• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.549-554
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• 2016

ConceptNet is a common sense knowledge base which is formed in a semantic graph whose nodes represent concepts and edges show relationships between concepts. As it is difficult to make knowledge base integrity, a knowledge base often suffers from incompleteness problem. Therefore the quality of reasoning performed over such knowledge bases is sometimes unreliable. This work presents neural tensor networks which can alleviate the problem of knowledge bases incompleteness by reasoning new assertions and adding them into ConceptNet. The neural tensor networks are trained with a collection of assertions extracted from ConceptNet. The input of the networks is two concepts, and the output is the confidence score, telling how possible the connection between two concepts is under a specified relationship. The neural tensor networks can expand the usefulness of ConceptNet by increasing the degree of nodes. The accuracy of the neural tensor networks is 87.7% on testing data set. Also the neural tensor networks can predict a new assertion which does not exist in ConceptNet with an accuracy 85.01%.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.153-165
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• 2010

In this paper, we proposed a Proactive Application Service Engine (PASE) supporting tailor-made distributed application services based on the Distributed Object Group Framework (DOGF) efficiently managing distributed objects, in the viewpoint of distributed application, composed application on network. The PASE consists of 3 layers which are the physical layer, the middleware layer, and the application layer. With the supporting services of the PASE, the grouping service manages the data gathered from H/W devices and the object's properties for application by user's request as a group. And the security service manages the access of gathered data and the object according to user's right. The data filtering service executes the filtering function to provide application with gathered data. The statistics service analysis past data. The diagnostic service diagnoses a present condition by using the gathered data. And the prediction service predicts a future's status based on the statistics service and the diagnostic service. For verifying the executability of the PASE's services, we applied to a greenhouse automatic control application in ubiquitous agriculture field.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.285-290
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• 2010

인터넷이 대중화됨에 따라 누구나 쉽게 자신의 의견을 온라인상에 표현할 수 있게 되었다. 그 결과 생각이나 느낌을 나타내는 의견 데이터들의 양이 급속도로 방대해졌으며, 이러한 데이터들을 이용한 여러 응용 사례들의 등장으로, 효율적인 검색 및 자동 분류 기술이 요구되고 있다. 이런 기술적 흐름에 맞추어 의견 데이터 분류에 관한 여러 연구들이 이루어져 왔다. 이러한 의견 분류에 대한 연구들을 살펴보면, 분류를 위해 자질(Feature)로서 사용한 단일어(Single word)가 아닌 2개 이상의 N-gram 단어, 어휘 구문 패턴 및 통사 구문 패턴 등을 사용한다. 특히, 패턴은 단일어나 N-gram 단어에 비해 유연하고, 언어학적으로 풍부한 정보를 표현할 수 있기 때문에 이를 주요 연구 주제로 사용되었다. 그럼에도 불구하고, 이러한 연구들은 주로 영어에 대한 연구들이었으며, 한국어에 패턴을 적용하여 주관성을 갖는 문장을 분류하거나, 극성을 분류하는 연구들은 아직 미비하다. 한국어의 특색으로 한국어는 용언의 활용이 발달되어 있어, 어미의 변화가 다양하며, 그 변화에 따라 의미가 미묘하게 변화한다. 그러나 기존 한국어에 대한 의견 분류 연구들은 단어의 핵심 의미만을 파악하기 위해 어미 부분을 제거하고 어간만을 취해서 처리하여 어미에 대한 의미변화를 고려하지 못하므로 분류 정확도가 영어권에 연구 결과에 비해 떨어진다. 그래서 본 연구는 영어에 적용된 패턴을 이용한 기존 방법들을 정리하고, 그 방법들 중에서 극성을 지닌 문장성분 패턴을 한국어에 적용하였다. 그리고 어미의 변화에 대한 패턴을 추출하여 이 변화가 의견 분류의 성능에 미치는 영향을 분석하였다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.65-70
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• 2019

In recently, ship trajectory data consisting of ship position, speed, course, and so on can be obtained from the Automatic Identification System device with which all ships should be equipped. These data are gathered more than 2GB every day at a crowed sea port and used for analysis of ship traffic statistic and patterns. In this study, we propose a method to process ship trajectory data efficiently with distributed computing resources using MapReduce algorithm. In data preprocessing phase, ship dynamic and static data are integrated into target dataset and filtered out ship trajectory that is not of interest. In mapping phase, we convert ship's position to Geohash code, and assign Geohash and ship MMSI to key and value. In reducing phase, key-value pairs are sorted according to the same key value and counted the ship traffic number in a grid cell. To evaluate the proposed method, we implemented it and compared it with IALA waterway risk assessment program(IWRAP) in their performance. The data processing performance improve 1 to 4 times that of the existing ship trajectory analysis program.

• Annual Conference of KIPS
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• 2012.11a
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• pp.844-847
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• 2012

인공위성의 개발은 오랜 기간에 걸쳐 다양한 분야의 전문가들에 의해 개발된 결과물들이 통합되어 완성될 수 있다. 위성개발과 같이 많은 개발자가 공동으로 작업하여 하나의 결과물을 생산하는 경우 개발과정에서 방대한 양의 문서작업이 수반된다. 특히 비행소프트웨어와 같이 서로 다른 개발자에 의해 작성된 코드들이 하나의 이미지로 통합되어 빌드될 경우 발생하는 문제점들을 해결하고 요구되는 기능들을 디버깅하기 위해서는 개발과정 및 소스코드에 대한 문서들이 필수적이다. 이러한 소프트웨어 설계에 대한 문서는 그 양이 방대하고 소스코드와의 연계성이 필요하기 때문에 소스코드를 작성한 각 개발자들이 직접 수작업으로 문서를 작성하였다. 예를 들면, 기존의 위성비행소프트웨어 개발과정에서는 이러한 문서들 중 전체 위성비행소프트웨어의 단위 코드별 입출력, 수행기능 등의 상세 설계 내용을 기록하는 SDD(Software Design Description)는 개발자가 작성한 코드를 기반으로 수작업을 통하여 작성되었다. 이러한 작성방식은 작성자의 입력오류가 발생할 수도 있으며 소프트웨어 개발과 별도로 수작업이 요구되어 문서작성에 소요되는 시간적 손해가 발생하게 된다. 유럽에서는 이러한 문제점을 보완하기 위하여 C, C++, C#, JAVA, VHDL 등 다양한 언어를 사용하는 소프트웨어 개발에 적용 가능한 자동적 문서작성 도구인 Doxygen을 설계 및 개발문서 작성에 활용하고 있다. Doxygen은 PDF, HTML, Latex, RTF 등 다양한 출력 포맷도 지원한다. 본 논문에서는 Doxygen을 활용하여 위성비행소프트웨어 개발문서의 작성 시 소요시간을 단축하고 소스코드로부터 해당 설계 내용을 추출하여 자동적으로 문서를 작성할 수 있는 방안에 대하여 소개한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.127-134
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• 2021

Recently, acquisition of high quality data has become an important issue as the expansion of the big data industry. In order to acquiring high quality data, accurate evaluation of data quality should be preceded first. The quality of data can be evaluated through meta-information such as statistics on data, and the task to extract such meta-information is called data profiling. Until now, data profiling software has typically been provided as a component or an additional service of traditional data quality or visualization tools. Hence, it was not suitable for utilizing directly in various environments. To address this problem, this paper presents the development result of data profiling software based on a microservice architecture that can be serviced in various environments. The presented data profiler provides an easy-to-use interface that requests of meta-information can be serviced through the restful API. Also, a proposed data profiler is independent of a specific environment, thus can be integrated efficiently with the various big data platforms or data analysis tools.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.33-42
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• 2022

In the existing intelligent command control system study, the analysis results of the commander's battlefield situation questions are provided from knowledge-based situation data. Analysis reporters write these results in various expressions of natural language. However, it is important to analyze situations about information and intelligence according to context. Analyzing the battlefield situation using artificial intelligence is necessary. We propose a virtual dataset generation method based on battlefield simulation scenarios in order to provide a dataset necessary for the battlefield situation analysis based on artificial intelligence. Dataset is generated after identifying battlefield knowledge elements in scenarios. When a candidate hypothesis is created, a unit hypothesis is automatically created. By combining unit hypotheses, similar identification hypothesis combinations are generated. An aggregation hypothesis is generated by grouping candidate hypotheses. Dataset generator SW implementation demonstrates that the proposed method can be generated the virtual battlefield situation dataset.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.256-264
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• 2023

Smart farm technology using IoT is one of the technologies that can increase productivity and improve the quality of agricultural products in agriculture, which is facing difficulties due to the decline in rural population, lack of rural manpower due to aging, and increase in diseases and pests due to climate change. . Smart farms using existing IoT simply monitor farms, implement smart plant growers, and have automatic greenhouse opening and closing systems. This paper implements a smart farm system based on MQTT, an industry standard protocol for the Internet of Things, and Node-RED, a representative development middleware for the Internet of Things. First, data is extracted from Arduino sensors, and data is collected and transmitted from IoT devices using the MQTT protocol. Then, Node-RED is used to process MQTT messages and store the sensing data in real time in MongoDB, a representative NoSQL, to store the data. Through this smart farm system, farm managers can use a computer or mobile phone to check sensing information on the smart farm in real time, anytime, anywhere, without restrictions on time and space.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.51-60
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• 2024

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.