• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.231-237
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• 2020

In this paper, we implemented a home IoT system based on MQTT protocol. In this system, data are collected from sensors in real time and transmitted to the server system. Based on collected data, home devices could be controlled automatically or manually. By using the MQTT protocol, we were able to see the data values of sensors collected in real time according to the topic setting. We implemented a system that automatically sets up home devices based on topic data, and it worked. The system is expected to be useful in applications that require monitoring and tracking of data in real time.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1125-1136
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• 2010

Lately there exist increasing demands for online abnormality monitoring over trajectory stream, which are obtained from moving object tracking devices. This problem is challenging due to the requirement of high speed data processing within limited space cost. In this paper, we present a FADA (Fuzzy Anomaly Detection Algorithm) which constructs normal profile by computing mobility feature information from the GPS (Global Positioning System) logs of mobile devices in MANETs (Mobile Ad-hoc Networks), computes a fuzzy dissimilarity between the current mobility feature information of the mobile device and the mobility feature information in the normal profile, and detects effectively the anomaly behaviors of mobile devices on the basis of the computed fuzzy dissimilarity. The performance of proposed FADA is evaluated through simulation.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.34-39
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• 2003

There have been several researches to trace intruders on the Internet. But, up to now, few of them has shown a satisfactory and practicable result of the study. Recently, a little bit more active methods such as 'counter-attacking' have been considered to be an alternative to solve the problem of hacking, and some people showed a tendency to accept the method as one possible way to protect their systems. And the new intruder-retracing method suggested in this study is an improved AIAA(Autonomous Intrusion Analysis Agent) model which has been achieved by attaching the counter-attacking method to the existing tracing system. In this paper, the automatic intruder-tracking system is proposed, which was achieved through the design of the following three modules, such as the intruder-retracing module, intruder-tracing module and AIAA dispatch module.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4153-4159
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• 2011

M2M can provide the bi-directional services between applications and objects which have the attached telecommunication module. It consists of the objects, platform, and (web based) applications. M2M can realize In-Transit Visibility and Asset Visibility combined with GIS. Although many applications of M2M, such as metering, monitoring, controlling, tracking et al., are being serviced, the interoperability among the M2Ms is not considered. In this paper, we described the interoperability. Specially, ROA, which is the state of art technology in mash-up, more than SOA, and URN, which is based on Open API in RESTful service, more than URL are studied. As the message protocol between platform and applications, MPM was defined.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.140-142
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• 2023

Federated learning (FL) is a new paradigm in machine learning (ML) that enables multiple devices to collaboratively train a shared ML model without sharing their local data. FL is well-suited for applications where data is sensitive or difficult to transmit in large volumes, or where collaborative learning is required. The Internet of Underwater Things (IoUT) is a network of underwater devices that collect and exchange data. This data can be used for a variety of applications, such as monitoring water quality, detecting marine life, and tracking underwater vehicles. However, the harsh underwater environment makes it difficult to collect and transmit data in large volumes. FL can address these challenges by enabling devices to train a shared ML model without having to transmit their data to a central server. This can help to protect the privacy of the data and improve the efficiency of training. In this view, this paper provides a brief overview of Fed-IoUT, highlighting its various applications, challenges, and opportunities.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.161-170
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• 2001

Vehicle detection is the basic of traffic monitoring. Video based systems have several apparent advantages compared with other kinds of systems. However, In video based systems, shadows make troubles for vehicle detection. especially active shadows resulted from moving vehicles. In this paper a new method that combines background subtraction and edge detection is proposed for vehicle detection and shadow rejection. The method is effective and the correct rate of vehicle detection is higher than 98(%) in experiments, during which the passive shadows resulted from roadside buildings grew considerably. Based on the proposed vehicle detection method, vehicle tracking, counting, classification and speed estimation are achieved so that traffic information concerning traffic flow is obtained to describe the load of each lane.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.59-66
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• 2021

This study proposes a requirements monitoring method that systematically performs traceability management and change management for requirements in order to improve the quality of software systems in the software development process. To this end, we present the artifact change history management models to systematically perform change management for individual artifacts and the traceability matrixes to define the relationship between artifacts. It also proposes a visual dashboard that makes it easy to grasp the situation in which changes are propagated to related artifacts when specific artifacts change.

• Korean Journal of Artificial Intelligence
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• v.5 no.2
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• pp.15-25
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• 2017

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tracking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face through a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.173-180
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• 2011

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.172-180
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• 2013

In this paper, we propose an energy-efficient camera operating scheme to save energy which can be used for mass surveillance cameras. This technique determines how many cameras should be turned on in the consideration of the velocity vector of monitoring targets, which is acquired by DSRC object tracking, the model of the specification of installed cameras, and the road model of installed sites. Also, we address other techniques used to save energy for the surveillance system as well. Throughout performance evaluation, we demonstrate the excellence of our proposed scheme compared with previous approaches.