• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1233-1243
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• 2023

Shell-and-tube heat exchanger (STHX) is widely used by virtue of its simple structure and high reliability, especially in a space-constrained surface ship. For the STHX of the surface ship, roll, pitch and other motion of the ship will affect the heat transfer performance, resistance characteristics and structural strength of the heat exchanger. Therefore, it is urgent to carry out numerical simulation research on three-dimensional thermal hydraulic characteristics of surface ship STHX under the marine conditions. In this paper, the numerical simulation of marine shell and tube heat exchanger of surface ship was carried out using the porous media model. Firstly, the mathematical physical model and numerical method are validated based on the experimental data of a marine engine cooling water shell and tube heat exchanger. The simulation results are in good agreement with the experimental results. The prediction errors of pressure drop and heat transfer are less than 10% and 1% respectively. The effect of marine conditions on the heat transfer characteristics of the heat exchanger is investigated by introducing the additional force model of marine condition to evaluate the effect of different motion parameters on the heat transfer performance of the heat exchanger. This study could provide a reference for the optimization of marine heat exchanger design.

• Membrane Journal
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• v.33 no.6
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• pp.369-376
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• 2023

In this study, the alternative to the energy-intensive conventional vacuum distillation process, an eco-friendly and energy-efficient pervaporation separation was employed in 1,2 hexane diol/water (HDO/water) mixture. The crosslinked PVA-glutaraldehyde was coated inside the alumina hollow fiber membrane (Al-HF). In the HDO/IPA pervaporation separation, optimization of the membrane concerning PVA/GA ratio, curing temperature, and pervaporation operating condition were performed. In the long-term stability test, the sustainable pervaporation separation performance giving flux in the range of 1.90~2.16 kg/m²h, and water content in permeate was higher than 99.5% (separation factor = 68) was obtained from the PVA/GA (molar ratio = 0.08, curing temperature = 80℃) coated Al-HF membrane from HDO/water (25/75, w/w, %) mixture at 40℃. Therefore, this work provides potential and inspiration for PVA-based membranes to mitigate excessive energy requirements in HDO/water separation by pervaporation.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.6
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• pp.424-432
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• 2023

This study deals with the design of a safety device around a leisure boat propeller. The safety device is to be designed to minimize performance degradation attached to propulsors in coastal waters. These devices, important for preventing propeller accidents, negatively gives influence boat performance, especially at higher speeds. In order to minimize the negative effect, the accelerating ducts, normally used in ESDs (Energy Saving Devices) have been chosen as a safety device. The present study aims to design an optimal duct (minimizing negative effect) through the parametric study. Based on the Marine 19A nozzle, the nozzle's thickness and angle were varied to obtain the optimum parameter in the preliminary design by the computational fluid dynamics program Star-CCM+ Ver. 15.02. In the detailed design, a NACA 4-digit Airfoil shape resembling the Marine 19A by modification at the trailing edge was chosen and the optimum shape was chosen according to variation of camber, thickness, and incidence angle for optimization. The optimally designed duct shows a speed decrease of about 10% in the sea trial result, which is much smaller than the normal speed decrease of at least 30%. The present designing method can give wide applications to the leisure boat because the wake is almost the same due to using the outboard propulsor.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.1-7
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• 2023

This paper developed a training model to classify normal roads and flooded roads using artificial intelligence technology. We expanded the diversity of learning data using various data augmentation techniques and implemented a model that shows good performance in various environments. Transfer learning was performed using the CNN-based Resnet152v2 model as a pre-learning model. During the model learning process, the performance of the final model was improved through various parameter tuning and optimization processes. Learning was implemented in Python using Google Colab NVIDIA Tesla T4 GPU, and the test results showed that flooding situations were detected with very high accuracy in the test dataset.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.312-318
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• 2006

The conventional lip-synch system has a two-step process, speech segmentation and recognition. However, the difficulty of speech segmentation procedure and the inaccuracy of training data set due to the segmentation lead to a significant Performance degradation in the system. To cope with that, the connected vowel recognition method using Head-Body-Tail (HBT) model is proposed. The HBT model which is appropriate for handling relatively small sized vocabulary tasks reflects co-articulation effect efficiently. Moreover the 7 vowels are merged into 3 classes having similar lip shape while the system is optimized by employing a class dependent SCHMM structure. Additionally in both end sides of each word which has large variations, 8 components Gaussian mixture model is directly used to improve the ability of representation. Though the proposed method reveals similar performance with respect to the CHMM based on the HBT structure. the number of parameters is reduced by 33.92%. This reduction makes it a computationally efficient method enabling real time operation.

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Advances in nano research
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• v.17 no.4
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• pp.369-383
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• 2024

Annular nano-electromechanical systems (NEMS) in intelligent prosthetic hands enhance precision by serving as highly sensitive sensors for detecting pressure, vibrations, and deformations. This improves feedback and control, enabling users to modulate grip strength and tactile interaction with objects more effectively, enhancing prosthetic functionality. This research focuses on the electro-thermal buckling behavior of multi-directional poroelastic annular NEMS used as temperature sensors in airplanes. In the present study, thermal buckling performance of nano-scale annular functionally graded plate structures integrated with piezoelectric layers under electrical and extreme thermal loadings is investigated. In this regard, piezoelectric layers are placed on a disk made of metal matrix composite with graded properties in three radials, thickness and circumferential directions. The grading properties obey the power-law distribution. The whole structure is embedded in thermal environment. To model the mechanical behavior of the structure, a novel four-variable refined quasi-3D sinusoidal shear deformation theory (RQ-3DSSDT) is engaged in obtaining displacement field in the whole structure. The validity of the results is examined by comparing to a similar problem published in literature. The results of the buckling behavior of the structure in different boundary conditions are presented based on the critical temperature rise and critical external voltage. It is demonstrated that increase in the nonlocal and gradient length scale factor have contradicting effects on the critical temperature rise. On the other hand, increase in the applied external voltage cause increase in the critical temperature. Effects of other parameters like geometrical parameters and grading indices are presented and discussed in details.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.175-183
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• 2008

In wireless sensor networks with ad hoc networking capability, sensor nodes are battery operated and are usually disposable once deployed. As a result, each sensor node senses and communicates with limited energy and, thus, energy efficiency has been studied as a key design factor which determines lifetime of a wireless sensor network, and it is more improved recently by using so-called cross-layer optimization technique. In this paper, we propose and implement a new energy saving mechanism that reduces energy consumption during data collection by controlling transmission power at sensor nodes and then measure its performance in terms of lifetime improvement for the wireless sensor network platform ZigbeX. When every sensor node transmits sensed data to its clusterhead, it controls its transmission power down to as low level as communication is possible, resulting in energy saving. Each sensor node controls its transmission power based on RSSI(Received Signal Strength Indicator) of the packet received from its clusterhead. In other words, the sensor node can save energy by controlling its transmission power down to an appropriate level that its clusterhead safely receives the packet it transmits. According to the repetitive experiment of the proposed scheme on the ZigbeX platform using the packet analyzer developed by us, it is observed that the network lifetime is prolonged by up to 21.9% by saying energy during the data collection occupying most amount of network traffic.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.