• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.2 s.344
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• pp.27-38
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• 2006

When an RFID reader attempts to read tags, interference might occur if the neighboring readers also attempt to communicate with the same tag at the same time or the neighboring readers use the same frequency at the same time. These interferences cause the RFID reader collision. When the RFID reader collision occirs, either the command from the reader can not be transmitted to the tags or the response from the tags can not be received by the reader correctly. RFID reader anti-collision algorithms have been developed to reduce it. One of the best blown reader anti-collision algorithms is the Colorwave algorithm proposed by MIT. The Colorwave algorithm reduces the reader collisions by having the readers operate at different times. In Colorwave the time is divided into frames and a frame is divided into a number of slots. Each reader can access the tags using the slot time assigned to it. Depending on the probability of the interference, the colorwave adjusts the frame size to improve the efficiency. In this paper, we analyze the operations and the performance of the Colorwave algorithm and identify the problems of the algorithm. We also show that by adding some modifications to the algorithm the performance can be improved significantly.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.527-534
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• 2017

The purpose of this study is to develop unmanned equipment that can automatically move to the desired point and measure water quality at the correct depth. For this purpose, we constructed a water sampling lift and water sampling container, an unmanned vessel equipped with a VRS-GPS, an acoustic echo sounder, and a water quality sensor. Also, we developed an automatic navigation algorithm and program, an automatic water sampling program, and a water quality map generation program. As a result of the experiment in the detention pond, the unmanned vessel sailed along the planned route with an accuracy of about 93% within the error range of 3m. In addition, the water quality sensor installed in the lift was able to acquire the water quality of the target area in real time and transmit it to the server via wireless Internet, and it was possible to monitor the water quality of each site in real time. Through field experiments, the water sampling lift was able to control the desired length with an accuracy of about 94%. The stretch length accuracy experiment of the water sampling lift was impossible to measure directly in the water, so it was replaced land-based experiment. We also found some unstable problems due to the weight of the water sampling lift and the weight of the air compressor to operate the water container. Except these two problems, we accomplished purpose of this study. An automated water quality measurement method using an unmanned vessel can be used to measure the quality of water in a difficult to access area and to secure the safety of the worker.

• Science of Emotion and Sensibility
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• v.23 no.3
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• pp.37-46
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• 2020

This study was aimed at investigating the activities of a badminton club and designing smart wear to prevent sports injuries during badminton club activities. Everyone is familiar with sports in an aging society and clubs are gradually developing. Popular badminton club activities lead to frequent sports injuries, especially ankle injuries, which are a serious problem that hampers members' participation in sports. Therefore, this study aims to propose a prototype design for smart wear to prevent sports injuries, including ankle injuries. First, we identified the characteristics and considerations of members of badminton clubs, and the components of smart wear to prevent sports injuries. Second, members of the badminton clubs and an elite badminton player participated in a survey on the issues and requirements associated with wearing smart wear. Third, usage scenarios for smart wear were created based on literature reviews and the user assessment lists. Fourth, a prototype of the smart wear to prevent sports injuries including ankle injuries was created based on the scenarios. With the proposed smart wear, members of badminton clubs who may require assistance with sports injuries will be able to monitor said injuries, as well as their health condition, as avatars in visual games through a smart terminal. The visual game system will provide easier access to information about sports injuries and health. This smart sportswear will allow members of badminton clubs to prevent sports injuries and review their performance. This study can be utilized to design smart wear to prevent sports injuries and monitor sporting activities or bio-signals.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.151-164
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• 2009

The importance of sensor networks as a base of ubiquitous computing realization is being highlighted, and espicially the security is recognized as an important research isuue, because of their characteristics.Several efforts are underway to provide security services in sensor networks, but most of them are preventive approaches based on cryptography. However, sensor nodes are extremely vulnerable to capture or key compromise. To ensure the security of the network, it is critical to develop security Intrusion Detection System (IDS) that can survive malicious attacks from "insiders" who have access to keying materials or the full control of some nodes, taking their charateristics into consideration. In this perper, we design a distributed and adaptive IDS architecture on sensor networks, respecting both of energy efficiency and IDS efficiency. Utilizing a modified HEED algorithm, a clustering algorithm, distributed IDS nodes (dIDS) are selected according to node's residual energy and degree. Then the monitoring results of dIDSswith detection codes are transferred to dIDSs in next round, in order to perform consecutive and integrated IDS process and urgent report are sent through high priority messages. With the simulation we show that the superiorities of our architecture in the the efficiency, overhead, and detection capability view, in comparison with a recent existent research, adaptive IDS.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2027-2034
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• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Convergence Security Journal
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• v.23 no.2
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• pp.27-36
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• 2023

In recent years, ransomware attacks have become more organized and specialized, with the sophistication of attacks targeting specific individuals or organizations using tactics such as social engineering, spear phishing, and even machine learning, some operating as business models. In order to effectively respond to this, various researches and solutions are being developed and operated to detect and prevent attacks before they cause serious damage. In particular, honeypots can be used to minimize the risk of attack on IT systems and networks, as well as act as an early warning and advanced security monitoring tool, but in cases where ransomware does not have priority access to the decoy file, or bypasses it completely. has a disadvantage that effective ransomware response is limited. In this paper, this honeypot is optimized for the user environment to create a reliable real-time dynamic honeypot file, minimizing the possibility of an attacker bypassing the honeypot, and increasing the detection rate by preventing the attacker from recognizing that it is a honeypot file. To this end, four models, including a basic data collection model for dynamic honeypot generation, were designed (basic data collection model / user-defined model / sample statistical model / experience accumulation model), and their validity was verified.

• Journal of the Korean Society of Marine Environment & Safety
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• v.14 no.1
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• pp.29-37
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• 2008

This study examined principles and techniques of efficient water quality management as well as total coastal pollutant loads and the relevant examples in the advanced countries from the viewpoints of water quality improvement and pollution control in coastal areas. The problems and improvements in an estimation of the current total pollutant loads were also pointed out. In addition, discussion was made on the relationship between total pollutant loads and environmental capacity as well as particulars requiring extensive examination on access to and study on water quality model used as prediction tool for marine environment. Furthermore, this study proposed details of and improvement plans for water quality control to be reflected and absorbed into systems and policies related to coastal water quality. In coastal areas, which are subject to total coastal pollutant loads, it is necessary to calculate pollutant loads reduction and allocation, to propose them in detail in statement in relations to new pollution sources for the corresponding projects or plans in environmental impact assessment and prior environmental review system. Also, in relations to regional plans for coastal management, the local government concerned must focus more on environmental management plan to implement data on pollution sources and pollutant loads flown into sea areas under basic jurisdiction, therefore it is required to actively respond to expansion and introduction of total coastal pollutant loads system in the future. Total coastal pollutant loads system must be expanded and executed by considering characteristics of sea area and changes in the environment of land. For pollution sources in land, the competent authorities in charge of coastal environment will need to initiatively administer supervision, monitoring activities and achieve integration and operation of the related laws by preparing legal bases for management system or adjusting the related laws.