• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.24-30
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• 2021

The accelerated growth of the internet and the enormous amount of data availability has become the primary reason for machine learning applications for data analysis and, more specifically, pattern recognition and decision making. In this paper, we focused on the crowdfunding site Kickstarter and collected the comments in order to apply neural networks to classify the projects based on the sentiments of backers. The power of customer reviews and sentiment analysis has motivated us to apply this technique in crowdfunding to find timely indications and identify suspicious activities and mitigate the risk of money loss.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1129-1139
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• 2015

Inference process generates additional triples from knowledge represented in RDF triples of semantic web technology. Tens of million of triples as an initial big data and the additionally inferred triples become a knowledge base for applications such as QA(question&answer) system. The inference engine requires more computing resources to process the triples generated while inferencing. The additional computing resources supplied by underlying resource pool in cloud computing can shorten the execution time. This paper addresses an algorithm to allocate the number of computing nodes "elastically" at runtime on Hadoop, depending on the size of knowledge data fed. The model proposed in this paper is composed of the layered architecture: the top layer for applications, the middle layer for distributed parallel inference engine to process the triples, and lower layer for elastic Hadoop and server visualization. System algorithms and test data are analyzed and discussed in this paper. The model hast the benefit that rich legacy Hadoop applications can be run faster on this system without any modification.

• Smart Structures and Systems
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• v.22 no.3
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• pp.335-340
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• 2018

The behavior of concrete slabs in composite beam with C and L shaped angle shear connectors has been studied in this paper. These two types of angle shear connectors' instalment have been commonly utilized. In this study, the finite element (FE) analysis and soft computing method have been used both to present the shear connectors' push out tests and providing data results used later in soft computing method. The current study has been performed to present the aforementioned shear connectors' behavior based on the variable factors aiming the study of diverse factors' effects on C and L shaped angle in shear connectors. ANFIS (Adaptive Neuro Fuzzy Inference System), has been manipulated in providing the effective parameters in shear strength forecasting by providing input-data comprising: height, length, thickness of shear connectors together with concrete strength and the respective slip of shear connectors. ANFIS has been also used to identify the predominant parameters influencing the shear strength forecast in C and L formed angle shear connectors.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.6
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• pp.50-63
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• 2000

In this paper, a new EMG pattern classification method based on soft computing techniques is proposed to help the disabled and the elderly handle rehabilitation robotic arm systems. First, it is shown that EMG is more useful than existing input devices such as voice, a laser pointer and a keypad in view of naturality, extensibility, and applicability. Then, a new procedure is proposed to select the minimal feature set. As methods of classifying the pre-defined motions, a fuzzy pattern classification and fuzzy min-max neural networks (FMMNN) are designed using the selected features. As results, the motions are recognized with success rates of 83 percent and 90 Percent using fuzzy pattern classification and FMMNN, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.69-74
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• 2005

In this paper, we suggested user assistant soft computing method for 3D effect optimization. In order to maximize 3D effect of image, intervals among cameras have to be set up properly according to distance between cameras and an object. Two data such as interval and distance was obtained to use in neural network as the data for learning. However, if the data for learning was obtained by only human's subjective views, it could be that the obtained data was not optimal for learning because the data had an accidental ewer To obtain optimal data lot learning, we added candidature data to obtained data through data analysis, and then selected the most proper data between the candidature data and the obtained data for learning in neural network. Usually, 3D effect of image was affected by both distance from an object to cameras and an object size. Therefore, we suggested fuzzy inference model which was able to represent two factors like distance and size. Candidature data was added by fuzzy model. In the simulation result, we verified that the mote the obtained data was affected by human's subjective views, the more effective the suggested system was.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.720-725
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• 2004

Robot systems have applied to manufacturing or industrial field for reducing the need for human presence in dangerous and/or repetitive tasks. However, robot applications are transformed from industrial field to human life in recent tendency Nowadays, final goal of robot is to make a intelligent robot that can understand what human say and learn by itself and have internal emotion. For example Home service robots are able to provice functions such as security, housework, entertainment, education and secretary To provide various functions, home robots need to recognize human`s requirement and environment, and it is indispensable to use artificial intelligence technology for implementation of home robots. In this paper, implemented robot system takes data from several sensors and fuses the data to recognize environment information. Also, it can select a proper behavior for environment using soft computing method. Each behavior is composed with intuitive motion and sound in order to let human realize robot behavior well.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.383-393
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• 2024

Monopile foundations of offshore wind turbines embedded in soft clay are subjected to the long-term cyclic lateral loads induced by winds, currents, and waves, the vibration of monopile leads to the accumulation of pore pressure and cyclic strains in the soil in its vicinity, which poses a threat to the safety operation of monopile. The researchers mainly focused on the hysteretic stress-strain relationship of soft clay and kinds of stiffness degradation models have been adopted, which may consume considerable computing resources and is not applicable for the long-term bearing performance analysis of monopile. In this study, a modified cyclic stiffness degradation model considering the effect of plastic strain and pore pressure change has been proposed and validated by comparing with the triaxial test results. Subsequently, the effects of cyclic load ratio, pile aspect ratio, number of load cycles, and length to embedded depth ratio on the accumulated rotation angle and pore pressure are presented. The results indicate the number of load cycles can significantly affect the accumulated rotation angle of monopile, whereas the accumulated pore pressure distribution along the pile merely changes with pile diameter, embedded length, and the number of load cycles, the stiffness of monopile can be significantly weakened by decreasing the embedded depth ratio L/H of monopile. The stiffness degradation of soil is more significant in the passive earth pressure zone, in which soil liquefaction is likely to occur. Furthermore, the suitability of the "accumulated rotation angle" and "accumulated pore pressure" design criteria for determining the required cyclic load ratio are discussed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.59-69
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• 2019

This paper focuses on the numerical simulation of ice abrasion induced by unbreakable ice floe. Under the assumption that unbreakable floes behave as rigid body, the Discrete Element Method (DEM) was applied to simulate the interaction between a fixed structure and ice floes. DEM is a numerical technique which is eligible for computing the motion and effect of a large number of particles. In DEM simulation, individual ice floe was treated as single rigid element which interacts with each other following the given interaction rules. Interactions between the ice floes and structure were defined by soft contact and viscous Coulomb friction laws. To derive the details of the interactions in terms of interaction parameters, the Finite Element Method (FEM) was employed. An abrasion process between a structure and an ice floe was simulated by FEM, and the parameters in DEM such as contact stiffness, contact damping coefficient, etc. were calibrated based on the FEM result. Resultantly, contact length and contact path length, which are the most important factors in ice abrasion prediction, were calculated from both DEM and FEM and compared with each other. The results showed good correspondence between the two results, providing superior numerical efficiency of DEM.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3361-3377
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• 2014

Synchronous computing models provided by real-time synchrony protocols, such as TTA [1] and PALS [2], greatly simplify the design, implementation, and verification of real-time distributed systems. However, their application to real systems has been limited since their assumptions on underlying systems are hard to satisfy. In particular, most previous real-time synchrony protocols hypothesize the existence of underlying fault tolerant real-time networks. This, however, might not be true in most soft real-time applications. In this paper, we propose a practical approach to a synchrony protocol, called Quality-Aware PALS (qPALS), which provides the benefits of a synchronous computing model in environments where no fault-tolerant real-time network is available. qPALS supports two flexible global synchronization protocols: one tailored for the performance and the other for the correctness of synchronization. Hence, applications can make a negotiation flexibly between performance and correctness. In qPALS, the Quality-of-Service (QoS) on synchronization and consistency is specified in a probabilistic manner, and the specified QoS is supported under dynamic and unpredictable network environments via a control-theoretic approach. Our simulation results show that qPALS supports highly reliable synchronization for critical events while still supporting the efficiency and performance even when the underlying network is not stable.