• Annual Conference of KIPS
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• 2007.11a
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• pp.815-818
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• 2007

최근 유비쿼터스 시대가 도래하면서 센서 네트워크의 중요성이 대두되고 있다. 센서 네트워크란 필요한 모든 곳에 전자태그를 부착하고, 이를 통해 사물의 인식 정보를 기본으로 주변의 환경정보까지 각종 센서를 통해 실시간으로 수집하여 관리, 통제할 수 있도록 구성한 네트워크를 말한다. 이러한 센서 네트워크에서 각 노드들은 에너지, 계산 능력, 대역폭 등에 상당한 제한을 받으며, 정보가 저장된 장치를 쉽게 도난 당할 수도 있다. 특히 보안 통신을 하기 위해 키 설정 및 관리는 필수적이며 지금까지 그로 인해 여러 가지 키 분배 및 관리 방법이 제안되었다. 본 논문은 군대 등의 특정 상황과 같이 계층적 구조를 가지는 센서 네트워크에서 더욱 효율적으로 통신을 할 수 있는 키 관리 방법을 소개하고자 한다. 기존의 계층적 구조의 취약점을 분석하고, 이를 바탕으로 레벨 키를 제안하여 같은 레벨에서 다른 그룹간 통신이 가능한 효율적인 키 분배 방안을 제시한다.

• Annual Conference of KIPS
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• 2007.11a
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• pp.936-940
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• 2007

본 논문은 무선 센서 네트워크의 에너지 효율적인 운영을 위해 무선 센서 네트워크 환경에 적합한 클러스터 헤드 선출 메커니즘을 제안한다. LEACH 와 같은 기존의 확률 모델 기반의 클러스터 헤드 선출 메커니즘들은 각 라운드마다 클러스터 헤드로 선출될 확률과 라운드 횟수 등을 바탕으로 클러스터 헤드를 선출한다. 그러나 이와 같은 방법은 각 노드의 상황을 고려하지 않아 네트워크의 수명을 단축시킬 수 있다. 이러한 문제점을 해결하기 위해서는 각 센서 노드의 에너지 및 노드 분포 상황을 고려하여 클러스터 헤드를 선출해야 한다. 하지만 실제 무선 센서 네트워크 환경에서는 클러스터 헤드 선출을 위해 정확한 정보를 수집하고 이를 계산하는데 있어 큰 오버헤드가 발생하는 문제점이 있다. 이에 본 논문에서는 정보 수집 및 계산에 있어서 오버헤드를 줄이고 네트워크의 수명을 극대화하기 위하여 퍼지 논리를 이용한 퍼지 논리 기반의 클러스터 헤드 선출 메커니즘을 제안한다. Matlab 을 통한 시뮬레이션 결과 LEACH 에 비해 퍼지 논리 기반의 클러스터 헤드 선출 메커니즘을 이용했을 경우 네트워크 수명이 약 16.3% 향상되었다.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.145-152
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• 2024

Closed quotient (CQ) represents the time ratio for which the vocal folds remain in contact during voice production. Because analyzing CQ values serves as an important reference point in vocal training for professional singers, these values have been measured mechanically or electrically by either inverse filtering of airflows captured by a circumferentially vented mask or post-processing of electroglottography waveforms. In this study, we introduced a novel algorithm to predict the CQ values only from audio signals. This has eliminated the need for mechanical or electrical measurement techniques. Our algorithm is based on a gated recurrent unit (GRU)-type neural network. To enhance the efficiency, we pre-processed an audio signal using the pitch feature extraction algorithm. Then, GRU-type neural networks were employed to extract the features. This was followed by a dense layer for the final prediction. The Results section reports the mean square error between the predicted and real CQ. It shows the capability of the proposed algorithm to predict CQ values.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.196-204
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• 2024

The security of data and information using encryption algorithms is becoming increasingly important in today's world of digital data transmission over unsecured wired and wireless communication channels. Hybrid encryption techniques combine both symmetric and asymmetric encryption methods and provide more security than public or private key encryption models. Currently, there are many techniques on the market that use a combination of cryptographic algorithms and claim to provide higher data security. Many hybrid algorithms have failed to satisfy customers in securing data and cannot prevent all types of security threats. To improve the security of digital data, it is essential to develop novel and resilient security systems as it is inevitable in the digital era. The proposed hybrid algorithm is a combination of the well-known RSA algorithm and a simple symmetric key (SSK) algorithm. The aim of this study is to develop a better encryption method using RSA and a newly proposed symmetric SSK algorithm. We believe that the proposed hybrid cryptographic algorithm provides more security and privacy.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.108-113
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• 2024

The purpose of this study is to analyze user perceptions of tourism platforms through big data. Data were collected from Naver, Daum, and Google as big data analysis channels. Using semantic network analysis with the keyword 'tourism platform,' a total of 29,265 words were collected. The collection period was set for two years, from August 31, 2021, to August 31, 2023. Keywords were analyzed for connected networks using TexTom and Ucinet programs for social network analysis. Keywords perceived by tourism platform users include 'travel,' 'diverse,' 'online,' 'service,' 'tourists,' 'reservation,' 'provision,' and 'region.' CONCOR analysis revealed four groups: 'platform information,' 'tourism information and products,' 'activation strategies for tourism platforms,' and 'tourism destination market.' This study aims to expand and activate services that meet the needs and preferences of users in the tourism field, as well as platforms tailored to the changing market, based on user perception, current status, and trend data on tourism platforms.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.27-41
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• 2024

Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (α), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.

• Current Optics and Photonics
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• v.8 no.5
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• pp.463-471
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• 2024

Multimode fibers (MMFs) possess high information throughput and small core diameter, making them highly promising for applications such as endoscopy and communication. However, modal dispersion hinders the direct use of MMFs for image transmission. By training neural networks on time-series waveforms collected from MMFs it is possible to reconstruct images, transforming blurred speckle patterns into recognizable images. This paper proposes a fully convolutional neural-network model, MSMDFNet, for image restoration in MMFs. The network employs an encoder-decoder architecture, integrating multiscale convolutional modules in the decoding layers to enhance the receptive field for feature extraction. Additionally, attention mechanisms are incorporated from both spatial and channel dimensions, to improve the network's feature-perception capabilities. The algorithm demonstrates excellent performance on MNIST and Fashion-MNIST datasets collected through MMFs, showing significant improvements in various metrics such as SSIM.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.5
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• pp.1099-1109
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• 2024

As a wide range of industries using iron, such as water and sewerage pipes, gas pipelines, heat pipes, electric engines, communication pipes, and oil pipelines, rapidly become active, there is a demand for reliability and low cost of DC power supplies that can prevent corrosion of pipe networks. In particular, high-efficiency corrosion prevention systems due to changes in the perception of carbon emissions and energy saving are essential elements. Therefore, the construction of a switching-type corrosion current controller is being activated. Also, in such systems, DC/DC converters capable of multi-channel current control are demanded for corrosion prevention functions and uniform consumption of sacrificial anodes. This paper proposes a new current supply system for preventing pipeline corrosion using a low-cost SMPS dedicated chip. The proposed method can maintain excellent parallel operation function, protection function, and response speed by configuring a current controller using a hybrid method using analog and digital. The proposed method verified its superiority through simulations and experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.31-40
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• 2016

In vehicular ad-hoc networks (VANETs), vehicles sense information on emergency incidents (e.g., accidents, unexpected road conditions, etc.) and propagate this information to following vehicles and a server to share the information. However, this process of emergency message propagation is based on multiple broadcast messages and can lead to broadcast storms. To address this issue, in this work, we use a novel approach to detect the vehicles that are farthest away but within communication range of the transmitting vehicle. Specifically, we discuss a signal-to-noise ratio (SNR)-based linear back-off (SLB) scheme where vehicles implicitly detect their relative locations to the transmitter with respect to the SNR of the received packets. Once the relative locations are detected, nodes that are farther away will set a relatively shorter back-off to prioritize its forwarding process so that other vehicles can suppress their transmissions based on packet overhearing. We evaluate SLB using a realistic simulation environment which consists of a NS-3 VANET simulation environment, a software-based WiFi-IP gateway, and an ITS server operating on a separate machine. Comparisons with other broadcasting-based schemes indicate that SLB successfully propagates emergency messages with latencies and hop counts that is close to the experimental optimal while reducing the number of transmissions by as much as 1/20.

• Journal of the Economic Geographical Society of Korea
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• v.6 no.1
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• pp.119-144
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• 2003

This study attempts to figure out the impact of Information and communication technologies (ICTs) on spatial structure and to speculate on spatial strategies in the electronic economy from a geographical perspective. The unprecedented development of ICTs based on the explosive use of the Internet was enough to lead to the expectation that physical distance would not be a significant barrier in business activities. In fact, however, at least at a current stage, the development of ICTs has not automatically removed the inequality in spatial structure. The accessibility to electronic space is different by economic and social status within a country as well as between countries. The importance of place, locality, and place-specific assets has been strengthened in the global economy. Physical proximity is still of great importance because it helps to minimize transaction costs, to exploit place-specific social networks, and to accumulate credibility for successful businesses. Likewise, the development of electronic commerce such as B2B and B2C EC also does not necessarily result in the ignorance of place and locality. Rather, the recognition of the importance of spatial strategies is extremely important for the success in online businesses. As a conclusion, the spatial dimension becomes more important in the digital era for successful businesses and balanced regional developments than ever before. The need for the improvement of ICT infrastructures, the development of human resources, and the establishment of regional innovation systems in peripheral areas cannot be overemphasized even in the digital era.