• 한국시뮬레이션학회논문지
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• 제19권2호
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• pp.17-28
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• 2010

음향 탐지나 항적 탐지 등을 이용하여 표적을 추적하는 유도 어뢰는 개념 설계 단계에서부터 군 요구를 분석하고 군요구에 따른 어뢰의 개략 설계 사양 도출을 필요로 한다. 이를 위해 이산 사건/이산 시간 혼합형 시뮬레이션 모델 구조를 적용하여 어뢰가 목적하고 있는 탐지 임무의 정량적인 달성 정도를 나타내는 탐지 효과도를 분석하였다. 어뢰의 탐지효과도 분석을 위해 초기 개념 설계 단계에서 주어지는 어뢰의 개략적인 설계 변수를 바탕으로 어뢰와 표적의 수학 모델을 설정하였으며, 이와 함께 이산 사건/이산 시간 혼합형 시뮬레이션 모델 구조를 적용하여 아 잠수함 모델, 어뢰 모델, 표적 모델을 구성하였다. 특히 어뢰 모델에는 유도 어뢰의 특성을 고려하여 탐색 운동 방법, 음향 탐지 방법 등을 적용하였으며. 각 모델을 구성하는 설계 변수에는 오차 모형을 반영하였다. 이를 바탕으로 어뢰가 표적을 탐지하는 과정에 대해 반복 시뮬레이션을 수행하여 설계 변수 변화에 따른 어뢰의 탐지 효과도를 분석하였다.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• International Journal of Advanced Culture Technology
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• 제4권1호
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• pp.28-30
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• 2016

Due to the high increment in usage and built-in advanced technology of smartphones, human activity recognition relying on smartphone sensor data has become a focused research area. In order to reduce noise of collected data, most of previous studies assume that smartphones are fixed at certain positions. This strategy is impractical for real life applications. To overcome this issue, we here investigate a framework that allows detecting the status of a traveller as idle or moving regardless the position and the direction of smartphones. The application of our work is to estimate the total energy consumption of a traveller during a trip. A number of experiments have been carried out to show the effectiveness of our framework when travellers are not only walking but also using primitive vehicles like motorbikes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.636-639
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• 2003

In this paper, we present a robust method for detecting other vehicles from n forward-looking CCD camera in a moving vehicle. This system uses edge and shape information to detect other vehicles. The algorithm consists of three steps: lane detection, ehicle candidate generation, and vehicle verification. First after detecting a lane from the template matching method, we divide the road into three parts: left lane, front lane, and right lane. Second, we set the region of interest (ROI) using the lane position information and extract a vehicle candidate from the ROI. Third, we use local orientation coding (LOC) edge image of the vehicle candidate as input to a pretrained neural network for vehicle recognition. Experimental results from highway scenes show the robustness and effectiveness of this method.

• 한국농공학회논문집
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• 제60권6호
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• pp.21-31
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• 2018

The result of examination of diagnostic method, which is composed of a combination of a thermal camera and a drone that visually shows the temperature of the object by detecting the infrared rays, for detecting the leakage of earth dam was driven in this research. The drone infrared thermography method was suggested to precise safety diagnosis through direct comparing the two method results of electrical resistivity survey and thermal image survey. The important advantage of the thermal leakage detection method was the simplicity of the application, the quickness of the results, and the effectiveness of the work in combination with the existing diagnosis method.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.243-260
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• 2008

To prevent low yields in the semiconductor industry is crucial to the success of that industry. However, to prevent low yields is difficult because of too many factors to affect yield variation and their complex relation in the semiconductor manufacturing process. This study presents a new efficient detection methodology for detecting abnormal yields including high and low yields, which can forecast the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors. In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our methodology using a semiconductor manufacturing company's field data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.98.3-98
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• 2001

The research for detecting of insulating deterioration in transformer has been studied from long ago. Analysis method of combustible gas, which is included in insulating oil, has been widely used in detection of transformer pre-fault detection due to the effectiveness of its method. Recently the fault effect of the large transformer is very critical in a power system, therefore the on-line monitoring and diagnostic system is needed. In addition, the more accurate method of detecting a Partial Discharge (PD) location should be developed. For preventive maintenance against discharge failures, it is important not only to detect the discharges, but also to accurately estimate their positions. However ...

• 한국IT서비스학회:학술대회논문집
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• 한국IT서비스학회 2009년도 추계학술대회
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• pp.544-548
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• 2009

The underlying success of logistics depends on the flow of data and information for effective management. Over the last 30 years, we have seen the power of microprocessors double about every 18months. This continuing trend means that computers will become considerably smaller, cheaper, and more abundant; indeed, they are becoming ubiquitous and are even finding their way into everyday objects, resulting in the creation of smart things. In the long term, ubiquitous technologies will take on great economic significance. Industrial products will become smart because of their integrated information processing capacity, or take on an electronic identity that can be queried remotely, or be equipped with sensors for detecting their environment, enabling the development of innovative products and totally new services. The global marketplace runs on logistics, security, speed, agility and flexibility..In this paper we report that pairing these traditional logistics functions with RFID technology can be a huge value-driver for companies. This winning combination yields increased logistics management effectiveness and more efficient visibility into the supply chain management.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2439-2441
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• 1998

In this research, the sensor and control system for self detecting masticatory muscle relaxation appliances. A strain gauge is used to measure a strength of tooth clenching force. A bridge circuit and voltage amplifier is designed to amplify measured signals and RF transmitter and receiver is also designed to communicate inner and outer mouth device. The experiments are performed to show the effectiveness of designed system.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.276-276
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• 2018

In this paper the potential of the Principle Component Analysis(PCA) technique that can be used to detect leaks in water pipe network blocks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study. The algorithms were designed to extract a partial set of flow data from the original 24 hour flow data so that the variability of the flows in the determined partial data set are minimal. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The results showed that the effectiveness of detecting leaks may improve by applying the developed algorithm. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks in real-world water pipe networks.