• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.10a
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• pp.286-289
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• 2004

This paper presents the fatigue characteristics of LIPCA (LIghtweight Piezo-Composite Actuator) device system. The LIPCA device system is composed of a piezoelectric ceramic layer and fiber reinforced lightweight composite layers. Typically a PZT ceramic layer is sandwiched by a top fiber layer with low CTE (coefficient of thermal expansion) and base layers with high CTE. The advantages of the LIPCA design are weight reduction by using the lightweight fiber reinforced plastic layers without compromising the generation of high force and large displacement and design flexibility by selecting the fiber direction and the size of prepreg layers. To predict the degradation of actuation performance of LIPCA due to fatigue, the cyclic electric loading tests using PZT specimens were performed and the strain for a given excitation voltage was measured during the test. The results from the PZT fatigue test were implemented into CLPT (Classical Laminated Plate Theory) model to predict the degradation of LIPCA's actuation displacement. The fatigue characteristic of PZT was measured using a test system composed of a supporting jig, a high voltage power supplier, data acquisition board, PC, and evaluated.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.1-12
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• 2006

For advanced civil engineering structures a service life of hundred up to hundred fifty and even two hundred years is sometimes required. The prediction of the performance of concrete structures over such a long period requires accurate and reliable predictive models. Most of the presently used, mostly experience based models don't have the quality and reliability that is required for reliable long-term predictions. The models designers are searching for should be based on an accurate description of the relevant degradation mechanisms. The starting point of such models is a realistic description of the microstructure of the concrete. In this presentation the need and the role of fundamental microstructural models for predicting the performance of concrete structures will be presented. An example will be given of a microstructural model with a proven potential for long-term predictions. Besides this also the role of models in general, i.e. in the whole design and execution process of concrete structures, will be dealt with. Finally recent trends in concrete research will be presented, like the research on self-healing cement-bases systems.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.290-305
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• 2022

Purpose: Currently, port facility informatization technology is focused on the planning and design phases, so the necessity of research and technology development on the port facility maintenance system based on life cycle-level information is emphasized. Method: Based on the maintenance history data of port facilities and facility operation information, from the perspective of the life cycle of port facilities, the system is configured to enable maintenance decisions for port facilities through analysis of aging patterns, performance degradation prediction models, and risk analysis and proposed a method of expressing information. Result: A function was developed to simultaneously display the SOC performance evaluation and the comprehensive performance evaluation developed in this study, so that mid-to long-term maintenance and reinforcement and facility expansion can be applied and comparatively judged. Conclusion: The integrated port performance system developed in this study induces and supports the risk minimization of port facility management by proactively promoting appropriate repair and reinforcement measures through historical and operational information on port facilities.

• Journal of Power Electronics
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• v.15 no.1
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• pp.177-189
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• 2015

The output of the controller is said to exceed the input limits of the plant being controlled when a control system operates in a non-linear region. This process is called the windup phenomenon. The windup phenomenon is not preferable in the control system because it leads to performance degradation, such as overshoot and system instability. Many anti-windup strategies involve switching, where the integral component differently operates between the linear and the non-linear states. The range of state for the non-overshoot performance is better illustrated by the boundary integral error plane than the proportional-integral (PI) plane in windup inspection. This study proposes a PI controller with a separate closed-loop integral controller and reference value set with respect to the input command and external torque. The PI controller is compared with existing conventional proportional integral, conditional integration, tracking back calculation, and integral state prediction schemes by using ScicosLab simulations. The controller is also experimentally verified on a direct current motor under no-load and loading conditions. The proposed controller shows a promising potential with its ability to eliminate overshoot with short settling time using the decoupling mode in both conditions.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2779-2789
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• 1999

In this paper, we propose a new neural Buffered Leaky Bucket algorithm for preventing the degradation of network performance caused by congestion and dealing with the traffic congestion in ATM networks. We networks. We justify the validity of the suggested method through performance comparison in aspects of cell loss rate and mean transfer delay under a variety of traffic conditions requiring the different QoS(Quality of Service). also, the cell scheduling algorithms such as DWRR and DWEDF used for multiplexing the incoming traffics are induced to get the delay time of the traffics fairly. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate is changed by the predicted values. The prediction of traffic loss rate by neural networks can effectively reduce the cell loss rate and the cell transfer delay of next incoming cells and be applied to other traffic control systems. Computer simulation results performed for traffic prediction show that QoSs of the various kinds of traffics are increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Steel and Composite Structures
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• v.46 no.3
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• pp.385-401
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• 2023

Steel is becoming increasingly popular due to its high strength, excellent ductility, great assembly performance, and recyclability. In reality, steel structures serving for a long time in atmospheric, industrial, and marine environments inevitably suffer from corrosion, which significantly decreases the durability and the service life with the exposure time. For the mechanical properties of corroded steel, experimental studies are mainly conducted. The existing numerical analyses only evaluate the mechanical properties based on corroded morphology at the isolated time-in-point, ignoring that this morphology varies continuously with corrosion time. To solve this problem, the relationships between pit depth expectation, standard deviation, and corrosion time are initially constructed based on a large amount of wet-dry cyclic accelerated test data. Successively, based on that, an in-situ pitting evolution method for evaluating the residual tensile strength of corroded steel is proposed. To verify the method, 20 repeated simulations of mass loss rates and mechanical properties are adopted against the test results. Then, numerical analyses are conducted on 135 models of corrosion pits with different aspect ratios and uneven corrosion degree on two corroded surfaces. Results show that the power function with exponents of 1.483 and 1.091 can well describe the increase in pit depth expectation and standard deviation with corrosion time, respectively. The effect of the commonly used pit aspect ratios of 0.10-0.25 on yield strength and ultimate strength is negligible. Besides, pit number ratio α equating to 0.6 is the critical value for the strength degradation. When α is less than 0.6, the pit number increases with α, accelerating the degradation of strength. Otherwise, the strength degradation is weakened. In addition, a power function model is adopted to characterize the degradation of yield strength and ultimate strength with corrosion time, which is revised by initial steel plate thickness.

• The KIPS Transactions:PartA
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• v.14A no.6
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• pp.357-362
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• 2007

In distributed systems like cluster systems, in order to get more performance improvement, the initial task placement system precisely estimates and correctly assigns the resource requirement by the process. The resource-based initial job placement scheme needs the prediction of resource usage of a task in order to fit it to the most suitable hosts. However, the wrong prediction of resource usage causes serious performance degradation in dynamic load balancing systems. Therefore, in this paper, to resolve the problem due to the wrong prediction, we propose a new load metric. By the new load metric, the resource-based initial job placement scheme can work without priori knowledge about the type of process. Simulation results show that the dynamic load balancing system using the proposed approach achieves shorter execution times than the conventional approaches.

• Corrosion Science and Technology
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• v.5 no.1
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• pp.33-38
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• 2006

Current coating practice requires the thickness of anti-corrosion organic coatings to be over $250{\mu}m$ for immersion parts of ships and offshore structures and the corrosion resistance of these coatings has been evaluated by destructive and qualitative analysis. Recently, Electrochemical Impedance Spectroscopy(EIS) method has been employed, as an alternative, to evaluate corrosion resistance of organic coatings. This method is characterized as being nondestructive, reproducible, and quantitative in evaluating aging of organic coatings. In this study, EIS method was adopted to quantitatively and effectively select the coating systems having optimized protective performance. Evaluations of several epoxy and epoxy/polyurethane coating systems typically used for ships and offshore structures were carried out in wet($50^{\circ}C$, $90^{\circ}C$) and dry(room temp.) environments to accelerate the degradation of the organic coatings. These results were compared with the conventional scribed(scratched) test results. The plausible prediction model for determining the remaining life-time of coating systems was also proposed based on variations of impedance data, FT-IR and $T_g$ measurements results.