• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.139-147
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• 2019

A false alarm, which is an incorrect report of an emergency, could trigger an unnecessary action. The predictive maintenance framework developed in our previous work has a feature whereby a machine alarm is triggered based on sensor data evaluation. The sensor data evaluator performs three essential evaluation steps. First, it evaluates each sensor data value based on its threshold (lower and upper bound) and labels the data value as "alarm" when the threshold is exceeded. Second, it calculates the duration of the occurrence of the alarm. Finally, in the third step, a domain expert is required to assess the results from the previous two steps and to determine, thereby, whether the alarm is true or false. There are drawbacks of the current evaluation method. It suffers from a high false-alarm ratio, and moreover, given the vast amount of sensor data to be assessed by the domain expert, the process of evaluation is prolonged and inefficient. In this paper, we propose a method for automatic false-alarm labeling that mimics how the domain expert determines false alarms. The domain expert determines false alarms by evaluating two critical factors, specifically the duration of alarm occurrence and identification of anomalies before or while the alarm occurs. In our proposed method, Hierarchical Temporal Memory (HTM) is utilized to detect anomalies. It is an unsupervised approach that is suitable to our main data characteristic, which is the lack of an example of the normal form of sensor data. The result shows that the technique is effective for automatic labeling of false alarms in sensor data.

• The Korean Nurse
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• v.30 no.3
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• pp.49-65
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• 1991

This study was carried out to assess the problems with the pregnant women, and check out the risk-factors in the high-risk pregnancies, using a simplified antepartum risk-scoring system, which was revised from Edwards' scoring system to be suitable for Korean situaition. This instrument was included 4 categories, demographic, obstetric, medical and miscellaneous factors. This survey was based on the 1300 pregnant women who were admitted, $x^2$-test, F-test, Pearsons correation, using statistical package SAS in NAS computer system, KIST. The results of the study were as follows; 1. 1313 infants were deliveried of these 560 infants(42.7%) were born to mothers with risk-scores > 7, and 753 infants(57.3%) were born to mothers risk-scores <7. 2. Maternal age" parity, education level, of the demographic factors were significant relation statistically to identify the high risk pregnancies($X^2$=20.88, 42.87, 15.60 P < 0.01). 3. C-section, post term, incompetent cervix, uterine anomaly, polyhydramnios, congenital anomaly, sensitized RH negative, abortion, preeclampsia, excessive size infant, premature, low birth weight infanl, abnormal presentation, perinatal loss, multiple pregnancy, of the obstetric factors were significant relation statistically to identify the high risk-pregnancies. ($X^2$ = 175.96, 87.5, 16.28, 21.78, 9.46, 8. 10, 6.75, 22.9, 64.84, 6.93, 361.43, 185.55, 78.65, 45.52, P < 0.01). 4. Abnormal nutrition, anemia, UTI, other medicalcondition(pulmonary disease, severe influenza), heart disease, V.D., of the miscellaneous and medical factors, were significant relation statistically to identify the high risk-pregnancies. 5. Premature, low birth weight infant, contracted pelvis, abnormal presentation, of the risk factors were significantly related with Apgar score at 1 '||'&'||' 5 minute after birth and neonatal body weight. 6. Apgar score at 1 '||'&'||' 5 minute after, birth and neonatal body weight were significantly negative correlated with risk-score. 7. There were statistically significant difference between risk-score and Apgar score at 1 '||'&'||' 5 minute after birth, 3 group(0-3, 4-6, above 7), and neonatal body weight, 2 group(below 2.5kg, the other group) (F=104.65, 96.61, 284.92, P<0.01). 8. Apgar score at 1 '||'&'||' 5 minute after birth(below 7), and neonatal body weight(below 2.5kg), were significant relation statistically with risk score.($x^2$=65.99, 60.88, 177.07, P<0.01) were 60.8 %, 60% . 9. Correct classifications of morbid infants(l '||'&'||' 5 minute Apgar score < 7) were 77.8%, 83.8% and that of nonmorbid infants(l '||'&'||' 5 minute Apgar score > 7) were 60.8%, 60%. 10. There were statistically significant difference between dislribution of maternal risk-score among the morbid infants(l '||'&'||' 5 minute Apgar score < 7) and non morbid infants(l '||'&'||' 5 minute Apgar score> 7) ($x^2$=64.8, 58.8, P < 0.001). 11. There were statistically significant difference between distribution of morbid infants(l '||'&'||' 5 minute Apgar score < 7) and fetal death. 12. The predictivity for classifying high.risk cases was 12 % and for classifying low-risk cases was 98.3 % in 5 minute Apgar score. Suggestions for further studies are as follows; 1. Contineous prospective studies, using this newly revised scoring system are strongly recommended in the stetric service. 2. Besides risk facto~s used in this study, assessmenl of risks by factors in another scoring system and paralled studies related to perinatal outcome are strongly recommended.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.525-534
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• 2014

Environmental problems typically occurring in abandoned mine lands (AML) include: contaminated and acidic surface water and groundwater; stockpiled waste rock and mill tailings; and ground subsidences due to mining operations. This study examines the effectiveness of various geophysical techniques for mapping potential hazard and contaminated zones. Four AML sites with sedimentation contamination problems, acid mine drainage (AMD) channels, ground subsidence, manmade liner leakage, and buried mine tailings, were selected to examine the applicability of various geophysical methods to the identification of the different types of mine hazards. Geophysical results were correlated to borehole data (core samples, well logs, tomographic profiles, etc.) and water sample data (pH, electrical conductivity (EC), and heavy metal contents). Zones of low electrical resistivity (ER) corresponded to areas contaminated by heavy metals, especially contamination by Cu, Pb, and Zn. The main pathways of AMD leachate were successfully mapped using ER methods (low anomaly peaks), self-potential (SP) curves (negative peaks), and ground penetrating radar (GPR) at shallow penetration depths. Mine cavities were well located based on composite interpretations of ER, seismic tomography, and well-log records; mine cavity locations were also observed in drill core data and using borehole image processing systems (BIPS). Damaged zones in buried manmade liners (used to block descending leachate) were precisely detected by ER mapping, and buried rock waste and tailings piles were characterized by low-velocity zones in seismic refraction data and high-resistivity zones in the ER data.