• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.100-113
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• 2019

Sarcoidosis is a multisystem disease characterized by noncaseating granulomas. Cardiac involvement is known to have poor prognosis because it can manifest as a serious condition such as the conduction abnormality, heart failure, ventricular arrhythmia, or sudden cardiac death. Although early diagnosis and early treatment is critical to improve patient prognosis, the diagnosis of CS is challenging in most cases. Diagnosis usually relies on endomyocardial biopsy (EMB), but its diagnostic yield is low due to the incidence of patchy myocardial involvement. Guidelines for the diagnosis of CS recommend a combination of clinical, electrocardiographic, and imaging findings from various modalities, if EMB cannot confirm the diagnosis. Especially, the role of advanced imaging such as cardiac magnetic resonance (CMR) imaging and positron emission tomography (PET), has shown to be important not only for the diagnosis, but also for monitoring treatment response and prognostication. CMR can evaluate cardiac function and fibrotic scar with good specificity. Late gadolinium enhancement (LGE) in CMR shows a distinctive enhancement pattern for each disease, which may be useful for differential diagnosis of CS from other similar diseases. Effectively, T1 or T2 mapping techniques can be also used for early recognition of CS. In the meantime, PET can detect and quantify metabolic activity and can be used to monitor treatment response. Recently, the use of a hybrid CMR-PET has introduced to allow identify patients with active CS with excellent co-localization and better diagnostic accuracy than CMR or PET alone. However, CS may show various findings with a wide spectrum, therefore, radiologists should consider the possible differential diagnosis of CS including myocarditis, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, amyloidosis, and arrhythmogenic right ventricular cardiomyopathy. Radiologists should recognize the differences in various diseases that show the characteristics of mimicking CS, and try to get an accurate diagnosis of CS.

• Journal of Medicine and Life Science
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• v.17 no.3
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• pp.65-73
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• 2020

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a single-gene disease of the cerebral small blood vessels caused by mutations in the NOTCH3 gene on chromosome 19. Although CADASIL was known as a rare disease, recent research has suggested that the NOTCH variants could be found frequently even in the general population. The main clinical features included recurrent stroke, migraine, psychiatric symptoms, and progressive cognitive decline. On brain magnetic resonance imaging, patients with CADASIL showed multifocal white matter hyperintensity lesions, lacunar infarcts, microbleeds, and brain atrophy. Among them, lacunar infarcts and brain atrophy are important in predicting the clinical outcomes of patients with CADASIL. In the Jeju National University Hospital, we have diagnosed 213 CADASIL patients from 2004 to 2020. Most NOTCH3 mutations were located in exon 11 (94.4%), and p.Arg544Cys was the most common mutation. The mean age at diagnosis was 61.0±12.8 years. The most common presenting symptoms were ischemic stroke (24.4%), followed by cognitive impairment(15.0%), headache (8.9%), and dizziness(8.0%). Although the exact prevalence of CADASIL in Jeju is still unknown, the disease prevalence could be as high as 1% of the population considering the prevalence reported in Taiwan. Therefore, it is necessary to discover efficient biomarkers and genetic tests that can accurately screen and diagnose patients suspected of having CADASIL in this region. Ultimately, it is urgent to explore the exact pathogenesis of the disease to identify leading substances of treatment potential, and for this, multi-disciplinary research through active support from the Jeju provincial government as well as the national government is essential.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.995-1002
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• 2006

Construction equipment plays an important role as one of resources in construction process. It is obvious that the machine intensive construction method achieves productivity and quality improvement as well as safety improvement. The number of registered construction equipment in Korea is over 320,000 in 2005 according to the national statistics safety, but recent crisis of construction equipment industrialist has been deepen by lack of skilled workers, rise in wages, diminution of working and etc. The main objective of this paper is to propose an improved management and application policy for the better construction equipment utilization. A classification method for construction equipment and a licensing system for construction equipment operators are newly suggested to revise the Construction Equipment Management Law. In order to satisfy the objective, this research performs literature reviews on domestic and overseas related laws and regulations for operating license, and conducts surveys and interviews with experts in the field of construction equipment industry. The results of this research can be considered as an important reference to update the Law that can improve construction productivity and equipment operating rate.

• Journal of the Korean Society of Radiology
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• v.82 no.4
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• pp.770-790
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• 2021

Idiopathic pulmonary fibrosis (IPF), based on the 2018 international clinical practice guidelines, can be diagnosed with a usual interstitial pneumonia (UIP) pattern on high-resolution computed tomography (HRCT) and compatible clinical findings. Given that imaging is pivotal for IPF evaluation and diagnosis, more emphasis should be placed on the integration of clinical, radiological, and pathologic findings for multidisciplinary diagnosis. Interstitial lung abnormality (ILA), on the other hand, has a purely radiological definition based on the incidental identification of CT abnormalities. Taken together, differentiation between ILA and clinically significant interstitial lung disease (ILD) must be based on proper clinical evaluation. With this review, the recent updates in IPF diagnosis and the radiologic considerations for ILA can be well understood, which can be helpful for the proper diagnosis and management of patients with diffuse interstitial pulmonary fibrosis.

• Journal of the Korean Society of Radiology
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• v.81 no.5
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• pp.1053-1068
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• 2020

In accordance with the new healthcare policy of government (Moon Jae-In Care) to strengthen health insurance coverage, the National Health Insurance (NHI) coverage of brain magnetic resonance imaging (MRI), brain/neck MR angiography (MRA), and head and neck MRI have been expanded since 2018 in Korea. This article has been reviewed focusing on the "Detailed matter concerning criteria and method for providing reimbursed services in the NHI. Some revisions" regarding reimbursement for MRI, which was revised from October 2018 to April 2020 and is currently in effect. It included the MRI reimbursement system in Korea, recent adjustment of the reimbursement coverage for patients with headache or dizziness, and reimbursement coverage, standard imaging, and radiologic report of brain MRI, brain/neck MRA and head and neck MRI. This article could help radiologists gain knowledge on health insurance to protect the expertise of the radiologist and to play a leading role in the hospital. As the policy changes, detailed matter concerning criteria and method for providing reimbursed services in the NHI may be revised. Therefore, radiologists should update issues related to insurance reimbursement for MRI continuously.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.27-80
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• 2023

Nowadays usage of different applications of identity management IDM demands prime attention to clarify which is more efficient regarding preserve privacy as well as security to perform different operations concerning digital identity. Those operations represent the available interactions with identity during its lifecycle in the digital world e.g., create, update, delete, verify and so on. With the rapid growth in technology, this field has been evolving with a number of IDM models being proposed to ensure that identity lifecycle and face some significant issues. However, the control and ownership of data remines in the hand of identity service providers for central and federated approaches unlike in the self-sovereign identity management SSIM approach. SSIM is the recent IDM model were introduced to solve the issue regarding ownership of identity and storing the associated data of it. Thus, SSIM aims to grant the individual's ability to govern their identities without intervening administrative authorities or approval of any authority. Recently, we noticed that numerous IDM solutions enable individuals to own and control their identities in order to adapt with SSIM model. Therefore, we intend to make comparative study as much of these solutions that have proper technical documentation, reports, or whitepapers as well as provide an overview of IDM models. We will point out the existing research gaps and how this study will bridge it. Finally, the study will propose a technical enhancement, everKEY solution, to address some significant drawbacks in current SSIM solutions.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.132-144
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• 2001

The broadcast environment is asymmetric communication aspect that is typically much greater communication capacity available from server to clients than in the opposite direction. In addition, most of mobile computing systems only allow the generation of read-only transactions from mobile clients for retrieving different types of information such as stock data, traffic information and news updates. Since previous concurrency control protocols, however, do not consider such a particular characteristics, the performance degradation occurs when those schemes are applied to the broadcast environment having quite a high data contention. In this paper, we propose OCC/2VTS (Optimistic Concurrency Control based on 2-Version and TimeStamp) that is most appropriate for broadcast environment. OCC/2VTS lets each client process and commit query transactions for itself by using two version data in cache. If the values of appropriate data items are not changed twice by invalidation report after a query transaction starts, the query transaction is committed safely independent of commitment of update transactions. OCC/2VTS decreases the number of informing server for the purpose of commitment. Due to broadcasting the validation reports including updated recent values, it reduces the opportunity of requesting a recent data values of server as well. As a result, OCC/2VTS makes full use of the asymmetric bandwidth. It also improves transaction throughput by increasing the query transaction commit ratio as much as possible.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.651-665
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• 2003

To reduce cache misses emerges as the most important issue in today's situation of main memory databases, in which CPU speeds have been increasing at 60% per year, and memory speeds at 10% per year. Recent researches have demonstrated that cache-conscious index structure such as the CR-tree outperforms the R-tree variants. Its search performance can be poor than the original R-tree, however, since it uses a lossy compression scheme. In this paper, we propose alternatively a cache-conscious version of the R-tree, which we call MR-tree. The MR-tree propagates node splits upward only if one of the internal nodes on the insertion path has empty room. Thus, the internal nodes of the MR-tree are almost 100% full. In case there is no empty room on the insertion path, a newly-created leaf simply becomes a child of the split leaf. The height of the MR-tree increases according to the sequence of inserting objects. Thus, the HeightBalance algorithm is executed when unbalanced heights of child nodes are detected. Additionally, we also propose the CCMR-tree in order to build a more cache-conscious MR-tree. Our experimental and analytical study shows that the two-dimensional MR-tree performs search up to 2.4times faster than the ordinary R-tree while maintaining slightly better update performance and using similar memory space.