• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.327-342
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• 2017

In this paper, we introduce an integrated processing system of smart sensor data for IoT service which collects sensor data and efficiently processes it. Based on the technology of collecting sensor data to the development of the IoT field and sending it to the network · Based on the receiving technology, as various projects such as smart homes, autonomous running vehicles progress, the sensor data is processed and effectively An autonomous control system to utilize has been a problem. However, since the data type of the sensor for monitoring the autonomous control system varies according to the domain, a sensor data integration processing system applying the autonomous control system to various different domains is necessary. Therefore, in this paper, we introduce the Smart Sensor Data Integrated Processing System, apply it and use the window as a reference to process internal and external sensor data 1) receiveData, 2) parseData, 3) addToDatabase 3 With the process of the stage, we provide and implement the automatic window opening / closing system "Smart Window" which ventilates to create a comfortable indoor environment by autonomous control system. As a result, standby information is collected and monitored, and machine learning for performing statistical analysis and better autonomous control based on the stored data is made possible.

• Convergence Security Journal
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• v.22 no.5
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• pp.69-80
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• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.715-720
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• 2023

The military is driving innovative changes such as AI, cloud computing, and drone operation through the Fourth Industrial Revolution. It is expected that such changes will lead to a rapid increase in the demand for information exchange requirements, reaching all lower-ranking soldiers, as networking based on IoT occurs. The flow of such information must ensure efficient information distribution through various infrastructures such as ground networks, stationary satellites, and low-earth orbit small communication satellites, and the demand for information exchange that is distributed through them must be appropriately dispersed. In this study, we redefined the DSCP, which is closely related to QoS (Quality of Service) in information dissemination, into 11 categories and performed research to map each cluster group identified by cluster analysis to the defense "information exchange requirement list" on a one-to-one basis. The purpose of the research is to ensure efficient information dissemination within a multi-layer integrated network (ground network, stationary satellite network, low-earth orbit small communication satellite network) with limited bandwidth by re-establishing QoS policies that prioritize important information exchange requirements so that they are routed in priority. In this paper, we evaluated how well the information exchange requirement lists classified by cluster analysis were assigned to DSCP through M&S, and confirmed that reclassifying DSCP can lead to more efficient information distribution in a network environment with limited bandwidth.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3B
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• pp.261-270
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• 2008

Even though there is a great deal of progress in a numerical method of high caliber like SPH, it is very rarely deployed in a water resources community. Despite the great stride in computing environment, depth averaged approach like a nonlinear shallow equation is still efficient tool for flood routing in large watershed, but it can give some misleading information like the inundation height of flood. In this rationale, we numerically simulate the flow into the dry channel, dry channel with an obstacle triggered by the collapse of a two dimensional water column using SPH (Smoothed Particle Hydrodynamics) in order to boost the application of numerical method of high caliber like SPH in a water resources community. As a most severe test of the robustness of SPH, we also carry out the simulation of the flow through a clearance into the wet channel driven by the rapid removal of a water gate. As a hydrodynamic model, we used the Navier-Stokes equation, a numerical integration of which was carried out using SPH. To verify the validity of newly proposed numerical model, we compare the numerically simulated flow with the others in the literature mainly from VOF and MAC, and hydraulic experiments by Martin and Moyce (1952), Koshizuka et al. (1995) and Janosi et al. (2004). It was shown that agreements between the numerical results in this study and hydraulic experiments are remarkable.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.107-117
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• 2023

It must be possible to assess how combat actions taking place in cyberspace affect the military's major mission systems and weapon systems. In order to analyze the mission impact caused by a cyber attack through cyber M&S, the target mission system and cyber warfare elements must be built as a model and a scenario for simulation must be authored. Many studies related to mission impact analysis due to cyber warfare have been conducted focusing on the United States, and existing studies have authored separate scenarios for physical battlefields and cyber battlefields. It is necessary to build a simulation environment that combines a physical battlefield model and a cyber battlefield model, and be able to integrate and author mission scenarios and cyber attack/defense scenarios. In addition, the physical battlefield and cyber battlefield are different work areas, so authoring two types of scenarios for simulation is very complicated and time-consuming. In this paper, we propose a method of using mission system information to prepare the data needed for scenario authoring in advance and using the pre-worked data to author an integrated scenario. The proposed method is being developed by reflecting it in the design of the scenario authoring tool, and an integrated scenario authoring in the field of counter-fire warfare is being performed to prove the proposed method. In the future, by using a scenario authoring tool that reflects the proposed method, it will be possible to easily author an integrated scenario for mission impact analysis in a short period of time.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.171-180
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• 2023

Hydrogen energy technology is gaining importance in the era of the Fourth Industrial Revolution, offering military advantages when applied to military vehicles due to its characteristics such as reduced greenhouse gas emissions, noise, and low vibration. Korea's military has initiated the Army Tiger 4.0 plan, focusing on hydrogen application, downsizing, and AI-based smart features. The Ministry of National Defense plans to collaborate with the Ministry of Environment to expand hydrogen charging stations nationwide, anticipating increased deployment of military hydrogen vehicles. However, considering the Jet Fire and VCE(Vapor Cloud Explosion) nature of hydrogen, ensuring safety during installation is crucial. Current military guidelines specify a minimum safety distance of 2m from adjacent buildings for charging stations. Scientific methods have been employed to quantitatively assess the accident damage range of hydrogen, proposing a minimum safety distance beyond the affected area.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.633-640
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• 2023

With the recent development of static and dynamic modulated brachytherapy methods in brachytherapy, which use radiation shielding to modulate the dose distribution to deliver the dose, the amount of parameters and data required for dose calculation in inverse treatment planning and treatment plan optimization algorithms suitable for new directional beam intensity modulated brachytherapy is increasing. Although intensity-modulated brachytherapy enables accurate dose delivery of radiation, the increased amount of parameters and data increases the elapsed time required for dose calculation. In this study, a GPU-based CUDA-accelerated dose calculation algorithm was constructed to reduce the increase in dose calculation elapsed time. The acceleration of the calculation process was achieved by parallelizing the calculation of the system matrix of the volume of interest and the dose calculation. The developed algorithms were all performed in the same computing environment with an Intel (3.7 GHz, 6-core) CPU and a single NVIDIA GTX 1080ti graphics card, and the dose calculation time was evaluated by measuring only the dose calculation time, excluding the additional time required for loading data from disk and preprocessing operations. The results showed that the accelerated algorithm reduced the dose calculation time by about 30 times compared to the CPU-only calculation. The accelerated dose calculation algorithm can be expected to speed up treatment planning when new treatment plans need to be created to account for daily variations in applicator movement, such as in adaptive radiotherapy, or when dose calculation needs to account for changing parameters, such as in dynamically modulated brachytherapy.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.71-81
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• 2024

In response to the COVID-19 pandemic, the global airport industry is actively introducing 4th Industrial Revolution technology-based systems for quarantine and passenger safety, and test bed construction and prior verification using airport infrastructure and resources are actively being conducted. Analysis of recent cases shows that despite the changing travel patterns of airport users and the diversification of airport service demands, most testbeds construction studies are still focused on suppliers, and task prioritization is also determined by decision makers. There is a tendency to rely on subjective judgment. In order to find practical ways to become a first mover that leads innovation in the aviation industry, this study selected tasks and derived priorities to build testbeds from a service perspective that reflects various customer service needs and changes. Research results using the AHP analysis method resulted in priorities in the order of access transportation and parking services (29.2%), security screening services (23.4%), and departure services (21.8%), and these analysis results were tested in the airport industry. It shows that innovation in testbeds construction is an important factor. In particular, the establishment of smart parking and UAM transportation testbeds not only helps strengthen airports as centers of technological innovation, but also promotes cooperation with companies, research institutes, and governments, and provides an environment for testing and developing new technologies and services. It can be a foundation for what can be done. The results and implications produced through this study can serve as useful guidelines for domestic and foreign airport practitioners to build testbeds and establish strategies.