• Membrane Journal
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• v.33 no.6
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• pp.377-382
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• 2023

In this study, a PEBAX/PVDF composite membrane was fabricated, and its pervaporation performance was tested in an ethanol/water mixture. In addition, we attempted to improve the pervaporation performance of the composite membrane by forming a ZIF-8 layer on the surface of the PVDF substrate. The thickness of selective layer was optimized by comparing the pervaporation performance depending on the PEBAX thickness. A pervaporation test was performed on the Ethanol/Water mixture. As a result, the composite membrane using PVDF substrate with ZIF-8 layer had a flux of 1.98 kg/m²h and separation factor of 3.88, showing higher values of both permeation flux and selectivity than the composite membrane using bare PVDF substrate.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.325-330
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• 2023

This paper proposes to apply machine learning to the process of predicting the slab thickness based on the structural analysis results or experience and subjectivity of engineers in the design of bridge data construction to enable digital-based decision-making. This study aims to build a reliable design environment by utilizing machine learning techniques to provide guide values to engineers in addition to structural analysis for slab thickness selection. Based on girder bridges, which account for the largest proportion of bridge data, a prediction model process for predicting slab thickness among superstructures was defined. Various machine learning models (Linear Regress, Decision Tree, Random Forest, and Muliti-layer Perceptron) were competed for each process to produce the prediction value for each process, and the optimal model was derived. Through this study, the applicability of machine learning techniques was confirmed in areas where slab thickness was predicted only through existing structural analysis, and an accuracy of 95.4% was also obtained. models can be utilized in a more reliable construction environment if the accuracy of the prediction model is improved by expanding the process

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.429-434
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• 2023

Kitchen cabinets are essential furniture for storing the kitchen tools, but their high installed location makes it difficult for users to access the upper of the cabinets. Therefore, in this paper, we propose a new type of kitchen cabinet that allows users to easily take out or store items by adding new height adjustment features while maintaining the function of the existing cabinet. For convenience and safety, an appropriate complex link mechanism is designed so that the selected floor, not the entire cabinet, can come down to a desired height with one operation. Moreover, the optimal descent path is set to prevent the floor tilting or interfloor interference during descent, and appropriate link shapes, lengths, and joint types are selected to implement it. FEA analysis is performed to ensure that the stretched complex linkage can support the load of the stored items and the feasibility of the height adjustable kitchen cabinet is verified through fabrication.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.378-384
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• 2023

In this study, we aim to derive the descriptor vector conditions that can simultaneously achieve the efficiency and accuracy of artificial Neural Network Potentials (NNP). The material system selected is silicon, a highly applicable material in various industries. Atomic structure-dependent energy data for training artificial neural networks were generated through density functional theory calculations. Behler-Parrinello type atomic-centered symmetric functions were employed as descriptors, and various length vector NNPs were generated. These NNPs were applied to reproduce the structure and mechanical properties of silicon materials in molecular dynamics simulations. In our findings, the minimum vector length for achieving both learning and computational efficiency while maintaining property reproducibility is approximately 50. It was also observed that, for the same conditions, incorporating more angle-dependent symmetric functions into the descriptor vector, could enhance the accuracy of NNP. Our results can provide guidelines for optimizing the conditions of descriptor vectors to achieve both efficiency and accuracy of NNP, simultaneously.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.113-118
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• 2024

In this paper, we propose an optimal wavelet in a system for canceling background noise of acoustic signals. This system performed Discrete Wavelet Transform(DWT) instead of the existing Short Time Fourier Transform(STFT) and then improved noise cancellation performance through a deep learning process. DWT functions as a multi-resolution band-pass filter and obtains transformation parameters by time-shifting the parent wavelet at each level and using several wavelets whose sizes are scaled. Here, the noise cancellation performance of several wavelets was tested to select the most suitable mother wavelet for analyzing the speech. In this study, to verify the performance of the noise cancellation system for various wavelets, a simulation program using Tensorflow and Keras libraries was created and simulation experiments were performed for the four most commonly used wavelets. As a result of the experiment, the case of using Haar or Daubechies wavelets showed the best noise cancellation performance, and the mean square error(MSE) was significantly improved compared to the case of using other wavelets.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.174-180
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• 2024

In this paper, we explore an emotion classification method through multimodal learning utilizing wav2vec 2.0 and KcELECTRA models. It is known that multimodal learning, which leverages both speech and text data, can significantly enhance emotion classification performance compared to methods that solely rely on speech data. Our study conducts a comparative analysis of BERT and its derivative models, known for their superior performance in the field of natural language processing, to select the optimal model for effective feature extraction from text data for use as the text processing model. The results confirm that the KcELECTRA model exhibits outstanding performance in emotion classification tasks. Furthermore, experiments using datasets made available by AI-Hub demonstrate that the inclusion of text data enables achieving superior performance with less data than when using speech data alone. The experiments show that the use of the KcELECTRA model achieved the highest accuracy of 96.57%. This indicates that multimodal learning can offer meaningful performance improvements in complex natural language processing tasks such as emotion classification.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.1-12
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• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.465-470
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• 2024

Kitchen cabinets are widely used for their spacious storage and efficient use of space, but their high installed location makes it difficult for the elderly and disabled to access. Therefore, in this paper, we propose a new height-adjustable kitchen cabinet that can be used more easily and safely. The lift-down range of cabinet was set considering the installation location of cabinet for efficient use of kitchen space and the maximum height accessible to the elderly and disabled, and the link geometry and driving method of the complex link mechanism were determined through the mechanism design procedure to ensure that the selected floor come down safely along the optimal descend path. In addition, the appropriate motor and control algorithm were added to allow the user to descend to the desired height with a simple button operation. It was confirmed through actual production that the proposed linkage mechanism performs the desired lift-down motion.

• Resources Recycling
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• v.33 no.2
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• pp.37-45
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• 2024

Molybdenum is used in various industries because of its high heat and corrosion resistance. It was selected as a critical mineral in Korea. However, there have been recent challenges in production because of the increased depth and decreased grade of molybdenum veins. Consequently, it is necessary to enhance the effectiveness of the molybdenum beneficiation process. In this study, a basic evaluation of beneficiation characteristics was conducted to enhance the effectiveness of the domestic molybdenum ore beneficiation process. The properties of the beneficiation process were assessed using mineralogical analysis, work index, and flotation kinetics. The results revealed that the allowable particle size of the molybdenum ore for liberation was ~100 ㎛. In addition, the work index was calculated to be 14.57 kWh/t. The operating conditions in the flotation units were achieved by determining the optimal flotation time for each process based on flotation kinetics. Finally, the characteristics of molybdenum ore beneficiation provided in this study can be utilized to diagnose the grinding and flotation processes of large-scale molybdenum beneficiation plants.

• Smart Media Journal
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• v.13 no.4
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• pp.23-32
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• 2024

Large-scale fires and their consequential damages are becoming increasingly common, but confidence in fire detection systems is waning. Recently, widely-used chemical fire detectors frequently generate lots of false alarms, while video-based deep learning fire detection is hampered by its time-consuming and expensive nature. To tackle these issues, this study proposes a fire detection model utilizing an autoencoder approach. The objective is to minimize false alarms while achieving swift and precise fire detection. The proposed model, employing an autoencoder methodology, can exclusively learn from normal data without the need for fire-related data, thus enhancing its adaptability to diverse environments. By amalgamating data from five distinct sensors, it facilitates rapid and accurate fire detection. Through experiments with various hyperparameter combinations, the proposed model demonstrated that out of 14 scenarios, only one encountered false alarm issues. Experimental results underscore its potential to curtail fire-related losses and bolster the reliability of fire detection systems.