• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.807-824
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• 2016

In this paper, a method of mood classification based on user brainwaves is proposed for real-time application in commercial services. Unlike conventional mood analyzing systems, the proposed method focuses on classifying real-time user moods by analyzing the user's brainwaves. Applying brainwave-related research in commercial services requires two elements - robust performance and comfortable fit of. This paper proposes a filter based on Regularized Common Spatial Patterns (RCSP) and presents its use in the implementation of mood classification for a music service via a wireless consumer electroencephalography (EEG) device that has only 14 pins. Despite the use of fewer pins, the proposed system demonstrates approximately 10% point higher accuracy in mood classification, using the same dataset, compared to one of the best EEG-based mood-classification systems using a skullcap with 32 pins (EU FP7 PetaMedia project). This paper confirms the commercial viability of brainwave-based mood-classification technology. To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated. Furthermore, as a prototype service, this paper introduces a mood-based music list management system called MyMusicShuffler based on the proposed mood-classification method.

• Journal of Sensor Science and Technology
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• v.18 no.5
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• pp.385-392
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• 2009

This paper describes the algorithm development of a new body pressure detection sensor for occupant classification system. U.S. Government has required that advanced airbag system should be installed to every automobiles after 2006 according to FMVSS 208 regulation. Therefore, Occupant Classification System should be provided the passenger with safety in order to protect the infants or children that sit in the front passenger seat. When an occupant sits on the chair of the vehicle, deployment of the airbag depends on passenger's weigh distribution and postures. Authors have been developed a new pattern recognition of passenger and weight distribution at the same time by Force Sensing Resistor for the safety.

• Journal of Information Technology Services
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• v.21 no.5
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• pp.65-79
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• 2022

The smart factory has spread to small and mid-size enterprises (SMEs) under the leadership of the government. Smart factory consists of a work area, an operation management area, and an industrial control system (ICS) area. However, each site is combined with the IT system for reasons such as the convenience of work. As a result, various breaches could occur due to the weakness of the IT system. This study seeks to discover the items and vulnerabilities that SMEs who have difficulties in information security due to technology limitations, human resources, and budget should first diagnose and check. First, to compare the existing domestic and foreign smart factory vulnerability classification systems and improve the current classification system, the latest smart factory vulnerability information is collected from NVD, CISA, and OWASP. Then, significant keywords are extracted from pre-processing, co-occurrence network analysis is performed, and the relationship between each keyword and vulnerability is discovered. Finally, the improvement points of the classification system are derived by mapping it to the existing classification system. Therefore, configuration and maintenance, communication and network, and software development were the items to be diagnosed and checked first, and vulnerabilities were denial of service (DoS), lack of integrity checking for communications, inadequate authentication, privileges, and access control in software in descending order of importance.

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.188-192
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• 2001

Magnetic separation and sulphidization-flotation for recovery of nickel and copper from two types of scraped condenser wastes, containing 8- l4% nickel and 2-4% copper, were studied. The effects of magnetic field intensities, classification, and grinding on the recovery of nickel and copper were investigated. According to the characteristics of nickel and copper in the scraps, classification-magnetic separation, different magnetic field intensities, and stages-grinding-cleaning of rough concentrate were investigated. The nickel concentrates containing 38-65% nickel with 84-97% recoveries and the copper concentrates containing 25-43% nickel with 35-60% recoveries were obtained by classification-magnetic separation. In addition, copper concentrates containing 26-45% copper with 76-88% recoveries were obtained by sulphidization-flotation from magnetic tailings and middling products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4092-4107
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• 2015

We propose a background subtraction method for moving cameras based on trajectory classification, image segmentation and label inference. In the trajectory classification process, PCA-based outlier detection strategy is used to remove the outliers in the foreground trajectories. Combining optical flow trajectory with watershed algorithm, we propose a trajectory-controlled watershed segmentation algorithm which effectively improves the edge-preserving performance and prevents the over-smooth problem. Finally, label inference based on Markov Random field is conducted for labeling the unlabeled pixels. Experimental results on the motionseg database demonstrate the promising performance of the proposed approach compared with other competing methods.

• Clean Technology
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• v.11 no.2
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• pp.91-104
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• 2005

We studied more effective Environmental R&D management system which can be used in the budget allocation of the national R&D budget by the newly opened governmental administration system of science and technology. As a first step, we surveyed classification systems for the environmental technology which can be applied to the analysis of the national R&D budget. Each organization, either inland and oversea, has its own classification system for the environmental technology. However, we found that each systems can lead different results. It would be beneficial if an efficient standard classification system could be generated. The new system classified the environmental technology by the environmental management area(media) like most present systems rather than by the technical characteristics. The new system included all the areas of the environmental management and maintained consistent principles. The new system would help efficient management of the national R&D of the environment technology, if the national standard classification can be changed by the new system. To the researchers and professionals the new system will help them noticing the status of the environmental R&D and avoiding duplicate research, so that they can do research more efficiently and can achieve maximized results.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.526-529
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• 2007

Storm and flood damage management systems in national disaster management system(NDMS) were organized into three operation systems. They are prevention, preparation, response, and recovery systems. Disaster resources in each system must be promptly and exactly applied to minimize casualties and loss of properties. However, the disaster resources in current management system can not be immediately used in calamity situation due to the lack of efficiency in statistical data. In this study, the classification system of the disaster resources in storm and flood damage systems was examined to develop the a standard technology in disaster resources management. Problems and reformation points of the classification system were also presented to improve the classification technique and to construct the data base.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1869-1886
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• 2018

This paper proposes a MIDI- and audio-based music genre classification method for Korean traditional music. There are many traditional instruments in Korea, and most of the traditional songs played using the instruments have similar patterns and rhythms. Although music information processing such as music genre classification and audio melody extraction have been studied, most studies have focused on pop, jazz, rock, and other universal genres. There are few studies on Korean traditional music because of the lack of datasets. This paper analyzes raw audio and MIDI phrases in Korean traditional music, performed using Korean traditional musical instruments. The classified samples and MIDI, based on our classification system, will be used to construct a database or to implement our Kontakt-based instrument library. Thus, we can construct a management system for a Korean traditional music library using this classification system. Appropriate feature sets for raw audio and MIDI phrases are proposed and the classification results-based on machine learning algorithms such as support vector machine, multi-layer perception, decision tree, and random forest-are outlined in this paper.

• Korean Institute of Interior Design Journal
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• v.24 no.1
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• pp.207-215
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• 2015

It seems obvious that the spatial information of existing architectural heritage will be re-structured utilizing BIM technology. In the future to be able to implement such task, a new system of classification of spatial information, which fit to the structural nature of architectural heritage is necessary. This paper intend to suggest the conceptual model that can be the base of establishing new classification system for architectural heritage. For this study we reviewed researches related to classification system of architectural heritage (CS-AH) and BIM based architectural heritage (BIM-AH), first. As a result, we found that CS-AH is focused on building elevation and type, and BIM-AH is biased on the Library and Parametric Modeling. Second, we figured out a relationship between the CS-AH and BIM-AH. From this analysis, we found that BIM-AH is biased on Library and Parametric since the building elevation and type was focused on CS-AH. This review suggests a potential of the 3D CS-AH to expand the range of research for BIM-AH. At last, we suggest the three concept of classification are: 1)horizontality-accumulation relationship, 2)structure-infill relationship, 3)segment-member relationship. These three concept, together as one system of classification, could provide useful framework of new classification system of spatial information for architectural heritage.