• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.6
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• pp.53-59
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• 2021

The ROK Navy operates many war ships despite its short history. Various types of war ships, such as submarines, destroyers, frigates, corvette etc., use suitable propulsion systems for the operational requirements of each war ship. A hybrid propulsion system was introduced to change from the current mechanical propulsion system to an electric propulsion system according to the changing patterns of naval warfare, and it is expected that an integrated electric propulsion system will also be introduced. Therefore, this paper investigates the propulsion system of major ships operated by the Korean Navy, predicts the changes in future naval warfare, and proposes a propulsion system for future ships.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.124-130
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• 2023

Implementing flexible production systems in domestic and foreign automotive design parts assembly has increased demand for automation and power reduction. Consequently, transition to a hybrid production method is observed where multiple vehicles are assembled in a single assembly line. Multiple robots, 3D vision sensors, mounting positions, and correction software have complex configurations in the automotive glass mounting system. Hence, automation is required owing to significant difficulty in the assembly process of automobile parts. This study presents a primer lighting and inspection algorithm that is robust to the assembly environment of real automotive design parts using high power 'ㄷ'-shaped LED inclined lighting. Furthermore, a 2D camera was developed in the primer coating inspection system-the core technology of the glass mounting system. A primer application demo line applicable to the actual automobile production line was established using the proposed high power lighting and algorithm. Furthermore, application inspection performance was verified using this demo system. Experimental results verified that the performance of the proposed system exceeded the level required to satisfy the automobile requirements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1276-1295
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• 2023

Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

• Tribology and Lubricants
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• v.38 no.2
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• pp.70-72
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• 2022

In this study, we report on a power system developed as a decelerator for a driving module in an electric vehicle. The system is to be mounted in a limited space. The research focus was on development of miniaturization, light weight, and high power density. In particular, we aimed to minimize the layout of existing external components as integrated or built-in, and to maximize the power density by applying optimal cooling technology to increased requirements for developing modular power systems applicable to various OEM models. South Korean automakers ranked fourth in global electric-vehicle sales in 2020, but domestic sales are relatively slow. Despite government's expansion in subsidies for eco-friendly cars, consumers are delaying purchases after 2021 considering the cost-effectiveness of electric vehicles. In major European markets, the demand for electric vehicles exceeded the demand for diesel cars, and sales of hybrid cars, which used to represent eco-friendly cars, are slowing down as Toyota, started selling electric vehicles. In this study, the internal lubrication characteristics of a decelerator installed in an electric vehicle were analyzed in terms of the deceleration time while driving. By selecting the proper oil and oil viscosity, it was confirmed that there is no problem in lubricating the bearings and gears of the decelerator.

• Coupled systems mechanics
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• v.12 no.3
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• pp.241-260
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• 2023

The objective of this study is to evaluate the effects of adding fibers to concrete and the distribution rate of the porosity on the interfacial stresses of the beams strengthened with various types of functionally graded porous (FGP) plate. Toward this goal, the beams strengthened with FGP plate were considered and subjected to uniform loading. Three types of beams are considered namely RC beam, RC beam reinforced with metal fibers (RCFM) and RC beam reinforced with Alfa fibers (RCFA). From an analytical development, shear and normal interfacial stresses along the length of the FGP plates were obtained. The accuracy and validity of the proposed theoretical formula are confirmed by the others theoretical results. The results showed clearly that adding fibers to concrete and the distribution rate of the porosity have significant influence on the interfacial stresses of the beams strengthened with FGP plates. Finally, parametric studies are carried out to demonstrate the effect of the mechanical properties and thickness variations of FGP plate, concrete and adhesive on interface debonding, we can conclude that, This research is helpful for the understanding on mechanical behavior of the interface and design of the FRP-RC hybrid structures.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3844-3853
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• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• Ocean Systems Engineering
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• v.13 no.3
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• pp.287-312
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• 2023

The global performance of a 15 MW floating offshore wind turbine, a newly designed semisubmersible floating foundation with multiple heave plates by CNOOC, is investigated with two independent turbine-floater-mooring coupled dynamic analysis programs CHARM3D-FAST and OrcaFlex. The semisubmersible platform hosts IEA 15 MW reference wind turbine modulated for VolturnUS-S and hybrid type (chain-wire-chain with clumps) 3×2 mooring lines targeting the water depth of 100 m. The numerical free-decay simulation results are compared with physical experiments with 1:64 scaled model in 3D wave basin, from which appropriate drag coefficients for heave plates were estimated. The tuned numerical simulation tools were then used for the feasibility and global performance analysis of the FOWT considering the 50-yr-storm condition and maximum operational condition. The effect of tower flexibility was investigated by comparing tower-base fore-aft bending moment and nacelle translational accelerations. It is found that the tower-base bending moment and nacelle accelerations can be appreciably increased due to the tower flexibility.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.