• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1507-1515
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• 2008

It is essential for electric power companies to have a quick restoration system of the faulted pole transformers which occupy most of transformers to supply stable electricity. However, it takes too much time to restore it when a transformer is out of order suddenly because we now count on operator in investigating causes of failure and making decision of recovery methods. This paper presents the concept of 'Fault pattern analysis and Restoration prediction model using Data mining techniques’, which is based on accumulated fault record of pole transformers in the past. For this, it also suggests external and internal causes of fault which influence the fault pattern of pole transformers. It is expected that we can reduce not only defects in manufacturing procedure by upgrading quality but also the time of predicting fault patterns and recovering when faults occur by using the result.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.523-526
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• 2015

In recent years, building commissioning has often been part of a standard delivery practice in construction, particularly in the high-performance green building market, to ensure the building is designed and constructed per owner's requirements. Commissioning, therefore, intends to provide quality assurance that buildings perform as intended by the design and often helps achieve energy savings. Commissioning, however, is not as widely adopted as its potential benefits are perceived. Owners are still skeptical of the cost-effectiveness claims by energy management and commissioning professionals. One of the issues in the current commissioning practice is that not every project is guaranteed to benefit from the commissioning services. This, coupled with its added cost, the commissioning service is not acquired with great acceptance and confidence by building owners. To overcome this issue, this paper presents a unique methodology to enhance owner's predicting capability of the degree of success of commissioning service using the Bayesian theorem. The paper analyzes a situation where a future building owner wants to use a pre-commissioning in an attempt to refine the success rate of the future commissioned building performance. The author proposes the Bayesian theorem based framework to improve the current commissioning practice where building owners are not given accurate information how much successful their projects are going to be in terms of energy savings from the commissioning service. What should be provided to the building owners who consider their buildings to be commissioned is that they need some indicators how likely their projects benefit from the commissioning process. Based on this, the owners can make better informed decisions whether or not they acquire a commissioning service.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1483-1495
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• 2007

Thanks to IEEE 802.11 technique, accessing Internet through a wireless LAN(Local Area Network) is possible in the most of the places including university campuses, shopping malls, offices, hospitals, stations, and so on. Most of the APs(access points) for wireless LAN are supporting 2.4 GHz band 802.11b and 802.11g protocols. This paper is introducing a C# library function which can be used to read RSSIs(Received Signal Strength Indicator) from APs. An LBS(Location Based Service) estimates the current location of the user and provides useful user's location-based services such as navigation, points of interest, and so on. Therefore, indoor, LBS is very desirable. However, an indoor LBS cannot be realized unless indoor position ing is possible. For indoor positioning, techniques of using infrared, ultrasound, signal strength of UDP packet have been proposed. One of the disadvantages of these techniques is that they require special equipments dedicated for positioning. On the other hand, wireless LAN-based indoor positioning does not require any special equipments and more economical. A wireless LAN-based positioning cannot be realized without reading RSSIs from APs. Therefore, our C# library function will be widely used in the field of indoor positioning. In addition to providing a C# library function of reading RSSI, this paper introduces implementation of indoor positioning modules making use of the library function. The methods used in the implementation are K-NN(K Nearest Neighbors), Bayesian and trilateration. K-NN and Bayesian are kind of fingerprinting method. A fingerprint method consists of off-line phase and realtime phase. The process time of realtime phase must be fast. This paper proposes a decision tree method in order to improve the process time of realtime phase. Experimental results of comparing performances of these methods are also discussed.

• Neurospine
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• v.15 no.4
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2027-2032
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• 2003

For action selection as well as learning, simple associations between stimulus and response have been employed in most of literatures. But, for a successful task accomplishment, it is required that an animat can learn and express behavioral sequences. In this paper, we propose a novel action-selection-mechanism to deal with sequential behaviors. For this, we define behavioral motivation as a primitive node for action selection, and then hierarchically construct a network with behavioral motivations. The vertical path of the network represents behavioral sequences. Here, such a tree for our proposed ASM can be newly generated and/or updated, whenever a new sequential behaviors is learned. To show the validity of our proposed ASM, three 2-D grid world simulations will be illustrated.

• International Journal of Advanced Culture Technology
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• v.2 no.2
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• pp.15-18
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• 2014

This paper presents a sensor location decision making method respect to Container-Grown Seedlings in view of precision agriculture (PA) when sensors involved in tree container measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the container-grown seedlings system have a known location, whereas the remaining locations must be estimated. We derive Rao-Cramer bounds and maximum-likelihood estimators under Gaussian and log-normal models for the TOA and RSS measurements, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.165-169
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• 2010

This study seeks to find the factors associated with social workers' turnover intention and show us how to manage turnovers by looking for some rules affecting turnover intentions. Our investigation surveying 331 social workers reveals that social workers' turnover intentions are affected by organizational commitment, job satisfaction, and burnout. Our pattern analyses using fuzzy ID3 show that the higher their commitment, the higher their job satisfaction stemming from promotion opportunities, rewards, and personal relations with peers and bosses. In addition, turnover intentions decreases (even if burnouts--the job-related stress--are very serious) when organizational commitment increases. We come to understand that organizational commitment could be a more important variable than job satisfaction and burnouts. Such results suggest that it would be necessary to consider how to improve social workers' organization-wide commitment rather than satisfaction and burnout related to jobs and environments.

• Proceedings of the Korean Society of Toxicology Conference
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• 2005.05a
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• pp.206-206
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• 2005

• Journal of Periodontal and Implant Science
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• v.28 no.4
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• pp.645-660
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• 1998

The purpose of this study was to make and ascertain a decision making process on the base of patient-oriented utilitarianism in the treatment of patients of chronic adult periodontitis. Fifty subjects were chosen in Yonsei Dental hospital and the other fifty were chosen in Severance dental hospital according to the selection criteria. Fifty four patients agreed in this study. NS group(N=32) was treated with scaling and root planing without any surgical intervention, the other S group(N=22) done with flap operation. During the active treatment and healing time, all patients of both groups were educated about the importance of oral hygiene and controlled every visit to the hospital. When periodontal treatment needed according to the diagnostic results, some patients were subjected to professional tooth cleaning and scaling once every 3 months according to an individually designed oral hygienic protocol. Probing depth was recorded on baseline and 18 months after treatments. A questionnaire composed of 6 kinds(hygienic easiness, hypersensitivity, post treatment comfort, complication, functional comfort, compliance) of questions was delivered to each patient to obtain the subjective evaluation regarding the results of therapy. The decision tree for the treatment of adult periodontal disease was made on the result of 2 kinds of periodontal treatment and patient's ubjective evaluation. The optimal path was calculated by using the success rate of the results as the probability and utility according to relative value and the economic value in the insurance system. The success rate to achieve the diagnostic goal of periodontal treatment as the remaining pocket depth less than 3mm and without BOP was $0.83{\pm}0.12$ by non surgical treatment and $0.82{\pm}0.14$ by surgical treatment without any statistically significant difference. The moderate success rate of more than 4mm probing pocket depth were 0.17 together. The utilities of non-surgical treatment results were 100 for a result with less than 3mm probing pocket depth, 80 for the other results with more than 4mm probing pocket depth, 0 for the extraction. Those of surgical treatment results were the same except 75 for the results with more than 4mm. The pooling results of subjective evaluation by using a questionnaire were 60% for satisfaction level and 40% for no satisfaction level in the patient group receiving nonsurgical treatment and 33% and 67% in the other group receiving surgical treatment. The utilities for 4 satisfaction levels were 100, 75, 60, 50 on the base of that the patient would express the satisfaction level with normal distribution. The optimal path of periodontal treatment was rolled back by timing the utility on terminal node and the success rate, the distributed ratio of patient's satisfaction level. Both results of the calculation was non surgical treatment. Therefore, it can be said that non-surgical treatment may be the optimal path for this decision tree of treatment protocol if the goal of the periodontal treatment is to achieve the remaining probing pocket depth of less than 3mm for adult chronic periodontitis and if the utilitarian philosophy to maximise the expected utility for the patients is advocated.