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Human activity recognition using smartphone has been attracting great interest. Since collecting large amount of labeled data is expensive and time-consuming for conventional machine learning techniques, transfer learning techniques have been proposed for activity recognition. However, existing transfer learning techniques typically rely on feature matching based on global domain shift and lack considering the intra-class knowledge transfer. In this paper, a novel transfer learning technique is proposed for cross-domain activity recognition, which can properly integrate feature matching and instance reweighting across the source and target domain in principled dimensionality reduction. The experiments using three real datasets demonstrate that the proposed scheme can achieve much higher precision (92%), recall (91%), and F1-score (92%), in comparison with the existing schemes.
Xianyao Chen; Kyung Tae Kim; Hee Yong Youn. Feature matching and instance reweighting with transfer learning for human activity recognition using smartphone. The Journal of Supercomputing 2021, 1 -28.
AMA StyleXianyao Chen, Kyung Tae Kim, Hee Yong Youn. Feature matching and instance reweighting with transfer learning for human activity recognition using smartphone. The Journal of Supercomputing. 2021; ():1-28.
Chicago/Turabian StyleXianyao Chen; Kyung Tae Kim; Hee Yong Youn. 2021. "Feature matching and instance reweighting with transfer learning for human activity recognition using smartphone." The Journal of Supercomputing , no. : 1-28.
Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.
Hai Xue; Kyung Tae Kim; Hee Yong Youn. Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization. Sensors 2019, 19, 311 .
AMA StyleHai Xue, Kyung Tae Kim, Hee Yong Youn. Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization. Sensors. 2019; 19 (2):311.
Chicago/Turabian StyleHai Xue; Kyung Tae Kim; Hee Yong Youn. 2019. "Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization." Sensors 19, no. 2: 311.
Event detection is an important task required in various applications of wireless sensor network (WSN). The existing approaches consider the spatial and temporal correlation of sensor data separately or not in a cohesive way. In this paper an event detection scheme with WSN is introduced, which adopts a hierarchical structure to efficiently integrate the spatial and temporal correlation of sensor data. Here a fusion algorithm considering both the weight of the sensors and spatial information is applied to Markov random field to properly fuse the decisions of the neighboring nodes. Markov chain is also adopted to effectively extract the temporal correlation after the spatial correlation is decided. The simulation results demonstrate that the proposed scheme can effectively increase the detection accuracy and reduce communication cost, in comparison with the existing schemes.
Xianda Chen; Kyung Tae Kim; Hee Yong Youn. Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network. Computer Networks 2016, 108, 108 -119.
AMA StyleXianda Chen, Kyung Tae Kim, Hee Yong Youn. Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network. Computer Networks. 2016; 108 ():108-119.
Chicago/Turabian StyleXianda Chen; Kyung Tae Kim; Hee Yong Youn. 2016. "Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network." Computer Networks 108, no. : 108-119.
Hee Jung Park; Kyung Tae Kim; Man Yun Kim; Hee Yong Youn. Enhanced Hypervisor-based SSD Cache with Dynamic Cache Scanning and Allocation for Virtualized Cloud System. International Journal of Networked and Distributed Computing 2015, 3, 224 -233.
AMA StyleHee Jung Park, Kyung Tae Kim, Man Yun Kim, Hee Yong Youn. Enhanced Hypervisor-based SSD Cache with Dynamic Cache Scanning and Allocation for Virtualized Cloud System. International Journal of Networked and Distributed Computing. 2015; 3 (4):224-233.
Chicago/Turabian StyleHee Jung Park; Kyung Tae Kim; Man Yun Kim; Hee Yong Youn. 2015. "Enhanced Hypervisor-based SSD Cache with Dynamic Cache Scanning and Allocation for Virtualized Cloud System." International Journal of Networked and Distributed Computing 3, no. 4: 224-233.
Kyung Tae Kim; Man Youn Kim; Ji Hyeon Choi; Hee Yong Youn. An Energy Efficient Clustering Algorithm for Maximizing the Lifetime of Wireless Sensor Network. International Journal of Networked and Distributed Computing 2015, 3, 214 -223.
AMA StyleKyung Tae Kim, Man Youn Kim, Ji Hyeon Choi, Hee Yong Youn. An Energy Efficient Clustering Algorithm for Maximizing the Lifetime of Wireless Sensor Network. International Journal of Networked and Distributed Computing. 2015; 3 (4):214-223.
Chicago/Turabian StyleKyung Tae Kim; Man Youn Kim; Ji Hyeon Choi; Hee Yong Youn. 2015. "An Energy Efficient Clustering Algorithm for Maximizing the Lifetime of Wireless Sensor Network." International Journal of Networked and Distributed Computing 3, no. 4: 214-223.