• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.670-679
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• 2015

With a rapid development of internet, many users these days refer to various recommender sites when buying items, movies, music and more. However, there are malicious users (Sybil) who raise or lower item ratings intentionally in these recommender sites. And as a result, a recommender system (RS) may recommend incomplete or inaccurate results to normal users. We suggest a recommender algorithm to separate ratings generated by users into normal ratings and outlier ratings, and to minimize the effects of malicious users. Specifically, our algorithm first ensures a stable RS against three kinds of attack models (Random attack, Average attack, and Bandwagon attack) which are the main recent security issues in RS. To prove the performance of the method of suggestion, we conducted performance analysis on real world data that we crawled. The performance analysis demonstrated that the suggested method performs well regardless of Sybil size and type when compared to existing algorithms.

• Journal of the Korea Society for Simulation
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• v.29 no.3
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• pp.73-82
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• 2020

In the fourth industrial revolution, wireless sensor networks (WSNs) are an important element of collecting and analyzing data in a variety of environments without human intervention. This sensor network is greatly affected by topology and routing protocols. Routing protocols, which affect energy consumption, are executed after deploying sensor nodes. Once built, they are difficult to change. Before the WSN is deployed, a routing protocol is carefully selected in view of various environments and the performance of the protocol is evaluated. In this paper, we propose a model to simulate multiple routing protocols using a discrete event system specification (DEVS). The DEVS-based proposed model simulates various situations without changes and structures of the its model as algorithms of the routing protocols are implemented in its coordinators model. To verify normal behaviors of the proposed model, the number of report delivery and the energy consumption of the sensor network were compared using representative protocols LEACH and Dijkstra. As a result, it was confirmed that the proposed model executes normally in both routing protocols.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.311-318
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• 2021

Research has been ongoing to detect ships from offshore photographs for a variety of reasons, including maritime security, identifying international trends, and social scientific research. Due to the development of artificial intelligence, R-CNN models for object detection in photographs and images have emerged, and the performance of object detection has risen dramatically. Ship detection in offshore photographs using the R-CNN model has also begun to apply to satellite photography. However, satellite images project large areas, so various objects such as vehicles, landforms, and buildings are sometimes recognized as ships. In this paper, we propose a novel methodology to improve the performance of ship detection in satellite photographs using R-CNN series models. We separate land and sea via marker-based watershed algorithm and perform morphology operations to specify RoI one more time, then detect vessels using R-CNN family models on specific RoI to reduce typology. Using this method, we could reduce the misdetection rate by 80% compared to using only the Fast R-CNN.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.190-197
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• 2020

Recently there was an incident that military radars, coastal CCTVs and other surveillance equipment captured a small rubber boat smuggling a group of illegal immigrants into South Korea, but guards on duty failed to notice it until after they reached the shore and fled. After that, the detection of such vessels before it reach to the Korean shore has emerged as an important issue to be solved. In the fields of marine navigation, Automatic Identification System (AIS) is widely equipped in vessels, and the vessels incessantly transmits its position information. In this paper, we propose a method of automatically identifying abnormally behaving vessels with AIS using convolutional autoencoder (CAE). Vessel anomaly detection can be referred to as the process of detecting its trajectory that significantly deviated from the majority of the trajectories. In this method, the normal vessel trajectory is gridded as an image, and CAE are trained with images from historical normal vessel trajectories to reconstruct the input image. Features of normal trajectories are captured into weights in CAE. As a result, images of the trajectories of abnormal behaving vessels are poorly reconstructed and end up with large reconstruction errors. We show how correctly the model detects simulated abnormal trajectories shifted a few pixel from normal trajectories. Since the proposed model identifies abnormally behaving ships using actual AIS data, it is expected to contribute to the strengthening of security level when it is applied to various maritime surveillance systems.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.115-124
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• 2021

The outbreak of covid-19 has brought the characteristics and advantages of non-contact services to increased prominence, and the development of smart hotels"has accelerated. This study aims to identify, categorize and define the smart service experience at different touch points of the customer experience. The concept and characteristics of the smart hotel were examined based on existing research and literature. An analytical framework was designed using smart experience factors and customer touch points of smart hotels. Selected Chinese smart hotels were then examined under this framework. The case analysis results show that the customer experience design of smart hotels has developed to different degrees, in terms of interactivity, personalization, accessibility, information and privacy security. Based on the above findings, this article suggests that the design of smart hotels should use integrated data to further enhance personalized service experience.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.78-88
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• 2016

Recently BIM technology has been expanded for using in construction project. However its spread has been delayed than the initial expectations, due to the high-cost of BIM infrastructure development, the lack of regulations, the lack of process and so forth. In design phase, especially, collaboration based on BIM system has being a key factor for successful next generation building project. Through the analysis of current research trend about IT technologies, virtualization and BIM service, data exchange such as drawing, 3D model, object data, properties using cloud computing and virtual server system is defined as a most successful solution. In various industrial fields, cloud computing technology is utilized as a promising solution which can reduce time and cost of hardware infrastructure. Among the cloud computing technology, VDI is receiving a great deal of attention from it market as an essential part cloud computing. VDI enables to host multiple individual virtual machines by using hypervisor. It has an advantage to easy main device management. Therefore, this study implements a step-by-step user's DaaS by analyzing the desktop resource data of the workers from Pre-design phase to Schematic design, Design develop and Construction design phase. It also develops BIM environment based on test of BIM modeler and designers in architectural design firm. The goal of the study is to enable the cloud computing BIM server. It provides cost saving, high-performance quality of working environment and cooperation's convenience and high security when doing BIM work in small and medium sized architectural design firm.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.31-45
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• 2018

Recent dramatical development of computer technology related to internet technology intensifies the dispute over software of computer or smart device. Research on software has been flourished with political issuing of fierce competition among nations for software development. Particularly industrial growth in ethernet based big data and IoT (Internet of Things) has promoted to build and develop open source programs based on java, xcode and C. On these circumstances, issue on software piracy has been confronted despite the basic security policy protecting intellectual property rights of software and thus it is of substantial importance to protect the rights of originality of source program license. However, the other issue on source technology protection of developer is the possibility of hindrance to advancement in industry and culture by developing programs. This study discuss the way of enhancing legal stability of IoT application program development and reinforcing precision in inspection of program plagiarism by analyzing the source programs with newly introducing text mining technique, thus suggests an alternative protective way of infringement of personal information due to duplicating program.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.79-87
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• 2005

In e-commerce applications, a public key cryptosystem is an important and indispensible element for the basic security operations such as authentication, digital signaturing, and key distribution. In wired network environments, the public key infrastructure certificate, which is based on X.509 specification, has been widely used. On the other hand, it still remains difficult to use the certificate information in wireless network environments due to the inherent limitations of the hand-held devices such as low computational power and short battery life. In this paper, we facilitate a codesign approach by implementing a software public-key cryptosystem and classifying its internal computation overheads quantitatively using a software profiling technique. Moreover, we propose a method to analyze the profiled data and apply it to the problem of software/hardware partitioning in a codesign approach. As an illustrative example, we analyze the computational overheads of an EC-Elfagamal application and examine a critical computational path.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.89-95
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• 2016

In this paper, we propose a locker system that can remotely control and manage locker. The main features of the proposed system are follows. First, connect the locker with server and then open the locker after passing through the certification process in server. Second, locker manager can more systematically manage a locker by using locker management system based on smart phone. Third, provide an instant locker for temporary using lockers to increase the ease of use. Fourth, unlike traditional analog locker, can support better security by using authentication process and log data. Performance evaluation is performed via simulation by making the dummy model. The results of performance evaluation show the proposed system can support efficiently the locker management.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.