• Journal of Information Technology Services
• /
• v.14 no.4
• /
• pp.257-267
• /
• 2015

We propose a vehicle-mounted, location-aware mobile digital signage system that can be used for public transportation through mobile communication. This paper proposes the installations of the LED display panels at the backside of the bus., which display traffic information to cars behind the bus. Information to be displayed would include, but is not limited to, road information, public commercials and private commercials. We propose the system architecture and further implement the prototype of mobile digital signage system for demonstration. The system is based on the Client-Server system. Each bus has a client terminal which detects the current location by a GPS receiver and sends its location information to the server using mobile communication function. The terminal device receives advertisements and traffic information from the server and displays it to the large LCD or LED panel installed at the inside and outside of the bus. We use the Android smartphone as a client system, which inherently equipped with GPS and mobile communication function. GPS detects the location of bus and reports its geo-location data to the traffic information center server via a wireless communication network. On the server side, we developed a specially designed control server, where it communicates with the other traffic information center and updates and manages the databases contents being displayed by each position. The server contains location dependent variable information and returns selected information back to the vehicle in real time. Spatial database is used to process location based data. Server system periodically receives the real time traffic information from the road information center database. And it process the information by bus location and bus line number. In this paper, we propose a mobile digital signage service and explain the system implementation of this service.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.754-760
• /
• 2012

This paper describes peak shaving algorithms as long-term cycle scheduling in the power management system (PMS) for MW-scale battery energy storage systems (BESS). The purpose of PMS is basically to manage the input and output power from battery modules placed in the systems. Assuming that an one-day ahead load curve is provided, off-line peak shaving algorithms can be employed, but applying the results of the off-line algorithm may result in the difference in the real-time performance because there is uncertainty in the provided load curve. This paper adopts fuzzy based LP (linear programming) algorithms for describing the peak shaving algorithm in PMS and discusses a solution technique and real-time operation strategies using the solution.

• KEPCO Journal on Electric Power and Energy
• /
• v.5 no.3
• /
• pp.157-163
• /
• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• Journal of Broadcast Engineering
• /
• v.26 no.2
• /
• pp.167-174
• /
• 2021

Recently, deep learning technology is widely used in various computer vision applications, such as object recognition, classification, and image generation. In particular, the deep learning-based super-resolution has been gaining significant performance improvement. Fast super-resolution convolutional neural network (FSRCNN) is a well-known model as a deep learning-based super-resolution algorithm that output image is generated by a deconvolutional layer. In this paper, we propose an FPGA-based convolutional neural networks accelerator that considers parallel computing efficiency. In addition, the proposed method proposes Optimal-FSRCNN, which is modified the structure of FSRCNN. The number of multipliers is compressed by 3.47 times compared to FSRCNN. Moreover, PSNR has similar performance to FSRCNN. We developed a real-time image processing technology that implements on FPGA.

• Journal of Korean Neurosurgical Society
• /
• v.62 no.3
• /
• pp.353-360
• /
• 2019

Epilepsy surgery that eliminates the epileptogenic focus or disconnects the epileptic network has the potential to significantly improve seizure control in patients with medically intractable epilepsy. Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has been an established option for epilepsy surgery since the US Food and Drug Administration cleared the use of MRgLITT in neurosurgery in 2007. MRgLITT is an ablative stereotactic procedure utilizing heat that is converted from laser energy, and the temperature of the tissue is monitored in real-time by MR thermography. Real-time quantitative thermal monitoring enables titration of laser energy for cellular injury, and it also estimates the extent of tissue damage. MRgLITT is applicable for lesion ablation in cases that the epileptogenic foci are localized and/or deep-seated such as in the mesial temporal lobe epilepsy and hypothalamic hamartoma. Seizure-free outcomes after MRgLITT are comparable to those of open surgery in well-selected patients such as those with mesial temporal sclerosis. Particularly in patients with hypothalamic hamartoma. In addition, MRgLITT can also be applied to ablate multiple discrete lesions of focal cortical dysplasia and tuberous sclerosis complex without the need for multiple craniotomies, as well as disconnection surgery such as corpus callosotomy. Careful planning of the target, the optimal trajectory of the laser probe, and the appropriate parameters for energy delivery are paramount to improve the seizure outcome and to reduce the complication caused by the thermal damage to the surrounding critical structures.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.35 no.11
• /
• pp.155-162
• /
• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.1
• /
• pp.81-92
• /
• 2009

Recently, there is a drastic increase in users interested in real-time multimedia services in the WLAN environment, as the demand of IEEE 802.11 WLAN-based services increases. However, the handoff delay based on 802.11i security policy is not acceptable for the seamless real-time multimedia services provided to MS frequently moving in the WLAN environment, and there is a possibility of DoS attacks against session key derivation process and handoff mechanism. In this paper, a secure and efficient handoff mechanism in the centralized WLAN environment is introduced to solve the security problems. The 4-way Handshake for both mutual authentication and session key derivation is replaced by the 2-way Reassociation process.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.1
• /
• pp.29-37
• /
• 2020

This paper implements a method of extracting an object about a dog through real-time image analysis and classifying dog behaviors from the extracted images. The Darknet YOLO was used to detect dog objects, and the Teachable Machine provided by Google was used to classify behavior patterns from the extracted images. The trained Teachable Machine is saved in Google Drive and can be used by ml5.js implemented on a node.js server. By implementing an interactive web server using a socket.io module on the node.js server, the classified results are transmitted to the user's smart phone or PC in real time so that it can be checked anytime, anywhere.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.12
• /
• pp.1618-1623
• /
• 2019

In this paper, inductive couplers realizing broadband powerline communication (PLC) are fabricated using Fe-based nanocrystalline alloy and their performance is analyzed. As a result of the field tests using the distribution automation system (DAS), the couplers achieve comparatively excellent data communication in the principal frequency band of broadband PLC although there is a difference in communication rate depending on the presence or absence of a branch. In addition, it has been confirmed that the communication speed is maintained for a real-time transmission even in a high current environment although there is a difference in the transmission rate depending on the distance. Hence, it is considered that the inductive couplers can be used as a core device to realize the intelligent power network by exploiting them for the monitoring and remote controlling of the power plant equipments for the DAS located in the inaccessible areas.

• Journal of Korea Multimedia Society
• /
• v.24 no.11
• /
• pp.1481-1491
• /
• 2021

Since most biosignals rely on contact-based measurement, there is still a problem in that it is hard to provide convenience to users by applying them to daily life. In this paper, we present a mobile application for estimating heart rate based on a deep learning model. The proposed application measures heart rate by capturing real-time face images in a non-contact manner. We trained a three-dimensional convolutional neural network to predict photoplethysmography (PPG) from face images. The face images used for training were taken in various movements and situations. To evaluate the performance of the proposed system, we used a pulse oximeter to measure a ground truth PPG. As a result, the deviation of the calculated root means square error between the heart rate from remote PPG measured by the proposed system and the heart rate from the ground truth was about 1.14, showing no significant difference. Our findings suggest that heart rate measurement by mobile applications is accurate enough to help manage health during daily life.