• Journal of Fashion Business
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• v.23 no.1
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• pp.14-24
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• 2019

Various types of functional bedding for inducing and maintaining sleep, are developed and launched with the importance of improving health through sleep emphasized currently. The purpose of this study is to examine development status and direction of functional bedding in the $4^{th}$ Industrial Revolution era, through systematic classification of elements of IoT-based smart bedding cases actively developed as functional bedding at home and abroad. Through previous research, literature and Internet data, characteristics and functional extension of smart bedding and the background of smart bed development was analyzed. And it was analyzed that smart bedding pursues recent functionalism and convergence of physical and digital concept such as IoT or AI, and also mental value to improve sleep quality. As bedroom where smart bedding place in has the private and limited characteristics and users are in sleep-conscious, that hard to ensure power and discomfort in carrying are moderated and the aesthetic elements are not very important, and that the smart bedding performance while sleeping were affected on developmental background. Based on CES case study and analysis on how smart beds are functionally expanded from conventional bedding, smart beds have gained information through digital sensing, and common properties that can be controlled anytime, anywhere, using a smart phone. Some set up the right environment and pose, while others stimulate nerves directly as active intervention. It is expected that smart bedding will be developed to cure user's body and mind, through active intervention when sleeping.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.725-728
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• 2014

Continue to have an increasingly violent crimes against children every year, and as you know all seriousness is classified as a felony. However, efforts to reduce the underlying crime is low. Therefore, it is necessary to solve this problem, the security system. Is to protect the children and adults that exist that can pose a threat to children to identify and monitor tracking method in this paper. Was based on a Korean standard body size of a person, such as keys, arm length, leg length, head vertical length, head width proposed method. Also, separate the adults and children through the comparison of the reference value, the ratio and the ratio of the number of pixels of the detected object, the proposed method. Processing speed is fast because it detects only a specific object region in the entire image in the handling method in the proposed method the five nine minutes. The advantage is to enable comparison of the specific object, through which there is.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.9-16
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• 2023

Technological advancement is being advanced in sports such as electronic protection of taekwondo competition and VAR of soccer. However, a person judges and guides the posture by looking at the posture, so sometimes a judgment dispute occurs at the site of the competition in Taekwondo Poomsae. This study proposes an artificial intelligence model that can more accurately judge and evaluate Taekwondo movements using artificial intelligence. In this study, after pre-processing the photographed and collected data, it is separated into train, test, and validation sets. The separated data is trained by applying each model and conditions, and then compared to present the best-performing model. The models under each condition compared the values of loss, accuracy, learning time, and top-n error, and as a result, the performance of the model trained under the conditions using ResNet50 and Adam was found to be the best. It is expected that the model presented in this study can be utilized in various fields such as education sites and competitions.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.33-47
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• 2023

Under the "Act on Safety Control of Small Public Facilities (enacted in 2015)", each local government selects and conducts annual safety inspections for small public facilities. Among small public facilities, small bridges pose high risks and are heavily utilized by local residents, making them challenging to manage due to their large numbers and limited resources. Therefore, there is a need for a rational selection method that considers the management capacity of local governments, as well as the establishment of objective risk evaluation and maintenance planning for small hazardous facilities. In this study, we propose a selection method based on structural and functional classification of small bridges, considering the management capacity of local governments. Additionally, we present quantitative evaluation indicators for ten risk evaluation criteria, aiming to facilitate objective risk evaluation.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.39-46
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• 2024

Many researchers make efforts to evaluate water quality using various models. Such models require a dataset without missing values, but in real world, most datasets include missing values for various reasons. Simple deletion of samples having missing value(s) could distort distribution of the underlying data and pose a significant risk of biasing the model's inference when the missing mechanism is not MCAR. In this study, to explore the most appropriate technique for handing missing values in water quality data, several imputation techniques were experimented based on existing KNN and MICE imputation with/without the generative neural network model, Autoencoder(AE) and Denoising Autoencoder(DAE). The results shows that KNN and MICE combined imputation without generative networks provides the closest estimated values to the true values. When evaluating binary classification models based on support vector machine and ensemble algorithms after applying the combined imputation technique to the observed water quality dataset with missing values, it shows better performance in terms of Accuracy, F1 score, RoC-AuC score and MCC compared to those evaluated after deleting samples having missing values.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1257-1257
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• 2001

Fourier transform near infrared (FT-NIR) spectroscopy was used as a rapid method to measure the $^{o}Brix$ content and to discriminate between different must samples in terms of their fee amino nitrogen (FAN) values. FT-NIR spectroscopy was also used as a rapid method to discriminate between Chardonnay wine samples in terms of the status of the male-lactic fermentation (MLF). This was done by monitoring the conversion of malic to lactic acid and thereby determining whether MLF has started, is underway or has been completed followed by classification of the samples. Furthermore, FT-NIR spectroscopy was applied as a rapid method to discriminate between table wine samples in terms of the ethyl carbamate (EC) content. EC in wine can pose a health threat and need to be monitored by determining the EC content in relation to the regulatory limits set by the authorities. For each of the above mentioned parameters, $QUANT+^{TM}$ methods were built and calibrations derived and it was found that a very strong correlation existed in the sample set for the FT-NIR spectroscopic predictions of $^{o}Brix$ (r = 0.99, SECV = 0.306), but the correlations for the FAN (r = 0.61, SECV = 272.1), malic acid (r = 0.58, SECV = 1.06), lactic acid (r = 0.51, SECV = 1.14) and EC predictions (r = 0.47, SECV = 3.67) were not as good. Soft Independent Modeling by Class Analogy (SIMCA) diagnostics and validation was applied as a sophisticated discrimination method. The must samples could be classified in terms of their FAN values when SIMCA was applied, obtaining results with recognition rates exceeding 80%. When SIMCA diagnostics and validation were applied to determine the progress of conversion of malic to lactic acid and the EC content, again results with recognition rates exceeding 80% were obtained. The evaluation of the applicability of FT-NIR spectroscopy measurement of FAN, $^{o}Brix$ values, malic acid, lactic acid and EC content in must and wine shows considerable promise. FT-NIR spectroscopy has the potential to reduce the analytical times considerably in a range of measurements commonly used during the wine making process. Where conventional FT-NIR calibrations are not effective, SIMCA methods can be used as a discriminative method for rapid classification of samples. SIMCA can replace expensive, time-consuming, quantitative analytical methods, if not completely, at least to some extent, because in many processes it is only needed to know whether a specific cut off point has been reach or not or whether a sample belongs to a certain class or not.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.703-714
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• 2005

Researches in text categorization have been confined to whole-document-level classification, probably due to lacks of full-text test collections. However, full-length documents availably today in large quantities pose renewed interests in text classification. A document is usually written in an organized structure to present its main topic(s). This structure can be expressed as a sequence of sub-topic text blocks, or passages. In order to reflect the sub-topic structure of a document, we propose a new passage-level or passage-based text categorization model, which segments a test document into several Passages, assigns categories to each passage, and merges passage categories to document categories. Compared with traditional document-level categorization, two additional steps, passage splitting and category merging, are required in this model. By using four subsets of Routers text categorization test collection and a full-text test collection of which documents are varying from tens of kilobytes to hundreds, we evaluated the proposed model, especially the effectiveness of various passage types and the importance of passage location in category merging. Our results show simple windows are best for all test collections tested in these experiments. We also found that passages have different degrees of contribution to main topic(s), depending on their location in the test document.

• Journal of Korean Neurosurgical Society
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• v.30 no.sup2
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• pp.203-210
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• 2001

Objective : Paraclinoidal aneurysms termed that aneurysms arising from proximal internal carotid artery(ICA) between the site of emergence of the carotid artery from roof of the cavernous sinus and origin of the posterior communicating artery(PCoA). These aneurysms pose conceptual and technical surgical problems with regard to acquisition of proximal control and safe intracranial exposure. The efficiency of surgical technique according to the location of paraclinoidal aneurysm was studied for minimal exposure. Materials and Methods : Over the past four years, the authors treated surgically 171 cases of cerebral aneurysm, among them ten patients were paraclinoidal aneurysms with two patients unruptured aneurysms. Mean age was 47 years old, and all patients were female. Three patients were proximal posterior carotid artery wall aneurysms(one large, one giant), four patients carotid-ophthalmic artery aneurysms and three patients superior hypophyseal artery aneurysms. Results : There could be done clip in all cases, there were no deaths and no complication. And no patient developed sustained neurological deficits including visual function except hydrocephalus in one case. Four patients complained of visual disturbance but two patients had recovery after postoperation and two patient were not longer to bad. Conclusion : Our recent experience suggests that preoperative scrutiny of diagnostic angiography allows classification of all paraclinoidal aneurysms regardless of size and surgical technique which this classification has focused on operative approaches unique to each aneurysm projection was helpful to improve the operative outcome with good visual function and to shorten the operative time.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences