• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.808-826
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• 2017

Most of conventional DSS's(Digital Signage Systems) have been built based on LAMP framework. Recent researches have shown that MEAN or MERN stack framework is simpler, more flexible, faster and more suitable for web-based application than LAMP stack framework. In this paper, we propose a design and implementation of MEARN (ME(A+R)N) stack-based real-time digital signage system, MR-DSS, which supports handing real-time tasks like urgent/instant messaging, system status monitoring and so on, efficiently in addition to conventional digital signage CMS service tasks. MR-DSCMS, CMS of MR-DSS, is designed to provide most of its normal services by REST APIs and real-time services like urgent/instant messaging by Socket.IO base under MEARN stack environment. In addition to architecture description of components composing MR-DSS, design and implementation issues are clarified in more detail. Through experimental testing, it is shown that 1) MR-DSS works functionally well, 2) the networking load performance of MR-DSCMS's REST APIs is better compared to a well-known open source Xibo CMS, and 3) real-time messaging via Socket.IO works much faster than REST APIs.

• Food Science of Animal Resources
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• v.41 no.1
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• pp.85-94
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• 2021

A pig-specific real-time PCR assay based on the mitochondrial ND5 gene was developed to detect porcine material in food and other products. To optimize the performance of assay, seven commercial TaqMan master mixes and two real-time PCR platforms (Applied Biosystems StepOnePlus and Bio-rad CFX Connect) were used to evaluate the limit of detection (LOD) as well as the PCR efficiency and specificity. The LODs and PCR efficiencies for the seven master mixes on two platforms were 0.5-5 pg/reaction and 84.96%-108.80%, respectively. Additionally, non-specific amplifications of DNA from other animal samples (human, dog, cow, and chicken) were observed for four master mixes. These results imply that the sensitivity and specificity of a real-time PCR assay may vary depending on master mix and platform used. The best combination of master mix and real-time PCR platform can accurately detect 0.5 pg porcine DNA, with a PCR efficiency of 100.49%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1363-1374
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• 2012

The number and use of Internet connected devices has dramatically increased in the last several years. Therefore many services synchronizing data in real-time is increasing such as mail, calendar and storage service. This service provides convenience to users. However, after devices are seized, the data could be changed because of characteristic about real-time synchronization. Therefore digital investigation could be difficult by this service. This work investigates the traces on each local device and proposes a method for the preservation of real-time synchronized data. Based on these, we propose the procedures of real-time synchronization data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.79-84
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• 2023

Apache Spark, which provides the fastest processing speed among recent distributed and parallel processing technologies, provides real-time functions and machine learning functions. Although official documentation guides for these functions are provided, a method for fusion of functions to predict a specific value in real time is not provided. Therefore, in this paper, we conducted a study to predict the value of data in real time by fusion of these functions. The overall configuration is collected by downloading stock price data provided by the Python programming language. And it creates a model of regression analysis through the machine learning function, and predicts the adjusted closing price among the stock price data in real time by fusing the real-time streaming function with the machine learning function.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• The Journal of Engineering Geology
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• v.34 no.2
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• pp.173-191
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• 2024

Landslides pose significant threats to many countries globally, yet the development and implementation of effective landslide early warning systems (LEWS) remain challenging due to multifaceted complexities spanning scientific, technological, and political domains. Addressing these challenges demands a holistic approach. Technologically, integrating thresholds, such as rainfall thresholds, with real-time data within accessible, open-source software stands as a promising solution for LEWS. This article introduces LandScient_EWS, a PHP-based program tailored to address this need. The software facilitates the comparison of real-time measured data, such as rainfall, with predefined landslide thresholds, enabling precise calculations and graphical representation of real-time landslide advisory levels across diverse spatial scales, including regional, basin, and hillslope levels. To illustrate its efficacy, the program was applied to a case study in Medellin, Colombia, where a rainfall event on August 26, 2008, triggered a shallow landslide. Through pre-defined rainfall intensity and duration thresholds, the software simulated advisory levels during the recorded rainfall event, utilizing data from a rain gauge positioned within a small watershed and a single grid cell (representing a hillslope) within that watershed. By identifying critical conditions that may lead to landslides in real-time scenarios, LandScient_EWS offers a new paradigm for assessing and responding to landslide hazards, thereby improving the efficiency and effectiveness of LEWS. The findings underscore the software's potential to streamline the integration of rainfall thresholds into both existing and future landslide early warning systems.

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1167-1176
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• 2002

In a (hard) real-time system, every time-critical task must meet its timing constraint, which is typically specified in terms of its deadline. Many computer systems, such as those for open system environment or multimedia services, need an efficient schedulability test for on-line real-time admission control of new jobs. Although various polynomial time schedulability tests have been proposed, they often fail to decide the schedulability of the system precisely when the system is heavily loaded. Furthermore, the most of previous studies on on-line real-time schedulability tests are concentrated on periodic task applications. Thus, this paper presents an efficient on-line real-time schedulability check algorithm which can be used for imprecise real-time system predictability before dispatching of on-line imprecise real-time task system consisted of aperiodic and preemptive task sets when the system is overloaded.

• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.1
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• pp.20-26
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• 2006

One of the computer application fields which started showing noticeable new growth trends in recent years is the real time communication distributed computing application field. Object -oriented(OO) real time(RT) distributed computing is a form of real-time distributed computing realized with a distributed computer system structured in the form of an object network. In this paper, we describes the application environment as the DHS (distributed high-precision simulation) with TMO structure. The TMO scheme is aimed for enabling a great reduction of the designer's effort in guaranteeing timely service capabilities of distributed computing application systems. It has been formulated from the beginning with the objective of enabling design-time guaranteeing of timely action. In the real time simulation techniques based on TMO object modeling, we have observed several advantages to the TMO structuring scheme. TMO object modeling has a strong traceability between requirement specification and design, cost-effective high-coverage validation, autonomous subsystems, easy maintenance and flexible framework for requirement specification.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.94-102
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• 2010

In recent years, embedded systems such as cellular phones, Portable Multimedia Player, intelligent appliance, automobile engine control are reshaping the way people live, work, and play. Thereby, services application to guarantee various requirements of users become increasingly sophisticated and complicated, such embedded computing platforms use real-time operating systems (RTOSs) with time determinism. These RTOSs must not only provide predictable services but must also be efficient and small in size. Kernel services should also be deterministic by specifying how long each service call will take to execute. Having this information allows the application designers to better plan their real-time application software so as not to miss the deadline of each task. In this paper, we present the complete generalized real-time scheduling algorithm using multi-dimensional methodology to determine the highest priority in the ready list with 2r levels of priorities in a constant time without additional memory overhead.