Papers by Keyword: Synchronization

Abstract: This study used the Morris-Lecar (ML) neuron model coupled with Short-Term Plasticity (STP) to simulate neuronal connectivity and synaptic patterns. We analyze this neural network synchronization activity, examined the post-synaptic conductance patterns in the modelled neural network, investigated the dynamics of the neural network membrane potentials in the synchronous state, and analyze the Short-Term Plasticity (STP) synaptic transmission patterns by varying the inter-neuron connection probability for both inhibitory (pi) and excitatory (pe). This computational-based study was executed using Brian2 Simulator. The results revealed that the higher the connection probability, the more connections and synapses are formed. The greater value of pe, the more synchronous the neural network activity. In contrast, the higher value of pi, the less synchronous the neural network activity. A synchronous neural network implies that the spikes occur coincidentally, where coincidental spikes lead to easily detectable membrane potentials and postsynaptic conductance. Furthermore, spikes affect the release of neurotransmitters, thereby affecting synaptic transmission patterns. We further determined the frequency of this neural network synchronization.

Abstract: In city, identification for 3 phase-4 wires power distribution lines is difficult due to compound installation of overhead and underground lines, transposition, bad view caused by trees or big buildings. It is necessary that the correct and fast identification method is required for load balancing among distribution lines. Tracing off-line method with high power impulse signal injection has been used recently. Our proposed method uses to identify live lines with very small power high frequency signal injection based on data communication technology. Two end communication terminals are required to be synchronized between them for determination on electrically same phases. Challenging issue is to achieve synchronization without GPS providing synchronizing time. A novel power transformer and phase identification system is designed and implemented. The system consists of a transmitter and a receiver with power-line communication module. Some experiments are conducted to verify the theoretical concepts in a big commercial building.

Abstract: Epilepsy is type of neurological disorder characterized by recurrent seizures that may cause injury to self and others. The ability to predict seizure before its occurrence, so that counter measures are considered, would improve the quality of life of epileptic patients. This research work proposes an adaptive seizure prediction approach based on electroencephalography (EEG) signals analysis. We use cross-correlation to estimate synchronization between EEG channels. Abnormal synchronization between brain regions may reveal brain condition and functionality. Two EEG synchronization baselines, normal and pre-seizure, are used to continuously monitor sliding windows of EEG recording to predict the upcoming seizure. The two baselines are continuously updated using distance-based method based on the most recent prediction outcome. Up to 570 hours continuous EEG recording taken from CHB-MIT dataset is used for validating the proposed method. An overall of 84% sensitivity (46 out of 55 seizures are correctly predicted) and 63% specificity are achieved with one hour prediction horizon. The proposed method is suitable to be implemented in mobile or embedded device which has limited processing resources due to its simplicity.

Abstract: In this paper, the performance of three different phase locked loop (PLL) algorithms is experimentally evaluated in a grid connected photovoltaic inverter. Robust PLL is a key requirement for ensuring system compatibility with existing grid codes. The double second order generalized integrator (DSOGI) and the decoupled double synchronous reference frame (DDSRF) PLL are compared against the conventional synchronous reference frame (SRF) PLL under grid disturbances. All synchronization algorithms are initially simulated in Matlab and subsequently transformed in discrete time for implementation in the digital controller. Grid distortions, such as phase voltage sags, frequency fluctuations and voltage harmonic distortion are emulated via a controllable three phase generator.

Abstract: A double-action three-stage synchronous expanding and contracting hydraulic cylinder has been designed based on the principle of synchronous expanding and contracting hydraulics cylinder. The Principle of linear synchronous expanding and contracting hydraulics cylinder and the main points of design have been discussed. Modeling and simulation are conducted in AMESim system and the result shows that the multi-stage cylinder can expand and contract synchronously and smoothly. As long as the effective area is proportional, the multi-stage cylinder can move linearly.

Abstract: The paper deals with a dynamic response of footbridge structure which is loaded by a movement of pedestrians. Interaction between crowd and footbridge structure is a complex problem. It is important to take into account load generated by the movement of pedestrians during the computation, but also change of load influenced by a response of the structure. It means practically that the response of the structure has influence on the movement of pedestrians. Computation of the response has to be performed in several steps. The first step is backward modification of load represented by the crowd movement. Next step is recalculation of the structure with newly compiled load. This approach can bring near the real behaviour of the structure which is exposed by the continuous movement of pedestrians..

Abstract: Recently, synchronization of complex networks has been a focus subject of technology fields. In this paper, we consider the adaptive control and synchronization of uncertain complex networks. By using the adaptive control techniques with the linear feedback updated law and the well-known invariant principle on dynamical system theory, some simple yet generic criteria are derived. Furthermore, the result is applied to typical chaotic cellular neural networks (CNN). Finally, numerical simulations are presented to demonstrate the feasibility and effectiveness of the proposed techniques.

Abstract: Locomotion consists of cyclic events controlled by the neuronal activity of networks called central pattern generators. For a correct management of pedestrian flows, under regular or safety-critical situations, a deep understanding of individual and crowd dynamics is crucial. Here, we examine the emergence of walking and running forms of human gait. Individual locomotion and its interaction with other pedestrians is studied. Another key aspect examined is the self-organization experienced by a group of individuals which is a key concept to understand crowd dynamics. Self-organization leads to emergent properties, meaning that the whole system has characteristics that differ qualitatively from those of the component parts. The mechanisms behind the emergence of self-organized pattern of motion are also studied.

Abstract: Practical networks have community and hierarchical structure. These complex structures confuse the community detection algorithms and obscure the boundaries of communities. This paper proposes a delicate method which synthesizes spectral analysis and local synchronization to detect communities. Communities emerge automatically in the multi-dimension space of nontrivial eigenvectors. Its performance is compared to that of previous methods and applied to different practical networks. Our results perform better than that of other methods. Besides, it’s more robust for networks whose communities have different edge density and follow various degree distributions. This makes the algorithm a valuable tool to detect and analysis large practical networks with various community structures.

Abstract: Synchronization problem between QCNN (Quantum Cellular Neural Networks) and duffing system was studied in the paper. Not only one new controller with less parameters in QCNN was designed, but it was proved by Lyapunov function that output signal would be convergent to reference signal, and meanwhile, forms of multi-dimension controllers were given. Then the synchronization structure was draw with the corresponding controllers. In the simulation results, the new controller has higher time efficiency, which shows applying the new controller to QCNN is feasible and effective and it can assure the achievement of synchronization. Furthermore, weak periodic signal can be detected in QCNN, which provides one new thought to detection of weak signal.