• Physical Therapy Rehabilitation Science
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• v.11 no.4
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• pp.384-390
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• 2022

Objective: The purpose of this study was to investigate the effect of the side-step tasks based circular training program (STCT) on balance and gait characteristics in stroke patients. Design: A randomized controlled trial Methods: Twenty-four stroke patients were randomly divided into two groups of twelve patients each. One group was applied with the STCT whereas the other group was treated with conservative physiotherapy (CP). The ability of gait was measured in 10m walking test and stride length on both side using BTS G-WALK (BTS Bioengineering S.p.A, Italy) and the ability of balance was measured in Berg Balance Scale (BBS) and Timed Up and Go Test (TUG). Results: The STCT group was significant differences in the balance parameters of BBS and TUG (p<0.05) and showed significant differences in gait variables in 10m walking speed, stride length of affected and non-affected side after the experiment before and after the experiment (p<0.05). In addition, the STCT group showed a significant difference in BBS compared to the control group (p<0.05). Conclusions: The results of this study confirmed that the side-step tasks based circular training program (STCT) improves balance and walking ability in stroke patients. STCT is expected to be used as a useful intervention method for stroke rehabilitation.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• Journal of KSNVE
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• v.4 no.1
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• pp.59-69
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• 1994

A new LMS algorithm titled constrained LMS' is proposed for problems with constrained structure. The conventional LMS algorithm can not be used because it destroys the constrained structures of the weights or parameters. Proposed method uses error-back propagation, which is popular in training neural networks, for error minimization. The illustrative examplesare shown to demonstrate the applicability of the proposed algorithm.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.675-677
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• 2005

This study proposes a fuzzy classification using EM algorithm. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.4
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• pp.1751-1755
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• 2014

Background: In this study prognostic correlations of histopathologic parameters and the Ki-67 proliferation index and as well as the diagnostic value of immunohistochemical markers in pheochromocytomas were evaluated. Materials and Methods: A total of 22 patients diagnosed with a pheochromocytoma between 2000-2010 in Izmir Katip Celebi University Ataturk Training and Research Hospital were included. Diagnostic value of the PASS scoring system, and prognostic correlations of histopathologic parameters and Ki-67 proliferation index were investigated. SPSS for Windows 17.0 software was used for statistical analysis. Results: There was no statistically significant correlation between recurrence and clinicopathologic parameters or the PASS score (PASS>4). In addition, there were no statistically significant correlations between PASS score and clinicopathologic parameters, such as diameter (5 cm), weight (>100g), gender (female/male ratio) and age (25-45/45-55/>55). Besides, there were no significant correlation between diameter and clinicopathological parameters and also recurrence. However, there was a statistically significant correlation between Ki-67 proliferation index and capsule invasion (p=0.047). Conclusions: Some but not most of the findings in our study were concordant with the literature. To clarify relationships, investigations with standard scoring systems which are not affected by subjective factors and feature appropriate histopathological criteria should be made on larger study groups.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1197-1205
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• 2018

In this paper, we perform Single Image Super Resolution(SISR) for acquired images of EOTS or IRST from naval combat system. In order to conduct super resolution, we use Generative Adversarial Networks(GANs), which consists of a generative model to create a super-resolution image from the given low-resolution image and a discriminative model to determine whether the generated super-resolution image is qualified as a high-resolution image by adjusting various learning parameters. The learning parameters consist of a crop size of input image, the depth of sub-pixel layer, and the types of training images. Regarding evaluation method, we apply not only general image quality metrics, but feature descriptor methods. As a result, a larger crop size, a deeper sub-pixel layer, and high-resolution training images yield good performance.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.7
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• pp.624-630
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• 2006

ZnO deposition parameters are not independent and have a nonlinear and complex property. To propose a method that could verify and predict the relations of process variables, neural network was used. At first, ZnO thin films were deposited by using RF magnetron sputtering process with various conditions. Si, GaAs, and Glass were used as substrates. The temperature, work pressure, and RF power of the substrate were $50\sim500^{\circ}C$, 15 mTorr, and $180\sim210W$, respectively : the purity of the target was ZnO 4 N. Structural properties of ZnO thin films were estimated by using XRD (0002) peak intensity. The structure of neural network was a form of 4-7-1 that have one hidden layer. In training a network, learning rate and momentum were selected as 0.2, 0.6 respectively. A backpropagation neural network were performed with XRD (0002) peak data. After training a network, the temperature of substrate was evaluated as the most important parameter by sensitivity analysis and response surface. As a result, neural network could capture nonlinear and complex relationships between process parameters and predict structural properties of ZnO thin films with a limited set of experiments.

• IMMUNE NETWORK
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• v.5 no.2
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• pp.117-123
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• 2005

The immune response to any stimulus is complex, requiring coordinated action by several types of cells in a tightly regulated sequence. Thus, a physical stress such as exercise may act at any number of points in the complex sequence of events collectively termed the immune response. Although exercise causes many propound changes in parameters of immune function, the nature and magnitude of such changes rely on several factors including the immune parameters of interest; type, intensity, and duration of exercise; fitness level or exercise history of the subject; environmental factors such as ambient temperature and humidity. Although regular moderate exercise appears to be important factor for increasing immunity, Athletes are susceptible to illness, in particular upper respiratory track infection, during periods of intense training and after competition. In addition, in elite athletes, frequent illness is associated with overtraining syndrome, a neuroendocrine disorder resulting from excessive training. Through this paper, we want to investigate the effects of exercise on the immunosuppression such as exercise induced lymphopenia, asthma, anaphylaxis, URT (upper respiratory track), and TB (tuberculosis) infection. and also, we want to suggest a direct mechanism, protection and therapy of exercise induced immunosuppression.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.