• 한국운동역학회지
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• 제31권2호
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• pp.113-118
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• 2021

Objective: The purpose of this study was to investigate differences of landing strategy between people with or without chronic ankle instability (CAI) during double-leg drop landing. Method: 34 male adults participated in this study (CAI = 16, Normal = 18). Participants performed double-leg drop landing task on a 30 cm height and 20 cm horizontal distance away from the force plate. Lower Extremities Kinetic and Kinematic data were obtained using 8 motion capture cameras and 2 force plates and loading rate was calculated. Independent samples t-test were used to identify differences between groups. Results: Compared with normal group, CAI group exhibits significantly less hip internal rotation angle (CAI = 1.52±8.12, Normal = 10.63±8.44, p = 0.003), greater knee valgus angle (CAI = -6.78±5.03, Normal = -12.38 ±6.78, p = 0.011), greater ankle eversion moment (CAI = 0.0001±0.02, Normal = -0.03±0.05, p = 0.043), greater loading Rate (CAI = 32.65±15.52, Normal = 18.43±10.87, p = 0.003) on their affected limb during maximum vertical Ground Reaction Force moment. Conclusion: Our results demonstrated that CAI group exhibits compensatory movement to avoid ankle inversion during double-leg drop landing compared with normal group. Further study about how changed kinetic and kinematic affect shock absorption ability and injury risk in participants with CAI is needed.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• 로봇학회논문지
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• 제17권4호
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• pp.417-424
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• 2022

In this paper, the developed trunk cargo unloading automation system is introduced, and the RGB-D sensor-based box loading situation recognition method and unloading plan applied to this system are suggested. First of all, it is necessary to recognize the position of the box in a truck. To do this, we first apply CNN-based YOLO, which can recognize objects in RGB images in real-time. Then, the normal vector of the center of the box is obtained using the depth image to reduce misrecognition in parts other than the box, and the inner wall of the truck in an image is removed. And a method of classifying the layers of the boxes according to the distance using the recognized depth information of the boxes is suggested. Given the coordinates of the boxes on the nearest layer, a method of generating the optimal path to take out the boxes the fastest using this information is introduced. In addition, kinematic analysis is performed to move the conveyor to the position of the box to be taken out of the truck, and kinematic analysis is also performed to control the robot arm that takes out the boxes. Finally, the effectiveness of the developed system and algorithm through a test bed is proved.

• International Journal of Korean Welding Society
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• 제1권1호
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• Journal of Digestive Cancer Research
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• 제6권1호
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• pp.6-10
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• 2018

The importance of signal transducer and activator of transcription 3 (STAT3) signaling in gastric carcinogenesis was firmly evaluated in the previous studies. Fully activated STAT3 induces various target genes involving tumor invasion and epithelial-mesenchymal transition (EMT), and mediates interaction between cancer cells and microenvironmental immune cells. Thus, suppression of STAT3 activity is an important issue for inhibition of gastric carcinogenesis and invasion. Unfortunately, data from clinical studies of direct inhibitor targeting STAT3 have been disappointing. SH2-containing protein tyrosine phosphatase 1 (SHP-1) effectively dephosphorylates and inhibits STAT3 activity, which has not been extensively studied gastric cancer research field. However, by summarizing recent data, it is evident that protein and gene expression of SHP-1 are minimal in gastric cancer cells, and induction of SHP-1 effectively downregulates phosphorylated STAT3 and inhibits cellular invasion in gastric cancer cells. Several SHP-1 inducers have been investigated in the experimental studies, including proton pump inhibitor, arsenic trioxide, and other natural compounds. Taken together, we suggest that modulation of SHP-1/STAT3 signaling axis may present a new way for treatment of gastric cancer, and development of effective SHP-1 inducer may be an important task in the future search field of gastric cancer.

• Educational Technology International
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• 제6권1호
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• pp.103-119
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• 2005

This study investigated to designand implement web-based collaborative learning system Co-Net and map out students' learning procedure using the system, based upon Student Team Achievement Division (STAD Slavin, 1990, 1996). There are technical process and instructional considerations to be made during the design process. The former are those that concern equipment requirements and specifications and include Ease of Use, Speed of Access, and Flexibility. On the other hand, instructional considerationsare concerned with the delivery and access of instructional materials and their outcomes on learners. They are cooperative interactions within groups and group heterogeneity, learner control, group incentives, individual accountability, equal opportunity for earning high scores and contributing to group effort, task specialization, and competition among groups. A web site for a virtual learning environment designed and built by the authors and known as Co-Net is then explained along with the whole process learners inside the environment. The main page of Co-Net consists of 15 menus to implement cooperative learning process. The cooperative learning activities using 15 menus are composed of six phases (1) preparation of the new knowledge (2) presentation of the new knowledge (3) knowledge assimilation and application (4) team and individual evaluation (5) team and individual recognition Throughout the five phases, the appropriate use of cooperative learning techniques has been shown to have both academic and social benefits to learners.

• 한국농공학회논문집
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• 제65권6호
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• pp.15-22
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• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

• 로봇학회논문지
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• 제19권1호
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• pp.65-70
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• 2024

Tool wear is considered an important issue in manufacturing and engineering, as worn tools can negatively impact productivity and product quality. Given that the wear status of tools plays a decisive role in the production process, measuring tool wear is a key task. Consequently, there is significant attention in manufacturing fields on the precise measurement of tool wear. Current domestic methods for measuring wear are limited in terms of speed and efficiency, with traditional methods being time-consuming and reliant on subjective evaluation. To address these issues, we developed a measurement module implementing the DeepContour algorithm, which uses image processing technology for rapid measurement and evaluation of tool wear. This algorithm accurately extracts the tool's outline, assesses its condition, determines the degree of wear, and proves more efficient than existing, subjective, and time-consuming methods. The main objective of this paper is to design and apply in practice an algorithm and measurement module that can measure and evaluate tool wear using image processing technology. It focuses on determining the degree of wear by extracting the tool's outline, assessing its condition, and presenting the measured value to the operator.

• 한국의류학회지
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• 제48권1호
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• pp.37-49
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• 2024

This study confirmed the relationship between variables developed by qualitative ground theory through quantitative research. The purpose of the study is to explain the effect of core sales tasks on sales services and the mediating effect of organizational member relationships on sales services. The data were collected through a survey of fashion brand salespeople in Gwangju from September to October 2020 with data from 235 responses analyzed using SPSS 27.0 and AMOS 26.0. The validity of the research model verified the confirmatory factor analysis and the research hypothesis was verified through path analysis and multi-mediated analysis of the structural model. The research results were as follows. First, sales management did not directly affect sales services, and customer management affected sales services. Second, a meaningful causal relationship was shown to exist between organizational member relationships and sales management, but organizational member relationships and customer management did not have a significant relationship. Third, the total and individual indirect effects of headquarters relations, colleague relations, and customer management were all statistically significant.

• 농업과학연구
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• 제49권4호
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• pp.785-793
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.