• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1503-1517
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• 1994

Intelligent Motion Planning System(IMPS) is presented for a robot to achieve an efficient path toward the given target point in two dimensional unknown environment is constructed with unrestricted obstacle shapes. IMPS consists of three components for making intelligent motion. These components are real-time motion planning algorithm based on a discontinous boundary method, fuzzy neural network decision system for heuristic knowledge representation, and world modeling with forgetting and reinforcing memory cells. First of all, in real-time motion planning algorithm, the behavior-based architectural method is used to generate subgoal. A behavior generates a subgoal independently by using the method of discontinuous boundary in sensed area. The discontinuous boundary method is a new proposed fast obstacle avoidance algorithm. The second component is fuzzy neural network decision system for accomplishing the subgoal. The heuristic rules are imbedded on the fuzzy neural network to make an intelligent decision. The last one is a forgetting, reinforcing memory technique for the construction of external world map. The activation values of all activated memory cells in grid space are decreased monotonically and after all they are burned out. Therefore, after sufficient journey, robot can have a stationary world map even if the dynaic obstacles exist. Using the IMPS, several simulations show the efficient achievement of target point in unknown enviroment with obstcles of various shapes.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.69-77
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• 2011

Modeling and Simulation(M&S) technology gets an attention from various parts such as industry and military. Especially, military uses the technology to cope with a different situation from the one in the Cold War and maximize the effect of training against the cost in the new environment. In order for the training based on M&S technology to be effective, the situations of a battlefield and a combat must be more realistically simulated. For this, a technique development on Computer-Generated Forces(CGF) which represents a unit's simulation logic and a human's simulated behaviors is focused. The CGF simulating a human's behaviors can be used in representing an enemy force, experimenting behaviors in a future war, and developing a new combat idea. This paper describes a methodology to accomplish Computer-Generated Forces' autonomous intelligence. It explains the process of applying a task behavior list based on the METT+T element onto CGFs. On the other hand, in the domain knowledge of military field manual, fuzzy facts such as "fast" and "sufficient" whose real values should be decided by domain experts can be easily found. In order to efficiently implement military simulation logics involved with such subjectivity, using a fuzzy inference methodology can be effective. In this study, a fuzzy inference methodology is also applied.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.563-568
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• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.821-828
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• 2011

This paper developed the algorithm for improving the performance the auto pilot in the autonomous vehicle system consisting of the Track keeping control, the Automatic steering, and the Automatic mooring control. The automatic steering is the control device that could save the voyage distance and cost of fuel by reducing the unnecessary burden of driving due to the continuous artificial navigation, and avoiding the route deviation. During the step of the ship autonomic navigation control, since the wind power or the tidal force could make the ship deviate from the fixed course, the automatic steering calculates the difference between actual sailing line and the set course to keep the ship sailing in the vicinity of intended course. first, we could get the transfer function for the modeling of ship according to the Nomoto model. Considering the maneuverability, we propose it as linear model with only 4 degree of freedoms to present the heading angle response to the input of rudder angle. In this paper, the model of ship is derived from the simplified Nomoto model. Since the proposed model considers the maximum angle and rudder rate of the ship auto pilot and also designs the Fuzzy controller based on existing PID controller, the performance of the steering machine is well improved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.268-276
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• 2015

A ship's sailing route or plan is determined by the master as the decision maker of the vessel, and depends on the characteristics of the navigational environment and the conditions of the ship. The trajectory, which appears as a result of the ship's navigation, is monitored and stored by a Vessel Traffic Service center, and is used for an analysis of the ship's navigational pattern and risk assessment within a particular area. However, such an analysis is performed in the same manner, despite the different navigational environments between coastal areas and the harbor limits. The navigational environment within the harbor limits changes rapidly owing to construction of the port facilities, dredging operations, and so on. In this study, a support vector machine was used for processing and modeling the trajectory data. A K-fold cross-validation and a grid search were used for selecting the optimal parameters. A complicated traffic route similar to the circumstances of the harbor limits was constructed for a validation of the model. A group of vessels was composed, each vessel of which was given various speed and course changes along a specified route. As a result of the machine learning, the optimal route and voyage data model were obtained. Finally, the model was presented to Vessel Traffic Service operators to detect any anomalous vessel behaviors. Using the proposed data modeling method, we intend to support the decision-making of Vessel Traffic Service operators in terms of navigational patterns and their characteristics.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.107-121
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• 2009

First reason for generation of standby power is because starting voltage must pass through from the source of electricity to IC. The second reason is due to current when IC is in operation. Purpose of this abstract is on structures of simple modules that automatically switch on or off through analysis of state on standby power and analysis of cutoff point patterns as well as application of algorithms. To achieve this, this paper is based on analysis of electric signals and modeling. Also, on/off cutoff criteria has been established for reduction of standby power. To find on/off cutoff point, that is executed algorithm of similar group and leading pattern group generation in the standby power state. Therefore, the algorithm was defined as an important parameter of the subtraction value of calculated between $1^{st}$ SCS, $2^{nd}$ SCS, and the median value of sampling coefficient per second from a wall outlet.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.343-348
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• 2004

Since the destruction of World Trade Center the attention of the united States and the wider international community has focussed upon the need to strengthen security and prevent terrorism. This paper suggests an analysis prior to risk factor and structure for anti-terrorism in the korean maritime society. For this, in this paper, maritime terror risk factor was extracted by type and case of terror using brainstorming method. Also, risk factor is structured by FSM method and analyzed for ranking of each risk factor by AHP. At the result, the evaluation of risk factor is especially over maximum factor for related external impact.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Smart Structures and Systems
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• v.16 no.2
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• pp.329-345
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• 2015

As commonly known, sensor errors and faulty signals may potentially lead structures in vibration to catastrophic failures. This paper presents a new approach to deal with sensor errors/faults in vibration control of structures by using the Fault detection and isolation (FDI) technique. To demonstrate the effectiveness of the approach, a space truss structure with semi-active devices such as Magneto-Rheological (MR) damper is used as an example. To address the problem, a Linear Matrix Inequality (LMI) based fixed-order $H_{\infty}$ FDI filter is introduced and designed. Modeling errors are treated as uncertainties in the FDI filter design to verify the robustness of the proposed FDI filter. Furthermore, an innovative Fuzzy Fault Tolerant Controller (FFTC) has been developed for this space truss structure model to preserve the pre-specified performance in the presence of sensor errors or faults. Simulation results have demonstrated that the proposed FDI filter is capable of detecting and isolating sensor errors/faults and actuator faults e.g., accelerometers and MR dampers, and the proposed FFTC can maintain the structural vibration suppression in faulty conditions.