• Journal of the Korean Geosynthetics Society
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• v.4 no.3
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• pp.21-33
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• 2005

This paper presented a mechanism of the soil structure reinforced by geosynthetics, in which the reinforcing mechanism is treated as the effect arising from the reinforcement process to prevent the dilative deformation of soil under shearing. A full-scale in-situ model test was carried out by introducing the prestress method to enhance the geosynthetic-reinforcement, and the prestress effect through the FEM is also examined. The elasto-plastic model and the initial parameters needed in the FEM are presented. Moreover, the theoretical prediction is compared with the experimental results, which were obtained by a full-scale in-situ model test.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.12
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• pp.104-109
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• 2001

With the increase of storage density and data transfer rates in optical disk drive, mechanical issues, mainly noise and vibration, become critical. Rubber materials are extensively used in various machine design application, mainly for vibration/shock/noise control devices. However, there are still a lot of difficulties in the use of designing the rubber components with complex shape and under pre-deformed state. It was demonstrated in that the variation of rubber component stiffness with the pre-deformed state were calculated by the finite element method and the reliability of numerical results were checked by compared with the measuring the deflection values. This paper presents a efficient design method of rubber mounts for anti-vibration of an optical disk thrive. With an empirical equation to estimate elastic modulus from hardness, and dynamic characteristics of rubber material of a cylindrical shape, this method is capable of predicting the dynamic characteristics of rubber components at design stage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.462-472
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• 2021

With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

• Nuclear Engineering and Technology
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• v.27 no.6
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• pp.853-858
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• 1995

The initial condition of a core transient should be consistent with real core state for the simulation of the core tansient. The initial xenon distribution, which can not be measured in the core, has a significant effect on the transient with xenon dynamics. In the simulation of the transient starting from non-equilibrium xenon state, the accurate initialization of the non-equilibrium xenon distribution is essential for the prediction of the core transient behavior. In this study, a xenon initialization method to predict the core transient more accurately was developed through the equivalent pre-xenon-oscillation which represents the tenon oscillation before the transient and verified by the application of the simulation for a startup test of Yonggwang Unit 3.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.96-96
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• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.10
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• pp.3060-3083
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• 2024

With the continuous advancements in malicious code polymorphism and obfuscation techniques, the performance of traditional machine learning-based detection methods for malware variant detection has gradually declined. Additionally, conventional pre-trained models could adequately capture the contextual semantic information of malicious code and appropriately represent polysemous words. To enhance the efficiency of malware variant detection, this paper proposes the MalEXLNet intelligent semantic analysis and detection architecture for malware. This architecture leverages malware API call sequences and employs an improved pre-training model for semantic vector representation, effectively utilizing the semantic information of API call sequences. It constructs a hybrid deep learning model, CBAM+AttentionBiLSTM,CBAM+AttentionBiLSTM, for training and classification prediction. Furthermore, incorporating the KMeansSMOTE algorithm achieves balanced processing of small sample data, ensuring the model maintains robust performance in detecting malicious variants from rare malware families. Comparative experiments on generalized datasets, Ember and Catak, the results show that the proposed MalEXLNet architecture achieves excellent performance in malware classification and detection tasks, with accuracies of 98.85% and 94.46% in the two datasets, and macro-averaged and micro-averaged metrics exceeding 98% and 92%, respectively.

• Fibers and Polymers
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• v.7 no.4
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• pp.404-413
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• 2006

This study examines multiple quality optimization of the injection molding for Polyether Ether Ketone (PEEK). It also looks into the dimensional deviation and strength of screws that are reduced and improved for the molding quality, respectively. This study applies the Taguchi method to cut down on the number of experiments and combines grey relational analysis to determine the optimal processing parameters for multiple quality characteristics. The quality characteristics of this experiment are the screws' outer diameter, tensile strength and twisting strength. First, one should determine the processing parameters that may affect the injection molding with the $L_{18}(2^1{\times}3^7)$ orthogonal, including mold temperature, pre-plasticity amount, injection pressure, injection speed, screw speed, packing pressure, packing time and cooling time. Then, the grey relational analysis, whose response table and response graph indicate the optimum processing parameters for multiple quality characteristics, is applied to resolve this drawback. The Taguchi method only takes a single quality characteristic into consideration. Finally, a processing parameter prediction system is established by using the back-propagation neural network. The percentage errors all fall within 2%, between the predicted values and the target values. This reveals that the prediction system established in this study produces excellent results.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.12 no.3
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• pp.49-56
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• 1990

Facial balance is the primary detevminant of good facial esthetics and is expressed externally by the shape of facial soft tissues. Balance of the facial skeleton is most important in prediction of orthognathic surgery, however, it is not alwags coincided to soft tissue balance because the soft tissue drapes overlying hard tissue varies in thickness and tones from case to case. So, soft tissue facial balance and esthetics also should always be considered in prediction of hard tissue changes preoperatively. The chin has a paramount importance in the overall appearance of the face and facial profile because it may express individual charactor or image. Therefore positional change of the chin must be considered in any cases as the last and important option to give an overall soft tissue balance. Two cases were referred from orthodontists only for anterior segmental of teortomuy of the chin. Pre-operative evaluation showed poor soft tissue chin profiles which were not coincided to hard tissue chin balance. We altered surgical plans to fulfill balancing soft tissue profile and then could improve overall esthetics after surgery.