• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.6
• /
• pp.2895-2899
• /
• 2016

Background: Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005-2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. Materials and Methods: BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. Results: BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per $10^5$ women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019-20. Conclusions: BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.50 no.8
• /
• pp.541-550
• /
• 2022

An effective real time fault diagnosis approach for UAV engine is drawn from IMM filter and GLRT methods. For this purpose based on the linear diagnosis model derived from engine dynamic performance analysis the Kalman filter for residual estimation and each method are applied to the fault diagosis of the actuator for engine control sensors. From the process of the IMM filter application the effective FDI measure is obtained and the state responses due to actuator fault are estimated. Likewise from the GLRT method the fault magnitudes of actuator and sensors are estimated associated with some FDI functionings. The numerical simulations verify the effectiveness of the IMM filter for FDI and the GLRT in estimating the fault magnitudes of each fault mode.

• Journal of The Korean Association For Science Education
• /
• v.29 no.8
• /
• pp.812-823
• /
• 2009

The purpose of this study is to search effective assessments methods by using the Fusion model of Cognitive diagnosis theory. Attributes are skills or cognitive processes that are required to perform correctly on a particular item. After test items were developed, item's attributes were decided and Q-matrix about item's attributes was made. After testing, the result was analyzed according to gender and achievement level. The results of the analysis showed that students mastered 'Interpreting data' best, and 'synthesizing' worst among the five attributes. Female students showed higher ability than male students in 'recalling.' Students of high achievement level mastered more scientific attributes than students of low achievement level. Conventional assessments only provided a single summary score but Cognitive diagnosis modeling provided useful information by estimating individual knowledge states by assessing whether an examinee has mastered specific attributes measured by the science test. The skill profiles can offer a skill level of strong, weak, or mixed for each student for each skill. Therefore, the skill profiles will provide useful diagnostic information in addition to single overall scores.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.31-36
• /
• 2001

We design the intelligent diagnosis system for deciding on operation state of TMS Analyzer in this paper. We propose the method to model the neno-fuzzy model for diagnosing the operation state of analyzer by using input and output signals of TMS to measure NOx and SOx. By using experiment data, neuro-fuzzy model is investigated. Validity of the proposed system is asserted by numerical simulation.

• Journal of Power Electronics
• /
• v.18 no.5
• /
• pp.1608-1617
• /
• 2018

The rapid and correct isolation of faulty submodules (SMs) is of great importance for improving the reliability of modular multilevel converters (MMCs). Therefore, a fast diagnosis method containing fault detection and fault location determination was presented in this paper. An improved incremental predictive model of arm current was proposed to detect failures, and the multi-step prediction method was used to eliminate the negative impact of disturbances. Moreover, a control method was proposed to strengthen the fault characteristics to rapidly locate faulty arms and faulty SMs by detecting the variation rate of the SM capacitor voltage. The proposed method can rapidly and easily locate faulty SMs under different load conditions without the need for additional sensors. The experimental results have validated the effectiveness of the proposed method by using a single-phase MMC with four SMs per arm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.9 no.2
• /
• pp.70-76
• /
• 1984

In this paper, a functional m-redundant system, which is m-fault tolerant, is defined based on the graph-theory. This system is designed to be t(t$\geq$m) fault-diagnosable by comparing its unit's outcomes without additive test functions, so, the system down for diagnosis is not needed. The diagnostic model for this system is presented. It is to avail the redundancy of the system effectively. It is shown that this model can be converted into Preparata's model. Thus, the diagnostic characteristics of a functional m-redundant system is analyzed by the method originated by Preparata et al.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.493-495
• /
• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2001.10a
• /
• pp.386-392
• /
• 2001

We design the intelligent diagnosis system for deciding on operation state of TMS Analyzer in this paper. We propose the method to model the neuro-fuzzy model for diagnosing theoperation state of analyzer by using input and output signals of TMS to measure Nox and SOx. By using experiment data, neuro-fuzzy model is investigated. Validity of the proposed system is asserted by numerical simulation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.11
• /
• pp.928-934
• /
• 2002

A natural way to cope with fault tolerant control (FTC) problems is to modify the control parameters according to an online identification of the system parameters when a fault occurs. However. due to not only difficulties Inherent to the online multivariable identification in closed-loop systems, such as modeling errors, noise or the lack of excitation signals, but also long time requirement to identify the post-fault system and implemeutation of control problems during the identification process, we propose an alternative approach based on the observer-based fault detection and isolation (FDI) and model reference adaptive control (MRAC). The proposed robust fault diagnosis method is based on a bank of observers. We also propose a model reference adaptive control with changeable reference models according to the occurred faults. Simulation results of a flight control example show the validity and applicability of the proposed algorithms.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.489-492
• /
• 2008

Nowadays in order to estimate safety diagnosis of bridge, a lot of data like static and dynamic displacement, accelerometer, wind velocity and so on are demanded. When it comes to measure these data, cabled sensor is essential equipment. But cabled sensors have also inefficient factors. From this point of view, considering practical aspects of using these expensive equipments which have been used to examine safety diagnosis, measuring by cabled sensors is restrictive in some respect. Recently to improve theses problems, Wireless sensor system was introduced. But this system can't perform intelligent reaction because database of this system is just based on internet. In this paper, the intelligent bridge safety management model which can be installed easily, measured at all times and dealing intelligently with various situations is developed to improve these problems.