• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.32-38
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• 2020

The deployment of geographically distributed wireless sensors has greatly elevated the capability of monitoring structural health in social-overhead capital (SOC) public infrastructures. This paper deals with the utilization of a distributed mobility management (DMM) approach for the deployment of wireless sensing devices in a structural health monitoring system (SHM). Then, a wireless sensing mechanism utilizing low-energy adaptive clustering hierarchy (LEACH)-based clustering algorithm for smart sensors has been analyzed to support the seamless data transmission of structural health information which is essentially important to guarantee public safety. The clustering of smart sensors will be able to provide real-time monitoring of structural health and a filtering algorithm to boost the transmission of critical information over heterogeneous wireless and mobile networks.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.2
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• pp.7-18
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• 2020

Communication is a process involving a group of interrelated elements working together for the purpose of information transfer. This paper discusses information retrieval as a communication process in the 21st century library. The difficulties associated with access to recorded knowledge through bibliographic control devices have been exacerbated by the interposition of additional encoding processes in the library and further decoding by the users. In addition, the innovation of internet/web has revolutionized the means and mode of communication process in the library by flooding information seekers with information and creating an illusion of self-sufficiency in many users. With these changes in information seeking behaviour and pattern, a cybernetic approach to information retrieval has emerged emphasizing adaptive control mechanisms and feedback processes. This paper argues that libraries should strive to continuously remain relevant by keeping abreast with changes in the behavior of information users. To this end, this paper proposes apomediatic-cybernetic model of communication, which illustrates information retrieval processes for the 21st-century library.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2964-2983
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• 2015

As the Android operating system has become a key target for malware authors, Android protection has become a thriving research area. Beside the proved importance of system permissions for malware analysis, there is a lot of overlapping in permissions between malware apps and goodware apps. The exploitation of them effectively in malware detection is still an open issue. In this paper, to investigate the feasibility of neuro-fuzzy techniques to Android protection based on system permissions, we introduce a self-adaptive neuro-fuzzy inference system to classify the Android apps into malware and goodware. According to the framework introduced, the most significant permissions that characterize optimally malware apps are identified using Information Gain Ratio method and encapsulated into patterns of features. The patterns of features data is used to train and test the system using stratified cross-validation methodologies. The experiments conducted conclude that the proposed classifier can be effective in Android protection. The results also underline that the neuro-fuzzy techniques are feasible to employ in the field.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1998.06a
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• pp.55-58
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• 1998

Recently, neural networks with self-adaptability like human brain have attracted much attention. It is desirable for the neuron-function to be implemented by exclusive hardware system on account of huge quantity in calculation. We have proposed a novel neuro-device composed of a MFSFET(ferroelectric gate FET) and oscillation circuit with CUJT(complimentary unijuction transistor) and PUT(programmable unijuction transistor). However, it is difficult to preserve ferroelectricity on Si due to existence of interfacial traps and/or interdiffusion of the constitutent elements, although there are a few reports on good MFS devices. In this paper, we have simulated CUJT and PUT devices instead of fabricating them and composed oscillation circuit. Finally, we have resented, as an approach to the MFSFET neuron circuit, adaptive learning function and characterized the elementary operation properties of the pulse oscillation circuit.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (Parallel Distributed Processing) network to complete patterns against the environmental situation. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.

• Journal of the Korea Computer Graphics Society
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• v.23 no.1
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• pp.17-24
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• 2017

We propose an efficient method to express water spray effects by adaptively modeling air particles in particle-based water simulation. In real world, water and air continuously interacts with each other around free surfaces and this phenomenon is commonly observed in waterfall or sea with rough waves. Due to thin spray water, the interfaces between water and air become vague and the interactions between them lead to heavy vortex phenomenon. To express this phenomenon, we propose methods of 1) generating adaptive air cell in particle-based water simulation, 2) expressing water spray effects by creating and evolving air particles in the adaptive air cells, and 3) guaranteeing robustness of simulation by solving drifting problem occurred when adjacent air particles are insufficient. Experiments convincingly demonstrate that the proposed approach is efficient and easy to use while delivering high-quality results.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.135-147
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• 2023

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.12-20
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• 1998

As an approach to develope a control system with high robustness in changing control environment conditions, this paper will propose a robust control system, using multilayer neural network and biological immune system. The proposed control system adjusts weights of the multilayer neural network(MNN) with the immune algorithm. This algorithm is made up of two major divisions, the innate immune algorithm as a first line of defence and the adaptive immune algorithm as a barrier of self-adjustment. Using the proposed control system based on immune algorithm, we will work out a design for the controller of a robot manipulator. And we will demonstrate the effectiveness of the control system of robot manipulator with computer simulations.

• Journal of the Korean Institute of Navigation
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• v.21 no.3
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• pp.55-66
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• 1997

In this paper, applications of neural networks to vibration control of flexible single link robot manipulator are ocnsidered. The architecture of neural networks is a hidden layer, which is comprised of self-recurrent one. Tow neural networks are utilized in a control system ; one as an identifier is called neuro identifier and the othe ra s a controller is called neuro controller. The neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by dynamic error-backpropagation algorithm(DEA). To guarantee concegence and to get faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. When a flexible manipulator is ratated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlle dinsuch as way, that the motor is rotated by a specified angle. while simulataneously stabilizing vibration of the flexible manipulators so that it is arrested as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large body motions, as well as the flexural vibrations. Therefore, dynamic models for a flexible single link manipulator is derived, and LQR controller and nerual networks controller are composed. The effectiveness of the proposed nerual networks control system is confirmed by experiments.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.184-194
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• 2016

In existing identification methods for on-orbit spacecraft, such as eigensystem realization algorithm (ERA) and subspace method identification (SMI), singular value decomposition (SVD) is used frequently to estimate the modal parameters. However, these identification methods are often used to process the linear time-invariant system, and there is a lower computation efficiency using the SVD when the system order of spacecraft is high. In this study, to improve the computational efficiency in identifying time-varying modal parameters of large spacecraft, a faster recursive algorithm called fast approximated power iteration (FAPI) is employed. This approach avoids the SVD and can be provided as an alternative spacecraft identification method, and the latest modal parameters obtained can be applied for updating the controller parameters timely (e.g. the self-adaptive control problem). In numerical simulations, two large flexible spacecraft models, the Engineering Test Satellite-VIII (ETS-VIII) and Soil Moisture Active/Passive (SMAP) satellite, are established. The identification results show that this recursive algorithm can obtain the time-varying modal parameters, and the computation time is reduced significantly.