Journal of information and
communication convergence engineering
(J. Inf. Commun. Converg. Eng.)

Vol.22 No.3, September 30, 2024 Article TypeRegular paper

• Regular paper September 30, 2024

  0 31 6

  Design of 5-order Interdigital Band-Pass Filter

  Phally Phan , Donghoon Kang , Dal Ahn , and Youna Jang

  Journal of information and communication convergence engineering 2024; 22(3): 173-180 https://doi.org/10.56977/jicce.2024.22.3.173

  

  

  Abstract

  Full Text Cite Share

  Abstract : This study proposed an interdigital band-pass filter based on the microstrip transmission line. When designing a conventional structure for an interdigital filter based on the characteristics of the 5th order transmission line, seven resonators are required. By changing the impedance of the resonator adjacent to the feed line, the proposed interdigital band-pass filter was designed to reduce the number of resonators compared to conventional interdigital band-pass filters. Consequently, the resonator order decreased, and return and insertion losses become comparable to that in case of a conventional interdigital filter design. The proposed band-pass filter was designed with a center frequency of 2.75 GHz and a bandwidth of 1.5 GHz. Furthermore, based on various transmission characteristics such as group delay and coupling coefficient, two band-pass filters were designed, compared, and analyzed.

• Regular paper September 30, 2024

  0 39 5

  Data Transmission Method Using SATIN-based NOMA to Enhance Future Combat Capabilities

  Juhyun Maeng , Jongwon Lim , and Jounghuem Kwon

  Journal of information and communication convergence engineering 2024; 22(3): 181-188 https://doi.org/10.56977/jicce.2024.22.3.181

  

  

  Abstract

  Full Text Cite Share

  Abstract : Herein, an innovative transmission technique that utilizes the satellite aerial terrestrial integrated network (SATIN) architecture in combination with non-orthogonal multiple access (NOMA) communications is proposed. This approach is designed to significantly enhance communication rates, which is critical for modern and future combat capabilities. The effectiveness of the proposed transmission system is validated by conducting a comparative analysis of the sum-throughput results, considering various numbers of transmission nodes within the SATIN structure. The results and analyses reveal that the proposed method outperforms traditional methods such as spatial division multiple access (SDMA) and time division multiple access (TDMA), especially in terms of reducing data loss. This superior performance is primarily due to the advanced capability of NOMA in minimizing interference between signals, resulting in improved sum-throughput outcomes. The implementation of this method is expected to significantly enhance command communications in manned-unmanned combat systems, thereby bolstering overall combat effectiveness through improved transmission rates.

• Regular paper September 30, 2024

  0 48 2

  5G and Internet of Things: Next-Gen Network Architecture

  Ahmed Jumaa Lafta , Aya Falah Mahmood , and Basma Mohammed Saeed

  Journal of information and communication convergence engineering 2024; 22(3): 189-198 https://doi.org/10.56977/jicce.2024.22.3.189

  

  

  Abstract

  Full Text Cite Share

  Abstract : This study examined the integrated benefits of 5G New Radio, network slicing, and reinforcement learning (RL) mechanisms in addressing the challenges associated with the increasing proliferation of intelligent objects in communication networks. This study proposed an innovative architecture that initially employed network slicing to efficiently segregate and manage various service types. Subsequently, this architecture was enhanced by applying RL to optimize the subchannel and power allocation strategies. This dual approach was proven through simulation studies conducted in a suburban setting, highlighting the effectiveness of the method for optimizing the use of available frequency bands. The results highlighted significant improvements in mitigating interference and adapting to the dynamic conditions of the network, thereby ensuring efficient dynamic resource allocation. Further, the application of an RL algorithm enabled the system to adjust resources adaptively based on real-time network conditions, thereby proving the flexibility and responsiveness of the scheme to changing network scenarios.

• Regular paper September 30, 2024

  0 71 8

  Differential Authentication Scheme for Electric Charging System through Light Gradient Boosting Machine

  Byung-Hyun Lim , Ismatov Akobir , and Ki-Il Kim , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(3): 199-206 https://doi.org/10.56977/jicce.2024.22.3.199

  

  

  Abstract

  Full Text Cite Share

  Abstract : The network security of Plug-and-Charge (PnC) technology in electric vehicle charging systems is typically achieved through the well-known Transport Layer Security (TLS) protocol, which causes high communication overhead. To reduce this overhead, a differential authentication method employing different schemes for individual users has been proposed. However, decisions use a simple threshold approach and no quantitative performance evaluation should be made. In this study, we determined each user’s trust using several machine learning algorithms with their charging patterns and compared them. The experimental results reveal that the proposed approach outperforms the conventional approach by 41.36% in terms of round-trip time efficiency, demonstrating its effectiveness in reducing the TLS overhead. In addition, we show the simulation results for three user authentication methods and capture the performance variations under CPU busy waiting scenarios.

• Regular paper September 30, 2024

  0 35 2

  New Tree Routing Protocol with Adaptive Metrics Based on Hop Count

  BeomKyu Suh , Ismatov Akobir , and Ki-Il Kim *

  Journal of information and communication convergence engineering 2024; 22(3): 207-214 https://doi.org/10.56977/jicce.2024.22.3.207

  

  

  Abstract

  Full Text Cite Share

  Abstract : In wireless sensor networks, the implementation of routing protocols is crucial owing to their limited computational capacities. Tree routing is a suitable method in wireless sensors owing to its minimal routing overhead. However, single-hop metric schemes, such as hop count, cause congestion at specific nodes, whereas multiple metric schemes cannot control dynamically changing network environments. To address these issues, we propose a scheme to implement enhanced tree routing with adaptive metrics based on hop count. This approach assigns different weights to metrics to select suitable parent nodes based on hop count. The parent-selection algorithm utilizes hop count, buffer occupancy, and received signal strength indicator (RSSI) as metrics. This study evaluates the performance through simulation scenarios to analyze whether improvements can be made to address problems encountered in traditional tree routing. The performance metrics include packet delivery speed, throughput, and end-to-end delay, which vary depending on the volume of network traffic.

• Regular paper September 30, 2024

  0 30 1

  Optimal Terminal Interconnections Using Minimum Cost Spanning Tree of Randomly Divided Planes

  Minkwon Kim , Yeonsoo Kim , Hanna Kim , and Byungyeon Hwang *, Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(3): 215-220 https://doi.org/10.56977/jicce.2024.22.3.215

  

  

  Abstract

  Full Text Cite Share

  Abstract : This paper presents an efficient method for expanding interconnections in scenarios involving the reconstruction of interconnections across arbitrarily divided planes. Conventionally, such situations necessitate rebuilding interconnections based on all targets, ensuring minimal cost but incurring substantial time expenditure. In this paper, we present a tinkered tree algorithm designed to efficiently expand interconnections within a Euclidean plane divided into m randomly generated regions. The primary objective of this algorithm is to construct an optimal tree by utilizing the minimum spanning tree (MST) of each region, resulting in swift interconnection expansion. Interconnection construction is applied in various design fields. Notably, in the context of ad hoc networks, which lack a fixed-wired infrastructure and communicate solely with mobile hosts, the heuristic proposed in this paper is anticipated to significantly reduce costs while establishing rapid interconnections in scenarios involving expanded connection targets.

• Regular paper September 30, 2024

  0 20 3

  Marine life Image Recognition using Deep Learning

  Jiyun Hong , Jiwon Lee , Somin Lee , Eun Ko , Gyubin Kim , Jungwoon Kang , and Mincheol Kim

  Journal of information and communication convergence engineering 2024; 22(3): 221-230 https://doi.org/10.56977/jicce.2024.22.3.221

  

  

  Abstract

  Full Text Cite Share

  Abstract : The aim of this study is to investigate the automatic recognition and analysis of Jeju marine-life images using artificial intelligence (AI) technology. The dataset of marine-life images was prepared using tools such as Python, TensorFlow, and Google Colab (Google Colaboratory). We also developed models by training deep learning AI in image recognition to automatically recognize the species found in these images and extract their associated information, such as taxonomy, characteristics, and distribution. This study is innovative in that it uses deep learning technology combined with image-recognition technology for marine biodiversity research. In addition, these results will lead to the development of the marine-life industry in Jeju by supporting marine environment monitoring and marine resource conservation. Furthermore, this study is anticipated to contribute to academic advancement, specifically in the study of marine species diversity.

• Regular paper September 30, 2024

  0 19 2

  Identification of Partial Discharge Defect Detection in Cast-Resin Power Transformers Using Back-Propagation Algorithm

  Sung-Wook Kim , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(3): 231-236 https://doi.org/10.56977/jicce.2024.22.3.231

  

  

  Abstract

  Full Text Cite Share

  Abstract : This paper presents a method used to identify partial discharge defects in cast-resin power transformers using a back-propagation algorithm. The Rogowski-type partial discharge (PD) sensor was designed with a planar and thin structure based on a printed circuit board to detect PD signals. PD electrode systems, such as metal protrusions, particle-on-insulators, delamination, and void defects, were fabricated to simulate the PD defects that occur in service. PD characteristics, such as rising time, falling time, pulse width, skewness, and kurtosis without phase-resolved partial discharge patterns, were extracted to intuitively analyze each PD pulse according to the type of PD defect. A backpropagation algorithm was designed to identify PD defects using a virtual instrument (VI) based on the LabVIEW program. The results show that the accuracy rate of back-propagation (BP) algorithm reaches over 92.75% in identifying four types of PD defects.

• Regular paper September 30, 2024

  0 41 3

  Implementation of an Intelligent Automatic Notification System Based on Bluetooth Beacons and Smart Phones

  Amsuk Oh, Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(3): 237-241 https://doi.org/10.56977/jicce.2024.22.3.237

  

  

  Abstract

  Full Text Cite Share

  Abstract : In this study, we aim to implement an intelligent automatic notification system based on Bluetooth beacons and smartphones to prevent accidents caused by children being left in vehicles while commuting to school during summer or winter. This is a problem that is emerging as safety accidents occur owing to negligence in vehicles. Although teachers solutions, such as checking children’s attendance, safety accidents o ccur f req uently e very y ear. B ecause the continuous occurrence of safety accidents, parents’ anxiety about accidents is increasing, and various accident prevention measures are being developed to solve this problem. However, these methods are difficult to implement as teachers have to manage a large number of children. To solve this problem, this study implements an intelligent automatic notification system based on Bluetooth beacons and smartphones. This notification system attaches a relatively low-power Bluetooth beacon to the child’s belongings to recognize boarding and disembarking using the teacher’s smartphone, and sends a message to the parent’s smartphone.

• Regular paper September 30, 2024

  0 37 4

  Improved Fuzzy-associated Memory Techniques for Image Recovery

  Zheng Zhao and Kwang Baek Kim

  Journal of information and communication convergence engineering 2024; 22(3): 242-248 https://doi.org/10.56977/jicce.2024.22.3.242

  

  

  Abstract

  Full Text Cite Share

  Abstract : This paper introduces an improved fuzzy association memory (IFAM), an advanced FAM method based on the T-conorm probability operator. Specifically, the T-conorm probability operator fuzzifies the input data and performs fuzzy logic operations, effectively handling ambiguity and uncertainty during image restoration, which enhances the accuracy and effectiveness of the restoration results. Experimental results validate the performance of IFAM by comparing it with existing fuzzy association memory techniques. The root mean square error shows that the restoration rate of IFAM reached 80%, compared to only 40% for the traditional fuzzy association memory technique.

• Regular paper September 30, 2024

  0 37 3

  Simulator-Driven Sieving Data Generation for Aggregate Image Analysis

  DaeHan Ahn , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(3): 249-255 https://doi.org/10.56977/jicce.2024.22.3.249

  

  

  Abstract

  Full Text Cite Share

  Abstract : Advancements in deep learning have enhanced vision-based aggregate analysis. However, further development and studies have encountered challenges, particularly in acquiring large-scale datasets. Data collection is costly and time-consuming, posing a significant challenge in acquiring large datasets required for training neural networks. To address this issue, this study introduces a simulation that efficiently generates the necessary data and labels for training neural networks. We utilized a genetic algorithm (GA) to create optimized lists of aggregates based on the specified values of weight and particle size distribution for the aggregate sample. This enabled sample data collection without conducting sieving tests. Our evaluation of the proposed simulation and GA methodology revealed errors of 1.3% and 2.7 g for aggregate size distribution and weight, respectively. Furthermore, we assessed a segmentation model trained with data from the simulation, achieving a promising preliminary F1 score of 78.18 on the actual aggregate image.

• Regular paper September 30, 2024

  0 57 6

  Realtime Analysis of Sasang Constitution Types from Facial Features Using Computer Vision and Machine Learning

  Abdullah , Shah Mahsoom Ali , and Hee-Cheol Kim

  Journal of information and communication convergence engineering 2024; 22(3): 256-266 https://doi.org/10.56977/jicce.2024.22.3.256

  

  

  Abstract

  Full Text Cite Share

  Abstract : Sasang constitutional medicine (SCM) is one of the best traditional therapeutic approaches used in Korea. SCM prioritizes personalized treatment that considers the unique constitution of an individual and encompasses their physical characteristics, personality traits, and susceptibility to specific diseases. Facial features are essential for diagnosing Sasang constitutional types (SCTs). This study aimed to develop a real-time artificial intelligence-based model for diagnosing SCTs using facial images, building an SCTs prediction model based on a machine learning method. Facial features from all images were extracted to develop this model using feature engineering and machine learning techniques. The fusion of these features was used to train the AI model. We used four machine learning algorithms, namely, random forest (RF), multilayer perceptron (MLP), gradient boosting machine (GBM), and extreme gradient boosting (XGB), to investigate SCTs. The GBM outperformed all the other models. The highest accuracy achieved in the experiment was 81%, indicating the robustness of the proposed model and suitability for real-time applications.