• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.117-130
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• 2015

A least squares method for adjusting the horizontal network satisfies the conditions which is minimizing the sum of the squares of errors based on probability theory. This research compared accuracy of 3rd cadastral control points adjusted by traditional and least square method with respect to the result of Network-RTK. Test results showed the least square method more evenly distribute closure error than traditional method. Mean errors of least square and traditional adjusting method are 2.7cm, 2.2cm respectively. In addition, blunder in angle observations can be detected by comparing position errors which calculated by forward and backward initial coordinates. However, distance blunder cannot offer specific observation line occurred mistake because distance error propagates several observation lines which have similar directions.

• Journal of KIISE:Information Networking
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• v.33 no.2
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• pp.131-138
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• 2006

To achieve security in wireless sensor networks(WSN), it is important to be able to encrypt and authenticate messages sent among sensor nodes. Due to resource constraints, many key agreement schemes used in general networks such as Diffie-Hellman and public-key based schemes are not suitable for wireless sensor networks. The current pre-distribution of secret keys uses q-composite random key and it randomly allocates keys. But there exists high probability not to be public-key among sensor nodes and it is not efficient to find public-key because of the problem for time and energy consumption. To remove problems in pre-distribution of secret keys, we propose a new cryptographic key management protocol, which is based on the clustering scheme but does not depend on probabilistic key. The protocol can increase efficiency to manage keys because, before distributing keys in bootstrap, using public-key shared among nodes can remove processes to send or to receive key among sensors. Also, to find outcompromised nodes safely on network, it selves safety problem by applying a function of lightweight attack-detection mechanism.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.231-235
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• 2008

Recently we witnessed a few scandals on plagiarism among academic paper and novels. Plagiarism on documents is getting worse more frequently. Although plagiarism on English had been studied so long time, we hardly find the systematic and complete studies on plagiarisms in Korean documents. Since the linguistic features of Korean are quite different from those of English, we cannot apply the English-based method to Korean documents directly. In this paper, we propose a new plagiarism detecting method for Korean, and we throughly tested our algorithm with one benchmark Korean text corpus. The proposed method is based on "k-mer" and "local alignment" which locates the region of plagiarized document pairs fast and accurately. Using a Korean corpus which contains more than 10 million words, we establish a probability model (or local alignment score (random similarity by chance). The experiment has shown that our system was quite successful to detect the plagiarized documents.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.1
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• pp.56-65
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• 2006

Recently the study on service robots has been proliferated in many fields, and there are active developments for indoor services such as supporting for elderly people. It is important for robot to recognize objects and situations appropriately for effective and accurate service. Conventional object recognition methods have been based on the pre-defined geometric models, but they have limitations in indoor environments with uncertain situation such as the target objects are occluded by other ones. In this paper we propose a Bayesian network model to reason the probability of target objects for effective detection. We model the relationships between objects by activities, which are applied to non-static environments more flexibly. Overall structure is constructed by combining common-cause structures which are the units making relationship between objects, and it makes design process more efficient. We test the performance of two Bayesian networks for verifying the proposed Bayesian network model through experiments, resulting in accuracy of $86.5\%$ and $89.6\%$ respectively.

• Convergence Security Journal
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• v.18 no.1
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• pp.93-102
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• 2018

The drug trade among the general public via the Internet and sns have been increasing, which is becoming a social problem. The general public believe that even if they do the drug trade via the Internet and sns the probability of detection is low. so they will conduct drug trade via the Internet and sns. Therefore, if the general public recognize that there is a high likelihood of disclosure, drug trade via the Internet and sns are likely to decline. If the possibility of punishment increases through specification of controlled delivery techniques and Introduction of entrapment investigator, it seems that the general public can not easily deal with drug trade via the Internet and sns. Also by further subdividing the penalties for drug offenses, for simple drug buyers through cure-oriented treatment rather than punishment drug demand be suppressed and penalties for drug suppliers should be strengthened.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.14-20
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• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.1-9
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• 2016

In accordance with the increased political and social utilization of social media, demands on online trend analysis and monitoring technologies based on social bigdata are also increasing rapidly. In this paper, we define 'risk' as issues which have probability of turn to negative public opinion among big social issues and classify their types in details. To define risk types, we conduct a complete survey on news documents and analyzed characteristics according to issue domains. We also investigate cross-medias analysis to find out how different public media and personalized social media. At the result, we define 58 risk types for 6 domains and developed automatic classification model based on machine learning algorithm. Based on empirical experiments, we prove the possibility of automatic detection for social issue risk in social media.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.571-577
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• 2010

Driving aircraft requires extremely complicated and detailed information processing. Pilots perform their tasks by selecting the information relevant to them. In this processing, spatial information presented simultaneously through crossmodal link is advantageous over the one provided in singular sensory mode. In this paper, probability to apply providing visual spatial information along with auditory information to enemy tracking system in aircraft navigation is empirically investigated. The result shows that auditory spatial information, which is virtually created through HRTF is advantageous to visual spatial information alone in attention processing. The findings suggest auditory spatial information along with visual one can be presented through crossmodal link by utilizing stereophonic sound such as HRTF. which is available in the existing simple stereo system.

• Aerospace Engineering and Technology
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• v.3 no.2
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• pp.229-237
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• 2004

In this paper, the design method and algorithms of the signal processor for a multipurpose radar system are analyzed. The signal processor, operating at the two modes-collision avoidance mode and weather mode, has 4 steps of ADC, NCI, STC, CFAR. Several algorithms of NCI and CFAR are analyzed and the optimal design is proposed to the system. CVI and CMLD algorithm have good performance in decreasing the false alarm rate and increasing detection probability, Regarding processor computational capacity, K=12 for CVI, M=16~20, Ko=M-4 for CMLD is suggested. CVI processing needs much time, two or more processors need to be allocated to CVI. So, for the system with four processors, two processors should be allocated to VID of NCI with ADC input and CFAR with STC, and two processors are should be allocated to CVI.