• Journal of Climate Change Research
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• v.9 no.2
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• pp.133-142
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• 2018

Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

• Tribology and Lubricants
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• v.37 no.5
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• pp.179-188
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• 2021

Reputed for their low friction coefficient and wear protection effect, diamond-like carbon (DLC) materials are considered amongst the most important lubricant coatings for tribological applications. In this framework, this investigation aims to elucidate the effect of a few operating parameters, such as applied stress and sliding amplitude on the friction lifetime of DLC coatings. Fretting wear tests are conducted using a 12.7 mm radius counterpart of 52100 steel balls slid against a substrate of the same material coated with a 2 ㎛ thickness DLC. Approximately, 5 to 57 N force is applied, generating a maximum Hertzian contact pressure of 430 to 662 MPa, corresponding to the applied force. The coefficient of friction (CoF) generates three regimes, first a running-in period regime, followed by a steady-state evolution regime, and finally a progressive increase of the CoF reaching the steel CoF value, as an indicator of reaching the substrate. To track the wear scenario, interrupted tests are performed with analysis combining scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), 3D profilometer and micro-Raman spectroscopy. The results show two endurance values: one characterizing the coating failure (Nc₁), and the other (Nc₂) indicating the friction failure which is situated where the CoF reaches a threshold value of μ_th = 0.3 in the third regime. The Archard energy density factor is used to determine the two endurance values (Nc₁, Nc₂). Based on this approach, a master curve is established delimitating both the coating and the friction endurances.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.21-30
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• 2021

This study examines the role of the manufacturing sector in stimulating economic growth in the Saudi economy. Even though the economic literature shows how the manufacturing sector stimulates economic growth, it does not clearly show the role of the manufacturing sector in economic growth. The study employed annual time-series data spanning the 1980-2018 period from the databases of the Saudi Arabian Monetary Authority. Moreover, the cointegration and VECM approaches were employed to examine the short- and long-run relationship causality between variables. The results show a two-way causal relationship exists between the manufacturing sector and economic growth. Furthermore, the results indicate that a unidirectional causal relationship exists, running from the manufacturing sector to the services sector. The study recommends that the determinants of the growth of the Saudi manufacturing sector must be investigated. Moreover, the most productive Saudi manufacturing industries must be identified, and the productivity of other sectors must be increased in a way that contributes to economic plans and policies. Thus, adopting economic policies that stimulate investment in the manufacturing sector contributes to increasing non-oil exports to diversify sources of income to achieve vision 2030 of the Kingdom of Saudi Arabia.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3398-3415
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• 2023

Internet-of-Things (IoT) is an emerging technology that interconnects millions of small devices to enable communication between the devices. It is heavily deployed across small scale to large scale industries because of its wide range of applications. These devices are very capable of transferring data over the internet including critical data in few applications. Such data is exposed to various security threats and thereby raises privacy-related concerns. Even devices can be compromised by the attacker. Modern cryptographic algorithms running on traditional machines provide authentication, confidentiality, integrity, and non-repudiation in an easy manner. IoT devices have numerous constraints related to memory, storage, processors, operating systems and power. Researchers have proposed several hardware and software implementations for addressing security attacks in lightweight encryption mechanism. Several works have made on lightweight block ciphers for improving the confidentiality by means of providing security level against cryptanalysis techniques. With the advances in the cipher breaking techniques, it is important to increase the security level to much higher. This paper, focuses on securing the critical data that is being transmitted over the internet by PRESENT using key-based dynamic S-Box. Security analysis of the proposed algorithm against other lightweight block cipher shows a significant improvement against linear and differential attacks, biclique attack and avalanche effect. A novel key-based dynamic S-Box approach for PRESENT strongly withstands cryptanalytic attacks in the IoT Network.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.423-426
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• 2011

The excessive axial load occurred in an immovable zone of continuous welded rail(CWR) tracks threatens the security of running trains due to the track buckling in extreme hot summer. The influence factors, such as rail temperature for compressive stress, ballast resistance for track stiffness and initial imperfection of track for tracks irregularity are uncertain track parameters that are randomly varied by climate conditions, operating conditions and maintenance of track etc. So, buckling of CWR tracks has very high uncertainties. Therefore, applying the probabilistic approach method is essential in order to rationally consider the uncertainty and randomness of the various parameters. In this study, buckling sensitivity analysis was carried out with respect to the characteristics of probability distribution of lateral ballast resistance using the buckling probability evaluation system of CWR tracks developed by our research team.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.476-490
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• 2017

In the centralized cloud controlled environment, the decision-making and monitoring play crucial role where in the host controller (HC) manages the resources across hosts in data center (DC). HC does virtual machine (VM) and physical hosts management. The VM management includes VM creation, monitoring, and migration. If HC down, the services hosted by various hosts in DC can't be accessed outside the DC. Decentralized VM management avoids centralized failure by considering one of the hosts from DC as HC that helps in maintaining DC in running state. Each host in DC has many VM's with the threshold limit beyond which it can't provide service. To maintain threshold, the host's in DC does VM migration across various hosts. The data in migration is in the form of plaintext, the intruder can analyze packet movement and can control hosts traffic. The incorporation of security mechanism on hosts in DC helps protecting data in migration. This paper discusses an approach for dynamic HC selection, VM selection and secure VM migration over cloud environment.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• Journal of Advanced Marine Engineering and Technology
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• v.10 no.4
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• pp.31-40
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• 1986

With the tendency toward high speed and high pressure in centrifugal pumps, the problem of sub-synchronous vibration has arisen, caused by the hydraulic forces of the working fluid, such as wearring, balance piston, impeller, etc.. These forces can drastically alter the rotor critical speeds and stability characteristics, and can be acted significant destabilizing forces. For preventing such self-excited vibration, the desing of the rotor system needs, which would secure the stability of the machine. In this paper, a procedure is presented for dynamic modeling of rotor-bearing-seal-impeller systems which consist of rigid disks, distributed parameter finite rotor elements and discrete bearings, seals and impellers. A finite element model including the effects of rotatory inertia and gyroscopic moments is developed using the consistent matrix approach. The technique of dynamic matrix reduction is applied to the shaft matrices to reduce them to a set of matrices of dynamic of significantly fewer degrees of freedom. The representation of bearing, seal and impeller elements is in term of linearized stiffness and damping matrices by reasonably small perturbations from equilibrium. The stability behavior of a typical double suction centrifugal pump is presented. Results show the influence of clearance and flow conditions on running speeds and stability characteristics.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.137-141
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• 2018

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.