• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.51-57
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• 2022

The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1852-1859
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• 2016

The effectiveness of a binarization algorithm in image processing depends on how to eliminate the uncertainty of determining threshold in a reasonable way and on minimizing information loss due to the binarization effect. Fuzzy binarization technique was proposed to handle that uncertainty with fuzzy logic. However, that method is known to be inefficient when the given image has low intensity contrast. In this paper, we propose an improved fuzzy binarization method to overcome such known drawbacks. Our method proposes a trapezoid type fuzzy membership function instead of most-frequently used triangle type one. We also propose an adaptive ${\alpha}$_cut determination policy. Our proposed method has less information loss than other algorithms since we do not use any stretching based preprocessing for enhancing the intensity contrast. In experiment, our proposed method is verified to be more effective in binarization with less information loss for many different types of images with low intensity contrast such as night scenery, lumber scoliosis, and lipoma images.

• Smart Structures and Systems
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• v.19 no.4
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• pp.341-350
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• 2017

Based on the super elastic properties of the shape memory alloy (SMA) and the inverse piezoelectric effect of piezoelectric (PZT) ceramics, a kind of hybrid semi-active control device was designed and made, its mechanical properties test was done under different frequency and different voltage. The local search ability of genetic algorithm is poor, which would fall into the defect of prematurity easily. A kind of adaptive immune memory cloning algorithm(AIMCA) was proposed based on the simulation of clone selection and immune memory process. It can adjust the mutation probability and clone scale adaptively through the way of introducing memory cell and antibody incentive degrees. And performance indicator based on the modal controllable degree was taken as antigen-antibody affinity function, the optimization analysis of damper layout in a space truss structure was done. The structural seismic response was analyzed by applying the neural network prediction model and T-S fuzzy logic. Results show that SMA and PZT friction composite damper has a good energy dissipation capacity and stable performance, the bigger voltage, the better energy dissipation ability. Compared with genetic algorithm, the adaptive immune memory clone algorithm overcomes the problem of prematurity effectively. Besides, it has stronger global searching ability, better population diversity and faster convergence speed, makes the damper has a better arrangement position in structural dampers optimization leading to the better damping effect.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.845-858
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• 2020

This paper proposes a modified end-to-end secure low energy adaptive clustering hierarchy (ME-LEACH) algorithm for enhancing the lifetime of a wireless sensor network (WSN). Energy limitations are a major constraint in WSNs, hence every activity in a WSN must efficiently utilize energy. Several protocols have been introduced to modulate the way a WSN sends and receives information. The end-to-end secure low energy adaptive clustering hierarchy (E-LEACH) protocol is a hierarchical routing protocol algorithm proposed to solve high-energy dissipation problems. Other methods that explore the presence of the most powerful nodes on each cluster as cluster heads (CHs) are the sparsity-aware energy efficient clustering (SEEC) protocol and an energy efficient clustering-based routing protocol that uses an enhanced cluster formation technique accompanied by the fuzzy logic (EERRCUF) method. However, each CH in the E-LEACH method sends data directly to the base station causing high energy consumption. SEEC uses a lot of energy to identify the most powerful sensor nodes, while EERRCUF spends high amounts of energy to determine the super cluster head (SCH). In the proposed method, a CH will search for the nearest CH and use it as the next hop. The formation of CH chains serves as a path to the base station. Experiments were conducted to determine the performance of the ME-LEACH algorithm. The results show that ME-LEACH has a more stable and higher throughput than SEEC and EERRCUF and has a 35.2% better network lifetime than the E-LEACH algorithm.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.131-143
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• 2007

The current trends in information technology and intelligent systems use data mining techniques to discover patterns and extract rules from distributed databases. In distributed environment, the extracted rules from data mining techniques can be used in dynamic replications, adaptive load balancing and other schemes. However, transmission of large data through the system can cause errors and unreliable results. This paper proposes the intelligent distributed platform based on dynamic group binding using mobile agents which addresses the use of intelligence in distributed environment. The proposed grouping service implements classification scheme of objects. Data compressor agent and data miner agent extracts rules and compresses data, respectively, from the service node databases. The proposed algorithm performs preprocessing where it merges the less frequent dataset using neuro-fuzzy classifier before sending the data. Object group classification, data mining the service node database, data compression method, and rule extraction were simulated. Result of experiments in efficient data compression and reliable rule extraction shows that the proposed algorithm has better performance compared to other methods.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Journal of the Korea Concrete Institute
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• v.28 no.6
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• pp.723-731
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• 2016

In pretensioned concrete members, a certain bond length from the end of the member is required to secure the effective prestress in the strands, which is defined as the transfer length. However, due to the complex bond mechanism between strands and concrete, most transfer length models based on the deterministic approach have uncertainties and do not provide accurate estimations. Therefore, in this study, Adaptive Neuro-Fuzzy Inference System (ANFIS), a Neuro-Fuzzy System, is introduced to reduce the uncertainties and to estimate the transfer length more accurately in pretensioned concrete member. A total of 253 transfer length test results have been collected from literatures to train ANFIS, and the trained ANFIS algorithm estimated the transfer length very accurately. In addition, a design equation was proposed to calculate the transfer length based on parametric studies and dimensional analyses. Consequently, the proposed equation provided accurate results on the transfer length which are comparable to the ANFIS analysis results.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.618-631
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• 2017

In this paper, we propose a modification of fuzzy logic based DASH adaptation algorithm(FDASH) for seamless media service in time-varying network conditions. The proposed algorithm selects more appropriate bit-rate for the next segment by the modification of the Fuzzy Logic Controller(FLC) and reduces the number of video bit-rate changes by applying Segment Bit-rate Filtering Module(SBFM). Also, we apply the Start Mechanism for clients not to watch the low quality videos in the very beginning stage of streaming service and add the Sleeping Mechanism to avoid any buffer overflow expected. Ultimately, we verified by using NS-3 Network Simulator that the proposed method shows better performance compared to FDASH. According to the experimental results, there is no buffer underflow/overflow within the limited buffer size, which is not guaranteed in FDASH on the other hand. Also, we confirmed that mFDASH has almost the same level of average video quality against FDASH and reduces about 50% of number of video bit-rate changes compared to FDASH in Point-to-Point network and Wi-Fi network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.47-57
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• 2005

In this research, the main theme is the system integration of driving simulator and unmanned vehicle. The total system is composed of the mater system and the slave system. The master system has a cockpit system and the driving simulator. The slave system means an unmanned vehicle, which is composed of the actuator system the sensory system and the vision system. The communication system is composed of RS-232C serial communication system which combines the master system with the slave system. To integrate both systems, the signal classification and system characteristics considered DSP(Digital Signal Processing) filter is designed with signal sampling and measurement theory. In addition, to simulate the motion of tele-operated unmanned vehicle on the driving simulator, the classical washout algorithm is applied to this filter, because the unmanned vehicle does not have a limited working space, while the driving simulator has a narrow working space and it is difficult to cover all the motion of the unmanned vehicle. Because the classical washout algorithm has a defect of fixed high pass later, fuzzy logic is applied to reimburse it through an adaptive filter and scale factor for realistic motion generation on the driving simulator.