• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.405-423
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• 2022

The distance vector-hop (DV-Hop) is one of the emblematic algorithms that use node connectivity for locating, which often accompanies by a large positioning error. To reduce positioning error, the bio-inspired algorithm and weight optimization model are introduced to address positioning. Most scholars argue that the weight value decreases as the hop counts increases. However, this point of view ignores the intrinsic relationship between the error and weight. To address this issue, this paper constructs the relationship model between error and hop counts based on actual communication characteristics of sensor nodes in wireless sensor network. Additionally, we prove that the error converges to 1/6CR when the hop count increase and tendency to infinity. Finally, this paper presents a modified error-oriented weight positioning model, and implements it with genetic algorithm. The experimental results demonstrate excellent robustness and error removal.

• ETRI Journal
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• v.44 no.6
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• pp.915-924
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• 2022

In this paper, we present a two-stage scalable channel estimator (TSCE), a deep learning (DL)-based scalable, and robust channel estimator for wireless cellular networks, which is made up of two DL networks to efficiently support different resource allocation sizes and reference signal configurations. Both networks use the transformer, one of cutting-edge neural network architecture, as a backbone for accurate estimation. For computation-efficient global feature extractions, we propose using window and window averaging-based self-attentions. Our results show that TSCE learns wireless propagation channels correctly and outperforms both traditional estimators and baseline DL-based estimators. Additionally, scalability and robustness evaluations are performed, revealing that TSCE is more robust in various environments than the baseline DL-based estimators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1234-1257
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• 2023

In this paper, we propose a framework for multi-focus image fusion called PATN. In our approach, by aggregating deep features extracted based on the U-type Transformer mechanism and shallow features extracted using the PSA module, we make PATN feed both long-range image texture information and focus on local detail information of the image. Meanwhile, the edge-preserving information value of the fused image is enhanced using a dense residual block containing the Sobel gradient operator, and three loss functions are introduced to retain more source image texture information. PATN is compared with 17 more advanced MFIF methods on three datasets to verify the effectiveness and robustness of PATN.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.510-512
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• 2022

컴퓨터 비전 분야에서 오랫동안 사용되었던 CNN(Convolution Neural Network)은 오분류를 일으키기 위해 악의적으로 추가된 섭동에 매우 취약하다. ViT(Vision Transformer)는 입력 이미지의 전체적인 특징을 탐색하는 어텐션 구조를 적용함으로 CNN의 국소적 특징 탐색보다 특성 픽셀에 섭동을 추가하는 적대적 공격에 강건한 특성을 보이지만 최근 어텐션 구조에 대한 강건성 분석과 다양한 공격 기법의 발달로 보안 취약성 문제가 제기되고 있다. 본 논문은 ViT가 CNN 대비 강건성을 가지는 구조적인 특징을 분석하는 연구와 어텐션 구조에 대한 최신 공격기법을 소개함으로 향후 등장할 ViT 파생 모델의 강건성을 유지하기 위해 중점적으로 다루어야 할 부분이 무엇인지 소개한다.

• ETRI Journal
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• v.45 no.6
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.725-734
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• 2016

SCADA(Supervisory Control and Data Acquisition) system is widely used for remote monitoring and control throughout the domestic industry. Due to a recent breach of security on SCADA systems, such as Stuxnet, the need of correctly established secure certification of a control system is growing. Currently, EDSA-CRT (Embedded Device Security Assurance-Communication Robustness Test), which tests the ability to provide core services properly in a normal/abnormal network protocol, is only focused on the testing of IP-based protocols such as IP, ARP, TCP, etc. Thus, in this paper, we propose test requirements for DNP3 protocol based on EDSA-CRT. Our analysis show that the specific test cases provide plentiful evidences that DNP3 should follow based on its functional requirements. As a result, we propose 33 specific test case for DNP3 protocol.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.29-45
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• 2017

Ad hoc sensor network is characterized by decentralized structure and ad hoc deployment. Sensor networks have all basic features of ad hoc network except different degrees such as lower mobility and more stringent energy requirements. Existing protocols provide different tradeoffs among some desirable characteristics such as fault tolerance, distributed computation, robustness, scalability and reliability. wireless protocols suggested so far are very limited, generally focusing on communication to a single base station or on aggregating sensor data. The main reason having such restrictions is due to maximum lifetime to maintain network activities. The network lifetime is an important design metric in ad hoc networks. Since every node does a router role, it is not possible for other nodes to communicate with each other if some nodes do not work due to energy lack. In this paper, we suggest an experimental ad-hoc on-demand distance vector routing protocol to optimize the communication of energy of the network nodes.The load distribution avoids the choice of exhausted nodes at the route selection phase, thus balances the use of energy among nodes and maximizing the network lifetime. In transmission control phase, there is a balance between the choice of a high transmission power that lead to increase in the range of signal transmission thus reducing the number of hops and lower power levels that reduces the interference on the expense of network connectivity.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.31-41
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• 2024

Deep learning models show excellent performance in tasks such as image classification and object detection in the field of computer vision, and are used in various ways in actual industrial sites. Recently, research on improving robustness has been actively conducted, along with pointing out that this deep learning model is vulnerable to hostile examples. A hostile example is an image in which small noise is added to induce misclassification, and can pose a significant threat when applying a deep learning model to a real environment. In this paper, we tried to confirm the robustness of the edge-learning classification model and the performance of the adversarial example detection model using it for adversarial examples of various algorithms. As a result of robustness experiments, the basic classification model showed about 17% accuracy for the FGSM algorithm, while the edge-learning models maintained accuracy in the 60-70% range, and the basic classification model showed accuracy in the 0-1% range for the PGD/DeepFool/CW algorithm, while the edge-learning models maintained accuracy in 80-90%. As a result of the adversarial example detection experiment, a high detection rate of 91-95% was confirmed for all algorithms of FGSM/PGD/DeepFool/CW. By presenting the possibility of defending against various hostile algorithms through this study, it is expected to improve the safety and reliability of deep learning models in various industries using computer vision.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.522-530
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• 2002

This paper introduces a neural network (NN) -based nonlinear predictor for the LP (Linear Prediction) residual. To evaluate the effectiveness of the NN-based nonlinear predictor for LP-residual, we first compared the average prediction gain of the linear long-term predictor with that of the NN-based nonlinear long-term predictor. Then, the effects on the quantization noise of the nonlinear prediction residuals were investigated for the NN-based nonlinear predictor A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Experimental results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.