• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.270-278
• /
• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• Journal of Drive and Control
• /
• v.20 no.4
• /
• pp.140-149
• /
• 2023

Wire harness is a component for efficient control when electronic parts are required such as construction machinery and electric vehicles. With emerging issues such as autonomous driving and automation in construction, a wire harness composed of multiple cables has become an essential part because more electronic parts are required. However, when a wire harness failure occurs, systems can be stopped, accidents can occur, and economic damage can be significant. Therefore, in this paper, we developed a digital fault screening system that could easily and quickly diagnose faults in the wire harness. The principle of the developed system was to sequentially send pulse signals to the wire harness and use returned signals to perform fault detection. As a result of diagnosing faults using the developed failure detection system, a detection accuracy of 99.9 % was confirmed through the experiments.

• Corrosion Science and Technology
• /
• v.23 no.2
• /
• pp.131-138
• /
• 2024

This paper introduces Primetals Technologies' Through-Process Optimization (TPO) Services and Through-Process Quality Control (TPQC) System, which integrate domain knowledge, software, and automation expertise to assist steel producers in achieving operational excellence. TPQC collects high-resolution process and product data from the entire production route, providing visualizations and facilitating quality assurance. It also enables the application of artificial intelligence techniques to optimize processes, accelerate steel grade development, and enhance product quality. The main objective of TPO is to grow and digitize operational know-how, increase profitability, and better meet customer needs. The paper describes the contribution of these systems to achieving operational excellence, with a focus on quality assurance. Transparent and traceable production data is used for manual and automatic quality evaluation, resulting in product quality status and guiding the product disposition process. Deviation management is supported by rule-based and AI-based assistants, along with monitoring, alarming, and reporting functions ensuring early recognition of deviations. Embedded root cause proposals and their corrective and compensatory actions facilitate decision support to maintain product quality. Quality indicators and predictive quality models further enhance the efficiency of the quality assurance process. Utilizing the quality assurance software package, TPQC acts as a "one-truth" platform for product quality key players.

• Smart Media Journal
• /
• v.13 no.3
• /
• pp.45-52
• /
• 2024

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

• Annual Conference of KIPS
• /
• 2007.11a
• /
• pp.395-398
• /
• 2007

오늘날 기업에서의 IT 는 급격한 비즈니스 환경의 변화와 다양한 비즈니스 모델의 출현으로 인해 점점 더 복잡해지고 있고, 비즈니스의 IT 의존도 또한 지속적으로 증가하고 있다. 이러한 상황에서 기업의 정보시스템을 운영하는 IT 운영조직은 사용자에게 IT 서비스의 품질향상과 비즈니스의 연속성을 보장할 수 있는 지원체계를 필요로 하고 있다. 1990년대 후반에 들어서 ITIL 을 기반으로 한 ITSM 개념이 국내에 도입 되면서 기존의 콜센터와 헬프데스크를 통한 IT 서비스지원 방식을 서비스데스크로 통합하게 되었고, 서비스데스크 자동화 시스템의 도입을 통해 기업의 모든 비즈니스 조직의 IT 와 관련된 요구사항을 관리하고 있다. 하지만 이러한 서비스데스크 자동화 시스템은 IT 서비스 제공자 관점에서 구축되었고 운영방향 또한 어플리케이션, 서버, 네트워크 등 기술적인 구성요소의 성능 향상에만 초점을 두었기 때문에 현업 사용자들이 만족 할 수 있는 실질적인 수준의 서비스 개선은 이루어지지 못하고 있다. 이에 본 연구에서는 고객관점에서의 요구사항 분석을 통해, 운영조직의 IT 서비스 요청에 대한 처리활동을 단순처리와 일반처리로 분류하고 이를 자동화 시스템에 반영 하였고, 실제 A 사(社)의 IT 운영조직에 적용하여, 비효율적인 저부가가치 활동과 고부가 가치의 활동이 시스템을 통해 분류가 가능함을 보였다. 아울러 IT 운영조직의 활동을 정량적으로 관리 할 수 있도록 관리지표를 발굴하고 자동화 시스템의 서비스요청관리 프로세스에 적용하여 IT 서비스를 제공받는 회사의 관리조직과 경영층이 IT 운영조직의 활동을 정량적으로 관리하고 측정할 수 있는 IT 서비스데스크 자동화 시스템의 개선모델을 제시 하였다.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.2
• /
• pp.59-67
• /
• 2024

Current straw mushroom farming in countries with large rice growing areas has great development potential, and was once considered a way to generate additional income and reduce poverty in rural areas. However, currently most people still grow mushrooms using traditional processes, leading to low productivity and unguaranteed output quality. Currently, due to climate change and unusual weather changes, people tend to switch to growing straw mushrooms indoors. In the process of growing straw mushrooms indoors, the design of an automatic control and monitoring system is very important to ensure the growing process is carried out effectively and achieves high yields. In this paper, we propose a system that can automatically control and monitor the humidity and temperature of the indoor straw mushroom growing process and other parameters that can be monitored through a network system using Internet of Things. The control algorithm automatically adjusts the grow house equipment based on feedback from sensors to maintain an optimal environment for growing straw mushrooms. Experimental results show that the straw mushroom growing system with automatically controlled and monitored environmental parameters helps improve efficiency, reduce costs and increase the sustainability of the current straw mushroom growing industry.

• Journal of Advanced Navigation Technology
• /
• v.28 no.2
• /
• pp.210-215
• /
• 2024

With increasing automation in ships and the development of autonomous ships, an IPMS (Integrated Platform Management System) in ship needs to integrate and process large amounts of real-time data from various equipment operating on different operating systems. A ship IPMS simulator based on data distribution service (DDS) was developed and its performance was evaluated to handle data processing similar in real ship environment. Errors were monitored while 1,000 topics/sec were asynchronously published and subscribed from data acquisitive units (DAUs) and an IPMS server operating on different operating systems. Except for the loss of some topics during the initial 4 seconds, topics were received without an error thereafter. The developed simulator demonstrates the feasibility of using DDS to integrate various control systems using different operating systems in actual ships.

• Journal of Sensor Science and Technology
• /
• v.33 no.3
• /
• pp.179-184
• /
• 2024

In the car industry, welding is a fundamental linking technique used for joining components, such as steel, molds, and automobile parts. However, accurate inspection is required to test the reliability of the welding components. In this study, we investigate the detection of weld beads using 2D image processing in an automatic recognition system. The sample image is obtained using a 2D vision camera embedded in a lighting system, from where a portion of the bead is successfully extracted after image processing. In this process, the soot removal algorithm plays an important role in accurate weld bead detection, and adopts adaptive local gamma correction and gray color coordinates. Using this automatic recognition system, geometric parameters of the weld bead, such as its length, width, angle, and defect size can also be defined. Finally, on comparing the obtained data with the industrial standards, we can determine whether the weld bead is at an acceptable level or not.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.30 no.3
• /
• pp.263-274
• /
• 2024

The digitization of ship environments has increased the risk of cyberattacks on ships. The smartization and automation of ships are also likely to result in cyber threats. The International Maritime Organization (IMO) has discussed the establishment of regulations at the autonomous level and has revised existing agreements by dividing autonomous ships into four stages, where stages 1 and 2 are for sailors who are boarding ships while stages 3 and 4 are for those not boarding ships. In this study, the level of a smart ship was classified into LEVELs (LVs) 1 to 3 based on the autonomous levels specified by the IMO. Furthermore, a risk assessment for smart ships at various LVs in different risk scenarios was conducted The cyber threats and vulnerabilities of smart ships were analyzed by dividing them into administrative, physical, and technical security; and mitigation measures for each security area were derived. A total of 22 cyber threats were identified for the cyber asset (target system). We inferred that the higher the level of a smart ship, the greater the hyper connectivity and the remote access to operational technology systems; consequently, the greater the attack surface. Therefore, it is necessary to apply mitigation measures using technical security controls in environments with high-level smart ships.

• Electronics and Telecommunications Trends
• /
• v.39 no.4
• /
• pp.54-62
• /
• 2024

The emergence and resurgence of novel respiratory infectious diseases since the turn of the millennium, including SARS, H1N1 flu, MERS, and COVID-19, have posed a significant global health threat. Efforts to combat these threats have involved various approaches, however, continued research and development are crucial to prepare for the possibility of emerging viruses and viral variants. Direct detection methods for viral pathogens include molecular diagnostic techniques and immunodiagnostic methods, while indirect diagnostic methods involve detecting changes in the condition of infected patients through imaging diagnostics, gas analysis, and biosignal measurement. Molecular diagnostic techniques, utilizing advanced technologies such as gene editing, are being developed to enable faster detection than traditional PCR methods, and research is underway to improve the efficiency of diagnostic devices. Diagnostic technologies for infectious diseases continue to evolve, and several key trends are expected to emerge in the future. Automation will facilitate widespread adoption of rapid and accurate diagnostics, portable diagnostic devices will enable immediate on-site diagnosis by healthcare professionals, and advancements in AI-based deep learning diagnostic models will enhance diagnostic accuracy.