• Nuclear Engineering and Technology
• /
• v.30 no.5
• /
• pp.403-415
• /
• 1998

The CANDU 6 reactor assembly consists of an array of 380 pressure tubes, which are installed horizontally in a large cylindrical vessel, the Calandria, containing the low pressure heavy water moderator. The pressure tube is located inside the calandria tube and the annulus between these tubes, which forms a closed loop with $CO_2$ gas recirculating, is called the Annulus Gas System(AGS). It is designed to give an alarm to the operator even for a small pressure tube leak by a very sensitive dew point meter so that he can take a preventive action for the pressure tube rupture incident. To judge whether the operator action time is enough or not in the design of Wolsong 2,3 & 4, the Leak Before Break(LBB) assessment is required for the analysis of the pressure tube failure accident. In order to provide the required data for the LBB assessment of Wolsong Units 2, 3, 4, a series of leak detection capability tests was performed by injecting controlled rates of heavy water vapour. The data of increased dew point and rates of rise were measured to determine the alarm set point for the dew point rate of rise of Wolsong Unit 2. It was found that the response of the dew point depends on the moisture injection rate, $CO_2$ gas flow rate and the leak location. The test showed that CANDU 6 AGS can detect the very small leaks less than few g/hr and dew point rate of rise alarm can be the most reliable alarm signal to warn the operator. Considering the present results, the first response time of dew point to the AGS $CO_2$ flow rate is approximated.

• Journal of Korea Foundry Society
• /
• v.42 no.6
• /
• pp.369-376
• /
• 2022

The die-casting process is an important process for various industries, but there are limitations in the profitability and productivity of related companies due to the high defect rate. In order to overcome this, this study has developed die-casting fault detection modules based on industrial AI technologies. The developed module is constructed from three-stage models depending on the characteristics of the dataset. The first-stage model conducts fault detection based on supervised learning from the dataset without labels. The second-stage model realizes one-class classification based on semi-supervised learning, where the dataset only has production success labels. The third-stage model corresponds to fault detection based on supervised learning, where the dataset includes a small amount of production failure cases. The developed fault detection module exhibited outstanding performance with roughly 96% accuracy for actual process data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.3
• /
• pp.1113-1135
• /
• 2018

Existing trust evaluation models for wireless sensor networks can accurately and objectively evaluate trust value of nodes, but the nodes' energy saving problem was ignored. Especially when there are a few malicious nodes in a network, the overall trust value calculation for all nodes would waste lots of energy. Beside that, the network failure caused by nodes death was also not considered. In this paper, we proposed a method for avoiding energy hole which applied trust evaluation models and a trust evaluation method based on information entropy, so as to achieve the purpose of improving nodes utilization. Simulation results show that the proposed method can effectively improve nodes utilization, and it has reasonable detection rate and lower false alert rate compared to other classical methods.

• Journal of Korean Society for Quality Management
• /
• v.37 no.4
• /
• pp.16-22
• /
• 2009

The inflection S-shaped software reliability growth model (SRGM) proposed by Ohba(1984) is one of the most commonly used models and has been discussed by many authors. The main purpose of this paper is to estimate the parameters of Ohba's SRGM within the Bayesian framework by applying the Markov chain Monte Carlo techniques. While the maximum likelihood estimates for these parameters are well known, the Bayesian method for the inflection S-shaped SRGM have not been discussed in the literature. The proposed methods can be quite flexible depending on the choice of prior distributions for the parameters of interests. We also compare the Bayesian methods with the maximum likelihood method numerically based on the real data.

• Clinical and Experimental Reproductive Medicine
• /
• v.31 no.3
• /
• pp.149-154
• /
• 2004

Objective: To assess the association with autoimmune endocrine diseases and detection rate of autoimmune antibodies and its clinical significance in patients with premature ovarian failure. Methods: Twenty eight patients with primary or secondary amenorrhea manifesting hormonal and clinical features of premature ovarian failure (primary POF: 7, secondary POF: 21) were investigated. We tested them TFT, 75 g OGTT, ACTH and S-cortisol for thyroiditis, IDDM, Addison's disease, and antithyoglobulin antibody, antimicrosomal antibody, antinuclear antibody, rheumatic factor, anti-smooth muscle antibody, anti-acetylcholine receptor antibody for non-organ specific autoimmune disorders. Results: Only one patient was diagnosed as IDDM and no patients had abnormal TFT or adrenal function test. More than one kind of autoantibody was detected in 11 patients of all (39.2%): 5 patients (71.4%) of primary POF group and 6 patients (21.4%) of secondary POF group. Eleven patients (39.3%) had antithyroglobulin antibody, 4 (14.3%) had antimicrosomal antibody, 2 (7.1%) had antinuclear antibody, 2 (7.1%) had rheumatic factor, 1 (3.6%) had anti-smooth muscle antibody, 1 (3.6%) had anti-acetylcholine receptor antibody. Conclusions: Premature ovarian failure may occur as a component of an autoimmune polyglandular syndrome, so patients should be measured with free thyroxine, thyroid-stimulating hormone, fasting glucose and electrolytes. Measurement of thyroid autoantibodies in POF patients may be important in identifying patients at risk of developing overt hypothyoidism, but other autoantibodies may not be suitable for screening test.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.7
• /
• pp.1088-1097
• /
• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.1
• /
• pp.30-38
• /
• 2017

In this paper, we propose an underwater acoustic sensor fault detection method for passive sonar systems. In general, a passive sonar system displays processed results of array signals obtained from tens of the acoustic sensors as a two-dimensional image such as displays for broadband or narrowband analysis. Since detection result display in the operation software is to display the accumulated result through the array signal processing, it is difficult to determine the possibility where signal may be contaminated by the fault or failure of a single channel sensor. In this paper, accordingly, we propose a detection method based on the analysis of RMSCR (Root Mean Square Crossing-Rate), and the processing techniques for the faulty sensors are analyzed. In order to evaluate the performance of the proposed method, the precision of detecting fault sensors is measured by using signals acquired from real array being operated in several coastal areas. Besides, we compare performance of fault processing techniques. From the experiments, it is shown that the proposed method works well in underwater environments with high average RMS, and mute (set to zero) shows the best performance with regard to fault processing techniques.

• Current Optics and Photonics
• /
• v.1 no.3
• /
• pp.196-202
• /
• 2017

Corona discharge is always a sign of failure processes of high-voltage electrical apparatus, including those utilized in electric railway systems. Solar-blind ultraviolet (UV) cameras are effective tools for corona inspection. In this work, we present an automatic railway corona-discharge detection system based on solar-blind ultraviolet detection. The UV camera, mounted on top of a train, inspects the electrical apparatus, including transmission lines and insulators, along the railway during fast cruising of the train. An algorithm based on the Hough transform is proposed for distinguishing the emitting objects (corona discharge) from the noise. The detection system can report the suspected corona discharge in real time during fast cruises. An experiment was carried out during a routine inspection of railway apparatus in Xinjiang Province, China. Several corona-discharge points were found along the railway. The false-alarm rate was controlled to less than one time per hour during this inspection.

• Fire Science and Engineering
• /
• v.28 no.6
• /
• pp.69-75
• /
• 2014

Recently Amore pacific rack-type warehouse fire broke out and argue an urgent improvement of fire protection design code including automatic sprinkler and detection design. Various type of commodities have their unique fire characteristics from fire spread rate and heat lease rate and fire hazard depends on storage height, rack arrangement, aisle width, fire load etc. With increasing ceiling height for more storage space prevent effective water spray of sprinkler head, also delays detection time causes failure of early suppression. To achieve fire protection code performance of this occupancy, Major code articles relating to a classification of commodity, sprinkler system installation, detection and fire fighting are reviewed and suggested based on fire case analysis, code review between country and field survey.

• Journal of Advanced Navigation Technology
• /
• v.24 no.4
• /
• pp.314-321
• /
• 2020

Logic automated stimulus and response (LASAR) software is an automatic test program development tool for logic function test and fault detection of avionics components digital circuit cards. LASAR software needs to the information for the logic circuit function and input and output of the device. If there is no component information, normal component modeling is impossible. In this paper, component modeling is carried out through reverse design of programmable logic device (PLD) device without element information. The developed LASAR program identified failure detection rates through fault simulation results and single-seated fault insertion methods. Fault detection rates have risen by 3% to 91% for existing limited modeling and 94% for modeling through the reverse design. Also, the 22 case of stuck fault with the I/O pin of EP310 PLD were detected 100% to confirm the good performance.