• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.107-108
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• 2014

현재의 지능형 안전 및 유지관리 방법은 자연재해 및 시설물 사용성능 향상 등의 환경변화 대응에 아직 미흡하고, 기술역량도 부족하다. 국내의 시설물의 점검 및 관리 시스템은 대부분 수작업으로 이루어진다. 이러한 수동적인 관리는 시설물의 상태 변화에 실시간으로 대응하지 못함으로서 여러 사고를 초래하기도 한다. 하지만 사람이 일일이 검사하는 수동적인 시설물 관리에서는 이러한 문제점을 완벽히 해결할 수 없으며, 시설물 관리를 위해 많은 유지보수 인력이 필요하지만 예산상의 문제로 인해 관리가 미흡하다. 본 논문에서는 4G 무선네트워크 기반의 영상카메라 및 감지센서 융합형 시각정보화 M2M 게이트웨이를 활용하여 간단한 시설물 관리 시스템 구성으로 인한 기존 원격 영상 감시 시스템과 차별화된 저전력, 저비용, 고효율, 고성능의 무인 시설물 관리 시스템을 설계하였다.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.251-257
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• 2015

Intrusion protection system is a security system that stop abnormal traffics through automatic activity by finding out attack signatures in network. Unlike firewall or intrusion detection system that defends passively, it is a solution that stop the intrusion before intrusion warning. The security performance of intrusion protection system is influenced by security auditability, user data protection, security athentication, etc., and performance is influenced by detection time, throughput, attack prevention performance, etc. In this paper, we constructed a convergence performance evaluation model about software product evaluation to construct the model for security performance evaluation of intrusion protection system based on CC(Common Criteria : ISO/IEC 15408) and ISO international standard about software product evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.132-146
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• 2020

Biometric technologies have become widely available in many different fields. However, biometric technologies using existing physical features such as fingerprints, facial features, irises, and veins must consider forgery and alterations targeting them through fraudulent physical characteristics such as fake fingerprints. Thus, a trend toward next-generation biometric technologies using behavioral biometrics of a living person, such as bio-signals and walking characteristics, has emerged. Accordingly, in this study, we developed a bio-signal authentication algorithm using electrocardiogram (ECG) signals, which are the most uniquely identifiable form of bio-signal available. When using ECG signals with our system, the personal identification and authentication accuracy are approximately 90% during a state of rest. When using fingerprints alone, the equal error rate (EER) is 0.243%; however, when fusing the scores of both the ECG signal and fingerprints, the EER decreases to 0.113% on average. In addition, as a function of detecting a presentation attack on a mobile phone, a method for rejecting a transaction when a fake fingerprint is applied was successfully implemented.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.27 no.1
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• pp.1-7
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• 2014

The current downturn of BIPV sector has an enormous potential to rebound and expand into the PV sector for construction market in the foreseeable future. Solar technology has already gained a significant market due to lack of natural resources in the Korean domestic market. Given the technical infrastructure of state-of-the-art fusion technology, the competitiveness of software-driven BIPV market in the world can be ver attractive and have the potential to develop as a key national technology. To do this, from the viewpoint of complexity, technical R&D, national political aspect, social aspect, economic aspect and institutional support systems need to be parallelly formulated. A dedicated BIPV sector has not yet been established, especially policy and institutional framework have very crucial impact on the establishment of BIPV sector.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.19-25
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• 2005

This paper presents a lateral control system for the autonomous navigation vehicle that was developed and tested by Robotics Centre of Ecole des Mines do Paris in France. A robust lane detection algorithm was developed for detecting different types of lane marker in the images taken by a CCD camera mounted on the vehicle. $^{RT}Maps$ that is a software framework far developing vision and data fusion applications, especially in a car was used for implementing lane detection and lateral control. The lateral control has been tested on the urban road in Paris and the demonstration has been shown to the public during IEEE Intelligent Vehicle Symposium 2002. Over 100 people experienced the automatic lateral control. The demo vehicle could run at a speed of 130km1h in the straight road and 50km/h in high curvature road stably.

• Journal of Power System Engineering
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• v.18 no.4
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• pp.85-90
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• 2014

In this paper, the transient thermal and residual stress analysis of the welding of 9% Ni steel plates using the FEA software ABAQUS are presented. The 9% Ni steel plates are welded manually with welding consumables of 70% Ni based Inconel type super-alloys (YAWATA WELD B (M)), producing a multi-pass/multi-layer butt weld. For these materials, temperature dependant mechanical and thermal material properties are used in the analysis. The back gouging is considered in welding process simulation. The FE thermal results are validated by comparing the real fusion profile and heat affected zone (HAZ). In addition, the continuous indentation test was conducted to measure the strength of base metal, HAZ and weld metal.

• Journal of Welding and Joining
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• v.17 no.2
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• pp.53-60
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• 1999

Conventionally, CCD camera and vision sensor using the projected pattern of light is generally used to inspect the weld bead defects. But with this method, a lot of time is needed for image preprocessing, stripe extraction and thinning, etc. In this study, laser vision sensor using the scanning beam of light is used to shorten the time required for image preprocessing. The software for deciding whether the weld bead is in proper shape or not in real time is developed. The criteria are based upon the classification of imperfections in metallic fusion welds(ISO 6520) and limits for imperfections(ISO 5817).

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.75-83
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• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.