Dong-Hoon Kim
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Dong-Hoon Kim has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Eun-Kyu Lee
Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Republic of Korea
2 shared publications - Hyun-Jong Lee
Kwangwoon University
1 shared publication - Hyung-Jung Kim
Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
1 shared publication Jae-Han Lim
1 shared publication- Jae-Han Lim
Department of Software, Kwangwoon University, Seoul, South Korea
1 shared publication - Jibum Kim
Department of Computer Science and Engineering, Incheon National University, Incheon, South Korea
1 shared publication
Latest Publications
- 15 June 2023
- Published by Elsevier in Computer Communications
- Vol. 208, 256-270
Wi-Fi frame detection via spiking neural networks with memristive synapses
- 15 June 2023
- Published by Elsevier in Computer Communications
- Vol. 208, 256-270
Wi-Fi frame detection via spiking neural networks with memristive synapses
- Open Access
Design of Optimized Coded LFM Waveform for Spectrum Shared Radar System
To meet the increasing demands for remote sensing, a number of radar systems using Linear Frequency Modulation (LFM) waveforms have been deployed, causing the problem of depleting frequency resources. To address this problem, several researchers have proposed the Spectrum Shared Radar System (SSRS) in which multiple radars share the same frequency band to transmit and receive their own signals. To mitigate the interferences caused by the signal transmission by other radars, SSRS employs orthogonal waveforms that inherit the orthogonality of the waveforms from orthogonal codes. However, the inherited orthogonality of the codes is significantly reduced when incorporating LFM waveforms with the codes. To solve this problem, in this paper, we propose a novel but simple scheme for generating a set of optimized coded LFM waveforms via new optimization framework. In the optimization framework, we minimize the weighted sum of autocorrelation sidelobe peaks () and cross-correlation peaks () of the coded LFM waveforms to maximize the orthogonality of the waveforms. Through computer simulations, we show that the waveforms generated by the proposed scheme outperform the waveforms created by previous proposals in terms of and .Open AccessDesign of Optimized Coded LFM Waveform for Spectrum Shared Radar System
To meet the increasing demands for remote sensing, a number of radar systems using Linear Frequency Modulation (LFM) waveforms have been deployed, causing the problem of depleting frequency resources. To address this problem, several researchers have proposed the Spectrum Shared Radar System (SSRS) in which multiple radars share the same frequency band to transmit and receive their own signals. To mitigate the interferences caused by the signal transmission by other radars, SSRS employs orthogonal waveforms that inherit the orthogonality of the waveforms from orthogonal codes. However, the inherited orthogonality of the codes is significantly reduced when incorporating LFM waveforms with the codes. To solve this problem, in this paper, we propose a novel but simple scheme for generating a set of optimized coded LFM waveforms via new optimization framework. In the optimization framework, we minimize the weighted sum of autocorrelation sidelobe peaks () and cross-correlation peaks () of the coded LFM waveforms to maximize the orthogonality of the waveforms. Through computer simulations, we show that the waveforms generated by the proposed scheme outperform the waveforms created by previous proposals in terms of and . - Open Access
- 13 August 2020
- Published by MDPI AG in Sustainability
- Vol. 12 (16) , 6539
Peak-Load Forecasting for Small Industries: A Machine Learning Approach
Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.
- 13 August 2020
- Published by MDPI AG in Sustainability
- Vol. 12 (16) , 6539
Open AccessPeak-Load Forecasting for Small Industries: A Machine Learning Approach
Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.
- Open Access
- 30 March 2019
- Published by MDPI AG in Sustainability
- Vol. 11 (7) , 1917
Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
The fundamental properties of long-range (LoRa) performance have been revealed by previous research, but advanced issues remain unresolved. This paper tackles three technical challenges that are confronted when establishing a LoRa network on a smart energy campus testbed in Korea. First, the communication range of LoRa in a combined indoor and outdoor environment has yet to be determined. To address this problem, this study builds a LoRa testbed from which we measure the propagation properties of radio signals in a combined environment. Then, we establish a simplified path loss model for LoRa PHY and compute the communication range. Next, national regulations enforce the use of the Listen-Before-Talk (LBT) scheduling scheme, but its performance has never been examined. This paper enumerates the impacts of LBT, threshold values, back-off algorithms, and application parameters on LoRa Medium Access Control (MAC) performance. Last, the application parameters required by the smart energy scenario influence LoRa performance, but their impacts have been scarcely reported. Based on the application parameters and experimental results, this paper proposes a strategy to deploy LoRa gateway(s) and nodes on the campus. To the best of the authors’ knowledge, this is the first work that considers performance issues when establishing a LoRa network on a real-world application testbed.
- 30 March 2019
- Published by MDPI AG in Sustainability
- Vol. 11 (7) , 1917
Open AccessExperiencing LoRa Network Establishment on a Smart Energy Campus Testbed
The fundamental properties of long-range (LoRa) performance have been revealed by previous research, but advanced issues remain unresolved. This paper tackles three technical challenges that are confronted when establishing a LoRa network on a smart energy campus testbed in Korea. First, the communication range of LoRa in a combined indoor and outdoor environment has yet to be determined. To address this problem, this study builds a LoRa testbed from which we measure the propagation properties of radio signals in a combined environment. Then, we establish a simplified path loss model for LoRa PHY and compute the communication range. Next, national regulations enforce the use of the Listen-Before-Talk (LBT) scheduling scheme, but its performance has never been examined. This paper enumerates the impacts of LBT, threshold values, back-off algorithms, and application parameters on LoRa Medium Access Control (MAC) performance. Last, the application parameters required by the smart energy scenario influence LoRa performance, but their impacts have been scarcely reported. Based on the application parameters and experimental results, this paper proposes a strategy to deploy LoRa gateway(s) and nodes on the campus. To the best of the authors’ knowledge, this is the first work that considers performance issues when establishing a LoRa network on a real-world application testbed.