• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.403-410
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• 2009

In this paper, we present an on-line quiz authoring software that helps teachers create an intelligent on-line quiz. It is designed to give each student appropriate diagnostic report using Bayesian inference networks that represent the relationships among knowledge-items. Once the authors design and edit quizzes in quiz authoring page, the authoring tool automatically produces a knowledge-model based on Bayesian inference network, on-line quizzes, and student report pages. It turns out that the on-line quizzes generated by this tool help students identify their weak parts of subject, make learning strategies for the next learning steps and carry out supplementary learning for their weak knowledge-items.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.51-60
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• 2006

The goal of qualitative analysis is to capture and formalize qualitative and intuitive knowledge about physical world. Qualitative reasoning has been successfully applied to electric and mechanical mechanism domains, in which most of reasoning has focused on simulation. This paper introduces a qualitative comparative analysis technique which predicts how a change in a given situation propagates. We developed a comparative analysis technique which extends previous research by including a reasoning technique about the relative rate of the change of a parameter. Previous research focuses only on the relative change of a parameter. Causal model for the given situation is generated from qualitative domain model. The propagation by the change in causal relations are traced by applying our comparative analysis. By providing explanation as well as prediction for the given change, our technique is expected to be used in design, diagnosis, intelligent tutoring system, environmental evaluation.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.574-579
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• 2010

The baseline wander is most fatal noise, because it obstructs reliable diagnosis of cardiac disorder. Thus, in this paper, the morphology-pair is proposed for estimation of baseline wander except P, T-wave and QRS-complex. Proposed Morphology-pair is able to except P, R, T-wave which have characteristics of local maxima. Likewise Q, S-wave such as local minima are excepted by proposed Morphology-pair. The final baseline wander eliminated ECG signal is deducted by subtraction of original ECG and estimated baseline wander. The experimental results based on the MIT/BIH database show that the proposed algorithms produce promising results.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.332-337
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• 2006

It has been suggested that the endoscopic color of intramucosal gastric carcinoma is correlated with mucosal vascularity within the carcinomatous tissue. The development of electronic endoscopy has made it possible to quantitatively measure the mucosal hemoglobin volume, using a hemoglobin index. The aim of this study was to make a software program to calculate the hemoglobin index (IHb) and then investigate whether the mucosal IHb determined from the electronic endoscopic data is a useful marker for evaluating the color of intramucosal gastric carcinoma, in particular with regard to its value for discriminating between the histologic types. The mean values of IHb for the carcinoma (IHb-C) and the mean values of IHb for the surrounding non-cancerous mucosa (IHb-N) were calculated in 75 intestinal-type and 34 diffuse-type gastric carcinomas. Then, we analyzed the ratio of the IHb-C to IHb-N. The mean IHb-C/IHb-N ratio in the intestinal-type carcinoma group was higher than that in the diffuse-type carcinoma group (1.28$\pm$0.19 vs. 0.81$\pm$0.18, respectively, p<0.001). When the cut-off point of the C/N ratio was set at 1.00, the accuracy rate, the sensitivity, the specificity, and the positive and negative predictive values of a C/R ratio below 1.00 for the differential diagnosis of diffuse-type carcinoma from intestinal-type carcinoma were 94.5%, 94.1%, 94.7%, 88.9% and 97.3%, respectively. IHb is useful for quantitative measurement of the endoscopic color in intramucosal gastric carcinoma and the IHb-C/IHb-N ratio would be helpful in distinguishing diffuse-type carcinoma from intestinal-type carcinoma.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.83-89
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• 2022

Internet of Things technology is rapidly developing due to the recent development of information and communication technology. IoT technology utilizes various sensors to generate unique data from each sensor, enabling diagnosis of system status. However, the equipment management system currently in effect is a post-preservation concept in which administrators must deal with the problem after the problem occurs, which could mean system reliability and availability problems due to system errors, and could result in economic losses due to negative productivity disruptions. Therefore, this study confirmed that edge controller control decision algorithms for more efficient operation of rectifiers in the factory by applying intelligent IoT (AIoT) technology and domain knowledge-based modeling for each sensor data collected based on this, outputting appropriate status messages for each scenario.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.175-189
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• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.

• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.193-206
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• 2008

Medical diagnosis can be considered a classification task which classifies disease types from patient's condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent's fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.65-93
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• 2005

Expert system implementation of human expert's diagnosis in art psychotherapy requires extensive knowledge on: (1) characteristics in a drawing; (2) psychological symptoms in a client; (3) relationships between the characteristics and the symptoms; (4) decision process; (5) knowledge elicitation and aquisition methods. Experts from many different fields provide such knowledge, ranging from art therapists who is on the spot, psychiatrists, psychologists, artists to knowledge engineers who know how to implement the decision system to a computer. The problems that make the implementation difficult are the expert's complex decision process and the ambiguity, the inconsistency and even the contradiction in the huge volume of the knowledge. Modeling the expert's decision process, we develope a framework of the system and then analyze and classify the knowledge. With the proposed classification, we present a suitable method of knowledge elicitation and aquisition. Then, we describe the subsets of knowledge in a unified structure using the ontology concept and Protege 2000 as a tool. Finally, we apply the system to a real case to show its usability and suitability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.40-45
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• 2018

Intelligent monitoring and diagnosis of production processes based on multivariate statistical methods has been one of important tasks for safety and quality issues. This is due to the fact that faults and unexpected events may have serious impacts on the operation of processes. This study proposes a diagnostic scheme based on effective representation of process measurement data and is evaluated using simulation process data. The effects of utilizing a preprocessing step and nonlinear statistical methods are also tested using fifteen faults of the simulation process. Results show that the proposed scheme produced more reliable results and outperformed other tested schemes with none of the filtering step and nonlinear methods. The proposed scheme is expected to be robust to process noises and easy to develop due to the lack of required rigorous mathematical process models or expert knowledge.