• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.33-40
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• 2019

As aerospace industry has been activated recently, it is required to commercialize composite analysis software. Until now, commercial software has been mainly used for analyzing composites, but it has been difficult to use due to high price and limited functions. In order to solve this problem, automatic analysis software for both in-plane and corner radius strength, which are all made on-line and generalized, has recently been developed. However, these have the disadvantage that they can not be analyzed simultaneously with multiple failure criteria. In this paper, we propose a method to greatly improve the processing speed while simultaneously handling the analysis of multiple failure criteria using a parallel processing platform that only works with a GPU equipped with a CUDA core. We have obtained satisfactory results when the analysis speed is experimented on the vast structure data.

• Journal of IKEEE
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• v.23 no.3
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• pp.852-857
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• 2019

In order to manage the demand resources of project participants and to provide appropriate strategies in the virtual power plant's power trading platform for consumers or operators who want to participate in the distributed resource collective trading market, it is very important to forecast the next day's demand of individual participants and the overall system's electricity demand. This paper developed a power demand forecasting model for the next day. For the model, we used LSTM algorithm of deep learning technique in consideration of time series characteristics of power demand forecasting data, and new scheme is applied by applying one-hot encoding method to input/output values such as power demand. In the performance evaluation for comparing the general DNN with our LSTM forecasting model, both model showed 4.50 and 1.89 of root mean square error, respectively, and our LSTM model showed high prediction accuracy.

• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.14-25
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• 2019

As the Internet of Things, artificial intelligence and big data have received a lot of attention as key growth engines in the era of the fourth industrial revolution, data acquisition and utilization in mobile, automotive, robotics, manufacturing, agriculture, health care and national defense are becoming more important. Due to numerous data-based industrial changes, demand for sensor technologies is exploding, especially for intelligent sensor technologies that combine control, judgement, storage and communication functions with the sensors's own functions. Intelligent sensor technology can be defined as a convergence component technology that combines intelligent sensor units, intelligent algorithms, modules with signal processing circuits, and integrated plaform technologies. Intelligent sensor technology, which can be applied to variety of smart IT convergence services such as smart devices, smart homes, smart cars, smart factory, smart cities, and others, is evolving towards intelligent and convergence technologies that produce new high-value information through recognition, reasoning, and judgement based on artificial intelligence. As a result, development of intelligent sensor units is accelerating with strategies for miniaturization, low-power consumption and convergence, new form factor such as flexible and stretchable form, and integration of high-resolution sensor arrays. In the future, these intelligent sensor technologies will lead explosive sensor industries in the era of data-based artificial intelligence and will greatly contribute to enhancing nation's competitiveness in the global sensor market. In this report, we analyze and summarize the recent trends in intelligent sensor technologies, especially those for four core technologies.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.179-187
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• 2019

Developing countries, including Africa, are experiencing very little human resources development due to the deprivation of equal educational opportunities, poor educational conditions, and the gap in information technology with developed countries. Developing countries that do not have excellent human resources are lagging behind in globalization competition with developed countries, and the problem of 'human resource development' in developing countries can not be avoided. In developing countries, education budgets are too low to meet education needs and compulsory education, and therefore they are not adequately responding to the increasing demand for education. The lack of education budget is due to the lack of education infrastructure. In this study, the NAS based server is configured to configure functions such as educational content and learning management, and the client area is presented with solutions for various media such as tablet, PC, and beam projector. And to support optimized e-learning services in developing countries by constructing a SCORM-based platform.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.229-236
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• 2019

IOT middleware is required to play a pivotal role in interpreting, managing, and controlling data information of Internet devices (sensors, etc.). In particular, the root industry has different process flows for different industries, and there are various data processing requirements for each company. Therefore, a general purpose IOT middleware is needed to accommodate this. The IOT middleware structure proposed by this paper is a plug-in that can be used as an engine part for middleware basic processes such as communication, data collection, processing and service linkage, We propose a flexible and effective smart process for root industry. In addition, we propose a method to strengthen prevention and security against tampering, deodorization, etc. through encryption of network data between middleware plug - in and related service layer. We propose a system that will be developed as an IOT middleware platform that is specialized in the root industry so that it can be extended in various network protocols such as MQTT, COAP, XAMP.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.53-62
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• 2018

As the performance of computer hardware has been increased in recent years, more realistic computer generated objects can be created and presented in personal computers and portable digital devices as well. For this reason, digital contents, including computer graphics, require virtual objects that are more realistic and representable in real-time on various devices, thus it requires more computational costs. In order to support the production of contents including computer graphics, games, and animations on multi-platform, Unity or unreal engines are mainly used. To represent more realistic behavior of virtual objects in a simulation, a virtual object must collide with other virtual objects and present the plausible interaction, as in the real world. However, such dynamic simulation requires a large amount of computational cost, and most portable devices cannot provide these dynamic simulations in real-time. In this paper, we proposed a GPGPU computation based dynamic cloth simulation to represent collision and response with spherical object in real-time. We believe that the proposed method can be useful for readily producing realistic digital contents.

• Journal of Coastal Disaster Prevention
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• v.4 no.4
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• pp.189-196
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• 2017

Ocean Research Stations (ORSs) is the ocean platform type observation towers and measured oceanic, atmospheric and environmental data. These station located on the offshore area far from the coast, so they can produce the data without land effect. This study focused to improve the wave data quality of ORS station. The wave observations at ORSs are used by the C-band (5.8 GHz, 5.17 cm) MIROS Wave and Current Radar (MWR). MWR is convenient to maintenance and produce reliability wave data under bad weather conditions. MWR measured significant wave height, peak wave period, peak wave direction and 2D wave spectrum, so it's can provide wave information for researchers and engineers. In order to improve the reliability of MWR wave data, Datawell Waverider Buoy was installed near the one ORS (Socheoncho station) during 7 months and validate the wave data of MWR. This study found that the wave radar tend to be overestimate the low wave height under wind condition. Firstly, this study carried out the wave Quality Control (QC) using wind data, however the quality of wave data was limited. So, this study applied the four filters (Correlation Check, Direction Filter, Reduce White Noise and Phillips Check) of MWR operating software and find that the filters effectively improve the wave data quality. After applying 3 effective filters in combination, the RMSE of significant wave height decreased from 0.81m to 0.23m, by 0.58m and Correlation increased from 0.66 to 0.96, by 0.32, so the reliability of MWR significant wave height was significantly improved.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.267-275
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• 2021

Smart application is developed in this paper by using an android-based platform to automatically determine the human emergency state (Lifesaver) by using different technology sensors of the mobile. In practice, this Lifesaver has many applications, and it can be easily combined with other applications as well to determine the emergency of humans. For example, if an old human falls due to some medical reasons, then this application is automatically determining the human state and then calls a person from this emergency contact list. Moreover, if the car accidentally crashes due to an accident, then the Lifesaver application is also helping to call a person who is on the emergency contact list to save human life. Therefore, the main objective of this project is to develop an application that can save human life. As a result, the proposed Lifesaver application is utilized to assist the person to get immediate attention in case of absence of help in four different situations. To develop the Lifesaver system, the GPS is also integrated to get the exact location of a human in case of emergency. Moreover, the emergency list of friends and authorities is also maintained to develop this application. To test and evaluate the Lifesaver system, the 50 different human data are collected with different age groups in the range of (40-70) and the performance of the Lifesaver application is also evaluated and compared with other state-of-the-art applications. On average, the Lifesaver system is achieved 95.5% detection accuracy and the value of 91.5 based on emergency index metric, which is outperformed compared to other applications in this domain.

• Journal of Microbiology and Biotechnology
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• v.31 no.7
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• pp.949-955
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• 2021

Previously, our research group isolated Bifidobacterium breve IDCC4401 from infant feces as a potential probiotic. For this study, we evaluated the safety of B. breve IDCC4401 using genomic and phenotypic analyses. Whole genome sequencing was performed to identify genomic characteristics and investigate the potential presence of genes encoding virulence, antibiotic resistance, and mobile genetic elements. Phenotypic analyses including antibiotic susceptibility, enzyme activity, production of biogenic amines (BAs), and proportion of D-/L-lactate were evaluated using E-test, API ZYM test, high-performance liquid chromatography (HPLC), and D-/L-lactic acid assay respectively. The genome of B. breve IDCC4401 consists of 2,426,499 bp with a GC content of 58.70% and 2,016 coding regions. Confirmation of the genome as B. breve was provided by its 98.93% similarity with B. breve DSM20213. Furthermore, B. breve IDCC4401 genes encoding virulence and antibiotic resistance were not identified. Although B. breve IDCC4401 showed antibiotic resistance against vancomycin, we confirmed that this was an intrinsic feature since the antibiotic resistance gene was not present. B. breve IDCC4401 showed leucine arylamidase, cystine arylamidase, α-galactosidase, β-galactosidase, and α-glucosidase activities, whereas it did not show production of harmful enzymes such as β-glucosidase and β-glucuronidase. In addition, B. breve IDCC4401 did not produce any tyramine, histamine, putrescine, cadaverine, or 2-phenethylamine, which are frequently detected BAs during fermentation. B. breve IDCC4401 produced 95.08% of L-lactate and 4.92% of D-lactate. Therefore, our findings demonstrate the safety of B. breve IDCC 4401 as a potential probiotic for use in the food industry.