• Journal of KIISE:Computing Practices and Letters
• /
• v.12 no.1
• /
• pp.56-65
• /
• 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
• /
• v.18 no.1
• /
• pp.93-102
• /
• 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
• /
• v.11 no.7
• /
• pp.14-20
• /
• 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
• /
• v.10 no.12
• /
• pp.561-568
• /
• 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
• /
• v.16 no.8
• /
• pp.1-9
• /
• 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
• /
• v.14 no.4
• /
• pp.571-577
• /
• 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
• /
• v.3 no.2
• /
• pp.229-237
• /
• 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.

• The Journal of Society for e-Business Studies
• /
• v.15 no.4
• /
• pp.303-317
• /
• 2010

In this paper, we propose a novel approach to fault prediction for real-time business process monitoring method using extended KNN imputation based LOF prediction. Existing rule-based approaches to process monitoring has some limitations like late alarm for fault occurrence or no indicators about real-time progress, since there exist unobserved attributes according to the monitoring phase during process executions. To improve these limitations, we propose an algorithm for LOF prediction by adopting the imputation method to assume unobserved attributes. LOF of ongoing instance is calculated by assuming next probable progresses after the monitoring phase, which is conducted during entire monitoring phases so that we can predict the abnormal termination of the ongoing instance. By visualizing the real-time progress in terms of the probability on abnormal termination, we can provide more proactive operations to opportunities or risks during the real-time monitoring.

• Journal of the Korea Society for Simulation
• /
• v.23 no.1
• /
• pp.33-42
• /
• 2014

In this paper we analyzed the search patterns for the anti-submarine warfare (ASW) surface ships cooperating with ASW helicopters. For this purpose, we modeled evasive motion of a submarine with a probabilistic method. And maneuvers and search actions of ships and helicopters participating in the anti-submarine search mission are designed. And for each simulation scenario, the case where a ship and a helicopter searches a submarine independently according to its optimized search pattern is compared with the case where the search platforms participate in the ASW mission cooperatively. Based on the simulation results, we proposed the reconfigured search patterns that help cooperative ASW surface ships increase the total cumulative detection probability (CDP).

• Journal of Korea Spatial Information System Society
• /
• v.11 no.1
• /
• pp.137-144
• /
• 2009

Understanding lane markings in a live video captured from a moving vehicle is essential to build services for intelligent vehicles such as LDWS(Lane Departure Warning Systems), unmanned vehicles, video-based car navigation systems. In this paper, we present a novel approach to recognize the color of lane markings and the lane number that he/she is driving on. More specifically, we present a background-color removal approach to understand the color of lane markings for various illumination conditions, such as backlight, sunset, and so on. In addition, we present a probabilistic network approach to decide the lane number. According to our experimental results, the proposed idea shows promising results to detect lane number in a various illumination conditions and road environments.