• Journal of Yeungnam Medical Science
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• v.29 no.2
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• pp.110-112
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• 2012

Intussusception in adult is a rare disease and laparotomy is usually considered because of the probability of malignancy. Especially with obstruction symptom or sign, it might be needed emergency operation. This case was a simultaneous development of small bowel intussusception and acute hepatitis A. The patient had abdominal pain and vomiting. Intitial laboratory examination with elevated aminotransferase revealed that the diagnosis was acute hepatitis. As managing acute hepatitis, the abdominal pain was not improved and the patient had tenderness on periumbilical area on physical examination. A jejunal intussusception with a lead point was proved on the abdominal computed tomography scan. Fortunately, symptom of intussusception was relieved while nulli per os (NPO) and intravenous hydration. After recovery of acute hepatitis, laparotomy was done. The lead point was $2.5{\times}3.0cm$ sized hamartoma. This was the case that the symptom of intussusception was confused with that of acute hepatitis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.227-247
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• 2018

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.951-958
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• 2017

In an attack context, the adversary wants to retrieve the message from the intercepted noisy bit stream without any prior knowledge of the channel codes used. The process of finding out the code parameters such as code length, dimension, and generator, for this purpose, is called the blind recognition of channel codes or the reconstruction of channel codes. In this paper, we suggest an improved algorithm of the blind recovery of rate k/n convolutional encoders in a noisy environment. The suggested algorithm improves the existing algorithm by Marazin, et. al. by evaluating the threshold value through the estimation of the channel error probability of the BSC. By applying the soft decision method by Shaojing, et. al., we considerably enhance the success rate of the channel reconstruction.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.555-562
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• 2010

After starting digital terrestrial broadcasting, there have been a number oftrials to provide new services like data broadcasting on a spare bandwidth of a DTV channel. Recently, the Push-VOD service, which provides A/V contents on that bandwidth, gets more attention and is being standardized as NRT(Non-Real-Time) by ATSC. However, it is highly probable that the contents transmitted in this way contain many errors due to the DTV receiving environment. Thus, in order to improve the reliability of transmission, the contents should be transmitted repeatedly several times, considering the unidirectional property of DTV terrestrial network. In this paper, we propose a method to calculate the optimal number of repetitions to transmit each contents in a way that minimizes the number of errors occured, when trying to transmit several contents to the receiver in a restricted time, using Markov-chain modeling and dynamic programming approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11C
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• pp.955-962
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• 2008

Multicast Strategy is one of the cost-saving methods in the large scale VOD environment. However, it does involve complicated problems to implement VCR-like interactions for user's convenience in the multicast streaming system under considering the limited-server and the network's bandwidth in the multicast-transmission system. Therefore, the proper solution of settling such a problem is necessary. Thus, this paper which revised P2Patching[l] proposes LGST(Level Group Streaming Technique) which supports the VCR's function through cooperation among peers with heterogeneous bandwidth under the environment of P2P. This strategy can reduce latency by improving the acceptance of server and using the bandwidth of network efficiently. And for evaluate the proposed scheme's performance, I simulated the performance of streaming delivery topology and streaming quality in comparison with P2Patching. In evaluation to service request refusal ratio and service quality according to bandwidth decrement, the result of simulation shows that proposed LGST improves about $11{\sim}18%$ of performance than P2Patching. In the test of latency recovery according to fault probability and influence of VCR function operation duration, it shows similar performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.32-39
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• 2009

Compressed Sensing is a method to sample analog signals at a rate under the Nyquist rate. With this scheme, it is possible to represent signals with a relatively smaller number of measurements than that of the conventional sampling method, and the original signals are reconstructed with high probability from the acquired measurements using the linear programming. Compressed sensing allows measurement time and/or the amount of ADC (analog-to-digital converter) resources for the signal acquisitions to be reduced. In this paper, we presents the backgrounds of the compressed sensing, a way to acquire measurements from an analog signal with a random basis, and the signal recovery method. Also we introduce applications of compressed sensing in wireless communications.

• Nuclear Engineering and Technology
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• v.21 no.1
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• pp.1-11
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• 1989

Numerous losses of decay heat removal capability have occurred at U during stutodwn while its significance to safety is needless to say. A study is carried out as an attempt to assess what could be done to lower the frequency of these events and to mitigate their consequences in the unlikely event that one occurs. The shutdown risk model is developed and analyzed using Event/Fault Tree for the typical pressurized water reactor. The human cognitive reliability (HCR) model, two-stage bayesian approach and staircase function model are used to estimate human reliability, initiating event frequency and offsite power non-recovery probability given loss of offsite power, respectively. The results of this study indicate that the risk of a Pm at shutdown is not much lower than the risk when the plant is operating. By examining the dominant accident sequences obtained, several design deficiencies are identified and it is found that some proposed changes lead to significant reduction in core damage frequency due to loss of cooling events.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.490-497
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• 2007

A Combined sewer overflows (CSOs) are themselves a significant source of water pollution. Therefore, the control of urban drainage for CSOs reduction and receiving water quality protection is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as stormwater detention storage is highly dependant on the temporal variability of storage capacity available (which is influenced by the duration of interevent dry periods) as well as the infiltration capacity of soil and recovery of depression storage. As a result, a continuous approach is required to adequately size such facilities. This study for the continuous long-term analysis of urban drainage system used analytical probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model have evolved that offer much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics of the subject area using analytical probabilistic model. This study presented the average annual COSs and number of COSs when the interceptor capacity is in the range $3{\times}DWF$ (dry weather flow). Also, calculated the average annual mass of pollutant lost in CSOs using Event Mean Concentration. Finally, this study presented a decision of storage volume for CSOs reduction and water quality protection.

• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.43-54
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• 2021

Since COVID-19 (Severe acute respiratory syndrome coronavirus type 2, SARS-Cov-2) was first discovered at the end of 2019, it has spread rapidly around the world. This study introduces an agent-based simulation model representing COVID-19 spread in South Korea to investigate the effect of detect level (contact tracing) on the virus spread. To develop the model, related data are aggregated and probability distributions are inferred based on the data. The entire process of infection, quarantine, recovery, and death is schematically described and the interaction of people is modeled based on the traffic data. A composite logistic functions are utilized to represent the compliance of people to the government move control such as social distancing. To demonstrate to effect of detect level on the virus spread, detect level is changed from 0% to 100%. The results indicate active contact tracing inhibits the virus spread and the inhibitory effect increases geometrically as the detect level increases.