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Linear compressor has a unique feature on capacity regulation by modifying the piston stroke of piston, with results a big dead volume in a low cooling capacity output condition. This paper provides an energy recovery control method to recovering the energy of the compressed gas in the clearance. A test bench was established and the results show that It is practical and reliable to apply energy recovery control and the optimal operating frequency decreases as the cooling capacity increases with energy recovery control.

Linear compressor employed linear motor driving the piston directly oscillating in cylinder, shows higher energy efficiency applied in refrigerator. And it also owns excellent variable cooling capacity with piston stroke adjustment. The stroke, or the peak to peak value of piston displacement, have a strong impact on the capacity of linear compressor as well as the efficiency. While collision may arise between piston and cylinder, if the operating condition changes without stroke control. Thus, the stroke is important parameter for linear compressor control. In this paper, an energy method for the stroke estimation is proposed, which omits the displacement sensor. And an experimental platform of the refrigerator stroke control system with PID algorithm is established employed energy method. The electrical parameters of linear motor are used for the stroke estimation. The estimated stroke results are verified with the measured stroke, and the errors would be achieved within ± 5% under different working condition. The method of stroke control for linear compressor startup process is investigated, and a segmentation stroke control could decrease the risk of collision between the cylinder and the piston when the linear compressor startup with pressure varying dramatically.

Linear compressor technology employed in refrigerator shows more energy-saving potentiality, for its compact design, high efficiency and excellent variable cooling capacity. The linear compressor is designed in resonance condition to reduce the input current required, so the dynamic parameters identification for linear compressor would make a positive contribution to a better performance prediction and efficiency operating. While the valves' motion coupled with the free piston structure in linear compressor, which make the gas force is nonlinear and harmonic distortion. It caused the dynamic parameters of linear compressor time varying and nonlinear. Hence the natural frequency estimation seems an impractical task in time domain due to the nonlinear gas force. As the linear oscillating motor has an ability of restricting high harmonics, the natural frequency estimation in frequency domain is practicable. In this paper, we generalize the Fourier Series Analysis applied for linear compressor vibration analysis, including the application for the gas force linearization, the signal processing and the natural frequency estimation. We established a test bench in a refrigerator control system with Fourier Series Analysis for the signal processing and the natural frequency estimation. The effective voltage, the effective current and the power measured by discrete Fourier transform (DFT) method agree well with the results measured by an electricity parameter meter, and the linear motor efficiency achieves a value of 95% in the refrigerator by tracking the nature frequency estimated in frequency domain.

Social propagation issue that appears in both the real world and the cyberspace recently gains great attentions in several communities. It typically is involved with a great number of autonomous participated individuals and their local interactions, and may arise global emergent behaviors or properties. It is therefore necessary to investigate what emergence may occur and how to influence the emergence in the social propagation. This paper proposes an agent-based artificial society approach to analyzing social propagation issue, which includes AVI (Agent-Virus-Interaction) model for modeling social propagation and the systematic method to investigate the emergence. The AVI model that borrows ideas from multiagent and organization metaphors has three core concepts: agent, virus and interaction, representing the propagation carrier, content, and channel, respectively. Based on the AVI model, a systematic method for analyzing social propagation that consists of five analysis activities including modeling artificial society, programing simulation codes, designing experiments, performing simulations, and analyzing results, is proposed. We study a case of an infectious virus propagation to illustrate the approach and show its usability and effectiveness. Besides, we design and conduct a number of experiments to analyze what/how factors influence the infectious virus propagation. Finally, we discuss some potential extensions to the AVI model for satisfying further requirements of social propagation applications.

Choosing whom to tweet information to promote information spread on social media is an interesting and significant topic for both academic and industrial areas. Effective policies to choose whom to tweet information on social media should make more users to be aware of the information and to be willing to retweet it. Aiming at investigating how information spreads on social media, this paper proposes an interest-based dissemination model to depict the information spread process. The interest is introduced as the foundation of users’ rationality to follow other users and retweet information. Meanwhile, this paper adopts artificial society and a lightweight social computing method to evaluate and analyze the effectiveness of policies, in which users on social media are modelled as agents with various social relationships and interests. Based on PZE approach, several policies for information promotion on social media have been made, and simulations are undertaken quantitatively to evaluate their effectiveness in various scenarios. The experimental results reveal that the effectiveness of a policy is tightly related to two kinds of users’ rational behaviors: retweeting information and following other users. Keywordsagent; information spread; interest; social media; artificial society

Complexity issues in the real world cannot be completely solved by experiments on the real society. ACP approach (Artificial society for modelling, Computations experiment for analysis and Parallel execution for control) has been proposed to solve to eliminate the gap of micro-to-macro emergent behaviours. Artificial society modelling is the consitituent of the ACP approach. However, there are no widely accepted and well-established methods to conduct and standardize artificial society modelling. This paper describes the main aspects of artificial society modelling, including agent population, environment and event. Based on the social organization metaphor, it presents an artificial society modelling approach, including the modelling process. This approach can effectively aid and guide artificial society modelling.