• Journal of Information Processing Systems
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• v.17 no.2
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.73-77
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• 2014

In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

• Journal of the Korean Institute of Gas
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• v.8 no.2 s.23
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• pp.28-34
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• 2004

Enhanced methodologies for process diagnosis and abnormal situation management have been developed for the last two decades. However, there is no single method that always shows better performance over all kinds of diagnostic problems. In this paper, a framework of message-passing, cooperative, intelligent diagnostic agents is presented for improved on-line fault diagnosis through cooperative problem solving of different expertise. A group of diagnostic agents in charge of different process functional perform local diagnoses in parallel; exchange related information with other diagnostic agents; and cooperatively solve the global diagnostic problem of the whole process plant or business units just like human experts would do. For their better understanding, sharing and exchanging of process knowledge and information, we also suggest a way of remodeling processes and protocols, taking into account semantic abstracts of process information and data. The benefits of the suggested multi-agents-based approach are demonstrated by the implementations for solving the diagnostic problems of various chemical processes.

• The Journal of Korean Academic Society of Nursing Education
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• v.5 no.1
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• pp.58-71
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• 1999

Nursing process is an essential part for nursing practice. Nursing faculty members must focus on the clinical application for students and try to identify the possible problems that students might face in the fields. The purpose of this study is to examine the actual condition of nursing process education in curricula and to investigate the response of students in clinical experience of nursing process. From 462 students in the 6 associate programs(ADN) and the 6 baccalaureate programs (BSN) data was collected by questionnaire. The results were as followed. 1. Seven programs (58.3%) opened the nursing process in mainly sophomore (BSN) or freshman(ADN). If not opened, the nursing process was taught at the major subjects(espcially fundamental nursing or adult nursing). 2. All Students responded they we supposed to use nursing process in preparing the case report. The majority(94.6%) used NANDA lists for nursing diagnosis and 55.7% of subjects consulted the Korean terms by KNA when translating. The tutors for nursing process in clinical settings were the professor in charge of the subject (68.6) or clinical instructors (48.1%) , assistants(34%). 3. The problems in clinical application that students experienced consisted of 17 items and the mean was 2.27. The biggest problem was 'the lack of the model for RN of applying the nursing process in clinical settings'(2.97). Next the big problem was 'the lack of the competency for implementing the established nursing plans'(2.69). All items were significantly different according to the level of educational programs(ADN or BSN). ADN students had more problems in applying the each step of nursing process and BSN students perceived the NANDA as a guidance of nursing diagnosis and the inconsistency of advices from several instructors or practicum to be mere problematic. 4. The mean of merits after application of nursing process was relatively fair (2.82). The best merit was 'they can identify nursing problems more exactly'(3.07). The second high merit was 'they can study the rational of nursing action' (3.03). BSN than ADN and the subjects of second year than of one year in clinical experience perceived the use of nursing process to be better. Based on this results we need to enforce the application of nursing diagnosis in the class. The use of sample cases can be the efficient method. Students can identify the possible health problems for patient from the cases in imaginary world and discuss them each other. Also we can use the discussion session after practice every other day or as needed. All this is on the good interaction between tutor and student. We must consider to have enough time for student to seize the essence of the nursing process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1604-1612
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• 2002

Rollers in the continuous process systems are ones of key components that determine the quality of web products. The condition of rollers (e.g. eccentricity, runout) should be consistently monitored in order to maintain the process conditions (e.g. tension, edge position) within a required specification. In this paper, a new diagnosis algorithm is suggested to detect the defective rollers based on the frequency analysis of web tension signals. The kernel of this technique is to use the characteristic features (RMS, Peak value, Power spectral density) of tension signals which allow the identification of the faulty rollers and the diagnosis of the degree of fault in the rollers. The characteristic features could be used to train an artificial neural network which could classify roller conditions into three groups (normal, warning, and faulty conditions) The simulation and experimental results showed that the suggested diagnosis algorithm can be successfully used to identify the defective rollers as well as to diagnose the degree of the defect of those rollers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.74-77
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• 2001

One of the most important technology in PA(Factory Automation) is to construct the diagnostic system for manufacturing process. To improve the productibility in the factory, the state of tools such as bite, drill, endmill should be monitored continuously. In this study, fuzzy logic was used to check the wear of drill in drilling process. The input variables to construct the fuzzy rules are cutting force and the rate of cutting force's change. The experiment was done with the fixed spindle speed and feed rate in cutting condition.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.101-108
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• 2004

It is very important to diagnose state of knife and strip cutting face in real time in the side trimming process due to improvement of quality of trimming face. In this study, diagnosis system for knife and strip cutting face was developed. CCD camera was installed at the both side of strip in the entry of burr masher roll. This system offer to operator the diagnosis result that was analyzed image obtained from CCD camera. As a result, bad quality of trimming face is decreased below 1/10 and knife breakage is detected $100\%$.

• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.82-87
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• 2014

The procedures of safety diagnosis assessment have been applied by synthetical consideration of structural safety, functionality, and economic efficiency in determining reconstruction or remodeling, but they have limitations in some special cases such as the non-association apartment. In this regard, this study was conducted to establish more desirable criteria and procedures in determining reconstruction or new construction and suggests a way to improve policy and laws of reconstruction correctly by reviewing the safety diagnosis in reconstruction process. Besides, the safety diagnosis of reconstruction for non-association apartments in government-owned public district was carried out preemptively to remove reckless procedures. As a result, the reconstruction of non-association apartments should be effectively without interference in the administrative procedures. This requires that the policy and laws of reconstruction safety diagnosis should be revised in priority in order that similar projects in future can be effectively executed.

• Journal of IKEEE
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• v.25 no.3
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• pp.548-555
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• 2021

This paper outlines the basic concepts, approaches and research trends of fault diagnosis and fault tolerant control applied to industrial processes, facilities, and motor drives. The main role of fault diagnosis for industrial processes is to create effective indicators to determine the defect status of the process and then take appropriate measures against failures or hazadous accidents. The technologies of fault detection and diagnosis have been developed to determine whether a process has a trend or pattern, or whether a particular process variable is functioning normally. Firstly, data-driven based and model-based techniques were described. Secondly, fault detection and diagnosis techniques for industrial processes are described. Thirdly, passive and active fault tolerant control techniques are considered. Finally, major faults occurring in AC motor drives were listed, described their characteristics and fault diagnosis and fault tolerant control techniques are outlined for this purpose.