• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1829-1846
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• 2021

Transient faults occur in computation units of a processor, which can cause control flow errors (CFEs) and compromise system reliability. The software-based methods perform illegal control flow detection by inserting redundant instructions and monitoring signature. However, the existing methods not only have drawbacks in terms of performance overhead, but also lack of configurability. We propose a configurable approach CCFCA for detecting CFEs. The configurability of CCFCA is implemented by analyzing the criticality of each region and tuning the detecting granularity. For critical regions, program blocks are divided according to space-time overhead and reliability constraints, so that protection intensity can be configured flexibly. For other regions, signature detection algorithms are only used in the first basic block and last basic block. This helps to improve the fault-tolerant efficiency of the CCFCA. At the same time, CCFCA also has the function of solving confusion and instruction self-detection. Our experimental results show that CCFCA incurs only 10.61% performance overhead on average for several C benchmark program and the average undetected error rate is only 9.29%. CCFCA has high error coverage and low overhead compared with similar algorithms. This helps to meet different cost requirements and reliability requirements.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• Steel and Composite Structures
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• v.43 no.5
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• pp.603-623
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• 2022

This article is dedicated to predict the natural frequencies of joined conical shell structures made of Functionally Graded Material (FGM). The structure includes two conical segments. The equivalent material properties are found by using the rule of mixture based on Voigt model. In addition, three well-known patterns are employed for distribution of material properties throughout the thickness of the structure. The main objective of the present research is to propose a novel exponential pattern and obtain the related equivalent material properties. Furthermore, the Donnell type shell theory is used to obtain the governing equations of motion. Note that these equations are obtained by employing First-order Shear Deformation Theory (FSDT). In order to discretize the governing system of differential equations, well-known and efficient semi-analytical scheme, namely Generalized Differential Quadrature Method (GDQM), is utilized. Different boundary conditions are considered for various types of single and joined conical shell structures. Moreover, an applicable modification is considered for the continuity conditions at intersection position. In the first step, the proposed formulation is verified by solving some well-known benchmark problems. Besides, some new numerical examples are analyzed to show the accuracy and high capability of the suggested technique. Additionally, several geometric and material parameters are studied numerically.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.3
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1789-1803
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• 2022

RAST-F is a new full-core analysis code based on the two-step approach that couples a multi-group cross-section generation Monte-Carlo code MCS and a multi-group nodal diffusion solver. To demonstrate the feasibility of using MCS/RAST-F for fast reactor analysis, this paper presents the coupled nodal code verification results for the MET-1000 and CAR-3600 benchmark cores. Three different multi-group cross-section calculation schemes are employed to improve the agreement between the nodal and reference solutions. The reference solution is obtained by the MCS code using continuous-energy nuclear data. Additionally, the MCS/RAST-F nodal solution is verified with results based on cross-section generated by collision probability code TULIP. A good agreement between MCS/RAST-F and reference solution is observed with less than 120 pcm discrepancy in k_eff and less than 1.2% root-mean-square error in power distribution. This study confirms the two-step approach MCS/RAST-F as a reliable tool for the three-dimensional simulation of reactor cores with fast spectrum.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.633-645
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• 2022

The active tuned mass damper (ATMD) is an efficient and reliable structural control system for mitigating the dynamic response of structures. The inertial force that an ATMD exerts on a structure to attenuate its otherwise large kinetic energy and undesirable vibrations and displacements is proportional to its excursion. Achieving a balance between the inertial force and excursion requires a control law or feedback mechanism. This study presents a technique for the optimum design of a sliding mode controller (SMC) as the control law for ATMD-equipped structures subjected to earthquakes. The technique includes optimizing an SMC under an artificial earthquake followed by testing its performance under real earthquakes. The SMC of a real 11-story shear building is optimized to demonstrate the technique, and its performance in mitigating the displacements of the building under benchmark near- and far-fault earthquakes is compared against that of a few other techniques (proportional-integral-derivative [PID], linear-quadratic regulator [LQR], and fuzzy logic control [FLC]). Results indicate that the optimum SMC outperforms PID and LQR and exhibits performance comparable to that of FLC in reducing displacements.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.165-172
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• 2023

Heterogeneous computing is a technology that utilizes different types of processors to perform parallel processing. It maximizes task processing and energy efficiency by leveraging various computing resources such as CPUs, GPUs, and FPGAs. On the other hand, edge computing has developed with IoT and 5G technologies. It is a distributed computing that utilizes computing resources close to clients, thereby offloading the central server. It has evolved to intelligent edge computing combined with artificial intelligence. Intelligent edge computing enables total data processing, such as context awareness, prediction, control, and simple processing for the data collected on the edge. If heterogeneous computing can be successfully applied in the edge, it is expected to maximize job processing efficiency while minimizing dependence on the central server. In this paper, experiments were conducted to verify the feasibility of various parallel programming models on high-end and low-end edge devices by using benchmark applications. We analyzed the performance of five parallel programming models on the Raspberry Pi 4 and Jetson Orin Nano as low-end and high-end devices, respectively. In the experiment, OpenACC showed the best performance on the low-end edge device and OpenSYCL on the high-end device due to the stability and optimization of system libraries.

• Steel and Composite Structures
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• v.47 no.1
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• pp.1-17
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• 2023

This paper deals with the free vibration behavior of rotating composite beams reinforced with carbon nanotubes (CNTs) under uniform thermal loads. The temperature-dependent beam material is assumed to be a mixture of single-walled carbon nanotubes (SWCNTs) in an isotropic matrix and five different functionally graded (FG) distributions of CNTs are considered according to the variation along the thickness, namely the UD-uniform, FG-O, FG-V, FG-Λ and FG-X distributions where FG-V and FG-Λ are unsymmetrical patterns. Considering the Timoshenko beam theory (TBT), a new finite element formulation of functionally graded carbon nanotube reinforced composite (FGCNTRC) beam is created for the first time. And the effects of several essential parameters including rotational speed, hub radius, effective material properties, slenderness ratio, boundary conditions, thermal force and moments due to temperature variation are considered in the formulation. By implementing different boundary conditions, some new results of both symmetric and non-symmetrical distribution patterns are presented in tables and figures to be used as benchmark for further validation. In addition, as an alternative advanced composite application for rotating systems exposed to thermal load, the positive effects of CNT addition in improving the dynamic performance of the system have been observed and the results are presented in several tables and figures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Structural Engineering and Mechanics
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• v.89 no.2
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• pp.199-211
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• 2024

The loading capacity of engineering structures/components reduces after the initiation and propagation of crack eventually leads to the final failure. Hence, it becomes essential to deal with the crack and its effects at the design and simulation stages itself, by detecting the prone area of the fracture. The phase-field (PF) method has been accepted widely in simulating fracture problems in complex geometries. However, most of the PF methods are formulated with second order continuity theoryinvolving C⁰ continuity. In the present study, PF method based on fourth-order (i.e., higher order) theory, maintaining C¹ continuity has been proposed for ductile fracture simulation. The formulation includes fourth-order derivative terms of phase field variable, varying between 0 and 1. Applications of fourth-order PF theory to ductile fracture simulation resulted in novelty in this area. The proposed formulation is numerically solved using a two-dimensional finite element (FE) framework in 3-layered manner system. The solutions thus obtained from the proposed fourth order theory for different benchmark problems portray the improvement in the accuracy of the numerical results and are well matched with experimental results available in the literature. These results are also compared with second-order PF theory and a comparison study demonstrated the robustness of the proposed model in capturing ductile behaviour close to experimental observations.