• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.93-101
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• 2020

Regional CRL(certificate revocation list) in vehicular communications is to partition Full CRL into several small CRLs according to geographic location to keep the size of individual CRLs with smaller. However, since a Regional CRL includes vehicle's revoked certificates within its administrative region, it has to know vehicle' location. For this, how to know vehicle' location effectively corresponding to every region represents a major challenge. This paper proposes a Regional CRL scheme which is envisioned to achieve vehicle's location and to make regional CRLs according to vehicles current location efficiently. The scheme is based on the short-lived pseudonyms defined by WAVE standard. It also acquires issued pseudonyms, vehicle's id and region information whenever a vehicle initiates pseudonyms refill after that, utilizes them to create and distribute the Regional CRL. To keep location privacy-preserving for vehicles, the scheme uses the blockchain technology in the network. The analysis results show that it reduces CRL size and database query time for finding revoked certificates sharply in the vehicle's on-board unit.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.433-436
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• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.

• Journal of Korea Water Resources Association
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• v.37 no.6
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• pp.449-460
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• 2004

The purpose of this study is to test the applicability of neuro-fuzzy system for monthly dam inflow forecasts by using weather forecasting information. The neuro-fuzzy algorithm adopted in this study is the ANFIS(Adaptive neuro-fuzzy Inference System) in which neural network theory is combined with fuzzy theory. The ANFIS model can experience the difficulties in selection of a control rule by a space partition because the number of control value increases rapidly as the number of fuzzy variable increases. In an effort to overcome this drawback, this study used the subtractive clustering which is one of fuzzy clustering methods. Also, this study proposed a method for converting qualitative weather forecasting information to quantitative one. ANFIS for monthly dam inflow forecasts was tested in cases of with or without weather forecasting information. It can be seen that the model performances obtained from the use of past observed data and future weather forecasting information are much better than those from past observed data only.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.91-97
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• 2011

In this paper we present an approach to the structure identification based on genetic algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy-genetic hybrid system in order to predicate the Mackey-Glass Chaotic time series. In this scheme the basic idea consists of two steps. One is the construction of a fuzzy rule base for the partitioned input space via genetic algorithm, the other is the corresponding parameters of the fuzzy control rules adapted by the backpropagation algorithm. In an attempt to test the performance the proposed system, three patterns, x(t-3), x(t-6) and x(t-9), was prepared according to time interval. It was through lots of simulation proved that the initial small error of learning owed to the good structural identification via genetic algorithm. The performance was showed in Table 2.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.25-32
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• 2008

This paper presents the reflection and transmission measurements conducted at the 60GHz suitable to provide a high speed wide band service. Mean received power and standard deviation are calculated and used to compare the characteristics of radio wave propagation to the various obstacles between transmitting and receiving antennas at the frequency. The results show that the transmitted signal strength by the steel door and copper plate are about 40dB lower than in free space, those by the rubber plate, glass and styroform are about 3dB lower than in free space. Also, the re(looted signal strengths at the 60 degree grazing angle show that in case by the partition is about 23dB lower, by the surface of a wall is about 6dB lower than by the copper plate. The presented results can be used for the design of 60 GHz picocell communication network that the reflected and transmitted waves affect to the service area.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.411-420
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• 2019

Camera model identification has been a subject of steady study in the field of digital forensics. Among the increasingly sophisticated crimes, crimes such as illegal filming are taking up a high number of crimes because they are hard to detect as cameras become smaller. Therefore, technology that can specify which camera a particular image was taken on could be used as evidence to prove a criminal's suspicion when a criminal denies his or her criminal behavior. This paper proposes a deep learning model to identify the camera model used to acquire the image. The proposed model consists of four convolution layers and two fully connection layers, and a high pass filter is used as a filter for data pre-processing. To verify the performance of the proposed model, Dresden Image Database was used and the dataset was generated by applying the sequential partition method. To show the performance of the proposed model, it is compared with existing studies using 3 layers model or model with GLCM. The proposed model achieves 98% accuracy which is similar to that of the latest technology.

• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.99-106
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• 2023

In this paper, we propose subregion-based area of interest management techniques for MMORPG(massively multiplayer online role playing games) integrating P2P(peer-to-peer) networking and cloud computing. For the crowded region, the proposed techniques partition it into several subregions and assign a player to manage each subregion as a coordinator. These techniques include a load balancing mechanism which regulates communication and computation overhead of such player below the specified threshold. We also provide a mechanism for satisfying the criterion, where subregions overlapped with each player's view must be switched quickly and seamlessly as the view moves around in the game world. In the proposed techniques where an efficient provisioning of resources is realized, they relieve a lot of computational power and network traffic, the load on the servers in the cloud by exploiting the capacity of the players effectively. Simulation results show that the MMORPG based on cloud and P2P architecture can reduce the considerable bandwidth at the server compared to the client server architecture as the available resources grow with the number of players in crowding or hotspots.

• Convergence Security Journal
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• v.22 no.1
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• pp.99-111
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• 2022

With current battlefield environment relying heavily on Network Centric Warfare(NCW), existing weaponary systems are evolving into a new concept that converges IT technology. Majority of the weaponary systems are implemented with numerous embedded softwares which makes such softwares a key factor influencing the performance of such systems. Furthermore, due to the advancements in both IoT technoogies and embedded softwares cyber threats are targeting various embedded systems as their scope of application expands in the real world. Weaponary systems have been developed in various forms from single systems to interlocking networks. hence, system level cyber security is more favorable compared to application level cyber security. In this paper, a secure real-time operating system has been designed, implemented and measured to protect embedded softwares used in weaponary systems from unknown cyber threats at the operating system level.

• Wind and Structures
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• v.39 no.1
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• pp.1-14
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• 2024

Widespread damages from extreme winds have attracted lots of attentions of the resilience assessment of power distribution systems. With many related environmental parameters as well as numerous power infrastructure components, such as poles and wires, the increased challenge of power asset management before, during and after extreme events have to be addressed to prevent possible cascading failures in the power distribution system. Many extreme winds from weather events, such as hurricanes, generate widespread damages in multiple areas such as the economy, social security, and infrastructure management. The livelihoods of residents in the impaired areas are devastated largely due to the paucity of vital utilities, such as electricity. To address the challenge of power grid asset management, power system clustering is needed to partition a complex power system into several stable clusters to prevent the cascading failure from happening. Traditionally, system clustering uses the Binary Decision Diagram (BDD) to derive the clustering result, which is time-consuming and inefficient. Meanwhile, the previous studies considering the weather hazards did not include any detailed weather-related meteorologic parameters which is not appropriate as the heterogeneity of the parameters could largely affect the system performance. Therefore, a fragility-based network hierarchical spectral clustering method is proposed. In the present paper, the fragility curve and surfaces for a power distribution subsystem are obtained first. The fragility of the subsystem under typical failure mechanisms is calculated as a function of wind speed and pole characteristic dimension (diameter or span length). Secondly, the proposed fragility-based hierarchical spectral clustering method (F-HSC) integrates the physics-based fragility analysis into Hierarchical Spectral Clustering (HSC) technique from graph theory to achieve the clustering result for the power distribution system under extreme weather events. From the results of vulnerability analysis, it could be seen that the system performance after clustering is better than before clustering. With the F-HSC method, the impact of the extreme weather events could be considered with topology to cluster different power distribution systems to prevent the system from experiencing power blackouts.