• KEPCO Journal on Electric Power and Energy
• /
• v.6 no.1
• /
• pp.29-33
• /
• 2020

Despite the same system architecture and operation principle, Advanced Metering Infrastructure (AMI) and Internet of Things (IoT) are recognized as a heterogeneous system. This is due to the different object modeling and communication protocols used in smart meters and sensors. However, data interworking between AMI and IoT is expected to be inevitable in the future. In this paper, we propose Device Language Message Specification (DLMS) to Lightweight Machine to Machine (LwM2M) conversion model. The proposed interworking model can reduce the packet size by 46.5% compared to that of the encapsulation method.

• Smart Media Journal
• /
• v.5 no.3
• /
• pp.35-41
• /
• 2016

In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

• Journal of Biomedical Engineering Research
• /
• v.43 no.6
• /
• pp.424-433
• /
• 2022

As the proportion of non-contact telemedicine increases and the number of electrocardiogram (ECG) data measured using portable ECG monitors increases, the demand for automatic algorithms that can precisely analyze vast amounts of ECG is increasing. Since the P, QRS, and T waves of the ECG have different shapes depending on the location of electrodes or individual characteristics and often have similar frequency components or amplitudes, it is difficult to distinguish P, QRS and T waves and measure each parameter. In order to measure the widths, intervals and areas of P, QRS, and T waves, a new algorithm that recognizes the start and end points of each wave and automatically measures the time differences and amplitudes between each point is required. In this study, the start and end points of the P, QRS, and T waves were measured using six Deep Neural Networks (DNN) that recognize the start and end points of each wave. Then, by synthesizing the results of all DNNs, 12 parameters for ECG characteristics for each heartbeat were obtained. In the ECG waveform of 10 subjects provided by Physionet, 12 parameters were measured for each of 660 heartbeats, and the 12 parameters measured for each heartbeat well represented the characteristics of the ECG, so it was possible to distinguish them from other subjects' parameters. When the ECG data of 10 subjects were combined into one file and analyzed with the suggested algorithm, 10 types of ECG waveform were observed, and two types of ECG waveform were simultaneously observed in 5 subjects, however, it was not observed that one person had more than two types.

• Smart Media Journal
• /
• v.11 no.2
• /
• pp.70-76
• /
• 2022

IoT application development using a cloud server causes problems such as data transmission and reception delay, network traffic, and cost for real-time processing support in network connected hardware. To solve this problem, edge cloud-based platforms can use neuromorphic hardware to enable fast data transfer. In this paper, we propose a model optimization method for supporting spiking neural networks on FPGA hardware. We focused on auto-adjusting network model parameters optimized for neuromorphic hardware. The proposed method performs optimization to show higher performance based on user requirements for accuracy. As a result of performance analysis, it satisfies all requirements of accuracy and showed higher performance in terms of expected execution time, unlike the naive method supported by the existing open source framework.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.6
• /
• pp.909-917
• /
• 2018

Recently, the Internet of Thing (IoT) technology is expanding explosively in the number of devices and applications in many application areas. In IoT systems, sensors and actuators are connected to the Internet and cooperate to do some applications by exchanging information. In this paper an IoT smart home system is developed to monitor and control home devices easily. Our system consists of sensor devices, IoT gateways, and an IoT server. Sensor devices developed using Rfduino sense the physical world and transmit their data using BLE to the IoT server thru the IoT gateway. We implemented the IoT gateway using Raspberry Pi and the IoT server using ARTIK cloud. We installed our system and made a test in our lab, which showed that our system can be installed and managed easily and extended its functionality in an easy way. By taking advantage of the rule mechanism and action messages of ARTIK cloud, we implemented the control of device parameters easily by sending action messages to the ARTIK cloud.

• Smart Media Journal
• /
• v.12 no.5
• /
• pp.28-35
• /
• 2023

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.

• Smart Media Journal
• /
• v.12 no.9
• /
• pp.159-170
• /
• 2023

It takes a lot of time and manpower to search for the missing. As part of the solution, a missing person search AI system was implemented using a YOLO-based model. In order to train object detection models, the model was learned by collecting recognition images (road fixation) of drone mobile objects from AI-Hub. Additional mountainous terrain datasets were also collected to evaluate performance in training datasets and other environments. In order to optimize the missing person search AI system, performance evaluation based on model size and hyperparameters and additional performance evaluation for concerns about overfitting were conducted. As a result of performance evaluation, it was confirmed that the YOLOv5-L model showed excellent performance, and the performance of the model was further improved by applying data augmentation techniques. Since then, the web service has been applied with the YOLOv5-L model that applies data augmentation techniques to increase the efficiency of searching for missing people.

• Smart Media Journal
• /
• v.13 no.1
• /
• pp.52-59
• /
• 2024

This paper proposes a plan to improve the reception radio environment of the mobile terminal and maintain a constant reception electric field by using small cells in a small closed environment. In order to configure an efficient communication infrastructure for small cells, both ends of wireless transmission and reception of an Ethernet-based wireless video recording system are connected using an L2 switch. The small cell connected to the receiving side L2 switch shares the wireless network section of the wireless video recording system and connects to the transmitting side L2 switch. After that, when it is normally linked to FMS, a management system for small cells, through the Internet network, the output of small cells is checked. In order to verify the results, a proposed network is formed on the elevator inside the building with a poor radio wave environment, and the radio wave environment is measured before and after the small cell application in the section where the elevator operates. As a result, the main parameters of the radio wave environment in all sections of the elevator are improved, as well as a constant receiving electric field strength within the moving elevator.

• Broadcasting and Media Magazine
• /
• v.16 no.4
• /
• pp.59-71
• /
• 2011

오디오와 음성 압축 기술적 근간은 서로 다르지만, 최근의 모바일 멀티미디어 기기 시장의 컨버전스 현상에 따라 압축하고자 하는 신호가 혼용되고 있으며, 비슷한 목표 전송률과 음질로 수렴하고 있다. 현재는 동일 기기에서 서로 다른 압축 기술을 적용하고 있으나, 음성과 음악이 동시에 서비스 되는 멀티미디어 기기에서는 단일 압축 방식으로 처리하고자 하는 이슈가 부각되고 있다. 특히, 스마트 폰 및 음악 콘텐츠 포탈 서비스의 대중화를 고려할 때, 음성 및 음악 신호 모두를 효율적으로 압축하는 음악 및 음성 신호의 융합 압축 기술이 더욱 필요해 보인다. 본 고에서는 MPEG 오디오 그룹에서 가장 최근 진행한 Unified Speech and Audio Coding(USAC)의 탄생 배경 및 표준화 현황을 소개한다. USAC는 64kbps 이하에서 기술적으로 최고 성능을 지닌 AMR-WB+ 및 HE-AAC v2보다도 우월한 음질을 보이며, 높은 비트율에서도 동등한 음질을 보장한다. 이런 우수한 음질에 기여한 USAC의 스위칭 구조와 더불어 기술적으로 향상된 주요 모듈인 파라미터 기반 스테레오 및 고주파 압축, 그리고 엔트로피 코딩 방식에 대해서 살펴 본다. 향후, 다양한 오디오 신호를 효율적으로 압축하는 USAC는 디지털 라디오, 모바일 TV, 그리고 오디오 북과 같은 사용자 시나리오에서 사용될 확률이 높아 보인다. 또한, USAC는 배경 잡음이나 배경 음악이 있는 경우에도 성능이 우수하기 때문에 YouTube 및 podcast 등과 같이 사용자가 콘텐츠를 생성할 때도 유용하게 사용 될 수 있다.

• Proceedings of the KIEE Conference
• /
• 2008.11a
• /
• pp.460-462
• /
• 2008

일반 수용가를 대상으로 한 효율적인 수요관리의 한 방법으로써 Dynamically-Administered Critical Peak Pricing[1] 요금제를 이용하여 일반 수용가 대상 수요관리를 스마트 미터기인 Smart Cabinet Panel(SCP)를 개발하여 적용하였다. 이 DA-CPP 요금제에는 핵심이 되는 최적 critical peak 시점을 푸는 하위 문제들 및 방법론들이 존재하는데, 우리는 energy service provider(ESP)가 관리해야 할 수용가의 수가 매우 많다는 점에 주목하여, 각 수용가의 1일 부하 사용량 패턴을 몇 개의 그룹으로 나누어 각 그룹에 대해 critical peak 최적 시점을 결정하는 연구를 수행하였다. 이러한 수용가 부하량 패턴그룹화를 위해 인공 지능의 여러 기법 중 하나인 self-organizing map(SOM)을 사용하였다 그리고 ESP와 수음가가 통신할 수 있도록 개발된 SCP를 통해 Critical Peak을 적용하였다.