• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3925-3932
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• 2024

During outages at nuclear power plants, much more care for radiation workers against internal exposure should be ensured given that more hot particles exist relative to the amount during normal operation. If fuel-type hot particles (FTHP) are inhaled, they can cause more severe health risks compared to activation-type hot particles (ATHP), which contain ⁶⁰Co, due to the alpha-emitting nuclides within FTHPs. The activities of difficult-to-measure nuclides within FTHPs inhaled by workers are inferred by the age-dating technique using a¹⁴¹Ce/¹⁴⁴Ce ratio as measured by whole-body counters. However, this method may be limited to outages that last for only a few months due to the short half-life (32.5 days) of ¹⁴¹Ce. We studied the feasibility of utilizing ²⁴¹Am, a nuclide with a long half-life of 432.6 years, as an alternative to ¹⁴¹Ce. Additionally, we improved the performance of a stand-type whole-body counter for low-energy gamma spectroscopy to meet the criterion (RMSE ≤0.25) specified in ANSI/HPS N13.30-2011 by employing an artificial neural network (ANN). This study can contribute to more rapid and accurate internal dose assessments for workers who have inhaled FTHPs during long-term outages at nuclear power plants.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.127-136
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• 2009

This paper proposes an on-line mining algorithm of moving trajectory patterns in RFID data streams considering changing characteristics over time and constraints of single-pass data scan. Since RFID, sensor, and mobile network technology have been rapidly developed, many researchers have been recently focused on the study of real-time data gathering from real-world and mining the useful patterns from them. Previous researches for sequential patterns or moving trajectory patterns based on stream data have an extremely time-consum ing problem because of multi-pass database scan and tree traversal, and they also did not consider the time-changing characteristics of stream data. The proposed method preserves the sequential strength of 2-lengths frequent patterns in binary relationship table using the time-evolving graph to exactly reflect changes of RFID data stream from time to time. In addition, in order to solve the problem of the repetitive data scans, the proposed algorithm infers candidate k-lengths moving trajectory patterns beforehand at a time point t, and then extracts the patterns after screening the candidate patterns by only one-pass at a time point t+1. Through the experiment, the proposed method shows the superior performance in respect of time and space complexity than the Apriori-like method according as the reduction ratio of candidate sets is about 7 percent.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.468-478
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• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.

• Journal of Biomedical Engineering Research
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• v.41 no.2
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• pp.107-119
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• 2020

The deep neural networks (DNN) that can replicate the behavior of the human expert who recognizes the characteristics of ECG waveform have been developed and studied to analyze ECG. However, although the existing DNNs can not provide the explanations for their decisions, those trials have attempted to determine whether patients have certain diseases or not and those decisions could not be accepted because of the absence of relating theoretical basis. In addition, these DNNs required a lot of training data to obtain sufficient accuracy in spite of the difficulty in the acquisition of relating clinical data. In this study, a small-sized continuous data processing DNN (C-DNN) was suggested to determine the simple characteristics of ECG wave that were not required additional explanations about its decisions and the C-DNN can be easily trained with small training data. Although it can analyze small input data that was selected in narrow region on whole ECG, it can continuously scan all ECG data and find important points such as start and end points of P, QRS and T waves within a short time. The star and end points of ECG waves determined by the C-DNNs were compared with the results performed by human experts to estimate the accuracies of the C-DNNs. The C-DNN has 150 inputs, 51 outputs, two hidden layers and one output layer. To find the start and end points, two C-DNNs were trained through deep learning technology and applied to a parameter acquisition algorithms. 12 lead ECG data measured in four patients and obtained through PhysioNet was processed to make training data by human experts. The accuracy of the C-DNNs were evaluated with extra data that were not used at deep learning by comparing the results between C-DNNs and human experts. The averages of the time differences between the C-DNNs and experts were 0.1 msec and 13.5 msec respectively and those standard deviations were 17.6 msec and 15.7 msec. The final step combining the results of C-DNN through the waveforms of 12 leads was successfully determined all 33 waves without error that the time differences of human experts decision were over 20 msec. The reliable decision of the ECG wave's start and end points benefits the acquisition of accurate ECG parameters such as the wave lengths, amplitudes and intervals of P, QRS and T waves.

• The Journal of Engineering Geology
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• v.16 no.4 s.50
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• pp.337-349
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• 2006

Seven hundred and fifty one fractures of the rhyolitic tuffaceous rock masses were mapped using 6 scanlines placed on rock slope exposures that were within 8.02 km of Busan-Ulsan highway. These data were analyzed to find the number of fracture sets that exist in the rock slopes and the probability distributions of orientation, spacing, trace length and fracture size in 3-D for each of the fracture sets. All the fracture set orientation distributions exhibit high variability. The Fisher distributions were found to be unsuitable to represent the statistical distribution of orientation for most of the fracture sets. The probability distributions, gamma, exponential and lognormal were found to be highly suitable to represent the distribution of spacing and semi-trace length of fracture sets. In obtain-ing these distributions, corrections were applied for sampling biases associated with spacing and trace length. The generated fracture system in 3-D was used to make predictions of fracture traces for each fracture set on 2-D win-dows. Developed stochastic 3-D fracture network for the rock mass was validated by comparing statistical proper-ties of the observed fracture traces on scanlines with the predicted fracture traces on the scanlines. This exercise fumed out to be successful.

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.317-326
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• 2010

Nowadays, transmission bandwidth for network traffic is increasing and the type is varied such as peer-to-peer (PZP), real-time video, and so on, because distributed computing environment is spread and various network-based applications are developed. However, as PZP traffic occupies much volume among Internet backbone traffics, transmission bandwidth and quality of service(QoS) of other network applications such as web, ftp, and real-time video cannot be guaranteed. In previous research, the port-based technique which checks well-known port number and the Deep Packet Inspection(DPI) technique which checks the payload of packets were suggested for solving the problem of the P2P traffics, however there were difficulties to apply those methods to detection of P2P traffics because P2P applications are not used well-known port number and payload of packets may be encrypted. A proposed algorithm for identifying P2P heavy traffics based on flow transport parameters and behavioral characteristics can solve the problem of the port-based technique and the DPI technique. The focus of this paper is to identify P2P heavy traffic flows rather than all P2P traffics. P2P traffics are consist of two steps i)searching the opposite peer which have some contents ii) downloading the contents from one or more peers. We define P2P flow patterns on these P2P applications' features and then implement the system to classify P2P heavy traffics.

• Journal of Veterinary Clinics
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• v.25 no.3
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• pp.187-191
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• 2008

A 7-year-old, intact female Yorkshire terrier dog was presented for coughing, anorexia, chest pain and dyspnea. Right lateral thoracic radiograph demonstrated a large mass shape on the heart base with decreased cardiac silhouette and severe right deviation of the trachea with the heart shifted to the left thoracic wall was observed on the ventrodorsal thoracic projection. Echocardiographic examination revealed a large rounded mass compressing left atrium around the heart base without signs of pericardial effusion. On computed tomographic (CT) findings, sagittal CT images depicted the possibility of cranial vena caval invasion and heart base involvement of the mass associated with biatrial compression. Dorsal CT image revealed the right deviation of trachea due to the heart base mass and markedly shrunk lung space was detected on the transverse CT image. Because the dog suddenly had died during the recovery from anesthesia after finishing CT scan, necropsy was performed. On gross findings, a large and lobulated mass was located at the base of the heart. A poorly-demarcated, infiltrative, multilobulated tumor composed of polyhedral cells in solid cellular sheets was confirmed based on histopathologic examination. This dog was diagnosed as a chemodectoma. This case report describes the clinical findings, diagnostic consistency of thoracic radiography, echocardiography and CT, and histopathologic confirmation in a spontaneously occurring chemodectoma with a Yorkshire terrier dog.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.737-744
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• 2012

Recently VoIP has provided voice(both wired and wireless from IP-based) as well as the transmission of multimedia information. VoIP used All-IP type, Gateway type, mVoIP etc. Wired and wireless VoIP has security vulnerabilities that VoIP call control signals, illegal eavesdropping, service misuse attacks, denial of service attack, as well as wireless vulnerabilities etc. from WiFi Zone. Therefore, the analysis of security vulnerabilities in wired and wireless VoIP and hacking incidents on security measures for research and study is needed. In this paper, VoIP (All-IP type, and for Gateway type) for system and network scanning, and, IP Phone to get the information and analysis of the vulnerability. All-IP type and Gateway type discovered about the vulnerability of VoIP hacking attacks (Denial of Service attacks, VoIP spam attacks) is carried out. And that is a real VoIP system installed and operated in the field of security measures through research and analysis is proposed.

• Journal of the Korean Ceramic Society
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• v.51 no.3
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• pp.170-176
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• 2014

Effective glass frit compositions enabled to absorb laser energy, and to seal a commercial dye-sensitized solar-cell-panel substrate were developed by using $V_2O_5$-based glasses with various amounts of $TeO_2$ substitution. The latter was intended to increase the lifetime of the solar cells. Substitution of $V_2O_5$ by $TeO_2$ provided a strong network structure for the glasses via the formation of tetrahedral pyramids in the glass, and changed the various glass properties, such as glass transition temperature ($T_g$), dilatometric softening point ($T_d$), crystallization temperature, coefficient of thermal expansion (CTE), and glass flowage without any detrimental effect on the laser absorption property of the glasses. The thermal expansion mismatch (${\Delta}{\alpha}$) between the glass frit and the substrate could be controlled within less than ${\pm}5%$ by addition of 10 wt% of ${\beta}$-eucryptite. An 810 nm diode laser was used for the sealing test. The laser sealing test revealed that the VZBT20 glass frit with 10 wt% ${\beta}$-eucryptite was successfully sealed the substrates without interfacial cracks and pores. The optimum sealing conditions were provided by a beam size of 3 mm, laser power of 40 watt, scan speed of 300 mm/s, and 200 irradiation cycles.