• Industry Promotion Research
• /
• v.8 no.2
• /
• pp.61-66
• /
• 2023

This study is a study on ways to revitalize traditional markets in the era of the 4th Industrial Revolution, and a total of 188 of the 2021 Cheonan City traditional market and shopping district fact-finding data were used for research and analysis. In order to revitalize the environment improvement of traditional markets, it can be seen that environmental changes suitable for the times are primarily required. The traditional market is an underdeveloped space, and through the parking environment improvement project and facility modernization project (toilet, facility equipment, etc.), space is improved through a specialization and subdivision process to meet the needs of the times, thereby increasing operational convenience, increasing visitor use, and improving natural perception. can promote In addition, the efficiency and effectiveness of the traditional market vitalization project can be increased when differentiated management vitalization contents and facility modernization projects are organically harmonized. This study is meaningful in that it analyzes the environmental improvement of facilities among the traditional market vitalization factors for the vitalization of the traditional market, and proposes a plan that can be a practical alternative to the government support policy for the traditional market.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.9
• /
• pp.259-269
• /
• 2017

As emerging the '4th Industrial Revolution' by increasing the necessity of the intelligent system recently, 'Autonomic Control System' also has been the important issue. It is necessary to develop the system collecting data of machines and sensors for the autonomic control system to monitor the target system. But it is difficult to collect data because data formats of machines and sensors of the existing factories differ between each manufacturer. Therefore, this paper presents and implements data collecting and processing system that comprise 3 steps including 'ParseBuffer', 'ProcessData' and 'AddToBuffer' by using 'MTConnect' that is standard manufacturing facility data collecting middleware. Through the suggested system, we can get data in a common format usable in an autonomous control system. As a case study, we experimented with the generation and collection of AGV (Automated Guided Vehicle) data, which is an unattended transportation system in the factory. To accomplish this, we defined the data type in accordance with the MTConnect standard and confirmed the data collected through the proposed system.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.6
• /
• pp.1-8
• /
• 2020

Communication between system modules is applied using the Modbus protocol in industrial sites including smart factories, industrial drones, building energy management systems, PLCs, ships, trains, and airplanes. The existing Modbus was used for serial communication, but the recent Modbus protocol is used for TCP/IP communication.The Modbus protocol supports RTU, TCP and ASCII, and implements and uses protocols in embedded systems. However, the transmission I/O devices for RTU, TCP, and ASCII-based protocols may differ. For example, RTU and ASCII communications transmit on a serial-based communication protocol, but in some cases, Ethernet TCP/IP transmission is required. In particular, since the C language (object-oriented) is used in embedded systems, the complexity of source code related to I/O registers increases. In this study, we designed software that can logically separate I/O functions from embedded devices, and designed the execution logic of each instance requiring I/O processing through a delegate class instance with Modbus RTU, TCP, and ASCII protocol generation. We designed and experimented with software that can separate communication I/O processing and logical execution logic for each instance.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.6
• /
• pp.834-841
• /
• 2022

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.5
• /
• pp.211-220
• /
• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.2
• /
• pp.48-59
• /
• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• Korean Chemical Engineering Research
• /
• v.60 no.3
• /
• pp.334-346
• /
• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• Journal of KIISE
• /
• v.44 no.2
• /
• pp.213-222
• /
• 2017

The Internet of Things (IoT) technology is rapidly developing in recent years, and is applicable to various fields. A smart factory is one wherein all the components are organically connected to each other via a WSN, using an intelligent operating system and the IoT. A smart factory technology is used for flexible process automation and custom manufacturing, and hence needs adaptive network management for frequent network fluctuations. Moreover, ensuring the timeliness of the data collected through sensor nodes is crucial. In order to ensure network timeliness, the power consumption for information exchange increases. In this paper, we propose an IEEE 802.15.4e DSME-based distributed scheduling algorithm for mobility support, and we evaluate various performance metrics. The proposed algorithm adaptively assigns communication slots by analyzing the network traffic of each node, and improves the network reliability and timeliness. The experimental results indicate that the throughput of the DSME MAC protocol is better than the IEEE 802.15.4e TSCH and the legacy slotted CSMA/CA in large networks with more than 30 nodes. Also, the proposed algorithm improves the throughput by 15%, higher than other MACs including the original DSME. Experimentally, we confirm that the algorithm reduces power consumption by improving the availability of communication slots. The proposed algorithm improves the power consumption by 40%, higher than other MACs.

• Korean Chemical Engineering Research
• /
• v.58 no.4
• /
• pp.565-572
• /
• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.6
• /
• pp.867-874
• /
• 2021

The IoT (Internet of Things) technology is rapidly becoming an important consideration in many engineering fields in the current 4th industrial era. In recent years, the concepts of digital shipbuilding and smart factories have been adopted as trends in shipyards. However, there is active interest in research on implementing autonomous driving in autonomous vehicles and airplanes, which is currently available in commercial form in a limited capacity. The present study is regarding the path-tracking performance of a boat to accomplish an autonomous driving mission using a flight controller (FC) and real-time kinematic (RTK) global positioning system (GPS) based on an open-source Ardupilot; an actual sea test is also performed using this system on a calm lake. The boat's mission is to evaluate the maneuverability of the self-driving process to a specific point and returning to the home position. For a given speed, the difference between the preset mission trajectory and actual operational trajectory was analyzed, and a series of studies were conducted on the applicability of the system to ships. In addition, the movements and maneuverability of the OmniX-type hull with four propellers were investigated, and the driving path-tracking performance was observed to increase by a maximum of 48%.