• Journal of Public Diplomacy
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• v.1 no.2
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• pp.49-74
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• 2021

In this paper, I applied grounded theory in exploring how Twitter became the battlefield for China's public diplomacy campaign. China's new move to global social media platforms, such as Twitter and Facebook, has been a controversial strategy in public diplomacy. This study analyzes Chinese Foreign Spokesperson Zhao Lijian's Twitter posts and comments. It models China's recent diplomatic move to Twitter as a "war of words" model, with features including "leadership," "polarization," and "aggression," while exerting possible effects as "resistance," "hatred," and "sarcasm" to the global community. Our findings show that by failing to gage public opinion and promote the country's positive image, China's current digital diplomacy strategy reflected by Zhao Lijian's tweets has instead constructed a polarized political public sphere, contradictory to the country's promoted "shared human destiny." The "war of words" model extends our understanding of China's new digital diplomacy move as a hybrid of state propaganda and self-performance. Such a strategy could spread hate speech and accelerate political polarization in cyberspace, despite improvements to China's homogenous network building on Twitter.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.137-150
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• 2009

This paper proposes the hybrid heuristic method to apply the opportunity time to solve the vehicle routing and scheduling problem with time constraints(VRSPTW). The opportunity time indicates the idle time which remains after the vehicle performs the unloading service required by each customer's node. In this proposed heuristic, we add the constraints to VRSPTW model for the opportunity time. We also obtain the initial solution by applying the cost evaluation function to the insertion strategy considering the opportunity time. In addition, we improve the former result by applying the opportunity time to the tabu search strategy by swapping the customer's node. Finally, we suggest the construction strategies of initial routing which can efficiently acquire the nearest optimal solution from various types of data in terms of geographical condition, scheduling horizon and vehicle capacity. Our experiment show that our heuristic can get the nearest optimal solution more efficiently than the Solomon's I1 heuristic.

• Transactions on Electrical and Electronic Materials
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• v.17 no.2
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• pp.57-68
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• 2016

In this study, we present the modern hybrid system based power generation for electric vehicle applications. We describe the hybrid structure of modified current source based DC - DC converters used to extract the maximum power from Photovoltaic (PV) and Fuel Cell system. Due to reduced dc-link capacitor requirement and higher reliability, the current source inverters (CSI) better compared to the voltage source based inverter. The novel control strategy includes Distributed Maximum Power Point Tracking (DMPPT) for photovoltaic (PV) and fuel cell power generation system. The proposed DC - DC converters have been analyzed in both buck and boost mode of operation under duty cycle 0.5>d, 0.5<d<1 and 0.5<d for capable electric vehicle applications. The proposed topology benefits include one common DC-AC inverter that interposes the generated power to supply the charge for the sharing of load in a system of hybrid supply with photovoltaic panels and fuel cell PEM. An improved control of Direct Torque and Flux Control (DTFC) based induction motor fed by current source converters for electric vehicle.In order to achieve better performance in terms of speed, power and miles per gallon for the expert, to accepting high regenerative braking current as well as persistent high dynamics driving performance is required. A simulation model for the hybrid power generation system based electric vehicle has been developed by using MATLAB/Simulink. The Direct Torque and Flux Control (DTFC) is planned using Xilinx ISE software tool in addition to a Modelsim 6.3 software tool that is used for simulation purposes. The FPGA based pulse generation is used to control the induction motor for electric vehicle applications. FPGA has been implemented, in order to verify the minimal error between the simulation results of MATLAB/Simulink and experimental results.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.2
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• pp.181-186
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• 2013

In recent years, eco-friendliness and high fuel economy have become important issues for commercial tractors. Electric tractors are often required for operation in a greenhouse. However, the battery capacity limits the available operation time. To overcome this problem, a plug-in hybrid electric tractor is considered a reasonable alternative. This tractor has a basic driving ability and can operate in various working modes such as mower, rotary, loader, and trailing. This study focuses on the energy management strategy by considering various working modes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Smart Structures and Systems
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• v.17 no.6
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• pp.957-980
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• 2016

Structural damage detection (SDD) is a challenging task in the field of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into the SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters, such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2595-2609
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• 2024

This paper introduces a base isolation system-tuned mass damper inerter (BIS-TMDI) hybrid system to the AP1000 nuclear power plant (NPP), which reduces seismic damage potential of the NPP structure. The effects of fluid-structure interaction (FSI) caused by the passive containment cooling system water storage tank (PCCWST) on NPP's seismic performance are investigated. The FSI of water tank theoretical model is considered based on the Housner's model, and a series of time history analyses are performed to prove the rationality of the proposed model. Three single-objective optimization strategies are employed to minimize the relative displacement variance and absolute acceleration variance of the upper structure, as well as the filtered energy index (FEI). Furthermore, a multi-objective optimization strategy considering all these three indexes is proposed to obtain optimal parameters of vibration control. The influence of vibration control strategies on the relative deformation and acceleration of the upper structure is explored with various water level ratios. The analytical results indicate that the proposed BIS-TMDI strategy has significantly reduced the NPP structure's seismic response. The effectiveness of the vibration control strategy is influenced by the water level ratio, emphasizing the significance of designing an appropriate water level ratio to reduce NPP structure's seismic response.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.523-531
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• 2014

Brushless Dual-Mechanical-Port Electrical Machine (BLDMPEM) is a new type of motor designed for Hybrid Electric Vehicle (HEV), which contains two mechanical ports and two electric ports. Compared with Dual-Mechanical-Port Electrical Machine (DMPEM), the brushless structure brings higher reliability and easier maintenance. In this paper, the model of BLDMPEM is discussed. In Chapter 2, the energy flow and mathematical model of BLDMPEM are analyzed. Then a novel three-phase half-bridge controlled rectifier topology and its control strategy for cruising mode of HEV based on BLDMPEM are proposed in Chapter 3. Compared with the Field Oriented Control (FOC) strategy of BLDMPEM, the proposed method does not require accurate motor parameters, and it is much simpler and easier to be implemented. At last, simulation and experiment results show the feasibility and validity of the proposed strategy.

• Journal of Power Electronics
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• v.18 no.1
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• pp.251-265
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• 2018

To enable a master-slave control independent microgrid system (MSCIMGS) to supply electricity continuously, the microgrid inverter should perform mode transfer between grid-connected and islanding operations. Transient oscillations should be reduced during transfer to effectively conduct a seamless mode transfer. This study uses a typical MSCIMGS as an example and improves the mode transfer strategy in three aspects: (1) adopts a status-tracking algorithm to improve the switching strategy of the outer loop, (2) uses the voltage magnitude and phase pre-synchronization algorithm to reduce transient shock at the time of grid connection, and (3) applies the hybrid-sensitivity $H_{\infty}$ robust controller instead of the current inner loop to improve the robustness of the controller. Simulations and experiments show that the proposed strategy is more practical than the traditional proportional-derivative control mode transfer and effective in reducing voltage and current oscillations during the transfer period.