Predictive control for voltage collapse avoidance using a modified discrete multi-valued PSO algorithm

Voltage stability is one of the most challenging concerns that power utilities are confronted with, and this paper proposes a voltage control scheme based on Model Predictive Control (MPC) to overcome this kind of instability. Voltage instability has a close relation with the adequacy of reactive power and the response of Under Load Tap Changers (ULTCs) to the voltage drop after the occurrence of a contingency. Therefore, the proposed method utilizes reactive power injection and tap changing to avoid voltage collapse. Considering discrete nature of the changes in the tap ratio and also in the reactive power injected by capacitor banks, the search area for the optimizer of MPC will be an integer area; consequently, a modified discrete multi-valued Particle Swarm Optimization (PSO) is considered to perform this optimization. Simulation results of applying the proposed control scheme to a 4-bus system confirm its capability to prevent voltage collapse.     

MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is a key, yet time-consuming functionality in wireless sensor networks (WSNs). Multi-channel design is a promising technique to alleviate interference as a primary reason for long latency of TDMA aggregation scheduling. Indeed, it provides more potential of parallel transmissions over different frequency channels, thus minimizing time latency. In this paper, we focus on designing a multi-channel minimum latency aggregation scheduling protocol, named MC-MLAS, using a new joint approach for tree construction, channel assignment, and transmission scheduling. To our best knowledge, this is the first work in the literature which combines orthogonal channels and partially overlapping channels to consider the total latency involved in data aggregation. Extensive simulations verify the superiority of MC-MLAS in WSNs.     

NUMERICAL SIMULATION OF FLOW OVER TWO CIRCULAR CYLINDERS IN TANDEM ARRANGEMENT

In this article,the 2-D unsteady viscous flow around two circular cylinders in a tandem arrangement is numerically simulated in order to study the characteristics of the flow in both laminar and turbulent regimes.The method applied alternatively is based on the finite volume method on a Cartesian-staggered grid.The great source term technique is employed to identify the cylinders placed in the flow field.To apply the boundary conditions,the ghost-cell technique is used.The implemented computational method is firstly validated through simulation of laminar and turbulent flows around a fixed circular cylinder.Finally,the flow around two circular cylinders in a tandem arrangement is simulated and analyzed.The flow visualization parameters,the Strouhal numbers,and drag and lift coefficients are comprehensively presented and compared for different cases in order to reveal the effect of the Reynolds number and gap spacing on the behavior of the flow.The obtained results have shown two completely distinct flow characteristics in laminar and turbulent regimes.     

Trust-based authentication scheme with user rating for low-resource devices in smart environments

In smart environments, pervasive computing contributes in improving daily life activities for dependent people by providing personalized services. Nevertheless, those environments do not guarantee a satisfactory level for protecting the user privacy and ensuring the trust between communicating entities. In this study, we propose a trust evaluation model based on user past and present behavior. This model is associated with a lightweight authentication key agreement protocol (Elliptic Curve-based Simple Authentication Key Agreement). The aim is to enable the communicating entities to establish a level of trust and then succeed in a mutual authentication using a scheme suitable for low-resource devices in smart environments. An innovation in our trust model is that it uses an accurate approach to calculate trust in different situations and includes a human-based feature for trust feedback, which is user rating. Finally, we tested and implemented our scheme on Android mobile phones in a smart environment dedicated for handicapped people.     

A NEW AGENT MATCHING SCHEME USING AN ORDERED FUZZY SIMILARITY MEASURE AND GAME THEORY

In this paper, an agent matching method for bilateral contracts in a multi-agent market is proposed. Each agent has a hierarchical representation of its trading commodity attributes by a tree structure of fuzzy attributes. Using this structure, the similarity between the trees of each pair of buyer and seller is computed using a new ordered fuzzy similarity algorithm. Then, using the concept of Stackelberg equilibrium in a leader–follower game, matchmaking is performed among the sellers and buyers. The fuzzy similarities of each agent with others in its personal viewpoint have been used as its payoffs in a bimatrix game. Through a case study for bilateral contracts of energy, the capabilities of the proposed agent-based system are illustrated.     

Congestion-aware channel assignment for multi-channel wireless mesh networks

In this paper, we propose a distributed congestion-aware channel assignment (DCACA) algorithm for multi-channel wireless mesh networks (MC–WMNs). The frequency channels are assigned according to the congestion measures which indicate the congestion status at each link. Depending on the selected congestion measure (e.g., queueing delay, packet loss probability, and differential backlog), various design objectives can be achieved. Our proposed distributed algorithm is simple to implement as it only requires each node to perform a local search. Unlike most of the previous channel assignment schemes, our proposed algorithm assigns not only the non-overlapped (i.e., orthogonal) frequency channels, but also the partially-overlapped channels. In this regard, we introduce the channel overlapping and mutual interference matrices which model the frequency overlapping among different channels. Simulation results show that in the presence of elastic traffic (e.g., TCP Vegas or TCP Reno) sources, our proposed DCACA algorithm increases the aggregate throughput and also decreases the average packet round-trip compared with the previously proposed Load-Aware channel assignment algorithm. Furthermore, in a congested IEEE 802.11b network setting, compared with the use of three non-overlapped channels, the aggregate network throughput can further be increased by 25% and the average round-trip time can be reduced by more than one half when all the 11 partially-overlapped channels are used.     

Copper/copper oxide nanoparticles synthesis using Stachys lavandulifolia and its antibacterial activity

Nanoparticles of copper/cuprous oxide (Cu/Cu₂ O) were successfully synthesised by a green chemistry route. The synthesis process was carried out using an extract of Stachys lavandulifolia as both reducing and capping agents with a facile procedure. The nanoparticles were characterised by different techniques including X‐ray diffraction, indicating that the synthesised sample comprised both copper and cuprous oxide entity. The nanoparticles had a mean size of 80 nm and represented an impressive bactericidal effect on Pseudomonas aeruginosa.Inspec keywords: copper, copper compounds, nanoparticles, nanofabrication, nanomedicine, antibacterial activity, X‐ray diffractionOther keywords: nanoparticles synthesis, Stachys lavandulifolia, antibacterial activity, green chemistry route, reducing agents, capping agents, X‐ray diffraction, bactericidal effect, Pseudomonas aeruginosa, Cu‐Cu₂ O     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.