• 제목/요약/키워드: intelligent diagnosis

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The Intelligent Clinical Laboratory as a Tool to Increase Cancer Care Management Productivity

  • Mohammadzadeh, Niloofar;Safdari, Reza
    • Asian Pacific Journal of Cancer Prevention
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    • 제15권6호
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    • pp.2935-2937
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    • 2014
  • Studies of the causes of cancer, early detection, prevention or treatment need accurate, comprehensive, and timely cancer data. The clinical laboratory provides important cancer information needed for physicians which influence clinical decisions regarding treatment, diagnosis and patient monitoring. Poor communication between health care providers and clinical laboratory personnel can lead to medical errors and wrong decisions in providing cancer care. Because of the key impact of laboratory information on cancer diagnosis and treatment the quality of the tests, lab reports, and appropriate lab management are very important. A laboratory information management system (LIMS) can have an important role in diagnosis, fast and effective access to cancer data, decrease redundancy and costs, and facilitate the integration and collection of data from different types of instruments and systems. In spite of significant advantages LIMS is limited by factors such as problems in adaption to new instruments that may change existing work processes. Applications of intelligent software simultaneously with existing information systems, in addition to remove these restrictions, have important benefits including adding additional non-laboratory-generated information to the reports, facilitating decision making, and improving quality and productivity of cancer care services. Laboratory systems must have flexibility to change and have the capability to develop and benefit from intelligent devices. Intelligent laboratory information management systems need to benefit from informatics tools and latest technologies like open sources. The aim of this commentary is to survey application, opportunities and necessity of intelligent clinical laboratory as a tool to increase cancer care management productivity.

프레스공정시스템에서 유도전동기 및 윤활유 레벨 상태모니터링을 위한 진단시스템 개발 (Diagnostic system development for state monitoring of induction motor and oil level in press process system)

  • 이인수
    • 한국지능시스템학회논문지
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    • 제19권5호
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    • pp.706-712
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    • 2009
  • 본 논문에서는 프레스공정라인에서 발생하는 고장을 감지하고 분류하기 위한 고장진단기법을 제안한다. 또한 윤활유 레벨을 자동감지 하기 위한 방법도 제안하다. 제안한 방법에서는 FFT 주파수해석과 여러 경계인수를 갖는 ART2 신경회로망을 사용하며, LabVIEW를 이용하여 고장진단 및 윤활유 레벨 자동감시를 위한 GUI(Graphical User Interface) 프로그램을 제작하여 고장진단을 수행하였다. 실험결과들로부터 제안한 유도전동기 고장진단 및 윤활유 레벨 자동감시시스템의 성능을 확인하였다.

유효 주파수 선택과 선형판별분석기법을 이용한 유도전동기 고장진단 시스템 (Induction Motor Diagnosis System by Effective Frequency Selection and Linear Discriminant Analysis)

  • 이대종;조재훈;윤종환;전명근
    • 한국지능시스템학회논문지
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    • 제20권3호
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    • pp.380-387
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    • 2010
  • 본 논문에서는 3상 유도전동기의 고장진단을 수행하기 위해 상호정보량과 선형판별분석기법에 기반을 둔 진단 알고리즘을 제안한다. 실험 장치는 유도전동기 구동의 기계적 모듈과 고장신호를 구하기 위한 데이터 획득 모듈로 구성하였다. 제안된 방법은 취득된 전류신호를 DFT에 의해 주파수 영역으로 변환한 후 분산정보를 이용하여 고장상태별로 차별성이 큰 순서대로 유효 주파수 성분을 추출한다. 다음 단계로 선택된 주파수 성분에 대해서 선형판별분석기법을 적용하여 고장상태별 특징들을 추출한 후 k-NN 분류기에 의해 유도전동기의 상태를 진단하게 된다. 제안된 방법의 타당성을 보이기 위해 다양한 조건하에서 실험한 결과 기존방법에 비하여 우수한 결과를 나타냈다.

차량 전원 안정성 향상을 위한 Diagnosis System 채택 Intelligent Smart Junction Box 개발 (Development of Diagnosis System Adopted Intelligent Smart Junction Box for Improving Vehicular Power Safety)

  • 정민수;김문겸;박영환;방순영
    • 전기학회논문지
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    • 제57권2호
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    • pp.276-285
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    • 2008
  • These days the automobile industry, which has rapidly progressed, has been an indispensable part in social and economic activities as well as its research and development have been activated in response to various needs of consumers and markets. The second and third generation control system, getting count on safety and convenience differently than early circuits, cause the hypertrophy of wire harness. The J/Box(Junction Box), which distributes power and wires, was developed to solve the problem. As vehicles have been better in quantity and intelligence, however, environment-friendly electric apparatus system has continuously increased and ITS(Intelligent Transport System) has been introduced in earnest. In result, wires got complicated and multilateral and also there has been a stronger probability that vehicles are out of order due to various problems including mechanical failure. In this study, ISJB(Intelligent Smart Junction Box) was introduced to solve the problem. The diagnosis system was applied to prevent the overload and short of ISJE. Also, the state of vehicles displayed so that drivers monitor it in motion. Likewise error data are saved in the memory so that such data can be analyzed retrospectively. The busbar was adopted in to the main power terminal and the part of power pattern was coverd by lead. Because ISJB is more sensitive to heat in comparison to the busbar type J/Box. With regard the circuits related with safe, alternative circuits were set up in order that electronic devices may be normally operated even when an error arises. ISJB is expected to improve the safety and quality of vehicles.

온톨로지에 기반한 간호진단 지식모델의 설계 (Design of Knowledge Model of Nursing Diagnosis based on Ontology)

  • 이인근;김화선;이성희
    • 한국지능시스템학회논문지
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    • 제22권4호
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    • pp.468-475
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    • 2012
  • 간호사는 NANDA, NIC, NOC과 같은 간호과정의 표준 가이드라인에 따라 간호 실무를 수행하고, 간호과정에 대한 정보를 전자의무기록 시스템에 기록하고 있다. 특히, NANDA는 간호진단 분류체계로써 간호진단의 추상적인 개념을 나타내고 있어, 상세한 간호진단 내용의 표현에 어려움이 있다. 그로 인해, 국내 병원에서는 자체적으로 간호진단 목록을 정의하여 사용하고 있으나, 이들은 표준이 적용되지 않아 간호기록의 전산화가 어려운 문제점이 있다. 따라서 본 논문에서는 NANDA와 SNOMED-CT와 같은 표준 용어체계를 참조하여 간호진단 개념을 표현하기 위한 온톨로지로 구축 방법론을 제시한다. 제안한 방법은 각 병원 및 분야에서 주로 사용하는 간호진단 목록을 체계적으로 구축함으로써 의료정보 시스템 간의 상호운용이 가능하고 지식의 확장이 용이하도록 한다. 제안한 방법에 따라 경북대학교병원의 여성건강 간호기록 진술문을 분석하고, 간호진단 정보의 추출 및 정련을 통해 112개의 간호진단 용어를 생성하였다. 그리고 이 용어를 이용하여 여성건강 간호진단 온톨로지를 구축하였고, 전문가 평가 및 실험을 통해 개발한 온토롤지의 타당도와 실용성을 확인하였다.

Intelligent Diagnosis of Broken Bars in Induction Motors Based on New Features in Vibration Spectrum

  • Sadoughi, Alireza;Ebrahimi, Mohammad;Moallem, Mehdi;Sadri, Saeid
    • Journal of Power Electronics
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    • 제8권3호
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    • pp.228-238
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    • 2008
  • Many induction motor broken bar diagnosis methods are based on evaluating special components in machine signals spectrums. Current, power, flux, etc are among these signals. Frequencies related to a broken rotor fault are slip dependent, therefore, correct diagnosis of fault - especially when obtrusive frequency components are present - depends on accurate determination of motor velocity and slip. The traditional methods typically require several sensors that should be pre-installed in some cases. This paper presents a diagnosis method based on only a vibration sensor. Motor velocity oscillation due to a broken rotor causes frequency components at twice slip frequency difference around speed frequency in vibration spectrum. Speed frequency and its harmonics as well as twice supply frequency, can easily and accurately be found in a vibration spectrum, therefore th motor slip can be computed. Now components related to rotor fault can be found. It is shown that a trained neural network - as a substitute for an expert person - can easily categorize the existence and the severity of a fault according to the features extracted from the presented method. This method requires no information about th motor internal and has been able to diagnose correctly in all the laboratory tests.

ELM 기반의 지능형 알고리즘과 퍼지 소속함수를 이용한 유입변압기 고장진단 기법 (Diagnosis Method for Power Transformer using Intelligent Algorithm based on ELM and Fuzzy Membership Function)

  • 임재윤;이대종;지평식
    • 전기학회논문지P
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    • 제66권4호
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    • pp.194-199
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    • 2017
  • Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

전력기기 열화 진단을 위한 부분방전 모의 및 측정 알고리즘 개발연구 (Investigation of Simulation and Measuring Algorithm of Partial Discharge for Diagnosis of Electric Machinery Deterioration)

  • 장형택;곽선근;신판석;김창업;정교범
    • 조명전기설비학회논문지
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    • 제25권8호
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    • pp.30-38
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    • 2011
  • This paper proposes a new intelligent diagnosis equipment for the partial discharge, which keeps deteriorating the insulating materials inside electric machineries, ultimately leading to electrical breakdown. In order to simulate experimentally the partial discharge inside the electric machinery, the tip-to-plate, the sphere-to-plate, the sphere-to-sphere and the plate-to-plate electrodes are used respectively, of which the gaps are 1[mm], 3[mm] or 5[mm] and the applied voltages are 3[kV], 5[kV] or 7[kV]. Ceramic coupler sensor and FIR digital filter are used to measure the partial discharge and the artificial neural network is used for the deterioration diagnosis of the electric machinery. The microprocessor of PD diagnosis equipment is DSP (TMS320C6713) with FPGA (Cyclone II). The results of the real-time and on-line experiments performed with the developed equipment are also explained.

Design and Implementation of Intelligent Medical Service System Based on Classification Algorithm

  • Yu, Linjun;Kang, Yun-Jeong;Choi, Dong-Oun
    • International Journal of Internet, Broadcasting and Communication
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    • 제13권3호
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    • pp.92-103
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    • 2021
  • With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

Extraction of Canine Cataract Object for Developing Handy Pre-diagnostic Tool with Fuzzy Stretching and ART2 Learning

  • Kim, Kwang Baek
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제16권1호
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    • pp.21-26
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    • 2016
  • Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. The first observation must be made by pet owners but they do not have proper equipment and knowledge to see the abnormalities. In this paper, we propose an intelligent image processing method to extract canine cataract suspicious object from non-professional equipment such as ordinary digital camera and cellular phone photographs so that even casual owners of pet dog can make a pre-diagnosis of such a surgery-needed disease as soon as possible. The experiment shows that the proposed method is successful in most cases except the dog has similar colored hair to the color of cataract.