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Se-Jung Lim
Liberal Arts & Convergence StudiesHonam University Gwangju South Korea

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Research article
Published: 16 April 2020 in International Transactions on Electrical Energy Systems
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This paper proposes a multiobjective optimization for fuel consumption (including fossil fuel and hydrogen) and polluted gas emission in a hybrid electric aircraft. The proposed convex multiobjective model aims at minimizing the total fuel consumption during the entire flight mission as well as the corresponding fuel cell size. The problem is formulated as mixed‐integer nonlinear programming using the Karush–Kuhn–Tucker optimality condition and solved by GAMS. Solving the proposed computationally efficient problem showed that, compared with the conventional hybrid‐electric aircraft, the optimum fuel cell power can improve the aircraft performance in terms of fuel consumption and polluted gas emission.

ACS Style

Mohammad J. Salehpour; Omid Zarenia; Seyyed Mohammad Hosseini Rostami; Jin Wang; Se‐Jung Lim. Convex multi‐objective optimization for a hybrid fuel cell power system of more electric aircraft. International Transactions on Electrical Energy Systems 2020, 30, 1 .

AMA Style

Mohammad J. Salehpour, Omid Zarenia, Seyyed Mohammad Hosseini Rostami, Jin Wang, Se‐Jung Lim. Convex multi‐objective optimization for a hybrid fuel cell power system of more electric aircraft. International Transactions on Electrical Energy Systems. 2020; 30 (7):1.

Chicago/Turabian Style

Mohammad J. Salehpour; Omid Zarenia; Seyyed Mohammad Hosseini Rostami; Jin Wang; Se‐Jung Lim. 2020. "Convex multi‐objective optimization for a hybrid fuel cell power system of more electric aircraft." International Transactions on Electrical Energy Systems 30, no. 7: 1.

Journal article
Published: 21 February 2020 in Sensors
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Many remote sensing scene classification algorithms improve their classification accuracy by additional modules, which increases the parameters and computing overhead of the model at the inference stage. In this paper, we explore how to improve the classification accuracy of the model without adding modules at the inference stage. First, we propose a network training strategy of training with multi-size images. Then, we introduce more supervision information by triplet loss and design a branch for the triplet loss. In addition, dropout is introduced between the feature extractor and the classifier to avoid over-fitting. These modules only work at the training stage and will not bring about the increase in model parameters at the inference stage. We use Resnet18 as the baseline and add the three modules to the baseline. We perform experiments on three datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our model combined with the three modules is more competitive than many existing classification algorithms. In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training with multi-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and 0.7%, respectively. The combination of the three modules improves the overall accuracy of the model by 1.61%. It can be seen that the three modules can improve the classification accuracy of the model without increasing model parameters at the inference stage, and training with multi-size images brings a greater gain in accuracy than the other two modules, but the combination of the three modules will be better.

ACS Style

Jianming Zhang; Chaoquan Lu; Jin Wang; Xiao-Guang Yue; Se-Jung Lim; Zafer Al-Makhadmeh; Amr Tolba. Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification. Sensors 2020, 20, 1188 .

AMA Style

Jianming Zhang, Chaoquan Lu, Jin Wang, Xiao-Guang Yue, Se-Jung Lim, Zafer Al-Makhadmeh, Amr Tolba. Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification. Sensors. 2020; 20 (4):1188.

Chicago/Turabian Style

Jianming Zhang; Chaoquan Lu; Jin Wang; Xiao-Guang Yue; Se-Jung Lim; Zafer Al-Makhadmeh; Amr Tolba. 2020. "Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification." Sensors 20, no. 4: 1188.

Journal article
Published: 06 June 2019 in Sensors
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A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods still have some drawbacks such as uneven distribution of cluster heads (CH) and unbalanced energy consumption. Recently, much attention has been paid to intelligent clustering methods based on machine learning to solve the above issues. In this paper, an affinity propagation-based self-adaptive (APSA) clustering method is presented. The advantage of K-medoids, which is a traditional machine learning algorithm, is combined with the affinity propagation (AP) method to achieve more reasonable clustering performance. AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids. Then the modified K-medoids is utilized to form the topology of the network by iteration. The presented method effectively avoids the weakness of the traditional K-medoids in aspects of the homogeneous clustering and convergence rate. Simulation results show that the proposed algorithm outperforms some latest work such as the unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H), the low-energy adaptive clustering hierarchy using affinity propagation (LEACH-AP) algorithm, and the energy degree distance unequal clustering (EDDUCA) algorithm.

ACS Style

Jin Wang; Yu Gao; Kai Wang; Arun Sangaiah; Se-Jung Lim. An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks. Sensors 2019, 19, 2579 .

AMA Style

Jin Wang, Yu Gao, Kai Wang, Arun Sangaiah, Se-Jung Lim. An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks. Sensors. 2019; 19 (11):2579.

Chicago/Turabian Style

Jin Wang; Yu Gao; Kai Wang; Arun Sangaiah; Se-Jung Lim. 2019. "An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks." Sensors 19, no. 11: 2579.

Journal article
Published: 17 April 2019 in Sensors
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In recent years, wireless sensor networks (WSNs) have been widely applied to sense the physical environment, especially some difficult environment due to their ad-hoc nature with self-organization and local collaboration characteristics. Meanwhile, the rapid development of intelligent vehicles makes it possible to adopt mobile devices to collect information in WSNs. Although network performance can be greatly improved by those mobile devices, it is difficult to plan a reasonable travel route for efficient data gathering. In this paper, we present a travel route planning schema with a mobile collector (TRP-MC) to find a short route that covers as many sensors as possible. In order to conserve energy, sensors prefer to utilize single hop communication for data uploading within their communication range. Sojourn points (SPs) are firstly defined for a mobile collector to gather information, and then their number is determined according to the maximal coverage rate. Next, the particle swarm optimization (PSO) algorithm is used to search the optimal positions for those SPs with maximal coverage rate and minimal overlapped coverage rate. Finally, we schedule the shortest loop for those SPs by using ant colony optimization (ACO) algorithm. Plenty of simulations are performed and the results show that our presented schema owns a better performance compared to Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-hop Weighted Revenue (MWR) algorithm and Single-hop Data-gathering Procedure (SHDGP).

ACS Style

Yu Gao; Jin Wang; Wenbing Wu; Arun Kumar Sangaiah; Se-Jung Lim. Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment. Sensors 2019, 19, 1838 .

AMA Style

Yu Gao, Jin Wang, Wenbing Wu, Arun Kumar Sangaiah, Se-Jung Lim. Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment. Sensors. 2019; 19 (8):1838.

Chicago/Turabian Style

Yu Gao; Jin Wang; Wenbing Wu; Arun Kumar Sangaiah; Se-Jung Lim. 2019. "Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment." Sensors 19, no. 8: 1838.

Journal article
Published: 30 January 2019 in Sensors
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Wireless Sensor Networks (WSNs) are usually troubled with constrained energy and complicated network topology which can be mitigated by introducing a mobile agent node. Due to the numerous nodes present especially in large scale networks, it is time-consuming for the collector to traverse all nodes, and significant latency exists within the network. Therefore, the moving path of the collector should be well scheduled to achieve a shorter length for efficient data gathering. Much attention has been paid to mobile agent moving trajectory panning, but the result has limitations in terms of energy consumption and network latency. In this paper, we adopt a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent. In HM-ACOPSO, the sensor field is divided into clusters, and the mobile agent traverses the cluster heads (CHs) in a sequence ordered by ACO. The anchor node of each CHs is selected in the range of communication by the mobile agent using PSO based on the traverse sequence. The communication range adjusts dynamically, and the anchor nodes merge in a duplicated covering area for further performance improvement. Numerous simulation results prove that the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency.

ACS Style

Yu Gao; Jin Wang; Wenbing Wu; Arun Kumar Sangaiah; Se-Jung Lim. A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs. Sensors 2019, 19, 575 .

AMA Style

Yu Gao, Jin Wang, Wenbing Wu, Arun Kumar Sangaiah, Se-Jung Lim. A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs. Sensors. 2019; 19 (3):575.

Chicago/Turabian Style

Yu Gao; Jin Wang; Wenbing Wu; Arun Kumar Sangaiah; Se-Jung Lim. 2019. "A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs." Sensors 19, no. 3: 575.

Journal article
Published: 17 January 2019 in Sensors
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Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations.

ACS Style

Gulshan Kumar; Rahul Saha; Mritunjay Kumar Rai; Reji Thomas; Tai-Hoon Kim; Se-Jung Lim; Jai Sukh Paul Singh. Improved Location Estimation in Wireless Sensor Networks Using a Vector-Based Swarm Optimized Connected Dominating Set. Sensors 2019, 19, 376 .

AMA Style

Gulshan Kumar, Rahul Saha, Mritunjay Kumar Rai, Reji Thomas, Tai-Hoon Kim, Se-Jung Lim, Jai Sukh Paul Singh. Improved Location Estimation in Wireless Sensor Networks Using a Vector-Based Swarm Optimized Connected Dominating Set. Sensors. 2019; 19 (2):376.

Chicago/Turabian Style

Gulshan Kumar; Rahul Saha; Mritunjay Kumar Rai; Reji Thomas; Tai-Hoon Kim; Se-Jung Lim; Jai Sukh Paul Singh. 2019. "Improved Location Estimation in Wireless Sensor Networks Using a Vector-Based Swarm Optimized Connected Dominating Set." Sensors 19, no. 2: 376.

Journal article
Published: 06 July 2011 in Sensors
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RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes.

ACS Style

Ali Kashif Bashir; Se-Jung Lim; Chauhdary Sajjad Hussain; Myong-Soon Park. Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks. Sensors 2011, 11, 7004 -7021.

AMA Style

Ali Kashif Bashir, Se-Jung Lim, Chauhdary Sajjad Hussain, Myong-Soon Park. Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks. Sensors. 2011; 11 (7):7004-7021.

Chicago/Turabian Style

Ali Kashif Bashir; Se-Jung Lim; Chauhdary Sajjad Hussain; Myong-Soon Park. 2011. "Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks." Sensors 11, no. 7: 7004-7021.