• Proceedings of the Korean Society of Applied Entomolohy Conference
• /
• 2000.05a
• /
• pp.92-92
• /
• 2000

• Journal of Korean Medicine for Obesity Research
• /
• v.16 no.1
• /
• pp.1-10
• /
• 2016

Objectives: The purpose of this study was to develop Russian version of Korean obese pattern identification questionnaire (KOPIQ) and classify the pattern of Uzbekistan obese patients. Methods: This study was conducted from 10 September 2014 to 10 December 2014 in Korea-Uzbekistan Oriental Hospital. The KOPIQ was translated to Russian language with the help of local expert in Russia and Korean Medicine. The patients who visited obese clinic in hospital were guided to join this study and informed consent was obtained. The inclusion criteria was >$23kg/m^2$ in body mass index. The Cronbach's alpha was used for it's inter consistency reliability and the KOPIQ result was compared with the experts one. Results: The Russian version of KOPIQ was developed. The 103 patient (25 males, 78 females; average age 57.2 years) joined in this study. The Cronbach's alpha of questionnaire was 0.787~0.883 according to individual pattern. The agreement rate of pattern Identification between local expert and KOPIQ was 13%. This developed questionnaire was realized as web version, which could be easily used in Uzbekistan. Conclusions: The Russian version of KOPIQ is developed in this study with suitable reliability. Further study is needed for KOPIQ to be applied in Uzbekistan with high validity.

• Journal of the military operations research society of Korea
• /
• v.23 no.2
• /
• pp.120-134
• /
• 1997

This paper illustrates the process of developing and configuring the prototype computer-assisted analysis system named as Work-Expert for analyzing occupational stress. A Work-Expert was developed to allow the nonexperts or line manager to utilize the existing knowledge in the area of occupational stress estimation, and to provide intelligent and computer-aided problem solving. The purpose of the system development is for future prediction and problem solving. Creating preventive measures, such as early detection of stress, proper placement and promotion of employees, job enlargement, employee identification, employee involvement, communication, and training of managers will be possible by using this system effectively.

• Proceedings of the KIEE Conference
• /
• 1997.07c
• /
• pp.927-929
• /
• 1997

In this paper, a new expert system scheme, called Logic Based Expert System (LBES), is proposed for real time fault diagnosis of power system. In LBES, Expertise is represented by logical connectives and converted into a Boolean function. The set of Prime Implicants (PIs) of the Boolean function contains all the sound inference results which can be obtained from the expertise. Therefore, off-line inference is possible by off-line PI identification, which reduces the on-line inference time considerably and makes it possible to utilize-the LBES in real-time environment.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1893-1896
• /
• 2002

Remediation Selection Expert System (RSES) is a rule-based expert system which is used far the selection of remediation techniques fur petroleum contaminated sites. In this paper, we describe a fuzzy logic-based sub-system: Site Characterization Sub-System (SCSS). It is an enhancement of the RSES, which is used to analyze the hydraulic properties of contaminated sites. This paper focuses on an explanation on how to apply fuzzy set theory for identification of soil types and hydraulic properties of a contaminated site. To illustrate application of fuzzy set theory to the problem, two sample cases are presented in detail.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.34 no.1
• /
• pp.1-5
• /
• 1998

This study presents intelligent deburring system which can transfer the exper's skill to deburring robot through neural network. The expert's skill is expressed as associate mapping between the characteristics of the burr and human expert's action. Under the fundamental idea that the state of the deburring process can be extracted via the visual sense of the human, we employ vision system for the perception and identification of the changing burr. From the demonstration of human experts, force data are measured and fitted impedance model. Finally the characteristics of the burr and coressponding force are associated by the neural network which is trained through many demonstrations. The proposed method is verified in the deburring process of welding burr.

• Imaging Science in Dentistry
• /
• v.51 no.3
• /
• pp.299-306
• /
• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.77-82
• /
• 2006

Automatic vessel collision-avoidance systems have been studied in the fields of artificial intelligence and navigation for decades. And to facilitate automatic collision-avoidance decision-making in two-vessel-encounter situation, several expert and fuzzy expert systems have been developed. However, none of them can negotiate with each other as seafarers usually do when they intend to make a more economic overall plan of collision avoidance in the COLREGS-COST-HIGH situations where collision avoidance following the International Regulations for Preventing Collisions at Sea(COLREGS) costs too much. Automatic Identification System(AIS) makes data communication between two vessels possible, and negotiation methods can be used to optimize vessel collision avoidance. In this paper, a negotiation framework is put forward to enable vessels to negotiate to optimize collision avoidance in the COLREGS-COST-HIGH situations at open sea. A vessel vector space is defined and therewith a cost model is put forward to evaluate the cost of collision-avoidance actions. Negotiations between a give-way vessel and a stand-on vessel and between two give-way vessels are considered respectively to reach overall low cost agreements. With the framework proposed in this paper, two vessels involved in a COLREGS-COST-HIGH situation can negotiate with each other to get a more economic overall plan of collision avoidance than that suggested by the traditional collision-avoidance expert systems.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.893-913
• /
• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• Journal of Oriental Neuropsychiatry
• /
• v.30 no.1
• /
• pp.1-11
• /
• 2019

Objectives: Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by a persistent pattern of inattention and/or hyperactivity impulsivity that interferes with function or development in children. In traditional Korean medicine (TKM) and traditional Chinese medicine (TCM), ADHD is classified by several patterns based on symptoms and signs. However, currently, there is no objective diagnostic tool for ADHD in traditional medicine. The objective of this study was to develop the Pattern Identification Questionnaire for ADHD (parents-survey style) to be used in Korean medicine, through a literature review and consultation with groups of experts. Methods: The types of pattern identifications of ADHD mentioned in 13 pieces of Korean and Chinese literatures and their symptoms and signs were analyzed. The advisory committee (15 Neuropsychiatrist and 11 Pediatrist in Korean Medicine) assessed the appropriateness of the literature selection and the types of pattern identification selection and their symptoms and signs, and weighed the significance of the symptoms and signs. The Pattern Identification Questionnaire for ADHD was developed using the calculated weights by evaluated significance. The translation of symptoms and signs to the Korean language was achieved through consultation with expert translators. Results: 1. Four pattern identification types and their symptoms and signs were selected according to frequency of appearance in the Korean and Chinese literatures, and were reviewed by the advisory committee: Kidney yin deficiency and liver yang ascendant hyperactivity (腎虛肝亢), Dual deficiencies in the heart and spleen (心脾兩虛), Phlegm-fire harassing the heart (痰火擾心), and Spleen weakness and liver energy preponderance (脾虛肝旺). 2. The weights of all the symptoms and signs in the four patterns were calculated using the means and standard deviations of the symptoms and signs' importance that were obtained from specialists' significance weighting. 3. The Pattern Identification Questionnaire for ADHD (parents-survey style) in Korean medicine composed of 38 questions was suggested. Conclusions: Using a review of the literature and expert advice, Pattern Identification Questionnaire for ADHD (parents-survey style) in Korean medicine was developed. Further clinical study is required to develop a final version of the questionnaire through the evaluation of reliability and validity.