• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.187-193
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• 2019

Key-value storage engines are an essential component of growing demand in many computing environments, including social networks, online e-commerce, and cloud services. Recent key-value storage engines offer many features such as transaction, versioning, and replication. In a key-value storage engine, transaction processing provides atomicity through Write-Ahead-Logging (WAL), and a synchronous commit method for transaction processing flushes log data before the transaction completes. According to our observation, flushing log data to persistent storage is a performance bottleneck for key-value storage engines due to the significant overhead of fsync() calls despite the various optimizations of existing systems. In this article, we propose a group synchronization method to improve the performance of the key-value storage engine. We also design and implement a transaction scheduling method to perform other transactions while the system processes fsync() calls. The proposed method is an efficient way to reduce the number of frequent fsync() calls in the synchronous commit while supporting the same level of transaction provided by the existing system. We implement our scheme on the WiredTiger storage engine and our experimental results show that the proposed system improves the performance of key-value workloads over existing systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.7-18
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• 2021

Recently, various connected industrial parks (CIPs) architectures using new technologies such as cloud computing, CPS, big data, fifth-generation mobile communication 5G, IIoT, VR-AR, and ventilation transportation AI algorithms have been proposed in Korea. Korea's small and medium-sized enterprises do not have the upper hand in technological competitiveness than overseas advanced countries such as the United States, Europe and Japan. For this reason, Korea's small and medium-sized enterprises have to invest a lot of money in technology research and development. As a latecomer, Korean SMEs need to improve their profitability in order to find sustainable growth potential. Financially, it is most efficient for small and medium-sized Korean companies to cut costs to increase their profitability. This paper made profitability improvement by reducing costs for small and medium-sized enterprises located in CIPs in Korea a major task. VJP (Vehicle Action Program) was noted as a way to reduce costs for small and medium-sized enterprises located in CIPs in Korea. The method of achieving minimum logistics costs for small businesses through the Korean CIPs payment system was analyzed. The details of the new Korean CIPs payment system were largely divided into four types: "Business", "Data", "Technique", and "Finance". Cost Benefit Analysis (CBA) was used as a performance analysis method for CIPs payment systems.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.15-29
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• 2021

Many countries and companies are pursuing and developing Artificial intelligence as it is the core technology of the 4th industrial revolution. Global IT companies such as Apple, Microsoft, Amazon, Google and Samsung have all released their own AI assistant hardware products, hoping to increase customer loyalty and capture market share. Competition within the industry for AI agent is intense. AI assistant products that command the biggest market shares and customer loyalty have a higher chance of becoming the industry standard. This study analyzed the current status of major overseas and domestic IT companies in the field of artificial intelligence, and suggested future strategic directions for voice UI technology development and user satisfaction. In terms of B2B technology, it is recommended that IT companies use cloud computing to store big data, innovative artificial intelligence technologies and natural language technologies. Offering voice recognition technologies on the cloud enables smaller companies to take advantage of such technologies at considerably less expense. Companies also consider using GPT-3(Generative Pre-trained Transformer 3) an open source artificial intelligence language processing software that can generate very natural human-like interactions and high levels of user satisfaction. There is a need to increase usefulness and usability to enhance user satisfaction. This study has practical and theoretical implications for industry and academia.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.381-394
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• 2022

Recently, due to the personalization and customization of data, Internet-based services have increased requirements for real-time processing, such as real-time AI inference and data analysis, which must be handled immediately according to the user's situation or requirement. Real-time tasks have a set deadline from the start of each task to the return of the results, and the guarantee of the deadline is directly linked to the quality of the services. However, traditional container systems are limited in operating real-time tasks because they do not provide the ability to allocate and manage deadlines for tasks executed in containers. In addition, tasks such as AI inference and data analysis basically utilize graphical processing units (GPU), which typically have performance impacts on each other because performance isolation is not provided between containers. And the resource usage of the node alone cannot determine the deadline guarantee rate of each container or whether to deploy a new real-time container. In this paper, we propose a monitoring technique for tracking and managing the execution status of deadlines and real-time GPU tasks in containers to support real-time processing of GPU tasks running on containers, and a node list management technique for container placement on appropriate nodes to ensure deadlines. Furthermore, we demonstrate from experiments that the proposed technique has a very small impact on the system.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.10-10
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• 2023

본질적으로 복잡하고 다양한 특성을 가지는 우리나라(도시, 농어촌, 도서산간, 섬 등)의 물 공급 시스템은 생활수준의 향상, 기후변화 및 가뭄위기, 소비환경 중심의 요구와 한정된 수자원을 잘 활용하기 위한 운영 및 관리가 매우 복잡하다. 이로 인한 수자원 고갈과 가뭄위기 등에 관련한 대책 및 방안으로 대체수자원인 지하수 활용방안들이 제시되고 있다. 따라서, 물 관리 시스템과 관련한 디지털 기술은 오늘날 플랫폼과 디지털 트윈의 도입을 통해 네트워크와 가상현실 세계의 연결이 통합되어진 4차 산업혁명 사업이 현실화되고 있다. 물 관리 시스템에 사용된 새로운 디지털 기술 "BDA(Big Data Analytics), CPS(Cyber Physical System), IoT(Internet of Things), CC(Cloud Computing), AI(Artificial Intelligence)" 등의 성장이 증가함에 따라 가뭄대응 위기와 도시 지하수 물 순환 시스템 운영이 증가하는 소비자 중심의 수요를 충족시키기 위해서는 지속가능한 지하수 공급을 효과적으로 관리되어야 한다. 4차 산업혁명과 관련한 기술성장이 증가함으로 인한 물 부문은 시스템의 지속가능성을 향상시키기 위해 전체 디지털화 단계로 이동하고 있다. 이러한 디지털 전환의 핵심은 데이터에 관한 것이며, 이를 활용하여 가치 창출을 위해서 "Digital Groundwater Technology/Twin(DGT)"를 극대화하는 방식으로 제고해야 한다. 현재 당면하고 있는 기후위기에 따른 가뭄, 홍수, 녹조, 탁수, 대체수자원 등의 수자원 재해에 대한 다양한 대응 방안과 수자원 확보 기술이 논의되고 있다. 이에 따른 "물 순환 시스템"의 이해와 함께 문제해결 방안도출을 위하여 이번 "기획 세션"에서는 지하수 수량 및 수질, 정수, 모니터링, 모델링, 운영/관리 등의 수자원 데이터의 플랫폼 동시성 구축으로부터 역동적인 "DGT"을 통한 디지털 트윈화하여, 지표수-토양-지하수 분야의 특화된 연직 프로파일링 관측기술을 다각도로 모색하고자 한다. "Digital Groundwater(DG)"는 지하수의 물 순환, 수량 및 수질 관리, 지표수-지하수 순환 및 모니터링, 지하수 예측 모델링 통합연계를 위해 지하수 플랫폼 동시성, ChatGPT, CPS 및 DT 등의 복합 디지털화 단계로 나가고 있다. 복잡한 지하환경의 이해와 관리 및 보존을 위한 지하수 네트워크에서 수량과 수질 데이터를 수집하기 위한 스마트 지하수 관측기술 개발은 큰 도전이다. 스마트 지하수 관측기술은 BD분석, AI 및 클라우드 컴퓨팅 등의 디지털 기술에 필요한 획득된 데이터 분석에 사용되는 알고리즘의 복잡성과 데이터 품질에 따라 영향을 미칠 수 있기 때문이다. "DG"는 지하수의 정보화 및 네트워크 운영관리 자동화, 지능화 등을 위한 디지털 도구를 활용함으로써 지표수-토양층-지하수 네트워크 통합관리에 대한 비전을 만들 수 있다. 또한, DGT는 지하수 관측센서의 1차원 데이터 융합을 이용한 지하수 플랫폼 동시성과 디지털 트윈을 연계할 수 있다.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.253-262
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• 2023

E-voting is a concept that includes actions such as kiosk voting at a designated place and internet voting at an unspecified place, and has emerged to alleviate the problem of consuming a lot of resources and costs when conducting offline voting. Using E-voting has many advantages over existing voting systems, such as increased efficiency in voting and ballot counting, reduced costs, increased voting rate, and reduced errors. However, centralized E-voting has not received attention in public elections and voting on corporate agendas because the results of voting cannot be trusted due to concerns about data forgery and modulation and hacking by others. In order to solve this problem, recently, by designing an E-voting system using blockchain, research has been actively conducted to supplement concepts lacking in existing E-voting, such as increasing the reliability of voting information and securing transparency. In this paper, we proposed an electronic voting system that introduced hybrid blockchain that uses public and private blockchains in convergence. A hybrid blockchain can solve the problem of slow transaction processing speed, expensive fee by using a private blockchain, and can supplement for the lack of transparency and data integrity of transactions through a public blockchain. In addition, the proposed system is implemented as BaaS to ensure the ease of type conversion and scalability of blockchain and to provide powerful computing power. BaaS is an abbreviation of Blockchain as a Service, which is one of the cloud computing technologies and means a service that provides a blockchain platform ans software through the internet. In this paper, in order to evaluate the feasibility, the proposed system and domestic and foreign electronic voting-related studies are compared and analyzed in terms of blockchain type, anonymity, verification process, smart contract, performance, and scalability.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.311-323
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• 2023

Oil spill accidents can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. In the case of oil spill detection using satellite imagery, it is possible to detect a wide range of oil spill areas by utilizing the information collected from various sensors equipped on the satellite. Previous studies have analyzed the reflectance of oil at specific wavelengths and have developed an oil spill index using bands within the specific wavelength ranges. When analyzing multiple images before and after an oil spill for monitoring purposes, a significant amount of time and computing resources are consumed due to the large volume of data. By utilizing Google Earth Engine, which allows for the analysis of large volumes of satellite imagery through a web browser, it is possible to efficiently detect oil spills. In this study, we evaluated the applicability of four types of oil spill indices in the area of various land cover using Sentinel-2 MultiSpectral Instrument data and the cloud-based Google Earth Engine platform. We assessed the separability of oil spill areas by comparing the index values for different land covers. The results of this study demonstrated the efficient utilization of Google Earth Engine in oil spill detection research and indicated that the use of oil spill index B ((B3+B4)/B2) and oil spill index C (R: B3/B2, G: (B3+B4)/B2, B: (B6+B7)/B5) can contribute to effective oil spill monitoring in other regions with complex land covers.