• Korean Association of Arts Management
• /
• no.50
• /
• pp.5-31
• /
• 2019

The main purpose of this study was to use Deep Learning based Topic Modeling and Semantic Network Analysis to examine research trend of arts management-related papers in korea. For this purpose, research subjects such as 'The Journal of Cultural Policy', 'The Journal of Cultural Economics', 'The Journal of Culture Industry', 'The Journal of Arts Management', and 'The Journal of Human Content', which are the registered journal of the National Research Foundation of Korea directly or indirectly related to arts management field. From 1988 to 2017, a total of 2,110 domestic journals' signature, abstract, and keyword were analyzed. We tried Big Data analysis such as Topic Modeling and Semantic Network Analysis to examine changes in trends in arts management. The analysis program used open software R and standard statistical software SPSS. Based on the results of the analysis, the implications and limitations of the study and suggestions for future research were discussed. And the potential for development of convergent research such as Arts & Artificial Intelligence and Arts & Big Data.

• The Journal of Korean Association of Computer Education
• /
• v.7 no.5
• /
• pp.93-99
• /
• 2004

The most important technology in the electronic commerce based on Internet is to guarantee the security of trading information exchange. Many technologies are proposed as a standard to support this security problem. One of them is an XML (eXtensible Markup Language). This is used in various applications as the document standard for electronic commerce system. The XML security has become very important topic. In this paper an XML security model for web services based electronic commerce system to guarantee the secure exchange of trading information. To accomplish the security of XML, the differences of XML signature, XML encryption and XML key management scheme respect to the conventional system should be provided. The new architecture is proposed based on unique characteristics of XML. Especially the method to integrate the process management system need to the electronic commerce is proposed.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.2
• /
• pp.559-568
• /
• 2014

Induction motors are critical components in industrial processes since their failure usually lead to an unexpected interruption at the industrial plant. The studies of induction motor behavior during abnormal conditions and the possibility to diagnose different types of faults have been a challenging topic for many electrical machine researchers. In this regard, an efficient and new method to detect the induction motor-fault may be the application of the Time Synchronous Averaging (TSA) to the stator current Park's Vector. The aim of this paper is to present a methodology by which defects in a three-phase wound rotor induction motor can be diagnosed. By exploiting the cyclostationarity characteristics of electrical signals, the TSA method is applied to the stator current Park's Vector, allowing the monitoring of the induction motor operation. Simulation and experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the hybrid Park's Vector-TSA approach.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2004.11a
• /
• pp.371-374
• /
• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.

• Journal of Digital Contents Society
• /
• v.19 no.2
• /
• pp.363-370
• /
• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.28 no.2
• /
• pp.159-162
• /
• 2017

Along with the popular use of commercial drones, there are increased concerns on the possible threats from drones intruding into secured areas. The difficulty of drone detection is attributed to its stealthy operation flying at low altitude with low level signature. Consequently, the anti-drone technique has been of major research topic in recent years and among others, the radar detection is considered as the most promising technique. However, the use of conventional radar detection may not be effective due to the low level radar cross sections of the commercial drones. In this paper, ISAR technique has been employed to implement drone detection in urban area. To this purpose, a pulsed radar system is set up on the ground to track flying drones and the corresponding ISAR images are produced by coherent processing.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.11 no.6
• /
• pp.89-103
• /
• 2001

Through the increment of requirement for group oriented communication services, the multicast infrastructure based on a wire and wireless network has become a widely discussed researching topic. However the research of the security properties safety, efficiency and scaleability in a multicast structure, has not been enough. In this study, we propose a scalable secure multicast key management structure based on PKI(Public Key Infrastructure), IPSec, domain subgroup and structural two mode scheme to provide a integrated multicast service. Also we discuss and propose the digital nominative group signature a refreshing method for satisfying the security and trusty on the network. At the base of this work we certify to the usability of new proposed scheme from comparing it with conventional schemes in the part of safety, efficiency and scaleability.

• International journal of advanced smart convergence
• /
• v.10 no.1
• /
• pp.1-11
• /
• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• Smart Media Journal
• /
• v.13 no.4
• /
• pp.76-85
• /
• 2024

In this paper, we propose a system that improves the data integrity of IoT(Internet of Things) devices in the virtual environment by combining Hyperledger Indy and MQTT(Message Queuing Telemetry Transport). The system complements the limitations of the centralized system by realizing a DPKI(Decentralized Public Key Infrastructure) structure that utilizes a distributed network in publish-subscribe(pub/sub) pattern communication. Digital signature technology was applied to ensure the data integrity of IoT devices and communication scenarios between the four core components of the client, IoT device, broker, and blockchain, as well as a topic structure using a decentralized identifier to ensure safety in the virtual environment. We present a systematic method for transparent data exchange. To prove the performance of the proposed system, this paper conducted experiments on four scenarios and evaluated communication performance in a virtual environment. The experimental results confirmed that the proposed system provides a reliable IoT data communication structure in a virtual environment.