• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.1-10
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• 2023

Open surface water body extraction is gaining popularity in recent years due to its versatile applications. Multiple techniques are used for water detection based on applications. Different applications of Radar as LADAR, Ground-penetrating, synthetic aperture, and sounding radars are used to detect water. Shortwave infrared, thermal, optical, and multi-spectral sensors are widely used to detect water bodies. A stereo camera is another way to detect water and different methods are applied to the images of stereo cameras such as deep learning, machine learning, polarization, color variations, and descriptors are used to segment water and no water areas. The Satellite is also used at a high level to get water imagery and the captured imagery is processed using various methods such as features extraction, thresholding, entropy-based, and machine learning to find water on the surface. In this paper, we have summarized all the available methods to detect water areas. The main focus of this survey is on water detection especially in small patches or in small areas. The second aim of this survey is to detect water hazards for unmanned vehicles and off-sure navigation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1301-1316
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• 2024

In modern warfare, missiles play a pivotal role but typically spend the majority of their lifecycle in long-term storage or standby mode, making it difficult to detect failures. Preemptive detection of missiles that will fail is crucial to preventing severe consequences, including safety hazards and mission failures. This study proposes a contamination-based stacking ensemble model, employing the local outlier factor (LOF), to detect such missiles. The proposed model creates multiple base LOF models with different contamination values and combines their anomaly scores to achieve a robust anomaly detection. A comparative performance analysis was conducted between the proposed model and the traditional single LOF model, using production-related inspection data from missiles deployed in the military. The experimental results showed that, with the contamination parameter set to 0.1, the proposed model exhibited an increase of approximately 22 percentage points in accuracy and 71 percentage points in F1-score compared to the single LOF model. This approach enables the preemptive identification of potential failures, undetectable through traditional statistical quality control methods. Consequently, it contributes to lower missile failure rates in real battlefield scenarios, leading to significant time and cost savings in the military industry.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 가을 학술발표회 논문집
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• pp.1185-1192
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• 2001

This paper reviews available techniques for monitoring corrosion of steel in concrete. The need for early detection and diagnosis of corrosion related deterioration in reinforced structures is widely acknowledged. This is particularly important in reinforced concrete structures on account of the economic and social significance of the problem. The current generally used on-site procedure for corrosion monitoring of reinforced structures employs a method of half-cell surface potential measurements. While the technique has provided a useful means of delineating areas of high or low corrosion risk, there are difficulties in its use and interpretation when assessing rates of deterioration. Electrochemical techniques are by far the most suitable for corrosion monitoring purpose and meet most of the requirements. The aim of this paper is to describe the electric resistance sensor(ER sensor) and electrochemical techniques employed to monitor and estimate corrosion rates of reinforcement. Early detection and diagnosis of corrosion hazards allows preventive measures to be taken, hence the typically expensive repair of severely deteriorated structures can be avoided.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.840-843
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• 2006

The aim of this study was to detect landslide using satellite image and apply the landslide to probabilistic landslide-susceptibility mapping at Gangneung area, Korea using a Geographic Information System (GIS). Landslide locations were identified by change detection technique of KOMSAT-1 (Korea Multipurpose Satellite) EOC (Electro Optical Camera) images and checked in field. For landslide-susceptibility mapping, maps of the topography, geology, soil, forest, lineaments, and land cover were constructed from the spatial data sets. Then, the sixteen factors that influence landslide occurrence were extracted from the database. Using the factors and detected landslide, the relationships were calculated using frequency ratio, one of the probabilistic model. Then, landslide-susceptibility map was drawn using the frequency ration and finally, the map was verified by comparing with existing landslide locations. As the verification result, the prediction accuracy showed 86.76%. The landslide-susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 춘계전력전자학술대회 논문집(2)
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• pp.761-764
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• 2003

Islanding phenomenon of a grid-connected photovoltaic (PV) power conditioning system (PCS) is said to occur if the PCS continues to power a section of the utility system after that section has been disconnected from the utility source. Since islanding creates hazards for personnel and equipment, PCSs are required to detect and prevent Et. In this paper, several islanding detection methods (IDMs) and reactive power variation method are described. Islanding test results for 9kW PCS are presented for verification.

• Journal of Biosystems Engineering
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• 제41권3호
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• pp.240-254
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• 2016

Background: Foodborne pathogens are a growing concern with respect to human illnesses and death. There is an increasing demand for improvements in global food safety. However, it is a challenge to detect and identify these harmful organisms in a rapid, responsive, suitable, and effective way. Results: Rapid developments in biosensor designs have contributed to the detection of foodborne pathogens and other microorganisms. Biosensors can automate this process and have the potential to enable fast analyses that are cost and time-effective. Various biosensor techniques are available that can identify foodborne pathogens and other health hazards. Conclusions: In this review, biosensor technology is briefly discussed, followed by a summary of foodborne pathogen detection using various transduction systems that exhibit specificity for particular foodborne pathogens. In addition, the recent application of biosensor technology to detect pesticides and heavy metals is briefly addressed.

• Journal of Microbiology
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• 제41권2호
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• pp.144-147
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• 2003

Listeria monocytogenes is a pathogen of major concern to the food industry and the potential cause of severe infections such as listeriosis. Early detection of this foodborne pathogen is important in order to eliminate its potential hazards. So, immunomagnetic separation (IMS) has been suggested as a means of reducing the total analysis time and for improving the sensitivity of detection. Atomic force microscopy (AFM) has been used for measuring the topographic properties of sample surfaces at nanometer scale. In this study, we used AFM to confirm both the sensitivity and the specificity of IMS. Regarding AFM analysis, the length and the width of the bacteria, which were in agreement with literature values, were found to be 2.993 $\mu\textrm{m}$ and 0.837 $\mu\textrm{m}$, respectively. As a result, AFM helped us both characterize and measure the bacterial and bead structures.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.252-254
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• 2001

Accurate detection and classification of faults on transmission lines is vitally important. High impedance faults(HIF) in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System(ANFIS). The performance of the proposed algorithm is tested on a typical 154[kV] Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle.

• 한국태양에너지학회 논문집
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• 제37권3호
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• pp.13-22
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• 2017

Anti-islanding scheme of grid-connected inverter is a key function of standards compliance, since unintentional islanding results in safety hazards, reliability, and many other issues. Therefore, many anti-islanding schemes have been researched, however, most of them have problems, which deteriorate performance of islanding detection under parallel-operation. Therefore, this paper proves the reason of problems and proposes a new anti-islanding scheme that has precise islanding detection under parallel-operation in single-phase and three-phase system. Finally, both simulation and experimental result validate the proposed scheme.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.