• Journal of the Korea Society of Computer and Information
• /
• v.15 no.12
• /
• pp.133-142
• /
• 2010

P-type fire control panel, currently in use, has a big hazard that can cause the large scaled human death tolls and property damage in the massive fire because it is difficult to identify in real time the location of fire outbreak and whether search-device are broken down or not. In this paper, I suggest that the integrated fire automation system on based p-type fire control panel should be used, which can detect in real time the signal that occur when whether search-device are broken down or not, and can detect the arisen circumstances information of p-type fire control panel on the fire signal in the far away. The devised systems have designed and embodied the analysis of circumstances information and module that can analyze the circumstances information from the p-type fire control panel and the part of internet access installment which can gather and deliver the circumstances information from the fire prevention facility receiver.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.4
• /
• pp.558-565
• /
• 2017

The vehicle-mounted mine detection system with large detection sensor modules can search wide areas with a fast detection speed. To mount the heavy mine detectors on a manned or unmanned vehicle, it is necessary to design the detector driving mechanism and control system based on the considerations driven from the characteristic analysis and the operation requirements of the detection system. Furthermore, while operating the mine detector mounted on a mobile vehicle, it is significant to keep the height from the ground to sensors within a certain distance in order to get a qualified detection performance. As the mine detection sensor, we used ground penetrating radar widely used to geotechnical exploration, mine detection and etc. In this paper, we introduce a driving mechanism through analyzing the characteristics of the vehicle-mounted mine detection system. We also suggest a method to automatically control the distance between the ground and GPR by utilizing the GPR output values, used to detect mines at the same time.

• Korean Journal of Remote Sensing
• /
• v.19 no.6
• /
• pp.465-478
• /
• 2003

This paper presents two methods for automatic estimation of threshold values in unsupervised change detection of multi-temporal remote sensing images. The proposed methods consist of two analytical steps. The first step is to compute the parameters of a 3-component Gaussian mixture model from difference or ratio images. The second step is to determine a threshold value using Bayesian rule for minimum error. The first method which is an extended version of Bruzzone and Prieto' method (2000) is to apply an Expectation-Maximization algorithm for estimation of the parameters of the Gaussian mixture model. The second method is based on an iterative thresholding algorithm that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here were illustrated by two experiments and one case study including the synthetic data sets and KOMPSAT-1 EOC images. The experiments demonstrate that the proposed methods can effectively estimate the model parameters and the threshold value determined shows the minimum overall error.

• Land and Housing Review
• /
• v.13 no.4
• /
• pp.105-114
• /
• 2022

Non-fire activated fire alarms caused by such actions as cigarette smoke, cooking, and high humidity are fire safety risk factors. In such instances, it is important to quickly locate and replace the actuated detector. However, it is difficult to locate those detectors because most do not have an address function. While new apartments can incorporate addressable fire alarm detectors, in existing apartments there are limitations in converting over to addressable detectors due to cost and power line issues. This study developed an efficient address function for fire alarms in existing apartments. The newly developed system consists of the existing receiver, and a proposed addressable repeater and detector. Utilizing an experimental setup, the performance of the proposed address monitoring system was confirmed to be stable and compatible with the receiver and existing detectors.

• Land and Housing Review
• /
• v.14 no.4
• /
• pp.95-102
• /
• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.189-196
• /
• 2024

Frequent false positives alarm from the Intelligent Selective Control System have raised significant concerns. These persistent issues have led to declines in operational efficiency and market credibility among agents. Developing a new model or replacing the existing one to mitigate false positives alarm entails substantial opportunity costs; hence, improving the quality of the training dataset is pragmatic. However, smaller organizations face challenges with inadequate capabilities in dataset collection and refinement. This paper proposes an automatic human pose data collection system centered around a human pose estimation model, utilizing camera-based sensor fusion techniques and edge devices. The system facilitates the direct collection and real-time processing of field data at the network periphery, distributing the computational load that typically centralizes. Additionally, by directly labeling field data, it aids in constructing new training datasets.

• Journal of KIISE:Software and Applications
• /
• v.36 no.3
• /
• pp.236-243
• /
• 2009

With advances in topic detection and tracking(TDT), automatic trend analysis from a collection of time-stamped documents, like patents, news papers, and blog pages, is a challenging research problem. Past research in this area has mainly focused on showing a trend line over time of a given concept by measuring the strength of trend-associated term frequency information. for detection of emerging trends, either a simple criterion such as frequency change was used, or an overall comparison was made against a training data. We note that in order to show most salient trends detected among many possibilities, it is critical to devise a ranking function. To this end, we define four properties(change, persistency, stability and volume) of trend lines drawn from frequency information, to quantify various aspects of trends, and propose a method by which trend lines can be ranked. The properties are examined individually and in combination in a series of experiments for their validity using the ranking algorithm. The results show that a judicious combination of the four properties is a better indicator for salient trends than any single criterion used in the past for ranking or detecting emerging trends.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.47 no.6
• /
• pp.12-18
• /
• 2010

Automatic Target Detection (ATD) systems that use forward-looking infrared (FLIR) consists of three stages. preprocessing, detection, and clutter rejection. All potential targets are extracted in preprocessing and detection stages. But, this results in a high false alarm rates. To reduce false alarm rates of ATD system, true targets are extracted in the clutter rejection stage. This paper focuses on clutter rejection stage. This paper presents a new clutter rejection technique using PCA features and stochastic features of clutters and targets. PCA features are obtained from Euclidian distances using which potential targets are projected to reduced eigenspace selected from target eigenvectors. CV is used for calculating stochastic features of edges in targets and clutters images. To distinguish between target and clutter, LDA (Linear Discriminant Analysis) is applied. The experimental results show that the proposed algorithm accurately classify clutters with a low false rate compared to PCA method or CV method

• Proceedings of KOSOMES biannual meeting
• /
• 2004.11a
• /
• pp.43-47
• /
• 2004

Two different sensors (here, KOMPSAT and RADARSAT) are considered for ship detection, and are used to delineate the detection performance for their data The experiments are set for coastal regions of Mokpo Port and Ulsan Port and field experiments on board pilot boat are conducted to collect in situ ship validation information such as ship type and length This paper introduce mainly the experiment result of ship detection by both RADARSAT SAR imagery and land-based RADAR data, operated by the local Authority of South Korean, so called vessel traffic system (VTS) radar. Fine imagery of Ulsan Port was acquired on June 19, 2004 and in-situ data such as wind speed and direction, taking pictures of ships and natural features were obtained aboard a pilot ship. North winds, with a maximum speed of 3.1 m/s were recorded Ship's position, size and shape and natural features of breakwaters, oil pipeline and alongside ship were compared using SAR and VTS. It is shown that KOMPSAT/EOC has a good performance in the detection of a moving ship at a speed of kts or more an hour that ship and its wake can be imaged. The detection capability of RADARSAT doesn't matter how fast ship is running and depends on a ship itself, e.g. its material, length and type. Our results indicate that SAR can be applicable to automated ship detection for a VTS and SAR combination service.

• Phonetics and Speech Sciences
• /
• v.15 no.4
• /
• pp.109-118
• /
• 2023

Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.