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

Vol.21 No.1, March 31, 2023 Article TypeRegular paper

• Regular paper March 31, 2023

  0 133 36

  An Accurate Design Method of Wideband BPF Considering Frequency Dependence of Inverters

  Youna Jang and Dal Ahn , Member, KIICE

  Journal of information and communication convergence engineering 2023; 21(1): 1-8 https://doi.org/10.56977/jicce.2023.21.1.1

  

  

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  Abstract : This paper presents a design method for a wideband bandpass filter (BPF) which compensates for frequency dependency based on the image admittance and image phase. In the proposed method, new compensation methods for the admittance and phase are integrated with the conventional method. The proposed method improves the frequency shift and reduces the unwanted bandwidth when designing more than 20% of the Fractional Bandwidth (FBW), whereas the conventional method exhibits frequency degradation at only 10% FBW. The proposed design theory was verified by applying it to both lumped elements and distributed lines through circuit simulation and measurements without an optimization process. The measurement results demonstrate improvements in the frequency shift and target bandwidth. In the future, an accurate design method based on frequency dependence can be implemented for the next-generation broadband communication system applications.

• Regular paper March 31, 2023

  0 123 27

  Text Classification on Social Network Platforms Based on Deep Learning Models

  YA Chen , Tan Juan , and Hoekyung Jung

  Journal of information and communication convergence engineering 2023; 21(1): 9-16 https://doi.org/10.56977/jicce.2023.21.1.9

  

  

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  Abstract : The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• Regular paper March 31, 2023

  0 121 30

  Comparison of the Effect of Interpolation on the Mask R-CNN Model

  Young-Pill Ahn , Kwang Baek Kim , and Hyun-Jun Park , Member, KIICE

  Journal of information and communication convergence engineering 2023; 21(1): 17-23 https://doi.org/10.56977/jicce.2023.21.1.17

  

  

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  Abstract : Recently, several high-performance instance segmentation models have used the Mask R-CNN model as a baseline, which reached a historical peak in instance segmentation in 2017. There are numerous derived models using the Mask R-CNN model, and if the performance of Mask R-CNN is improved, the performance of the derived models is also anticipated to improve. The Mask R-CNN uses interpolation to adjust the image size, and the input differs depending on the interpolation method. Therefore, in this study, the performance change of Mask R-CNN was compared when various interpolation methods were applied to the transform layer to improve the performance of Mask R-CNN. To train and evaluate the models, this study utilized the PennFudan and Balloon datasets and the AP metric was used to evaluate model performance. As a result of the experiment, the derived Mask R-CNN model showed the best performance when bicubic interpolation was used in the transform layer.

• Regular paper March 31, 2023

  0 139 32

  SSD PCB Component Detection Using YOLOv5 Model

  Pyeoungkee Kim , Xiaorui Huang , and Ziyu Fang

  Journal of information and communication convergence engineering 2023; 21(1): 24-31 https://doi.org/10.56977/jicce.2023.21.1.24

  

  

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  Abstract : The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual detection of malfunctioning components. With the rapid development of artificial intelligence technologies, automatic detection of components through convolutional neural networks (CNN) can provide a sound solution for this area. This study proposes applying the YOLOv5 model to SSD PCB component detection, which is the first step in detecting defective components. It achieves pioneering state-of-the-art results on the SSD PCB dataset. Contrast experiments are conducted with YOLOX, a neck-and-neck model with YOLOv5; evidently, YOLOv5 obtains an mAP@0.5 of 99.0%, essentially outperforming YOLOX. These experiments prove that the YOLOv5 model is effective for tiny object detection and can be used to study the second step of detecting defective components in the future.

• Regular paper March 31, 2023

  0 131 27

  Information Security on Learning Management System Platform from the Perspective of the User during the COVID-19 Pandemic

  Mujiono Sadikin , Rakhmat Purnomo , Rafika Sari , Dyah Ayu Nabilla Ariswanto , Juanda Wijaya , and Lydia Vintari

  Journal of information and communication convergence engineering 2023; 21(1): 32-44 https://doi.org/10.56977/jicce.2023.21.1.32

  

  

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  Abstract : Information security breach is a major risk in e-learning. This study presents the potential information security disruptions in Learning Management Systems (LMS) from the perspective of users. We use the Technology Acceptance Model approach as a user perception model of information security, and the results of a questionnaire comprising 44 questions for instructors and students across Indonesia to verify the model. The results of the data analysis and model testing reveals that lecturers and students perceive the level of information security in the LMS differently. In general, the information security aspects of LMSs affect the perceptions of trust of student users, whereas such a correlation is not found among lecturers. In addition, lecturers perceive information security aspect on Moodle is and Google Classroom differently. Based on this finding, we recommend that institutions make more intense efforts to increase awareness of information security and to run different information security programs.

• Regular paper March 31, 2023

  0 72 22

  Implementation of a Sightseeing Multi-function Controller Using Neural Networks

  Jae-Kyung Lee and Jae-Hong Yim

  Journal of information and communication convergence engineering 2023; 21(1): 45-53 https://doi.org/10.56977/jicce.2023.21.1.45

  

  

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  Abstract : This study constructs various scenarios required for landscape lighting; furthermore, a large-capacity general-purpose multi-functional controller is designed and implemented to validate the operation of the various scenarios. The multi-functional controller is a large-capacity general-purpose controller composed of a drive and control unit that controls the scenarios and colors of LED modules and an LED display unit. In addition, we conduct a computer simulation by designing a control system to represent the most appropriate color according to the input values of the temperature, illuminance, and humidity, using the neuro-control system. Consequently, when examining the result and output color according to neuro-control, unlike existing crisp logic, neuro-control does not require the storage of many data inputs because of the characteristics of artificial intelligence; the desired value can be controlled by learning with learning data.

• Regular paper March 31, 2023

  0 95 21

  Optimization for Large-Scale n-ary Family Tree Visualization

  Kyoungju Min , Jeongyun Cho , Manho Jung , and Hyangbae Lee

  Journal of information and communication convergence engineering 2023; 21(1): 54-61 https://doi.org/10.56977/jicce.2023.21.1.54

  

  

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  Abstract : The family tree is one of the key elements of humanities classics research and is very important for accurately understanding people or families. In this paper, we introduce a method for automatically generating a family tree using information on interpersonal relationships (IIPR) from the Korean Classics Database (KCDB) and visualize interpersonal searches within a family tree using data-driven document JavaScript (d3.js). To date, researchers of humanities classics have wasted considerable time manually drawing family trees to understand people’s influence relationships. An automatic family tree builder analyzes a database that visually expresses the desired family tree. Because a family tree contains a large amount of data, we analyze the performance and bottlenecks according to the amount of data for visualization and propose an optimal way to construct a family tree. To this end, we create an n-ary tree with fake data, visualize it, and analyze its performance using simulation results.

• Regular paper March 31, 2023

  0 68 21

  Imputation Method Using Local Linear Regression Based on Bidirectional k-nearest-components

  Yonggeol Lee , Member, KIICE

  Journal of information and communication convergence engineering 2023; 21(1): 62-67 https://doi.org/10.56977/jicce.2023.21.1.62

  

  

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  Abstract : This paper proposes an imputation method using a bidirectional k-nearest components search based local linear regression method. The bidirectional k-nearest-components search method selects components in the dynamic range from the missing points. Unlike the existing methods, which use a fixed-size window, the proposed method can flexibly select adjacent components in an imputation problem. The weight values assigned to the components around the missing points are calculated using local linear regression. The local linear regression method is free from the rank problem in a matrix of dependent variables. In addition, it can calculate the weight values that reflect the data flow in a specific environment, such as a blackout. The original missing values were estimated from a linear combination of the components and their weights. Finally, the estimated value imputes the missing values. In the experimental results, the proposed method outperformed the existing methods when the error between the original data and imputation data was measured using MAE and RMSE.

• Regular paper March 31, 2023

  0 67 22

  Emotional Correlation Test from Binary Gender Perspective using Kansei Engineering Approach on IVML Prototype

  Nur Faraha Mohd Naim and Mintae Hwang , Member, KIICE

  Journal of information and communication convergence engineering 2023; 21(1): 68-74 https://doi.org/10.56977/jicce.2023.21.1.68

  

  

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  Abstract : This study examines the response of users' feelings from a gender perspective toward interactive video mobile learning (IVML). An IVML prototype was developed for the Android platform allowing users to install and make use of the app for m-learning purposes. This study aims to measure the level of feelings toward the IVML prototype and examine the differences in gender perspectives, identify the most responsive feelings between male, and female users as prominent feelings and measure the correlation between user-friendly feeling traits as an independent variable in accordance with gender attributes. The feelings response could then be extracted from the user experience, user interface, and human-computer interaction based on gender perspectives using the Kansei engineering approach as the measurement method. The statistical results demonstrated the different emotional reactions from a male and female perspective toward the IVML prototype may or may not have a correlation with the user-friendly trait, perhaps having a similar emotional response from one to another.

• Regular paper March 31, 2023

  0 98 35

  Implementation and Evaluation of Harmful-Media Filtering Techniques using Multimodal-Information Extraction

  Yeon-Ji Lee , Ye-Sol Oh , Na-Eun Park , and Il-Gu Lee

  Journal of information and communication convergence engineering 2023; 21(1): 75-81 https://doi.org/10.56977/jicce.2023.21.1.75

  

  

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  Abstract : Video platforms, including YouTube, have a structure in which the number of video views is directly related to the publisher’s profits. Therefore, video publishers induce viewers by using provocative titles and thumbnails to garner more views. The conventional technique used to limit such harmful videos has low detection accuracy and relies on follow-up measures based on user reports. To address these problems, this study proposes a technique to improve the accuracy of filtering harmful media using thumbnails, titles, and audio data from videos. This study analyzed these three pieces of multimodal information; if the number of harmful determinations was greater than the set threshold, the video was deemed to be harmful, and its upload was restricted. The experimental results showed that the proposed multimodal information extraction technique used for harmful-video filtering achieved a 9% better performance than YouTube’s Restricted Mode with regard to detection accuracy and a 41% better performance than the YouTube automation system.

• Regular paper March 31, 2023

  0 102 27

  Performance Evaluation of a Feature-Importance-based Feature Selection Method for Time Series Prediction

  Hyun Ahn , Member, KIICE

  Journal of information and communication convergence engineering 2023; 21(1): 82-89 https://doi.org/10.56977/jicce.2023.21.1.82

  

  

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  Abstract : Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• Regular paper March 31, 2023

  0 89 27

  Phase Differences Averaging (PDA) Method for Reducing the Phase Error in Digital Holographic Microscopy (DHM)

  Hyun-Woo Kim , Jaehoon Lee , Arun Anand , Myungjin Cho , and Min-Chul Lee , Member, KIICE

  Journal of information and communication convergence engineering 2023; 21(1): 90-97 https://doi.org/10.56977/jicce.2023.21.1.90

  

  

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  Abstract : Digital holographic microscopy (DHM) is a three-dimensional (3D) imaging technique that uses the phase information of coherent light. In the reconstruction process of DHM, a narrow region around the positive or negative sideband from the Fourier domain is windowed to avoid noise due to the DC spectrum of the hologram spectrum. However, the limited size of the window also degrades the high-frequency information of the 3D object profile. Although a large window can have more detailed information of the 3D object shape, the noise is increased. To solve this trade-off, we propose phase difference averaging (PDA). The proposed method yields high-frequency information of the specimen while reducing the DC noise. In this paper, we explain the reconstruction algorithm for this method and compare it to various conventional filtering methods including Gaussian, Wiener, average, median, and bilateral filtering methods.

• Regular paper March 31, 2023

  0 100 26

  Human Detection using Real-virtual Augmented Dataset

  Jongmin Lee , Yongwan Kim , Jinsung Choi , Ki-Hong Kim , and Daehwan Kim

  Journal of information and communication convergence engineering 2023; 21(1): 98-102 https://doi.org/10.56977/jicce.2023.21.1.98

  

  

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  Abstract : This paper presents a study on how augmenting semi-synthetic image data improves the performance of human detection algorithms. In the field of object detection, securing a high-quality data set plays the most important role in training deep learning algorithms. Recently, the acquisition of real image data has become time consuming and expensive; therefore, research using synthesized data has been conducted. Synthetic data haves the advantage of being able to generate a vast amount of data and accurately label it. However, the utility of synthetic data in human detection has not yet been demonstrated. Therefore, we use You Only Look Once (YOLO), the object detection algorithm most commonly used, to experimentally analyze the effect of synthetic data augmentation on human detection performance. As a result of training YOLO using the Penn-Fudan dataset, it was shown that the YOLO network model trained on a dataset augmented with synthetic data provided high-performance results in terms of the Precision-Recall Curve and F1-Confidence Curve.