• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.73-78
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• 2006

Conventional caption extraction methods use the difference between frames or color segmentation methods from the whole image. Because these methods depend heavily on heuristics, we should have a priori knowledge of the captions to be extracted. Also they are difficult to implement. In this paper, we propose a method that uses little heuristic and simplified algorithm. We use topographical features of characters to extract the character points and use MST(Minimum Spanning Tree) to extract the candidate regions for captions. Character regions are determined by testing several conditions and verifying those candidate regions. Experimental results show that the candidate region extraction rate is 100%, and the character region extraction rate is 98.2%. And then we can see the results that caption area in complex images is well extracted.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.83-90
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• 2009

Wireless Sensor Network (WSN) is a wireless network that gathers information from remote area with autonomously configured routing path. We propose a fusion based routing for a 'convergecast' in which all sensors periodically forward collected data to a base station. Previous researches dealt with only full-fusion or no-fusion case. Our Fusion rate based Spanning Tree (FST) can provide effective routing topology in terms of total cost according to all ranges of fusion rate f ($0{\leq}f{\leq}1$). FST is optimum for convergecast in case of no-fusion (f = 0) and full-fusion (f = 1) and outperforms the Shortest Path spanning Tree (SPT) or Minimum Spanning Tree (MST) for any range of f (0 < f < 1). Simulation of 100-node WSN shows that the total length of FST is shorter than MST and SPT nearby 31% and 8% respectively in terms of topology lengths for all range of f. As a result, we confirmed that FST is a very useful WSN topology.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1019-1026
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• 2012

This paper presents a method for the identification of "edge observations" located on a boundary area constructed by a truncation variable as well as for the identification of outliers and the after fit of multivariate skew $t$-distribution(MST) to asymmetric data. The detection of edge observation is important in data analysis because it provides information on a certain critical area in observation space. The proposed method is applied to an Australian Institute of Sport(AIS) dataset that is well known for asymmetry in data space.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.65-71
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• 2008

In this study, two residential complexes, both built in the New City area after 2006 and both composed of over 500 households, were selected to estimate an efficient accessibility and connectivity index using accessibility and connectivity theories and methods from previous studies. With architectural blueprints, the nodes and edges of both residential complexes were drawn out and the accessibility matrixes were derived using these nodes and edges. As a result, the connectivity indices of both residential complexes were lower than 1, the accessibility index of Dong-tan was 86.922 being smaller than the accessibility index of Ilsan, 96.199, and the efficiency index of both residential complexes were similar being 0.26. With these indices it is expected to geometrically estimate the residential complexes and predict the pedestrian passing conveniences.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.516-516
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• 2000

We propose a neuron model that is possible to learn three-dimensional movement. The neuron model by imitating structure of a neuron, has the system resemble a neuron. We considered a neuron system based on the arguments, and wished to examine whether the system had reasonable function. Koch, Poggio and Torre believed that inhibition signal would shunt excitation signal on the dendrites. They believed that excitation signal operated input-signals and inhibition did as delayed ones. Thus, they were sure that function for directional selectivity was arisen by the shunting. Koch's concept is so important; therefore, we construct the neuron system with their concept. The neuron system makes the shunting function; thus, the model may have a function for directional selectivity. We initialized the connections and the dendrites by random data, and trained them by the back-propagation algorithm for three-dimensional movement. We made sure the defection of three-dimensional movement in the system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1C
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• pp.89-96
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• 2012

Since tactical robots are going to be grown and tactical data communications will be more network-centric, the reliability of wireless tactical data networks is going to be very important in the future. However, the connectivity of such wireless tactical data networks can be extremely uncertain in practical circumstances. In this paper, we propose a searching technique to find out the weak boundary area of the network connectivity using a small UAV(unmanned aerial vehicle) which has a simple polling access function to wireless nodes on the ground in wireless tactical data networks. The UA V calculates the network topology of the wireless tactical data networks and coverts it to the Lapalcian matrix. In the proposed algorithm, we iteratively search the eigenvalues and find a minimum cut in the network resulting in finding the weak boundary of the connectivity for the wireless tactical data networks. If a UAV works as a relay nodes for the weak area, we evaluate that the throughput performance of the proposed algorithm outperforms star connection method and MST(minimum Spanning Tree) connection method. The proposed algorithm can be applied for recovering the connectivity of wireless tactical data networks.

• Korean Journal of Biological Psychiatry
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• v.24 no.3
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• pp.95-109
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• 2017

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technique which can change cortical excitability in targeted area by producing magnetic field pulses with an electromagnetic coil. rTMS treatment has been used to treat various neuropsychiatric disorders including depression. In this review, we evaluate the literature on rTMS for depression by assessing its efficacy on different subtypes of depression and different technical parameters. In particular, we focus on the results of randomized clinical trials and meta-analyses for depression after the US Food and Drug Administration approval in 2008, which acknowledged its efficacy and acceptability. We also review the new forms of rTMS therapy including deep TMS, theta-burst stimulation, and magnetic seizure therapy (MST) that have been under recent investigation. High frequency rTMS over left dorsolateral prefrontal cortex (DLPFC), low frequency rTMS over right DLPFC, or bilateral rTMS is shown to be effective and acceptable in treatment for patients with non-psychotic, unipolar depression either as monotherapy or adjuvant. Deep TMS, theta-burst stimulation and MST are promising new TMS techniques which warrant further research.

• The Korean Journal of Pain
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• v.20 no.2
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• pp.163-168
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• 2007

Background: Temporomandibular joint disorder (TMD) is a group of musculoskeletal conditions characterized by pain in the pre-auricular area, limitation of jaw movement and palpable muscle tenderness. Thermography is a nonionizing, noninvasive diagnostic alternative for the evaluation of TMD. This study was conducted to evaluate the usefulness of thermography in the assessment of TMD. Methods: Thermography was conducted on the 61 patients who had been diagnosed with TMD, and on the 34 normal symptom-free volunteers. The temperature differences between opposite sides of the temporomandibular joint (${\Delta}T_{TMJ}$) and the masseter muscle (${\Delta}T_{MST}$) were calculated. The sensitivity and specificity of thermography was calculated at the cut off values of 0.2, 0.3, and $0.4^{\circ}C$. Results: In the patient group, the ${\Delta}T_{TMJ}$ was $0.42{\pm}0.38^{\circ}C$ and the ${\Delta}T_{MST}$ was $0.38{\pm}0.33^{\circ}C$, whereas in the control group the ${\Delta}T_{TMJ}$ was $0.10{\pm}0.07^{\circ}C$ and the ${\Delta}T_{MST}\;0.15{\pm}0.10^{\circ}C$. In addition, the patient group demonstrated a significantly lower level of thermal symmetry than the control group (P < 0.001) in both the temporomandibular joints and the masseter muscles. The sensitivity of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 67.2, 49.2, and 42.6% in the temporomandibular joint (TMJ) and 60.7, 49.2 and 37.7% in the masseter muscle, respectively. The specificity of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 88.2, 100, and 100% in the TMJ and 61.8, 91.2 and 100% in the masseter muscles, respectively. The accuracy of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 74.7, 67.4, and 63.2% in TMJ and 61.1, 64.2 and 60.0% in the masseter muscles, respectively. Conclusions: Temperature differences exist between the opposite sides of the TMD and masseter muscles in patients with TMD. Although the sensitivity of thermography in the diagnosis of TMD is low, it has high specificity in the evaluation of TMD, and is therefore applicable to patients with TMD.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.493-498
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• 2017

Capsule endoscopy is specialized for the observation of small intestine that is difficult to access by general endoscopy. The diagnostic procedure through capsule endoscopy consists of three stages: examination of indicant, endoscopy, and diagnosis. At this time, key information needed for diagnosis includes indicant, lesions, and suspected disease information. In this paper, these information are defined as semantic features and the extracting process is defined as semantic-based analysis. It is performed in whole capsule endoscopy. First, several symptoms of patient are checked before capsule endoscopy to get some information on suspected disease. Next, capsule endoscopy is performed by checking the suspected diseases. Finally, diagnosis is concluded by using supporting information. At this time, some association are used to conclude diagnosis. For example, there are the disease association between the symptom and the disease to identify the expected disease, and the anatomical association between the location of the lesion and supporting information. However, existing knowledge models such as MST and CEST only lists the simple term related to endoscopy and cannot consider such semantic associations. Therefore, in this paper, we propose association-based knowledge model for supporting diagnosis of capsule endoscopy. The proposed model is divided into two; a disease model and anatomical model of small intestine, interesting area(organs) of capsule endoscopy. It can effectively support diagnosis by providing key information for capsule endoscopy.