• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.59-66
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• 2020

In this paper, we propose a pipe leak detection system through data collection using low-power wireless acoustic sensor modules and data analysis using deep auto-encoder. Based on the Fourier transform, we propose a low-power wireless acoustic sensor module that reduces data traffic by reducing the amount of acoustic sensor data to about 1/800, and we design the system that is robust to noise generated in the audible frequency band using only 20kHz~100kHz frequency signals. In addition, the proposed system is designed using a deep auto-encoder to accurately detect pipe leaks even with a reduced amount of data. Numerical experiments show that the proposed pipe leak detection system has a high accuracy of 99.94% and Type-II error of 0% even in the environment where high frequency band noise is mixed.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.525-534
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• 2023

Gas leak detection system is a key to minimize the loss of life due to the explosiveness and toxicity of gas. Most of the leak detection systems detect by gas sensors or thermal imaging cameras. To improve the performance of gas leak detection system using single-modal methods, the paper propose multimodal approach to gas sensor data and thermal camera data in developing a gas type identification model. MultimodalGasData, a multimodal open-dataset, is used to compare the performance of the four models developed through multimodal approach to gas sensors and thermal cameras with existing models. As a result, 1D CNN and GasNet models show the highest performance of 96.3% and 96.4%. The performance of the combined early fusion model of 1D CNN and GasNet reached 99.3%, 3.3% higher than the existing model. We hoped that further damage caused by gas leaks can be minimized through the gas leak detection system proposed in the study.

• Nuclear Engineering and Technology
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• v.30 no.5
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• pp.403-415
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• 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 the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.227-236
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• 2003

Leaks in underground pipelines can cause social, environmental and economical problems. One of relevant countermeasures against leaks is to find and repair of leak points of the pipes. Leak noise is a good source to identify the location of leak points of the pipelines. Although there have been several methods to detect the leak location with leak noise, such as listening rods, hydrophones or ground microphones, they have not been so efficient tools. In this paper, acoustic emission (AE) sensors and accelermeters are used to detect leak locations which could provide all easier and move efficient method. Filtering, signal processing and algorithm of raw input data from sensors for the detection of leak location are described. A 120m-long pipeline system for experiment is installed and the results with the system show that the algorithm with the AE sensors and accelerometers offers accurate pinpointing of leaks. Theoretical analysis of sound wave propagation speed of water in underground pipes, which is critically important in leak locating, is also described.

• Journal of Korean Neurosurgical Society
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• v.59 no.1
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• pp.52-57
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• 2016

Objective : Many surgeons advocate for watertight dural reconstruction after posterior fossa surgery given the significant risk of cerebrospinal fluid (CSF) leak. Little evidence exists for posterior fossa dural reconstruction utilizing monolayer collagen matrix onlay graft in a non-watertight fashion. Our objective was to report the results of using collagen matrix in a non-watertight fashion for posterior fossa dural reconstruction. Methods : We conducted a retrospective review of operations performed by the senior author from 2004-2011 identified collagen matrix (DuraGen) use in 84 posterior fossa operations. Wound complications such as CSF leak, infection, pseudomeningocele, and aseptic meningitis were noted. Fisher's exact test was performed to assess risk factor association with specific complications. Results : Incisional CSF leak rate was 8.3% and non-incisional CSF leak rate was 3.6%. Incidence of aseptic meningitis was 7.1% and all cases resolved with steroids alone. Incidence of palpable and symptomatic pseudomeningocele in follow-up was 10.7% and 3.6% respectively. Postoperative infection rate was 4.8%. Previous surgery was associated with pseudomeningocele development (p<0.05). Conclusion : When primary dural closure after posterior fossa surgery is undesirable or not feasible, non-watertight dural reconstruction with collagen matrix resulted in incisional CSF leak in 8.3%. Incidence of pseudomeningocele, aseptic meningitis, and wound infection were within acceptable range. Data from this study may be used to compare alternative methods of dural reconstruction in posterior fossa surgery.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.2
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• pp.73-86
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• 2016

The purpose of this study is to examine a security investment for firms experienced in confidential information leak. Information security is an apparatus for protection of secret information. The competence of information security is a competitiveness to avoid information leakage in changing business environment. The type of information security is divided into administrative security, technical security and physical security. It is necessary to improve the incident correspondence competence through information security investment of the three types. Therefore, the investment of information security is to enhance information-asset protection of firms. To reinforce accident response competence, an organization discussed an establishment, security technology development, expand investment and legal system of the security system. I have studied empirically targeting the only information leak of firms. This data is a technical security competence and technology leakage situation of firms happened in 2010. During recovery of the DDos virus damage on countries, company and individual, the collected data signify a reality of information security. The data also identify a security competence of firms worrying information security management. According to the study, the continuous investment of information security has a high competence of accident correspondence. In addition, the most of security accidents showed a copy and stealing of paper and computer files. Firm on appropriate security investment is an accident correspondence competence higher than no security investment regardless of a large, small and medium-sized, and venture firm. Furthermore, the rational security investment should choose the three security type consideration for firm size.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.7-13
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• 2019

The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.617-621
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• 2003

The purpose of this paper is to analyze piping failure trend of safety pipings In domestic nuclear power plants. First, database for the piping failure was constructed with 105 data fields. The database includes plant population data, event data, and service history data. 7 kinds of piping failures in domestic NPPs were investigated. Among the 7 cases, detailed root causes were investigated for 3 cases. The first one is pipe wall thinning in main feedwater pipings of Westinghouse 3 loop type plants. The root cause of the wall thinning was flow accelerated corrosion near welding area. The next one is leak event in chemical and volume control system(CVCS) due to vibration. Some cracks occurred in socket welding area. The events showed that the integrity or socket weld is very vulnerable to vibration. The last one is also a leak event in primary sampling line in Korean standard reactor due to thermal fatigue. Although the structural integrity was not maintained by the events, there was no effect on nuclear safety in the above 3 piping failure eases.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.1-6
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• 2018

Widely used around the world, smartphones contain many features and can store content such as contacts, photos, and videos. Information that can be leaked in proportion to the information that the smartphone can store has also been increased. In recent years, accidents such as personal information leakage have occurred frequently. Personal information leakage is happening in the Android environment, which accounts for more than half of the smartphone operating system market share. Analyzing malicious apps that leak information can tell you how to prevent information leakage. Malicious apps that leak information will send importantinformation to the hacker's (C & C) server, which will use network communication. Malicious apps that are emerging nowadays encrypt and transmit important information through SSL communication. In this case, it is difficult to knowwhat kind of information is exposed to network. Therefore, we suggest a method to analyze malicious apps when leak important information through SSL communication. In this paper, we identify the way malicious apps leak information. And we propose a method for analyzing information leaked by SSL communication. Data before encryption was confirmed in the device through SSL hooking and SSL Strip method.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.