• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.29-35
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• 2021

While the population of pet dogs and pet-related markets are increasing, there is no convenient and reliable tool for pet health monitoring for pet owners/caregivers. In this paper, we propose a mobile platform-based pre-diagnosis system that pet owners can use for pre-diagnosis and obtaining information on coping strategies based on their observations of the pet dog's abnormal behavior. The proposed system constructs symptom-disease association databases for 100 frequently observed diseases under veterinarian guidance. Then, we apply the possibilistic fuzzy C-means algorithm to form the "probable disease" set and the "doubtable disease" set from the database. In the experiment, we found that the proposed system found almost all diseases correctly, with an average of 4.5 input symptoms and outputs 1.5 probable and one doubtable disease on average. The utility of this system is to alert the owner's attention to the pet dog's abnormal behavior and obtain an appropriate coping strategy before consult a veterinarian.

• Clinical and Experimental Reproductive Medicine
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• v.24 no.3
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• pp.393-398
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• 1997

Cytogenetic analysis was performed in 1321 couples and 141 women with history of abnormal reproductive outcome during 1988-1996. The use of high resolution banding technique and fluorescence in situ hybridization (FISH) in the chromosome analysis has made the precise evaluation of chromosome aberrations. The prevalence of balanced chromosomal translocation carriers were 3.74% (104/2783 patients). 70 cases (2.52%) were reciprocal translocation carriers and 34 (1.22%) had Robertsonian translocations. Chromosome aberrations were more frequent in women (73 cases) than in men (31 cases). No phenotypical abnormalities were found in all carriers, but they experienced abnormal reproductive outcomes such as recurrent spontaneous abortions, anomalous offsprings or infertility problem. Prenatal diagnosis was carried out on 36 subsequent pregnancies in balanced translocation carriers. The fetal karyotypes showed that 12 cases (33%) were normal, 22 (61%) were balanced translocations, and two (6%) were unbalanced translocations. It is concluded that the prevalence of balanced chromosomal translocations in patients with abnormal reproductive outcome is higher than that of the normal population. Most of the fetal samples showed normal karyotypes or balanced translocations. Although the incidence of chromosomal imbalance in the fetuses was relatively low in prenatal diagnosis, individuals with balanced translocations are predisposed to abnormal offspring with partial trisomy or monosomy. Therefore we recommend that genetic counselling and cytogenetic prenatal diagnosis for translocation carriers have to be offered to prevent recurrent chromosomal abnormal babies.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.3
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• pp.11-16
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• 2004

In this study, the characteristics of signals such as acoustic emission, vibration amplitude and noise level which are derived from the abnormal condition of compressor are investigated. The normal condition, vane stick sound and roller defect condition are chosen to analyze the signal in each cases. From the feature extraction of each signals, the dominant parameters of each signals which can identify the abnormal condition are suggested.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.3-9
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• 2003

The task for chromosome analysis and diagnosis by experienced cytogenetists are being concerned as repetitive, time consuming job and expensive. For that reason, intelligent agent based on chromosome knowledge base has been established to be able to analyze chromosomes and obtain necessary advises from the knowledge base instead of human experts. That is to say, knowledge base by IF THEN production rule was implemented to a knowledge domain with normal and abnormal chromosomes, and then the inference results by knowledge base could enter the inference data into the database. Experimental data were composed of normal chromosomes of 2,736 patients 'cases and abnormal chromosomes of 259 patients' cases that have been obtained from GTG-banding metaphase peripheral blood and amniotic fluid samples. The completed intelligent agent for chromosome knowledge base provides variously morphological information by analysis of normal or abnormal chromosomes and it also has the advantage of being able to consult with user on chromosome analysis and diagnosis.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.215-222
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• 2001

The task for chromosome analysis and diagnosis by experienced cytogenetists are being concerned as repetitive, time consuming job and expensive. FOr that reason, chromosome analysis system based on knowledge base for CAI had been established to be able to analyze chromosomes and obtain necessary advises from the knowledge base instead of human experts. That s to say, knowledge base by IF THEN production rule was implemented to a knowledge domain with normal and abnormal chromosomes, and then the inference results by knowledge base could enter the inference data into the database. Experimental data were composed of normal chromosome of 2,736 patients'cases and abnormal chromosomes of 259 patients'cases that have been obtained from GTG-banding metaphase peripheral blood and amniotic fluid samples. The complete system provides variously morphological information by analysis of normal or abnormal chromosomes and it also has the advantage of being able to consult with user on chromosome analysis and diagnosis.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1329-1332
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• 1987

This paper describes a design of the expert system for diagnosis of abnormal gait patients. This system makes the rule-based representation that can easily extend the knowledge-base and naturally represent the uncertainty, and the inference engine that uses forward chaining which covers the reasoning from the first condition to the goal. The results of inferring various maladies using this system are as follows: 1) In cases of progressive muscular dystrophy, cerebral vascular accident, peripheral neuropathic lesion and peroneal nerve injury, the result of inference is the same as that of medical specialists' with 100% accuracy. 2) In cases of Neuritis, Paralysis agitan and Brain tumor, the accuracy of inference is less than 50% compared to that of medical specialists. With above results, we decide that the rule-based representations of some maladies ard accurate relatively, but that the correction and the extention of some rules and some methods of problem solving are required in order to construct the complete expert system for diagnosis of abnormal gait patients.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.14-17
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• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.47-54
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• 2009

Spin coaters are the essential instruments in micro-fabrication processes, which apply uniform thin films to flat substrates. In this research, a spin coater diagnosis system is developed to detect the abnormal operation of TFT-LCD process in real time. To facilitate the real-time data acquisition and analysis, the circular-buffered continuous data transfer and the short-time Fourier transform are applied to the fault diagnosis system. To determine whether the system condition is normal or not, a steady-state detection algorithm and a frequency spectrum comparison algorithm using confidence interval are newly devised. Since abnormal condition of a spin coater is rarely encountered, algorithm is tested on a CD-ROM drive and the developed program is verified by a function generator. Actual threshold values for the fault detection are tuned in a spin coater in process.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.