• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.301-310
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• 2000

An autonomous tractor system was developed and its performance was evaluated. The system consisted of a tractor system of and a remote control station. The tractor and the remote control station communicated each other via wireless modems. The tractor had a DGPS(differential global positioning system), sensors, a controller and a modem. The DGPS collected position data and the tractor status was estimated. The information of tractor status and sensors was transferred to the remote control station. Then, the control station determined the control data such as steering angles using a fuzzy controller. The fuzzy controller used the information from the DGPS, sensors, and GIS(geographic information system) data. The control data were obtained by remote signal processing at the control station The control data for autonomous operation were transferred to the tractor controller. The performances of an autonomous tractor were evaluated for various speeds, different initial positions and different initial headings. About 1.3 seconds of time lag was occurred in transferring the tractor status data and the control data. Compensation the time lag, about 27cm deviation was observed at the speed of 0.5m/s and 37cm at the speed of 1m/s. Error caused mainly by the time lag and it would be reduced by developing a full-duplex radio module for controlling the remote tractor.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.313-314
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• 2007

AlN is used a wide variety of applications such as electroacoustic devices, blue diode and metal-insulator-semiconductor structures. AlN thin films were deposited on Si substrates by rf sputter technique with low temperature process. The orientation and morphology of AlN thin films at various power in the range from 150 to 300 w was studied. X-ray diffraction (XRD), full width at half-maximum (FWHM) and field emission scanning electron microscopy were employed to characterize the deposited films. The c-axis orientation along (002) Plane at experimental results was enhanced with the increasing of the rf power from 150 to 300 w and the surface morphology of the films showed a homogeneous and nano-sized microstructure.

• Annual Conference of KIPS
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• 2015.10a
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• pp.332-334
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• 2015

Nowadays a disaster event such as a building on fire, an earthquake or typhoon could occur any time, and any where. In such event, a warning notification system is a vital tool to send warning notifications to at-risk people in advance and provide them useful information to escape the dangerous area. Though some systems have been proposed such as emergency alert system using android, SMS or P2P overlay network, these works mainly focus on a reliable message distribution methods. In this work, we introduce a full prototype implementation of a personalized warning notification system based on geosocial information, which generates a personalized warning message for each user and delivers the messages through email or an android application. The system consists of four main modules: a web interface, database, a knowledge-based message generator, and message distributor. An android application is also created for user to receive warning messages on their smart phone. The prototype has been demonstrated successfully with a building-on-fire scenario.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.913-921
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• 2012

On the strength of the rapid development and propagation of U-healthcare service, the service technologies are full of important changes. However, U-healthcare service has security problem that patient's biometric information can be easily exposed to the third party without service users' consent. This paper proposes a distributed model according authority and access level of hospital officials in order to safely access patients' private information in u-Healthcare Environment. Proposed model can both limit the access to patients' biometric information and keep safe system from DoS attack using time stamp. Also, it can prevent patients' data spill and privacy intrusion because the main server simultaneously controls hospital officials and the access by the access range of officials from each hospital.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1286-1306
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• 2022

Searchable symmetric encryption (SSE) provides a safe and effective solution for retrieving encrypted data on cloud servers. However, the existing SSE schemes mainly focus on single keyword search in single client, which is inefficient for multiple keywords and cannot meet the needs for multiple clients. Considering the above drawbacks, we propose a scheme enabling dynamic multi-client and Boolean query in searchable symmetric encryption for cloud storage system (DMC-SSE). DMC-SSE realizes the fine-grained access control of multi-client in SSE by attribute-based encryption (ABE) and novel access control list (ACL), and supports Boolean query of multiple keywords. In addition, DMC-SSE realizes the full dynamic update of client and file. Compared with the existing multi-client schemes, our scheme has the following advantages: 1) Dynamic. DMC-SSE not only supports the dynamic addition or deletion of multiple clients, but also realizes the dynamic update of files. 2) Non-interactivity. After being authorized, the client can query keywords without the help of the data owner and the data owner can dynamically update client's permissions without requiring the client to stay online. At last, the security analysis and experiments results demonstrate that our scheme is safe and efficient.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.175-186
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• 2022

This research article develops an Arabic books' recommendation system, which is based on the content similarity that assists users to search for the right book and predict the appropriate and suitable books pertaining to their literary style. In fact, the system directs its users toward books, which can meet their needs from a large dataset of Information. Further, this system makes its predictions based on a set of data that is gathered from different books and converts it to vectors by using the TF-IDF system. After that, the recommendation algorithms such as the cosine similarity, the sequence matcher similarity, and the semantic similarity aggregate data to produce an efficient and effective recommendation. This approach is advantageous in recommending previously unrated books to users with unique interests. It is found to be proven from the obtained results that the results of the cosine similarity of the full content of books, the results of the sequence matcher similarity of Arabic titles of the books, and the results of the semantic similarity of English titles of the books are the best obtained results, and extremely close to the average of the result related to the human assigned/annotated similarity. Flask web application is developed with a simple interface to show the recommended Arabic books by using cosine similarity, sequence matcher similarity, and semantic similarity algorithms with all experiments that are conducted.

• The Journal of Information Technology and Database
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• v.1 no.1
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• pp.31-62
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• 1994

Information systems for application areas like office automation, customer service or computer aided manufacturing are usually highly interactive and deal with complex document structures composed of multiple media formats. For the realization of these systems, nonstandard database systems, which we call document databases, need to handle different types of coarse-and fine-grained document objects(like full-text documents, graphics and images), hierarchical and non-hierarchical relationships between objects(like composition-links and cross-references using hypertext structures) and document attributes of different types such as formatting/presentation information and access control. In this paper, we present the underlying data model for document databases based on descriptive markup languages that provide mechanisms for specifying the logical structure(or schema) of individual documents stored in the database. We then describe extensions to the data model for supporting notion of composite structures("join" operators for documents) --composition and hyperlinking mechanisms for representing compound documents and inter-linked documents as unique entites separate from their components. Furthermore, due to the interactive nature of the application domains, the database system in conjunction with clients(or browsers) has to support visual navigation and graphical query mechanisms. We describe the functionality of a new user interface paradigm called HyBrow for meeting the above mentioned requirements. The underlying implementation strategy is also discussed.discussed.

• Journal of The Korean Association of Information Education
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• v.20 no.6
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• pp.629-644
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• 2016

The purpose of this study was to investigate the effects of whole-task sequencing emphasis manipulation on expertise acquisition in web based complex task. To achieve the purpose, emphasis manipulation sequencing type is composed of a simple emphasis manipulation, a snowballing manipulation, and a full emphasis manipulation sequencing and participants was drawn from a pool of 93 undergraduate students sampled for the study. According to the findings, a snowballing manipulation group invested significantly lower cognitive load than a full emphasis manipulation group but did not a simple emphasis manipulation group. Based on these findings, though complex task is included of high interactivity owing to real task, learner cannot suffer cognitive overload because emphasis manipulation which can view the whole task and the part task in parallel provides meta cognition for learner. And whole-task sequencing emphasis manipulation affects to transfer. The snowballing emphasis manipulation group invested significantly higher than simple emphasis manipulation group and full emphasis manipulation group. Based on these findings, the snowballing manipulation which learner use whole-task sequencing and part-task sequencing simultaneously contribute to understandings and ability to solve problems for complex task and it will in turn, lead to expertise acquisition.