• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.3
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• pp.509-516
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• 2008

Recently, the operator modules to control external devices are concerned about automatic management system to process continuously changed signals. These signals are the stream data of which characteristics are several numbers. a short report interval and asynchronous report time. It is necessary that the system brings about high accuracy and real time process for stream data. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query at a past time, the sliding window query from a past time to current. In this paper, we propose the efficient method to manage the above signals by using a file structured database in small-size operating systems. We also propose a query model to accommodate various queries including the sliding window query. The file database in the QNX adopts a delta version and a shared memory buffering method for the resource limit of a small storage and a low computing power.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1147-1159
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• 2018

This study focuses on the verification of a rapid protection automation system using an inflatable structure. The inflatable structure is an automatic rapid protection system against human and material damage when the subsea tunnel is flooded. Especially, it is essential for construction and operation of subsea tunnels. In this study, we have experimentally verified the rapid protection automation system using the inflatable structure designed for this problem. In order to verify this, a model tunnel with a 40: 1 reduction ratio was constructed, and air pressure of 0.1 bar and 0.15 bar was injected to divide the tunnel according to the expansion rate at 10 sec and 20 sec. According to the results of the study, the protection efficiency was better at 0.15 bar than 0.1 bar when the expansion structure was expanded, and the protection efficiency and influent control efficiency were different according to the pneumatic injection time of the inflating structure. As a result of this study, it was found that the higher the internal air pressure of the inflated structure and the faster the inflation of rate, the more effectively the inflated structure was inflated. As a result of this study, it is necessary to further study the wedge type structure which is useful for the storage method of expansion structure, shape and expansion derivative, inhibition of expansion structure during protection and control of inflow water.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.517-524
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• 2003

There have been worldwide research and development efforts to automate various processes of bio-production and those efforts will be expanded with priority given to tasks which require high intensive labor or produce high value-added product and tasks under hostile environment. In the field of bio-production capabilities of the versatility and robustness of automated system have been major bottlenecks along with economical efficiency. This paper introduces a new concept of automation based on tole-operation, which can provide solutions to overcome inherent difficulties in automating bio-production processes. Operator(farmer), computer, and automatic machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. Among processes of greenhouse watermelon cultivation tasks such as pruning, watering, pesticide application, and harvest with loading were chosen based on the required labor intensiveness and functional similarities to realize the proposed concept. The developed system was composed of 5 major hardware modules such as wireless remote monitoring and task control module, wireless remote image acquisition and data transmission module, gantry system equipped with 4 d.o.f. Cartesian type robotic manipulator, exchangeable modular type end-effectors, and guided watermelon loading and storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. The proposed system showed practical and feasible way of automation in the field of volatile bio-production process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.36-42
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• 2014

Domestic 105mm HE (High Explosive) shell is composed of three parts that are Fuze, Projectile and Propellants. Among three parts, propelling charge of propellants part consists of single base propellants. It has been known that the lifespan of single base propellants is affected by a storage period. These are because Nitrocellulose (NC) which is the main component of propelling gunpowder can be naturally decomposed to unstable substances similar with other nitric acid ester. Even though it cannot be prevented fundamentally from being disassembled, a decomposition product ($NO_2$, $NO_3$, and $HNO_3$) and tranquillizer DPA (Diphenylamine) having high reactivity are added into a propellant to restrain induction of automatic catalysis by a decomposition product. The decay rate of the tranquillizer is also affected by a production rate of the decomposition product of NC. Therefore, an accurate prediction of the Self-Life is required to ensure against risks such as explosion. Hereupon, this paper presents a new methodology to estimate the shelf-life of single base propellants using data of ASRP (Ammunition Stockpile Reliability Program) to domestic 105mm HE (propelling charge of propellants part). We selected four attributes that are inferred to have influence on distribution of the DPA amount in a propellant from the ASRP dataset through data mining processes. Then the selected attributes were used as independent variables in a regression analysis in order to estimate the shelf-life of single base propellants.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.461-465
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• 2006

Recently, the operator modules to control external devices are concerned about automatic management system to process continuously changed signals. They need a efficient data management with high reliability and real time processing. The characteristics of these data are a large volume, a short report interval and asynchronous report time. The typical queries of these systems consist of the current query to search the latest signal value, the snapshot query to search the signal value of a past time, the historical query to search the signal value of a past tine to current. In this paper, we propose the efficient method to manage the above signals by using a file structured database in QNX operating systems. The data communications among the devices are done by Profibus-FMS protocol and the file databases are used for adjusting monitoring frequency and storing signals. The file database adopts a delta version and a periodical back up in due consideration of the resource limit of a small storage and a low computing power in QNX COM(Cabinet Operator Module).

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.255-263
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• 2021

Smart grid is a fully-automated, bi-directional, power transmission network based on the physical grid system, which combines sensor measurement, computer, information communication, and automatic control technology. Blockchain technology, with its security features, can be integrated with Smart Grids to provide secure and efficient power management and transmission. This paper dicusses the deployment of Blockchain technology in Smart Grid. It presents application areas and protocols in which blockchain can be applied to in securing smart grid. One application of each area is explored in detail, such as efficient peer-to-peer transaction, lower platform costs, faster processes, greater flexibility in power generation to transmission, distribution and power consumption in different energy storage systems, current barriers obstructing the implementation of blockchain applications with some level of maturity in financial services but concepts only in energy and other sectors. Wide range of energy applications suggesting a suitable blockchain architecture in smart grid operations, a sample block structure and the potential blockchain technicalities employed in it. Also, added with efficient data aggregation schemes based on the blockchain technology to overcome the challenges related to privacy and security in the smart grid. Later on, consensus algorithms and protocols are discussed. Monitoring of the usage and statistics of energy distribution systems that can also be used to remotely control energy flow to a particular area. Further, the discussion on the blockchain-based frameworks that helps in the diagnosis and maintenance of smart grid equipment. We have also discussed several commercial implementations of blockchain in the smart grid. Finally, various challenges have been discussed for integrating these technologies. Overall, it can be said at the present point in time that blockchain technology certainly shows a lot of potentials from a customer perspective too and should be further developed by market participants. The approaches seen thus far may have a disruptive effect in the future and might require additional regulatory intervention in an already tightly regulated energy market. If blockchains are to deliver benefits for consumers (whether as consumers or prosumers of energy), a strong focus on consumer issues will be needed.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.143-154
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• 2004

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multi-spectral automatic Aerial photographic system(PKNU 2). This system's Multi-spectral camera can catch the visible(RGB) and infrared(NIR) bands($3032{\times}2008$ pixels) image. Visible and infrared bands images were obtained from each camera respectively and produced Color-infrared composite images to be analyzed in the purpose of the environment monitor but that was not very good data. Moreover, it has a demerit that the stereoscopic overlap area is not satisfied with 60% due to the 12s storage time of each data, while it was possible that PKNU 2 system photographed photos of great capacity. Therefore, we have been developing the advanced PKNU 2(PKNU 3) that consists of color-infrared spectral camera can photograph the visible and near infrared bands data using one sensor at once, thermal infrared camera, two of 40 G computers to store images, and MPEG board to compress and transfer data to the computer at the real time and can attach and detach itself to a helicopter. Verification and calibration of each sensor(REDLAKE MS 4000, Raytheon IRPro) were conducted before we took the aerial photographs for obtaining more valuable data. Corrections for the spectral characteristics and radial lens distortions of sensor were carried out.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.74-82
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• 2015

In this study, a BMW plant process control system model which produces BMW is suggested and the BMW plant process controller with the following functions is developed. The first function is to operate the electronic overload relays to stop the blower for a certain period of time and to re-operate it again when the blower is overloaded. The second function is to close the motor operated valve automatically in case of power failure to prevent the circulation from the guided tank to the compost throwing tank and to block leak from the compost throwing tank due to the failure of ball valve. The third function is to transfer produced BMW from the concentration tank to 4 storage tanks for automatic managing of the BMW output. A device to measure the signal of the BMW plant process controller and a test equipment are developed. The designed BMW plant process controller is checked to see if it operates correctly according to the design specifications. The sequence control method based on BMW plant process controller is developed at a low cost in this study, so it is expected to bring improvements in the stability and the efficiency of system and to cause reductions in the operation and the management costs in the future.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.46-54
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• 2006

Recently, the important of a personal identification is increasing according to expansion using each on-line commercial transaction and personal ID-card. Although a personal ID-card embedded RFID(Radio Frequency Identification) tag is gradually increased, the way for a person's identification is deficiency. So we need automatic methods. Because RFID tag is vary small storage capacity of memory, it needs effective feature extraction method to store personal biometrics information. We need new recognition method to compare each feature. In this paper, we studied the face verification system using Hippocampal neuron modeling algorithm which can remodel the hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature vector of the face images very fast. and construct the optimized feature each image. The system is composed of two parts mainly. One is feature extraction using NMF(Non-negative Matrix Factorization) and LDA(Linear Discriminants Analysis) mixture algorithm and the other is hippocampal neuron modeling and recognition simulation experiments confirm the each recognition rate, that are face changes, pose changes and low-level quality image. The results of experiments, we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to the existing method.