• Journal of Information Management
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• v.35 no.4
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• pp.1-16
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• 2004

In this paper, we outline the user interface problems that the text messaging users face to provide empirical basis for developing better improved mobile text messaging system. Our initial hypothesis was that the majority of the problems that the text messaging users face, namely, 1) difficulty in correctly understanding the intent of the incoming messages and 2) problem with frequently mis-addressing the recipient of the outgoing messages, can be accounted for by the poor usability of the text messaging user interface. Our analysis is based on the text message-based communication diaries, which were recorded for one week by each and every one of 75 college students, and survey taken from the same subjects. The data was collected in 2004. The students listed various difficulties including the limited message length, obscure input method, lack of mean to express emotional content, lack of receipt confirmation, lack of auto save feature when preparing messages to send, and lack of means to permanently save messages. Some of these problems were also identified in the previous studies. However, we were able to gather additional problems that the users face and also elicit potential solutions to remedy the problems. From these findings and analysis, we attempted to provide ways to improve the text messaging user interface.

• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.104-120
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• 2022

The Small Scale magNetospheric and Ionospheric Plasma Experiment (SNIPE)'s scientific goal is to observe spatial and temporal variations of the micro-scale plasma structures on the topside ionosphere. The four 6U CubeSats (~10 kg) will be launched into a polar orbit at ~500 km. The distances of each satellite will be controlled from 10 km to more than ~1,000 km by the formation flying algorithm. The SNIPE mission is equipped with identical scientific instruments, Solid-State Telescopes(SST), Magnetometers(Mag), and Langmuir Probes(LP). All the payloads have a high temporal resolution (sampling rates of about 10 Hz). Iridium communication modules provide an opportunity to upload emergency commands to change operational modes when geomagnetic storms occur. SNIPE's observations of the dimensions, occurrence rates, amplitudes, and spatiotemporal evolution of polar cap patches, field-aligned currents (FAC), radiation belt microbursts, and equatorial and mid-latitude plasma blobs and bubbles will determine their significance to the solar wind-magnetosphere-ionosphere interaction and quantify their impact on space weather. The formation flying CubeSat constellation, the SNIPE mission, will be launched by Soyuz-2 at Baikonur Cosmodrome in 2023.

• Journal of the korean academy of Pediatric Dentistry
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• v.35 no.3
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• pp.418-426
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• 2008

The objectives of this study were to examine the properties of fluoride-releasing resin composite restorative materials. Four commercially available compomer materials (Compoglass F: CF, $Dyract^{(R)}$ AP: DA, $Dyract^{(R)}$ flow: DF, F2000: FT) and one fluoride-releasing composite resin ($Tetric^{(R)}$ Ceram: TC) were selected as experimental materials. Rectangular-shaped tensile test specimens were fabricated in a teflon mold giving 5mm in gauge length and 2mm in thickness. Disk-shaped specimens were fabricated in the split teflon mold with diameter of 15mm and thickness of 1mm. After curing for an hour, specimens were immersed in deionized water at $37^{\circ}C{\pm}1^{\circ}C$ for 30 days. All specimens were thermocycled for 10,000 cycles with 15 seconds of dwelling time in each $5^{\circ}C$ and $55^{\circ}C$ water baths. Toothbrush abrasion test was conducted under a load of 1.5 N and the abraded surfaces were examined with surface roughness tester (SV-3000, Mitutoyo Co, Japan) and SEM (JSM-5800, JEOL, Japan). Fluoride recharging was done by toothbrushing for 3 min. using a fluoride toothpaste (Perio Alpine Herb, LG Household & Health Care, Korea). The results obtained were summarized as follows; 1. The highest tensile strength value of 32.3 MPa was observed in TC group and the lowest value of 16.8 MPa was observed in CF group. The tensile strength of TC group was significantly higher than those of CF and DF groups (P<0.05). 2. The lowest Ra value of 0.287 was observed in TC group and the highest value of 1.516 was observed in FT group. The Ra value of FT group was significantly higher than other groups (P<0.05). 3. The abraded surfaces revealed the increase of surface roughness due to the protrusion and missing of filler particles. 4. The release of fluoride of compomers after tooth brushing by Perio Alpine Herb was initially large and then followed by small and continuously. But it remains small and constant in fluoride-releasing composite resin of TC. 5. The highest value of fluoride release after toothbrushing by Perio Alpine Herb was $2.064{\mu}g/cm^2$ in CF group and the lowest value was $0.1119{\mu}g/cm^2$ in TC group. The amount of fluoride release of CF group was significantly higher than other groups (P<0.05).

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.109-116
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• 2021

Purpose: The purpose of this study is to evaluate the usefulness of Surface-Guided Patient Setup by comparing the patient positioning accuracy when image-guided radiation therapy was used for Markerless patients(unmarked on the skin) using Surface-Guided Patient Setup and Marker patients(marked on the skin) using Laser-Based Patient Setup. Materials And Methods: The position error during IGRT was compared between a Markerless patient initially set up with SGPS using an optical surface scanning system using three cameras and a Marker patient initially set up with LBPS that aligns the laser with the marker drawn on the patient's skin. Both SGPS and LBPS were performed on 20 prostate cancer patients and 10 Stereotactic Radiation Surgery patients, respectively, and SGPS was performed on an additional 60 breast cancer patients. All were performed IGRT using CBCT or OBI. Position error of 6 degrees of freedom was obtained using Auto-Matching System, and comparison and analysis were performed using Offline-Review in the treatment planning system. Result: The difference between the root mean square (RMS) of SGPS and LBPS in prostate cancer patients was Vrt -0.02cm, Log -0.02cm, Lat 0.01cm, Pit -0.01°, Rol -0.01°, Rtn -0.01°, SRS patients was Vrt 0.02cm, Log -0.05cm, Lat 0.00cm, Pit -0.30°, Rol -0.15°, Rtn -0.33°. there was no significant difference between the two regions. According to the IGRT standard of breast cancer patients, RMS was Vrt 0.26, Log 0.21, Lat 0.15, Pit 0.81, Rol 0.49, Rtn 0.59. Conclusion:. As a result of this study, the position error value of SGPS compared to LBPS did not show a significant difference between prostate cancer patients and SRS patients. In the case of additionally performed SGPS breast cancer patients, the position error value was not large based on IGRT. Therefore, it is considered that it will be useful to replace LBPS with SGPS, which has the great advantage of not requiring patient skin marking..

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• The Journal of the Korean bone and joint tumor society
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• v.11 no.2
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• pp.141-147
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• 2005

Purpose: The most important thing in curing Malignant melanoma is surgical excision, operating method is wide excision. The author et al. studied 5-year survival rate of each stage and appropriate surgical margin after operating wide excision and immuno-chemotherapy. Materials and methods: From March 1995 to August 2003, wide excision and immunochemotherapy were operated to 35 patients (17 males and 18 females) who were diagnosed as malignant melanoma and followed up. Excision was done around 2 cm from edge of tumor regardless of the size or effected degree of the skin, and flap or full thickness skin graft was used for skin deficit that was not covered after excision. As for immuno-chemotherapy, method that prescribes 400 mg of dacarbazine (DTIC) and 3 million IU of interferone-${\alpha}$ in combination was used. Immuno-chemotherapy was operated to patients in over stage III. We used AJCC stage that was revised in 2002. Local recurrence, local metastasis and distant metastasis were investigated for these patients as well as the 5-year survival rate of each stage. Results: Most frequently 15 cases(42.8%) occurred in foot, 5 cases(14.2%) occured in ankle, 2 cases(5.7%) in leg, 2 cases(5.7%) in thigh and 5 cases(14.2%) in hand. The incidence of each stage were 8 cases(22.8%) in IA, 9 cases(25.7 %) in IB, 4 cases(11.4%) in IIA, 2 cases(5.7%) in IIB, 1 cases(2.8%) in IIIA, 2 cases(5.7%) in IIIB, 2 cases(5.7%) in IIIC and 7 cases(20.0%) in stage IV. 5-year survival rate of each stage were 94.1% in stage I, 66.8% in stage II, 40% in stage III and 14.3% in stage IV. Conclusion: 5-year survival rate of stage IV was low in malignant melanoma. In treatment of malignant melanoma, staging before operation is important as operation methods are different from each stage. We recommend wide excision which remove around 1~3 cm from margin of tumor up to each thickness.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.