• International journal of advanced smart convergence
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• v.11 no.1
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• pp.42-47
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• 2022

As smart factory is the factory which produces the products according to the customer's diverse demand and the changing conditions in it, it can be characterized by flexible production, dynamic reconstruction, and optimized production environment. To implement these characteristics, many kind of configuration elements in the smart factory should be connected to and communicated with each other. So the network is responsible for playing this role in the smart factory. As SDN (Software Defined Network) is the technology that can dynamically cope with the explosive increasing data amount and the hourly changing network condition, it is one of network technologies that can be applied to the smart factory. In this paper, we address SDN function and operation, SDN model suitable for the smart factory, and then performs the simulation for measuring this model.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.351-356
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• 2007

Factory location selection is very important to the success of operation of the whole supply chain, but few effective solutions exist to deliver a good result, motivated by this, this paper tries to introduce a new factory location selection methodology by employing the artificial neural networks technology. First, we reviewed previous research related to factory location selection problems, and then developed a (neural network-based factory selection model) NNFSM which adopted back-propagation neural network theory, next, we developed computer program using C++ to demonstrate our proposed model. then we did case study by choosing a Korean steelmaking company P to show how our proposed model works,. Finnaly, we concluded by highlighting the key contributions of this paper and pointing out the limitations and future research directions of this paper. Compared to other traditional factory location selection methods, our proposed model is time-saving; more efficient.and can produce a much better result.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1305-1311
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• 2016

The smart factory, a combination of ICT technology to the entire production process of a product, means can you intelligent factory is to achieve such reduction and process improvement of the production cost. To implement the smart factory, inevitably must have an internal equipment connections to the external network, this is by equipment which is operated by the existing closure network is exposed to the outside network, the security vulnerability so that gender is increased. In order to solve this problem, it is possible to apply security solutions that are used in normal environments. However, it is impossible to have just completely blocking security threats that can occur in a smart factory network. Further, considering the economic damage that can occur during security breach accident, which cannot be not a serious problem. Therefore, in this paper, a look to know the security measures that can be applied to smart factory, to introduce the main fusion security technology necessary to smart factory dedicated security gateway.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.586-592
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• 2000

There is currently no satisfactory way to optimize supplemental lighting in a greenhouse-type plant factory especially concerning plant production. In a commercial plant factory, we got outside radiation data, inside radiation data and lamp running data. They have a correlation, but have much disorder. By using regression, tendency between the outside and the inside including supplemental lighting was found. We could estimate the average transmittance of this plant factory. From this estimation, we could admit the amount of inside radiation was supplied as much supplied compared to natural radiation. Then we are trying to investigate of the production amount and the supplemental lighting. Plant factory is environmentally controlled, the temperature and humidity are not actually controlled stable. We propose a design of neural network model could be useful to estimate the profit resulting from the operation of supplemental lighting.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3C
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• pp.233-240
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• 2012

Unlike traditional factory environment, in an industrial factory network applied wireless sensor network technologies, all procedures of discovery, identification and verification of devices should be performed in an automatic fashion. To address these challenges, we design a management system using the device registry server that we propose in this paper. In the phase of device discovery, the proposed system utilizes properties of routing protocol running in factories. Also, in the phase of identification and verification, the system uses unique and general information of a device stored within the device registration server. Such a way allows management system to reduce implementation complexity and to easily manage devices in a factory applied with a wireless network consisting of heterogeneous devices.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.173-181
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• 2023

Data collection is an essential element in the construction and operation of a smart factory. The quality of data collection is greatly influenced by network conditions, and existing wireless network systems for IoT inevitably lose data due to wireless signal strength. This data loss has contributed to increased system instability due to misinformation based on incorrect data. In this study, I designed a distributed MQTT IoT smart sensor and gateway structure that supports wireless multicasting for smooth sensor data collection. Through this, it was possible to derive significant results in the service latency and data loss rate of packets even in a wireless environment, unlike the MQTT QoS-based system. Therefore, through this study, it will be possible to implement a data collection management system optimized for the domestic smart factory manufacturing environment that can prevent data loss and delay due to abnormal data generation and minimize the input of management personnel.

• Journal of the Korean Applied Science and Technology
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• v.26 no.2
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• pp.151-160
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• 2009

At this study, we are developing a possible control system through remote monitoring for fire protection in various chemical factory facilities. It's possible to do real time confirmation of a normal operation presence of the various equipment installed in a chemical factory through the internet network at a fire fighting head office, an area fire department and a chemical factory situation room using this remote monitoring control system. When occurring, abnormal operation is the remote monitoring control system, which can check this immediately and notify the situation room administrator. After it was tested using developed remote monitoring control system, the remote monitoring for which the internet network was used confirmed possible.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.203-208
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• 2024

The convergence of deep learning and smart factory is drawing a lot of attentions from not only industrial but also academic circles. The objective of this article is to quantitatively review on deep learning and smart factory from 2010 to 2023. This research analyzed the 138 articles, extracted from the Core Collection of Web of Science, in terms of four dimensions such as the main trend in article publications, the main trend in article citations, the distribution of article publications by research area, and the keywords representing the main contents of published articles. The quantitative review results reveal the following four points: First, the article publications drastically grew from 2019 to 2022 in its annual trend. Second, the article citations have rapidly grown since 2018. Third, Engineering, Computer Science, and Telecommunications are the top 3 research areas composing the 138 articles. Fourth, it is the top 10 keywords such as 'deep', 'learning', 'smart', 'detection', factory', 'data', 'system', 'manufacturing', 'neural', and 'network' that represent the main contents of the 138 articles published from 2010 to 2023 in deep learning and smart factory. These findings revealed by this quantitative review will be significantly useful for deepening and widening relevant future research on deep learning and smart factory.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.95-98
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• 2002

Monitoring of the cutting trouble is necessarily required to do Factory Automation and Intelligent manufacturing system. Therefore, we constructed a monitoring system using neural network in order to monitor of the cutting trouble. From obtained result, it is shown that the cutting trouble can be monitored effectively by neural network

• Convergence Security Journal
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• v.7 no.4
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• pp.1-8
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• 2007

Currently, the needs of open standard and integrated network, and also applications of Ethernet network based system are increasing. By applying and expanding TCP/IP based network technology from high to low control level, factory automation application has been implemented, and thus integrated factory automation system has been accomplished, which is directly interoperable between high level management program and DB system. This paper proposes a communication control device which is based on Ethernet/IP standard system for factory automation. The proposed system is workable in the environment of a network system, which is based on CIP standard interface technology. The proposed system can also contribute to improve data communication characteristics.