• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• Structural Engineering and Mechanics
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• v.78 no.4
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• pp.387-401
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• 2021

This paper provides a methodology to analyze the seismic performance of different component designs in hybrid masonry structures (HMS). HMS, comprised of masonry panels, steel frames and plate connectors is a relatively new structural system with potential applications in high seismic areas. HMS dissipate earthquake energy through yielding in the steel components and damage in the masonry panels. Currently, there are no complete codes to assist with the design of the energy dissipation components of HMS and there have been no computational studies performed to aid in the understanding of the system energy dissipation mechanisms. This paper presents parametric studies based on calibrated computational models to extrapolate the test data to a wider range of connector strengths and more varied reinforcement patterns and reinforcement ratios of the masonry panels. The results of the numerical studies are used to provide a methodology to examine the effect of connector strength and masonry panel design on the energy dissipation in HMS systems. We use as test cases two story structures subjected to cyclic loading due to the availability of experimental data for these configurations. The methodology presented is however general and can be applied to arbitrary panel geometries, and column and story numbers.

• Computers and Concrete
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• v.27 no.5
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• Journal of Drive and Control
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• v.17 no.4
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• pp.80-86
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• 2020

Autonomous driving is one of the most important new technologies of our time; it has benefits in terms of safety, the environment, and economic issues. Path following algorithms, such as automated lane keeping systems (ALKSs), are key level 3 or higher functions of autonomous driving. Pure-Pursuit and Stanley controllers are widely used because of their good path tracking performance and simplicity. However, with the Pure-Pursuit controller, corner cutting behavior occurs on curved roads, and the Stanley controller has a risk of divergence depending on the response of the steering system. In this study, we use the advantages of each controller to propose a hybrid control strategy that can be stably applied to complex driving environments. The weight of each controller is determined from the global and local curvature indexes calculated from HD map information and the current driving speed. Our experimental results demonstrate the ability of the hybrid controller, which had a cross-track error of under 0.1 m in a virtual environment that simulates K-City, with complex driving environments such as urban areas, community roads, and high-speed driving roads.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.918-927
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• 2020

Flows induced by hybrid CRP pod propulsion systems (CRP-POD) are fundamentally characterized by unsteadiness. This work presents a numerical study on the unsteady flow of a CRP-POD at behind-hull condition based on CFD (Computational Fluid Dynamics). Unsteady RANS method is adopted, coupled with SST k-u turbulence model and sliding mesh method. The propeller thrusts and torques obtained by CFD is validated by model tests and acceptable agreements are obtained. The time histories of shingle-blade loads and pressures near the hull surface are recorded for the analysis of unsteady flow features. The cases of forward propeller alone and aft propeller alone are also computed to distinguish the hull-propeller interaction and propeller-propeller interaction. The results show the blade loads of both forward and aft propellers strongly fluctuate with phase angles. For the forward propeller, the blade load fluctuation is mainly governed by the hull-propeller interaction, while the aft blade load is remarkably affected by the propeller-propeller interaction in terms of the load average and fluctuation pattern. The fields of pressure, vorticity and velocity are also analyzed to reveal the unsteady flow features.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.234-240
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• 2021

As the number of pet dog-related businesses is rising rapidly, there is an increasing need for reliable pet dog health information systems for casual pet owners, especially those caring for older dogs. Our goal is to implement a mobile pre-diagnosis system that can provide a first-hand pre-diagnosis and an appropriate coping strategy when the pet owner observes abnormal symptoms. Our previous attempt, which is based on the fuzzy C-means family in inference, performs well when only relevant symptoms are provided for the query, but this assumption is not realistic. Thus, in this paper, we propose a hybrid inference structure that combines fuzzy association memory and a double-layered fuzzy C-means algorithm to infer the probable disease with robustness, even when noisy symptoms are present in the query provided by the user. In the experiment, it is verified that our proposed system is more robust when noisy (irrelevant) input symptoms are provided and the inferred results (probable diseases) are more cohesive than those generated by the single-phase fuzzy C-means inference engine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3419-3437
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• 2022

Anonymization technology is an important technology for privacy protection in the process of data release. Usually, before publishing data, the data publisher needs to use anonymization technology to anonymize the original data, and then publish the anonymized data. However, for data publishers who do not have or have less anonymized technical knowledge background, how to configure appropriate parameters for data with different characteristics has become a more difficult problem. In response to this problem, this paper adds a historical configuration scheme resource pool on the basis of the traditional anonymization process, and configuration parameters can be automatically recommended through the historical configuration scheme resource pool. On this basis, a privacy model hybrid recommendation algorithm for user satisfaction is formed. The algorithm includes a forward recommendation process and a reverse recommendation process, which can respectively perform data anonymization processing for users with different anonymization technical knowledge backgrounds. The privacy model hybrid recommendation algorithm for user satisfaction described in this paper is suitable for a wider population, providing a simpler, more efficient and automated solution for data anonymization, reducing data processing time and improving the quality of anonymized data, which enhances data protection capabilities.

• Archives of Plastic Surgery
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• v.48 no.4
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• pp.427-432
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• 2021

The conventional approach of looking down a microscope to perform microsurgical procedures is associated with occupational injuries, anti-ergonomic postures, and increased tremor and fatigue, all of which predispose microsurgeons to early retirement. Recently, three-dimensional (3D) visualization of real-time microscope magnification has been developed as an alternative. Despite its commercial availability, no supermicrosurgical procedures have been reported using this technology to date. Lymphovenous anastomoses (LVAs) often require suturing vessels with diameters of 0.2-0.8 mm, thus representing the ultimate microsurgical challenge. After performing the first documented LVA procedure using 3D-augmented visualization in our unit and gaining experience with this technique, we conducted an anonymized in-house survey among microsurgeons who had used this approach. The participants considered that 3D visualization for supermicrosurgery was equivalent in terms of handling, optical detail, depth resolution, and safety to conventional binocular magnification. This survey revealed that team communication, resident education, and ergonomics were superior using 3D digital hybrid visualization. Postoperative muscle fatigue, tremor, and pain were also reduced. The major drawbacks of the 3D visualization microscopic systems are the associated costs, required space, and difficulty of visualizing the lymphatic contrast used.

• Smart Structures and Systems
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• v.29 no.3
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• pp.433-444
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• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.