• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.453-458
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• 2024

Due to global issues such as climate crisis and rising energy costs, there is an increasing focus on energy conservation and management. In the case of South Korea, approximately 53.5% of the total energy consumption comes from industrial complexes. In order to address this, we aimed to improve issues through the 'Shared Network Utility Plant' among companies using similar energy utilities to find energy-saving points. For effective energy conservation, various techniques are utilized, and stable data supply is crucial for the reliable operation of factories. Many anomaly detection and alert systems for checking the stability of data supply were dependent on Energy Management Systems (EMS), which had limitations. The construction of an EMS involves large-scale systems, making it difficult to implement in small factories with spatial and energy constraints. In this paper, we aim to overcome these challenges by constructing a data collection system and anomaly detection alert system on embedded devices that consume minimal space and power. We explore the possibilities of utilizing anomaly detection alert systems in typical institutions for data collection and study the construction process.

• Journal of Energy Engineering
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• v.19 no.2
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• pp.128-135
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• 2010

This paper introduces step by step procedures for the fabrication and operation of an embedded solar tracker. The system presented consists of application software, compactRIO, C-series interface module, analogue input module, step drive, step motor, feedback devices and other accessories to support its functional stability. CompactRIO that has a real-tim processor allows the solar tracker to be a stand-alone real time system which operates automatically without any external control. An astronomical method and an optical method were used for a high-precision solar tracker. CdS sensors are used to constantly generate feedback signals to the controller, which allow a solar tracker to track the sun even under adverse conditions. The database of solar position and sunrise and sunset time was compared with those of those of the Astronomical Applications Department of the U.S. Naval Observatory. The results presented here clearly demonstrate the high-accuracy of the present system in solar tracking, which are applicable to many existing solar systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.187-190
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• 2011

Until now, water pollution environmental monitoring system has been used at to acquire and measure data for streams and rivers. Recently coastal and marine environment monitoring system is becoming most important and urgent thing. The realtime automatic coastal and marine environment monitoring system using CDMA data transmission technique is developed in this study. The Embedded field server is designed and developed to acquire and measure underwater environment information such as pH, DO, water temperature using the water quality sensor. The obtained data is sent to the server via CDMA modem connected to the embedded field server and stored in database. Our purpose is to provide and monitor underwater environment information with CDMA communication in coastal areas.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.347-356
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• 2015

Due to the rapid development of IoT(Internet of Things) technology, traditional taxis are connected through dispatchers and location systems. Typically, modern taxis have embedded with GPS(Global Positioning System), which aims for obtaining the route information. By analyzing the frequency of taxi trip events, we can find the frequent route for a given query time. However, a scalability problem would occur when we convert the raw location data of taxi trip events into the analyzed frequency information due to the volume of location data. For this problem, we propose a NoSQL based top-K query system for taxi trip events. First, we analyze raw taxi trip events and extract frequencies of all routes. Then, we store the frequency information into hash-based index structure of MongoDB which is a document-oriented NoSQL database. Efficient top-K query processing for frequent route is done with the top of the MongoDB. We validate the efficiency of our algorithms by using real taxi trip events of New York City.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.187-194
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• 2003

Main Memory storage system can increase the performance of the system by assigning enough slack time to real-time transactions. Due to its high response time of main memory devices, main memory resident data management systems have been used for location management of personal mobile clients to cope with urgent location related operations. In this paper we have developed a multi-threaded main memory storage system as a core component of real-time retrieval system to handle a huge amount of readers and writers of main memory resident data. The storage system is implemented as an embedded component which is working with the help of a disk resident database system. It uses multi-threaded executions and utilizes latches for its concurrency control rather than complex locking method. It only saves most recent data on main memory and data synchronization is done only when disk resident database asks for update transactions. The system controls the number of read threads and update threads to guarantee the minimum requirements of real-time retrievals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.415-420
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• 2010

This paper describes the design overview of shared file system $SANique^{TM}$ and analyzes the performance evaluation results of I/O intensive stress test based on various cluster file system architectures. Especially, we illustrate the performance analysis for the comparison results between the $SANique^{TM}$ and the Linux file system EXT3 system that is used to generally in Unix world. In order to perform our evaluation, Oracle 10g database system is operated on the top of cluster file system, and we developed the various kinds of testing tools which are compiled by ESQL/C from Oracle. Three types of architectures are used in this performance evaluation. Those are the cluster file system $SANique^{TM}$, EXT3 and the combined architecture of $SANique^{TM}$ and EXT3. In this paper, we present that the results of $SANique^{TM}$ outperforms other cluster file systems in the overhead for providing the true sharing over the connecting server nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.

• International Journal of Railway
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• v.2 no.2
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• pp.50-59
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• 2009

Numerous train operating companies provide training by means of driving simulators. A detailed analysis in the course of the rail research project 2TRAIN has shown that the simulation technology, the purposes of training and the overall concept of simulator-based training are rather diverse (Schmitz & Maag, 2008). A joint factor however are weak assessment capabilities and the fact that the simulator training is often not embedded into the overall competence management. This fact hinders an optimal use of the simulators. Therefore, 2TRAIN aims at the development of enhanced training and assessment tools. Taking into account that several simulators are already in use, the focus lays on the extension of existing simulation technology instead of developing entirely new systems. This extension comprises (1) a common data simulation interface (CDSI), (2) a rule-based expert system (ExSys), (3) a virtual instructor (VI), and (4) an _assessment database (AssDB). The foundation of this technical development is an assessment concept (PERMA concept) that is based on performance markers. The first part of the paper presents this assessment concept and a process model for the two major steps of driver performance assessment, i.e. (1) the specification of exercise and assessment and (2) the assessment algorithm and execution of the assessment. The second part describes the rationale and the functionalities of the simulator add-on tools. Finally, recommendations for further technical improvement and appropriate usage are given. based on the results of a pilot study.

• Steel and Composite Structures
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• v.49 no.1
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• pp.109-123
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• 2023

Headed studs welded to steel beams and embedded within the concrete of deck slabs are vital components of modern composite floor systems, where safety and economy depend on the accurate predictions of the stud shear resistance. The multitude of existing deck profiles and the complex behavior of studs in deck slab ribs makes developing accurate and reliable mechanical or empirical design models challenging. The paper addresses this issue by presenting a machine learning (ML) model developed from the natural gradient boosting (NGBoost) algorithm capable of producing probabilistic predictions and a database of 464 push-out tests, which is considerably larger than the databases used for developing existing design models. The proposed model outperforms models based on other ML algorithms and existing descriptive equations, including those in EC4 and AISC 360, while offering probabilistic predictions unavailable from other models and producing higher shear resistances for many cases. The present study also showed that the stud shear resistance is insensitive to the concrete elastic modulus, stud welding type, location of slab reinforcement, and other parameters considered important by existing models. The NGBoost model was interpreted by evaluating the feature importance and dependence determined with the SHapley Additive exPlanations (SHAP) method. The model was calibrated via reliability analyses in accordance with the Eurocodes to ensure that its predictions meet the required reliability level and facilitate its use in design. An interactive open-source web application was created and deployed to the cloud to allow for convenient and rapid stud shear resistance predictions with the developed model.