• Journal of Information Processing Systems
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• v.17 no.4
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• pp.690-706
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• 2021

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

• ETRI Journal
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• v.40 no.6
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• pp.759-773
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• 2018

Uncore components such as on-chip memory systems and on-chip interconnects consume a large amount of energy in emerging embedded applications. Few studies have focused on next-generation analytical models for future chip-multiprocessors (CMPs) that simultaneously consider the impacts of the power consumption of core and uncore components. In this paper, we propose a convex-optimization approach to design heterogeneous uncore architectures for embedded CMPs. Our convex approach optimizes the number and placement of memory banks with different technologies on the memory layer. In parallel with hybrid memory architecting, optimizing the number and placement of through silicon vias as a viable solution in building three-dimensional (3D) CMPs is another important target of the proposed approach. Experimental results show that the proposed method outperforms 3D CMP designs with hybrid and traditional memory architectures in terms of both energy delay products (EDPs) and performance parameters. The proposed method improves the EDPs by an average of about 43% compared with SRAM design. In addition, it improves the throughput by about 7% compared with dynamic RAM (DRAM) design.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.5
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• pp.52-57
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• 2018

Most current ships have adopted on-board diesel generators to produce electricity, but the overall efficiency of equipment is down to about 50% due to thermal losses from operations such as exhaust gas, jacket water cooler, scavenge air cooler, etc. Recently, fuel cells have been highlighted as a promising technology to reduce the effect on the environment and have a higher efficiency. Therefore, this paper suggested a solid oxide fuel cell (SOFC)-gas turbine (GT) using waste heat from a SOFC and SOFC-GT-steam turbine (ST) with Rankine cycle. To compare both configurations, the fuel flow rate, current density, cell voltage, electrical power, and overall efficiency were evaluated at different operating loads. The overall efficiency of both SOFC hybrid systems was higher than the conventional system.

• Journal of Power Electronics
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• v.19 no.3
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• pp.761-770
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• 2019

This paper proposes a hybrid phase windings excitation method for improving the efficiency of a 7-phase brushless DC (BLDC) motor in the electric propulsion system of a ship. The electrical losses of a BLDC motor system depend on the operating region and the number of excited phase windings (2-phase, 4-phase or general 6-phase windings). In this paper the operating region and torque/speed characteristics according to the motor rotation speed and propeller load are analyzed for a number of excitation methods. In addition, it analyzes the electrical losses of the system under each of the excitation methods in the entire operating region of the motor. In every sampling time, the proposed control method calculates the electrical loss of the system for each of the excitation methods and operates a 7-phase BLDC motor by selecting the excitation method that results a decreased electrical loss at the operating speed. The usefulness of the proposed control algorithm is verified through experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1481-1501
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• 2019

In opportunistic network, the behavior of a node is autonomous and has social attributes such as selfishness.If a node wants to forward information to another node, it is bound to be limited by the node's own resources such as cache, power, and energy.Therefore, in the process of communication, some nodes do not help to forward information of other nodes because of their selfish behavior. This will lead to the inability to complete cooperation, greatly reduce the success rate of message transmission, increase network delay, and affect the overall network performance. This article proposes a hybrid incentive mechanism (Mim) based on the Reputation mechanism and the Credit mechanism.The selfishness model, energy model (The energy in the article exists in the form of electricity) and transaction model constitute our Mim mechanism. The Mim classifies the selfishness of nodes and constantly pay attention to changes in node energy, and manage the wealth of both sides of the node by introducing the Central Money Management Center. By calculating the selfishness of the node, the currency trading model is used to differentiate pricing of the node's services. Simulation results show that by using the Mim, the information delivery rate in the network and the fairness of node transactions are improved. At the same time, it also greatly increases the average life of the network.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.271-280
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• 2021

The code developed using a pressure-based method for unified conservation laws of incompressible/compressible fluids is expanded to handle moving or deforming body boundaries using the hybrid Cartesian/immersed boundary method. An instantaneous pressure field is calculated from a pressure Poisson equation for the whole fluid domain, including the compressible gas region. The polytropic gas is assumed for the compressible fluid so that the energy equation is decoupled. Immersed boundary nodes are identified based on edges crossing body boundaries. The velocity vector is reconstructed at the immersed boundary node using an interpolation along the assigned local normal line. The developed code is validated by comparing the time histories of pressure and wave elevation for sloshing in a rectangular and a membrane-type tank. The validated code is applied to simulate air cushion effects in a rectangular tank under sway motion. Time variations of pressure fields are analyzed in detail as the air pocket pulsates. It is shown that the contraction and expansion of the air pocket dominate the pressure loads on the wall of the tank. The present results are in good agreement with other experimental and computational results for the amplitude and the decay of the pressure oscillations measured at the pressure gauges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1590-1609
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• 2021

Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2467-2474
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• 2022

Nuclear Power Plants (NPPs) are the most protected facilities among all critical infrastructures (CIs). In addition to physical security, cyber security becomes a significant concern for NPPs since swift digitalization and overreliance on computer-based systems in the facility operations transformed NPPs into targets for cyber/physical attacks. Despite technical competencies, humans are still the central component of a resilient NPP to develop an effective nuclear security culture. Turkey is one of the newcomers in the nuclear energy industry, and Turkish Akkuyu NPP has a unique model owned by an international consortium. Since Turkey has limited experience in nuclear energy industry, specific multinational and multicultural characteristics of Turkish Akkuyu NPP also requires further research in terms of the Facility's prospective nuclear security. Yet, the link between "national cultures" and "nuclear security" is underestimated in nuclear security studies. By relying on Hofstede's national culture framework, our research aims to address this gap and explore possible implications of cross-national cultural differences on nuclear security. To cope with security challenges in the age of hybrid threats, we propose a security management model which addresses the need for cyber-physical security integration to cultivate a robust nuclear security culture in a multicultural working environment.