• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.15-21
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• 2020

In order to utilize big data in general industrial sites, the structured big data collected from facilities, processes, and environments of industrial sites must first be processed and stored, and in the case of unstructured data, it must be stored as unstructured data or converted into structured data and stored in a database. In this paper, we study a method of collecting big data based on open IoT standards that can converge and utilize measurement information, environmental information of industrial sites to collect big data. The platform for collecting big data proposed in this paper is capable of collecting, processing, and storing big data at industrial sites to process real-time sensing information. For processing and analyzing data according to the purpose of the stored industrial, various big data technologies also can be applied.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.1-9
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• 2022

As various services are linked to IoT(Internet of Things) and portable communication terminals, cyber attacks that exploit security vulnerabilities of the devices are rapidly increasing. In particular, cyber attacks targeting heterogeneous devices in large-scale network environments through advanced persistent threat (APT) attacks are on the rise. Therefore, in order to improve the effectiveness of the response system in the event of a breach, it is necessary to apply a data enrichment mechanism for the collected artifact data to improve threat analysis and detection performance. Therefore, in this study, by analyzing the data supplementation common elements performed in the existing incident management framework for the artifacts collected for the analysis of intrusion accidents, characteristic elements applicable to the actual system were derived, and based on this, an improved accident analysis framework The prototype structure was presented and the suitability of the derived data supplementary extension elements was verified. Through this, it is expected to improve the detection performance when analyzing cyber incidents targeting artifacts collected from heterogeneous devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.54-61
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• 2023

With the development of IoT technologies and artificial intelligent, diverse digital image equipments are being used in industrial sites. Because image data can be easily damaged by noise while it's obtained with a camera or a sensor and the damaged image has a bad effect on the process of image processing, noise removal is being demanded as preprocessing. In this thesis, for the restoration of image damaged by the noise of random impulse, a switching filter algorithm based on noise estimation was suggested. With the proposed algorithm, noise estimation and error distraction were carried out according to the similarity of the pixel values in the local mask of the image, and a filter was chosen and switched depending on the ratio of noise existing in the local mask. Simulations were conducted to analyze the noise removal performance of the proposed algorithm, and as a result of magnified image and PSNR comparison, it showed superior performance compared to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.731-742
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• 2022

PIPO is the latest domestic lightweight block cipher announced in ICISC'20, which is characterized by being lightweight to facilitate implementation on IoT with limited resources. In this paper, PIPO 64/128-bit and 64/256-bit were implemented using web-based languages such as Javascript and WebAsembly. Two methods of performance evaluation were conducted by implementing bitsice and TLU, and the performance was compared by implementing Looped written using for statements and Unrolled written for statements. It performs performance evaluations in various web browsers such as Google Chrome, Mozilla Firefox, Opera, and Microsoft Edge, as well as OS-specific environments such as Windows, Linux, Mac, iOS, and Android. In addition, a performance comparison was performed with PIPO implemented in C language. This can be used as an indicator for applying PIPO block cipher on the web.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3544-3564
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• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

• Convergence Security Journal
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• v.23 no.1
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• pp.45-54
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• 2023

Recently, with the development of the Internet of Things, research on protocols that can easily connect devices without a UI to the network has been steadily conducted. To this end, the Wi-Fi Alliance announced Wi-Fi Easy Connect, which can connect to a network using a QR code. However, since Wi-Fi Easy Connect requires a large amount of computation for safety, it is difficult to apply to low-power and miniaturized IoT devices. In addition, Wi-Fi Easy Connect considering scalability is designed to operate in a wired environment, but problems such as duplicate encryption occur because it does not consider a security environment like TLS. Therefore, in this paper, we analyze the Wi-Fi Easy Connect protocol and propose a protocol that can operate efficiently in the TLS environment. It was confirmed that the proposed protocol satisfies the existing security requirements and at the same time reduces about 67% of ECC scalar multiplication operations with a large amount of computation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.250-252
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated systems are progressing in various fields, and various application technologies are being studied in systems using algorithms such as object detection, recognition, and tracking. In the case of a system operating based on an image, noise removal is performed as a pre-processing process, and precise noise removal is sometimes required depending on the environment of the system. In this paper, we propose a modified central weight filter algorithm using pixel division of local regions to minimize the blurring that tends to occur in the filtering process and to emphasize the details of the resulting image. In the proposed algorithm, when a pixel of a local area is divided into two areas, the center of the dominant area among the divided areas is set as a criterion for the weight filter algorithm. The resulting image is calculated by convolving the transformed center weight with the pixel value inside the filtering mask.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.1
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• pp.25-37
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• 2019

Power-line communication (PLC) has influenced smart grid development. In addition, PLC has also been instrumental in current research on internet-of-things (IoT). Due to the implementation of PLC in smart grid and IoT environments, strides have been made in PLC and its combination with the wireless system to form a hybrid communication system. Also, PLC has evolved from a single-input-single-output (SISO) configuration to multiple-input-multiple-output (MIMO) configuration system, and from a point-to-point communication system to cooperative communication systems. In this work, we extend a MIMO wireless two-way amplify-and-forward (AF) cooperative communication system to a hybrid PLC and wireless (PLC/W) system configuration. We then maximize the weighted sum-rate for the hybrid PLC/W by optimizing the precoders at each node within the hybrid PLC/W system. The sum-rate problem was found to be non-convex, therefore, an iterative algorithm is used to find the optimal precoders that locally maximize the system sum-rate. For our simulation results, we compare our proposed hybrid PLC/W configuration to a PLC only and wireless only configuration at each node. Due to an improvement in system diversity, the hybrid PLC/W configuration outperformed the PLC only and wireless only system configurations in all simulation results presented in this paper.

• Convergence Security Journal
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• v.24 no.2
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• pp.31-38
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• 2024

Recent expansion in cloud service usage has heightened the importance of cloud security. The purpose of this study is to analyze current research trends in the field of cloud security and to derive implications. To this end, R&D project data provided by the National Science and Technology Knowledge Information Service (NTIS) from 2010 to 2023 was utilized to analyze trends in cloud security research. Fifteen core topics in cloud security research were identified using LDA topic modeling and ARIMA time series analysis. Key areas identified in the research include AI-powered security technologies, privacy and data security, and solving security issues in IoT environments. This highlights the need for research to address security threats that may arise due to the proliferation of cloud technologies and the digital transformation of infrastructure. Based on the derived topics, the field of cloud security was divided into four categories to define a technology reference model, which was improved through expert interviews. This study is expected to guide the future direction of cloud security development and provide important guidelines for future research and investment in academia and industry.