Tissue P systems are a class of distributed and parallel models of computation inspired by the way of communication among living cells or between cells and their environment. In this work, we investigate the computati...
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Tissue P systems are a class of distributed and parallel models of computation inspired by the way of communication among living cells or between cells and their environment. In this work, we investigate the computational power of tissue P systems, where each rule is assigned either with a label chosen from an alphabet or with the empty label A. The sequence of labels of rules applied during a halting computation is defined as the result of the computation, and the set of all results computed by a given tissue P system is called a control language. We prove that tissue P systems with antiport rules of weight one and without symport rules characterize regular languages;tissue P systems with antiport rules of weight at most two (resp., symport rules of weight at most two) without symport rules (resp., antiport rules) are universal. Tissue P systems with antiport rules of weight one and symport rules of weight one are also proved to be universal. These results show that the rule complexity is crucial for tissue P systems to achieve a desired computational power. (C) 2014 Elsevier Inc. All rights reserved.
Distributed control technology has significantly improved the regulation of dc microgrid systems. However, it also introduces potential cyber-security threats during the communication process. In particular, the syste...
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Distributed control technology has significantly improved the regulation of dc microgrid systems. However, it also introduces potential cyber-security threats during the communication process. In particular, the system is vulnerable to two types of attacks: denial-of-service (DoS) and false data injection (FDI) attacks. If both attacks occur simultaneously, they can destabilize the dc microgrid and jeopardize its safe operation. To ensure system stability under mixed DoS and unbounded FDI attacks, a consensus-based secondary control strategy is proposed. This strategy includes an adaptive feedback term to counteract FDI signals in the presence of DoS attacks. The stability of the system is rigorously analyzed using the Lyapunov method. Finally, numerical and experimental tests confirm the effectiveness of the proposed strategy.
Resource-constrained project scheduling problem is a classic problem in construction project. Aimed at solving this problem, an effective approach with decomposition on time windows is proposed in this paper. This app...
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Resource-constrained project scheduling problem is a classic problem in construction project. Aimed at solving this problem, an effective approach with decomposition on time windows is proposed in this paper. This approach is to select one activity to do decomposition arid to partition the feasible space of the original problem into some feasible subspaces, in which solutions are generated by using an extended serial scheduling scheme. Double justification is also performed in the process of searching in subspace. Four strategies for selecting activity to do decomposition, three strategies for decomposition and a strategy on sampling size in various Subspaces are designed. The results of experiments on two real construction projects show that the strategy based on degree for selecting activity and the strategy based on initial schedule for decomposition can obtain the best results. When compared with some other exiting algorithms, it is proven that the decomposition-based approach is effective and competitive. (C) 2016 Elsevier B.V. All rights reserved.
To achieve better performance with various load and system parameters in controlling a current-source rectifier (CSR) with less computing cost, a neural-network-based implementation of three-logic space-vector modulat...
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Memristors possess inherent nonlinearity and synaptic attributes, rendering them more suited for integration as synapses within neural networks compared to resistors. Furthermore, their utilization induces significant...
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Memristors possess inherent nonlinearity and synaptic attributes, rendering them more suited for integration as synapses within neural networks compared to resistors. Furthermore, their utilization induces significant alterations in the dynamic behavior of neural networks, hence bestowing considerable value in the realm of secure communication applications. In this paper, an improved cosine memristor is proposed, three representative memristor Hopfield neural networks (MHNNs) are constructed using it as synapse. The MHNNs exhibit rich initial offset boost behaviors, we summarize these phenomena into four types based on the offset characteristics. In particular, these offset types can be converted by adjusting internal parameters of the memristor. In addition, the MHNNs are constructed and tested using both software and hardware components, validating the accuracy of theoretical simulation and the implementation of systems. Ultimately, a set of bitplane level medical image encryption algorithm is proposed on the basis of MHNNs, which has excellent encryption performance and can effectively protect the privacy of patients.
With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWMVSC) have been investigated. Conventional PI controller has sh...
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The protocol stack plays a critical role in determining the performance of networked control system (NCS), which governs the communication activities and directly affects the communication quality of service (QoS). Fu...
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In this paper, we address the problem of person reidentification (re-id), which remains to be challenging due to view point changes, pose variations, different camera settings, etc. Different from common methods that ...
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Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recog...
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Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model's generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.
Additional consideration of recovery rapidity makes resilience-based solutions on critical infrastructure protection conceptually more advantageous than robustness-based solutions, but existing studies have not uncove...
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Additional consideration of recovery rapidity makes resilience-based solutions on critical infrastructure protection conceptually more advantageous than robustness-based solutions, but existing studies have not uncovered how large their differences could be. By considering a pre-event protection strategy frequently adopted both in practice and in the literature, i.e., protecting or retrofitting a set of weak components under a limited budget, this paper introduces four mathematical models and their solution algorithms for exactly identifying the optimal robustness-based and resilience-based protection strategies for critical infrastructure systems against worst-case malicious attacks and natural hazards, respectively. By comparing with the optimal robustness-based protection strategy, the value of resilience-based solution is then quantified by how much the system resilience can be improved and how much the system loss can be mitigated. Taking the electric power transmission system in Shelby County, USA as an example, results show that the optimal resilience-based solutions improve the worst-case system resilience by at most 1.29% and reduce the worst-case system loss by at most 13.25%, and enhance the seismic resilience by at most 0.16% and mitigate the system loss by at most 5.27% under seismic hazards at a 2% probability of being exceeded in 50 years. Other model parameters and several other systems are also investigated.
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