• The Journal of Advanced Prosthodontics
• /
• v.8 no.5
• /
• pp.333-338
• /
• 2016

PURPOSE. The aim of this study was to compare the effects of different denture cleansers on the surface roughness and microhardness of various types of posterior denture teeth. MATERIALS AND METHODS. 168 artificial tooth specimens were divided into the following four subgroups (n=42): SR Orthotyp PE (polymethylmethacrylate); SR Orthosit PE (Isosit); SR Postaris DCL (double cross-linked); and SR Phonares II (nanohybrid composite). The specimens were further divided according to the type of the denture cleanser (Corega Tabs (sodium perborate), sodium hypochlorite (NaOCl), and distilled water (control) (n=14)) and immersed in the cleanser to simulate a 180-day immersion period, after which the surface roughness and microhardness were tested. The data were analyzed using the Kruskal-Wallis test, Conover's nonparametric multiple comparison test, and Spearman's rank correlation analysis (P<.05). RESULTS. A comparison among the denture cleanser groups showed that NaOCl caused significantly higher roughness values on SR Orthotyp PE specimens when compared with the other artificial teeth (P<.001). Furthermore, Corega Tabs resulted in higher microhardness values in SR Orthotyp PE specimens than distilled water and NaOCl (P<.005). The microhardness values decreased significantly from distilled water, NaOCl, to Corega Tabs for SR Orthosit PE specimens (P<.001). SR Postaris DLC specimens showed increased microhardness when immersed in distilled water or NaOCl when compared with immersion in Corega Tabs (P<.003). No correlation was found between surface roughness and microhardness (r=0.104, P=.178). CONCLUSION. NaOCl and Corega Tabs affected the surface roughness and microhardness of all artificial denture teeth except for the new generation nanohybrid composite teeth.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.5
• /
• pp.825-832
• /
• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.11
• /
• pp.1173-1178
• /
• 2011

The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.

• Computers and Concrete
• /
• v.10 no.4
• /
• pp.379-390
• /
• 2012

In this paper, the mechanical strength of different lightweight mortars made with 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 percentage of scoria instead of sand and 0.55 water-cement ratio and 350 $kg/m^3$ cement content is investigated. The experimental result showed 7.9%, 16.7% and 49% decrease in compressive strength, tensile strength and mortar density, respectively, by using 100% scoria instead of sand in the mortar. The normalized compressive and tensile strength data are applied for artificial neural network (ANN) generation using generalized regression neural network (GRNN). Totally, 90 experimental data were selected randomly and applied to find the best network with minimum mean square error (MSE) and maximum correlation of determination. The created GRNN with 2 input layers, 2 output layers and a network spread of 0.1 had minimum MSE close to 0 and maximum correlation of determination close to 1.

• Journal of the Korea Society of Computer and Information
• /
• v.7 no.3
• /
• pp.99-108
• /
• 2002

Users location management and handoff management is constructed more effective for giving satisfaction to QoS of multimedia service in Mobility network. For this, We have the HLR of IMT-2000, difference of the HLR of PCS, and connectivity Artificial Intelligent network and to provide data service of packet that is analyze to effect on the HLR. We have a study on effective for satisfaction of QoS, that is the VHE of full concepts for provide of service in the IMT-2000, packet data service for internet connectivity, for satisfaction of QoS on basis mobility network technologies of the 2nd generation.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.2
• /
• pp.107-114
• /
• 1999

This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

• Journal of Embryo Transfer
• /
• v.33 no.2
• /
• pp.75-84
• /
• 2018

The cryopreservation has been extensively applied in many cells including spermatozoa (semen) during past several decades. Especially, the canine spermatozoa cryopreservation has contributed on generation of progeny of rare/genetically valuable dog breeds, genome resource banking and transportation of male germplasm at a distant place. However, severe and irreversible damages to the spermatozoa during cryopreservation procedures such as the thermal shock (cold shock), formation of intracellular ice crystals, osmotic shock, stress of cryoprotectants and generator of reactive oxygen species (ROS) have been addressed. According as a number of researches have been conducted to overcome these problems and to advance cryopreservation technique, several analytical methods have been employed to evaluate the quality of the fresh or cryopreserved canine spermatozoa in regards to the motility, morphology, integrity of membrane and DNA, mitochondrial activity, ROS generation, binding affinity to oocytes, in vitro fertilization potential and fertility potential by artificial insemination. Because the study designs with certain application of analytical methods are selective and varied depending on each experimental objective and laboratory condition, it is necessary to establish the normal reference data of the fresh or cryopreserved canine spermatozoa for each analytical method to monitor experimental procedure, to translate raw data and to discuss results. Here, we reviewed the recent articles to introduce various analytical methods for the canine spermatozoa as well as to establish the normal reference data for each analytical method in the fresh or cryopreserved canine spermatozoa, based on the results of the previous articles. We hope that this review contributes to the advancement of cryobiology in canine spermatozoa.

• Progress in Medical Physics
• /
• v.31 no.3
• /
• pp.111-123
• /
• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Knowledge Management Research
• /
• v.20 no.2
• /
• pp.25-42
• /
• 2019

AI speakers which are wireless speakers with smart features have released from many manufacturers and adopted by many customers. Though smart features including voice recognition, controlling connected devices and providing information are embedded in many mobile phones, AI speakers are sitting in home and has a role of the central en-tertainment and information provider. Many surveys have investigated the important factors to adopt AI speakers and influ-encing factors on satisfaction. Though most surveys on AI speakers are cross sectional, we can track customer attitude toward AI speakers longitudinally by analyzing customer reviews on AI speakers. However, there is not much research on the change of customer attitude toward AI speaker. Therefore, in this study, we try to grasp how the attitude of AI speaker changes with time by applying text mining-based analysis. We collected the customer reviews on Amazon Echo which has the highest share of AI speakers in the global market from Amazon.com. Since Amazon Echo already have two generations, we can analyze the characteristics of reviews and compare the attitude ac-cording to the adoption time. We identified all sub topics of customer reviews and specified the topics for smart features. And we analyzed how the share of topics varied with time and analyzed diverse meta data for comparisons. The proportions of the topics for general satisfaction and satisfaction on music were increasing while the proportions of the topics for music quality, speakers and wireless speakers were decreasing over time. Though the proportions of topics for smart fea-tures were similar according to time, the share of the topics in positive reviews and importance metrics were reduced in the 2nd generation of Amazon Echo. Even though smart features were mentioned similarly in the reviews, the influential effect on satisfac-tion were reduced over time and especially in the 2nd generation of Amazon Echo.

• Electronics and Telecommunications Trends
• /
• v.33 no.5
• /
• pp.103-110
• /
• 2018

Studies on artificial intelligence have been developed for the past couple of decades. After a few periods of prosperity and recession, a new machine learning method, so-called Deep Learning, has been introduced. This is the result of high-quality big- data, an increase in computing power, and the development of new algorithms. The main targets for deep learning are 1D audio and 2D images. The application domain is being extended from a discriminative model, such as classification/segmentation, to a generative model. Currently, deep learning is used for processing 3D data. However, unlike 2D, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become more popular owing to advances in 3D vision technology, the generation/acquisition of 3D data remains a very difficult problem. Moreover, it is not easy to directly apply an existing network model, such as a convolution network, owing to the variety of 3D data representations. In this paper, we summarize the 3D deep learning technology that have started to be developed within the last 2 years.