDF Web ontology security model based on relational database to resolve the issues. The proposed security model provides high practicality and usability, and also we can easily make it stable owing to the stability of the relational database security model.

• Journal of Internet Computing and Services
• /
• v.13 no.5
• /
• pp.21-31
• /
• 2012

Threat Evaluation(TE) which has air intelligence attained by identifying friend or foe evaluates the target's threat degree, so it provides information to Weapon Assignment(WA) step. Most of TE data are passed by sensor measured values, but existing techniques(fuzzy, bayesian network, and so on) have many weaknesses that erroneous linkages and missing data may fall into confusion in decision making. Therefore we need to efficient Threat Evaluation system that can refine various sensor data's linkages and calculate reliable threat values under unpredictable war situations. In this paper, we suggest new threat evaluation system based on information fusion JDL model, and it is principle that combine fuzzy which is favorable to refine ambiguous relationships with bayesian network useful to inference battled situation having insufficient evidence and to use learning algorithm. Finally, the system's performance by getting threat evaluation on an air defense scenario is presented.

• Journal of Navigation and Port Research
• /
• v.34 no.5
• /
• pp.349-354
• /
• 2010

Cheonan, Republic of Korea Navy patrol ship sank had happened by an unknown incident in the vicinity of Baekryeongdo southwest 1.6km(1 mile) sea at 21:45 on March 26, 2010. In terms of coastal researcher's point of view, it is meaningful to provide the sea condition of basic data necessary for search and rescue, more detailed predictions and inference data through the numerical simulations. Thus, in this study, we investigated the weather, wave, tide, tidal current, bottom soil conditions, and suspended sediment are investigated at the coast of Baekryeong-Daechung islands. And based on these data, the characteristics of sea conditions were analyzed. The tidal period at the time of incident corresponds between neap tide to mean tide. Until April 3-4 after March 26, the date of incident, the strongest velocity was progressed towards the spring tide. Thus, it was considered to be difficult to search and rescue operations. Also, because the ebb tide was in progress during 21:00 to 22:00, mass transport seems to be prevailed to the southeast. In particular, as the sudden turbulence due to the irregular topography existed was anticipated, we had carried out particle tracking experiment. From this experiment, depending on the situation of flow, the initial movement of the particles were directed to the southeast but it turned out moving towards the offshore based on the long term prediction. Through this result, it is considered that the scope of the search operation should be expanded towards the open sea.

• School Mathematics
• /
• v.15 no.3
• /
• pp.603-617
• /
• 2013

The major purpose of this article is to examine what kind of gap exists between mathematically gifted students' probability knowledge and the reality actually applying that knowledge and then analyze the cause of the gap. To attain the goal, 23 elementary mathematically gifted students at the highest level from G region were provided with problem situations internalizing a probability and expectation, and the problems are in series in which conditions change one by one. The study task is in a gaming situation where there can be the most reasonable answer mathematically, but the choice may differ by how much they consider a certain condition. To collect data, the students' individual worksheets are collected, and all the class procedures are recorded with a camcorder, and the researcher writes a class observation report. The biggest reason why the students do not make a decision solely based on their own mathematical knowledge is because of 'impracticality', one of the properties of probability, that in reality, all things are not realized according to the mathematical calculation and are impossible to be anticipated and also their own psychological disposition to 'avoid loss' about their entry fee paid. In order to provide desirable probability education, we should not be limited to having learners master probability knowledge included in the textbook by solving the problems based on algorithmic knowledge but provide them with plenty of experience to apply probabilistic inference with which they should make their own choice in diverse situations having context.

• Journal of the Korean Institute of Gas
• /
• v.26 no.4
• /
• pp.41-57
• /
• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.4B
• /
• pp.221-232
• /
• 2012

In this study, the model enhancing the safety of reservoirs and reducing the downstream flood damage by reservoirs system operation during abnormal flood was developed. Linear programming was used for the optimal reservoirs system operation during an abnormal flood and fuzzy inference system was introduced to solve the uncertainty problem which is included in hydrological factors like inflow, water level and inflow variation of reservoir operation. The linear programming model determined the optimal reservoir system operation rules and could be used in situation where water demands varies rapidly during the abnormal flood events using fuzzy control technique. In this study, the optimal reservoirs system operation for Andong and Imha reservoirs located in the upper basin of Nakdong river was performed in order that the design flood discharge at Andong city would not be exceeded for the design flood of 100 year and PMF(Probable Maximum Flood). And the model that determines the release according to the downstream flow discharge, the reservoir storage, the inflow and the inflow variation of each reservoir was developed using the optimal system operation result and fuzzy control technique. The developed model consisted of 224 fuzzy rules according to the conditions of Andong reservoir, Imha reservoir and Andong city. And the release from each reservoir could be determined when the current data are used as input data through the developed GUI.

• The KIPS Transactions:PartB
• /
• v.9B no.1
• /
• pp.1-6
• /
• 2002

Many customers consult the Internet before making purchase goods and using contents. The systems in the Internet could store a lot of data and classify the data into information to get relationship between a company and customers. To do that, let's consider a knowledge-assisted hybrid system that utilizes individually a customer's preference to make an optimal solution in the his/her decision making. The knowledge made by using the preference is employed to select an domain set appropriate to him/her business, and the process of selecting definitely provides the customer some benefits: elimination of discomfort from unknown information and reduction of costs and search time for forming an suitable domain set. To effectively adopt individual customer's preference and actively adapt change of business situation, this study propose an architecture of the system which includes rule presentations and an inference engine, and integrates a knowledge-based component into a quadratic programming component. In the experimental results, it is found that a knowledge-assisted hybrid system implemented by this idea is more flexible than existing systems in extension of knowledge about an customer's preference and goes beyond the traditional models.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.59-66
• /
• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.