• Journal of Dental Anesthesia and Pain Medicine
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• v.23 no.4
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• pp.213-220
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• 2023

Background: Third molar extraction is the most commonly performed minor oral surgical procedure in outpatient settings and requires regional anesthesia for pain control. Extraction of the maxillary molars commonly requires both posterior superior alveolar nerve block (PSANB) and greater palatine nerve block (GPNB), depending on the nerve innervations of the subject teeth. We aimed to study the effectiveness of PSANB alone in maxillary third molar (MTM) extraction. Methods: A sample size comprising 100 erupted and semi-erupted MTM was selected and subjected to study for extraction. Under strict aseptic conditions, the patients were subjected to the classical local anesthesia technique of PSANB alone with 2% lignocaine hydrochloride and adrenaline 1:80,000. After a latency period of 10 min, objective assessment of the buccal and palatal mucosa was performed. A numerical rating scale and visual analog scale were used. Results: In the post-latency period of 10 min, the depth of anesthesia obtained in our sample on the buccal side extended from the maxillary tuberosity posteriorly to the mesial of the first premolar (15%), second premolar (41%), and first molar (44%). This inferred that anesthesia was effectively high until the first molars and was less effective further anteriorly due to nerve innervation. The depth of anesthesia on the palatal aspect was up to the first molar (33%), second molar (67%), and lateromedially; 6% of the patients received anesthesia only to the alveolar region, whereas 66% received up to 1.5 cm to the mid-palatal raphe. In 5% of the cases, regional anesthesia was re-administered. An additional 1.8 ml PSANB was required in four patients, and another patient was administered a GPNB in addition to the PSANB during the time of extraction and elevation. Conclusion: The results of our study emphasize that PSANB alone is sufficient for the extraction of MTM in most cases, thereby obviating the need for poorly tolerated palatal injections.

• The Journal of Korean Academy of Prosthodontics
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• v.35 no.1
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• pp.211-220
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• 1997

This article describes verticsal dimension in its histologic and clinical aspect. Determination of correct vertical dimension of occlusion is one of the most important steps in prosthodontic rehabilitation. It is considered essential for improvement of facial esthetics and stomatognatic functions. Many techniques have been sued for measurement of the vertical dimension in dentulous and edentulous patients : pre-extraction record, physiologic rest position, swallowing, phonetics, esthetics, etc. But, there is no universally accepted or completely accurate method. Though a great deal of energy has been spent trying to find the exact position of the mandible, there is an controversial aspect of vetical dimension.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.135-140
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• 2009

In this paper, we synthesized active sonar target signals based on highlights model, and then carried out target classification using the synthesized signals. If the target aspect angle is changed, the different signals are synthesized. To know the result, two different experiments are done. First, The classification results with respect to each aspect angle are shown. Second, the results in two group in aspect angle are acquired. Time domain feature extraction is done using matched filter and envelope detection. It shows the pattern of each highlights. Artificial neural networks and multi-class SVM are used for classifying target signals.

• Journal of the Korean Academy of Esthetic Dentistry
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• v.31 no.2
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• pp.40-46
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• 2022

In maxilary anteriors, aesthetic aspect are of critical importance. but it is difficult to achieve esthetic results because of the narrow buccal-lingual alveolar bone width compared to the posterior teeth and alveolar bone resorption during tooth extraction. This case report describes how to minimize alveolar bone resorption and soft tissue collapse when immediate implant placement is done after extraction of the maxillary anterior teeth due to trauma.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.287-298
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• 1997

The main object of this study was to prove the effectiveness of satellite image data for extraction of the Matsucoccus Thenbergianae Miller ビt Park damaged area. The effectiveness of extraction of damaged area was improved by using the BRCT(Backwards radiance correction transformation) with DEM for normalization of topographic effects. The surface analysis of the extracted damaged area was revealed that the damage was started at south-west slope with the aspect of 7 to 18 degrees, and 50% to 70% of the highest altitude mountains. The direction of damage attached by the Matsucoccus Thunbergianae Miller et Park was able to predict through the analysis of periodical of years' images

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.823-828
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• 2003

The main objective of this paper is to provide a methodology of feature extraction using shape of image objects for content-based image retrieval. The shape of most real-life objects is irregular, and hence there is no universal approach to quantify the shape of an arbitrary object. In particular. electronic catalogs contain many image objects for their products. In this paper, we perform feature extraction based on individual objects in images rather than on the whole image itself, since our method uses a shape-based approach of objects using RLC lines within an image. Experiments show that shape parameters distinctly represented image objects and provided better classification and discrimination among image objects in an image database compared to Texture.

• Journal of Periodontal and Implant Science
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• v.45 no.6
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• pp.216-222
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• 2015

Purpose: After extraction, the alveolar bone tends to undergo atrophy in three-dimensions. The amount of alveolar bone loss in the horizontal dimension has been reported to be greater than the amount of bone loss in the vertical dimension, and is most pronounced in the buccal aspect. The aim of this study was to monitor the predictive alveolar bone level following the extraction of anterior teeth seriously involved with advanced chronic periodontitis. Methods: This study included 25 patients with advanced chronic periodontitis, whose maxillary anterior teeth had been extracted due to extensive attachment loss more than one year before the study. Periapical radiographs were analyzed to assess the vertical level of alveolar bone surrounding the edentulous area. An imaginary line connecting the mesial and the distal ends of the alveolar crest facing the adjacent tooth was arbitrarily created. Several representative coordinates were established in the horizontal direction, and the vertical distance from the imaginary line to the alveolar crest was measured at each coordinate for each patient using image analysis software. Regression functions predicting the vertical level of the alveolar bone in the maxillary anterior edentulous area were identified for each patient. Results: The regression functions demonstrated a tendency to converge to parabolic shapes. The predicted maximum distance between the imaginary line and the alveolar bone calculated using the regression function was $1.43{\pm}0.65mm$. No significant differences were found between the expected and actual maximum distances. Likewise, the predicted and actual maximum horizontal distances did not show any significant differences. The distance from the alveolar bone crest to the imaginary lines was not influenced by the mesio-distal spans of the edentulous area. Conclusions: After extraction, the vertical level of the alveolar ridge increased to become closer to the reference line connecting the mesial and distal alveolar crests.

• Archives of design research
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• v.16 no.3
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• pp.221-230
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• 2003

In product design, many designers incorporate psychological aspects to express the "fun" factor. If so, is it possible to apply the fun inducing mechanism utilized in psychology to product design and are there any problems in the application\ulcorner Psychology defines the sense of fun as the result of relief to a situation that causes a mental accumulation through the discovery of a due. The psychological extraction of fun inducing mechanism tends to lean too much to the cognitive aspect. Therefore, when dealing with product design, psychology's particular disposition is inadequate in explaining the important perceptive factor of fun. This study hypothesizes that perceptive aspect of should be induced along with the cognitive aspect in order to rationalize fun in product design. In order to understand the perception of fun, this study will introduce the amusement aspect within Kitsch products. Methods of developing a humorous form will be studied and these methods will be applicable to perceptive aspects. This paper hypothesizes that through the theoretical basis of internal characteristic of cognitive factors and external characteristic of perceptive factors, the mechanism of fun can be determined.etermined.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.