• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Computers and Concrete
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• v.24 no.2
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.203-211
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• 2021

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• Korean Journal of Applied Biomechanics
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• v.31 no.1
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• pp.50-58
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• 2021

Objective: This study aimed to determine the effect of wrist and trunk weight loading using sandbags in stroke patients in order to provide the quantitative data for enhancement of gait movement. Method: Twelve stroke patients, who have been diagnosed with hemiplegia over a year ago, were participated in this study. All subjects were asked to perform normal walking [N], wrist sandbag walking [W], wrist & trunk sandbag walking [WT], and both wrist sandbag walking [B] and both wrist & trunk sandbag walking [BT], respectively. Eight infrared cameras were used to collect the raw data. Gait parameters, arm swing, shoulder-pelvic kinematics, and lower extremity joint angle were calculated to examine the differences during walking. Results: As a result, there were no significant differences in the gait parameters, shoulder-pelvis, and lower extremities joint angles, but significant differences were found in the range of motion and the anteversion in arm swing. Conclusion: Wrist and trunk weight loading using sandbags affected the movement of the upper extremities only while it did not affect the movement of the lower extremities. It implies that it can reduce the risk of falling caused by a sudden movement change in lower extremities. In addition, the wrist and trunk weight loading using sandbags can induce changes in movement of the upper extremities independently and contribute to functional rehabilitation through resistance training.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.481-493
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• 2021

Low rise masonry structures are relatively inexpensive and easier to construct compared to other types of structures such as steel and reinforced concrete buildings. However, masonry structures are relatively heavier and less ductile and more vulnerable to damages in earthquakes. In this research, a new innovative low-cost seismic isolator using steel rings (SISR) is employed to reduce the seismic vulnerability of masonry structures. FEA of a masonry structure, made of concrete blocks is used to evaluate the effect of the proposed SISR on the seismic response of the structure. Two systems, fixed base and isolated from the base with the proposed SISRs, are considered. Micro-element approach and ABAQUS software are used for structural modeling. The nonlinear structural parameters of the SISRs, extracted from a recent experimental study by the authors, are used in numerical modeling. The masonry structure is studied in two separate modes, fixed base and isolated base with the proposed SISRs, under Erzincan and Imperial Valley-06 earthquakes. The accelerated response at the roof level, as well as the deformation in the masonry walls, are the parameters to assess the effect of the proposed SISRs. The results show a highly improved performance of the masonry structure with the SISRs.

• International Journal of High-Rise Buildings
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• v.9 no.4
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• pp.351-360
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• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• Journal of the Korean Society of Physical Medicine
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• v.17 no.1
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• pp.63-74
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• 2022

PURPOSE: This study aimed to compare the effects of manual therapy with stabilization exercises to manual therapy alone, on neck pain and body functions in patients with chronic mechanical neck pain. METHODS: Twenty patients with chronic mechanical neck pain were recruited and randomly allocated into two groups. A control group(n = 10) was given the manual therapy alone and an experimental group(n = 10) was given the manual therapy with stabilization exercises. The intervention was carried out 3 days per week for 4 weeks. The cervical resting pain, the most painful motion pain, craniocervical flexor endurance, forward head posture and neck disability index were used to assess participants at baseline and after 4 weeks. RESULTS: A comparison of the parameters before and after the intervention showed that both groups experienced significant improvements in the resting pain, the most painful motion pain, craniocervical flexor endurance, and forward head posture except for the forward head posture in the control group. A comparison of the parameters between the groups did not show a significant difference. CONCLUSION: The results of this study suggest that the combined intervention of manual therapy with stabilization exercise does not seem to be more effective than manual therapy alone for improving neck pain, craniocervical flexor endurance, forward head posture, and the neck disability index in patients with chronic mechanical neck pain.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• Advances in materials Research
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• v.11 no.1
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• pp.75-90
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• 2022

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.