Optimal operation of cascaded hydroelectric power plants in the power market

An improved network flow algorithm, which includes the minimum cost network flow and the same period network flow, is proposed to solve the optimization of cascaded hydroelectric power plants in a competitive electricity market. The typical network flow is used to find the feasible flow and add the discharge water to different cascaded hydroelectric power plants at the same step. The same period network flow is used to find the optimal flow and add the power output at a different step. This new algorithm retains the advantages of the typical network flow, such as simplicity and ease of realization. The result of the case analysis indicates that the new algorithm can achieve high calculation precision and can be used to calculate the optimal operation of eascaded hydroelectric power plants.     

Novel flow control mechanism based on improved BP neural network in cognitive packet network

In this paper, a novel flow control mechanism in cognitive packet network (CPN) based on the improved back propagation (BP) neural network is proposed, considering the flow distribution status predicted by BP neural network when packets are routed. The objective is to increase the capacity of CPN and improve the quality of service (QoS) by achieving flow balance. Besides, considering the slow convergence speed of traditional BP algorithm and the quick change of the flow status in cognitive packet network, an improved BP algorithm with dynamic learning rate is designed in order to achieve a higher convergence speed. The mechanism, which we propose, regards the predicated traffic data as an important factor when packets are routed to implement flow control. By achieving balance, the quality of network can be improved obviously. The simulation results show that the proposed mechanism provides better average time delay and packets loss ratio.     

Sequential feasible optimal power flow in power systems

A sequential feasible optimal power flow (OPF) method is developed for large-scale power systems. One of the outstanding features of this method is that it can maintain feasibility for both equality and inequality constraints during iterations. In sequential feasible OPF, every iteration consists of two stages: Objective improving stage and feasibility enforcing stage. Analytical basis for each stage is provided. Numerical studies on various power systems up to 2383 buses indicate that the proposed feasible approach is promising. Compared with the conventional OPF algorithms, such as interior point method, the proposed sequential feasible OPF approach can be terminated at any iteration and yield a feasible operating point simultaneously.     

An optimal method for prediction and adjustment on gasholder level and self-provided power plant gas supply in steel works

An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas.     

Preference Value-Based Network Selection and Resource Allocation in Heterogeneous Wireless Networks

Network selection and resource allocation( NS-RA) are the processes of determining network and radio resource which provide the service to user. Optimizing these processes is an important step towards maximizing the utilization of current and future networks. In this paper,we proposed a preference value-based network selection and resource allocation,in which the NS scheme was performed by the joint radio resource management( JRRM) entity and the RA scheme was performed by the network. In the NS step,the JRRM entity selected the preferable network for users according to the preference value of each network,which took the load balance,the received signal strength( RSS) and the relative position between the user and the network into account. In the second step,the network allocated the optimal sub-carrier to user for the downlink transmission each round according to the preference value of each user and the maximum reachable data rate calculated by users’ perceived channel information,maximizing the spectrum efficiency as well as guaranteeing the fairness. The simulation results showed that the proposed NS-RA scheme achieves better performance in terms of load distribution,spectrum efficiency and user fairness,compared to the conventional strategies.     

An integrated scheme of neural network and optimal predictive control

An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good control performance. The output of nonlinear network model is adopted as a measured disturbance that is therefore weakened in predictive feed-forward control. Simulation and practical application show the effectiveness of control by the proposed approach.     

Resource allocation for cognitive WLAN over fiber

In this paper,a novel WLAN system,Cognitive WLAN over Fiber (CWLANoF),is introduced in the first place.Moreover,when CWLANoF has more channels than STAs,a new channel allocation scheme is proposed using the Hungarian algorithm,which is demonstrated to be the optimal one.Furthermore,when CWLANoF has fewer channels than STAs,it is possible for more than one STA to share the same channel simultaneously based on the new features of CWLANoF.And the power control scheme is proposed for this kind of sharing,considering efficiency and fairness.Finally,extensive simulation results illustrate the significant performance improvement of the proposed channel allocation scheme and power control scheme.     

Optimal power allocation for a video layered multicasting relay strategy in cellular system

Within a cell of cellular system,cooperative relay technique can improve the performance of multicast efficiently,but it can cause the stream frequent interruptions because of the mobility of relay terminals.A video layered cooperative relay strategy is proposed to guarantee the continuity of multicast stream and retain high-bandwidth of the cooperative relay channel.Based on the capacity analysis for layered relay channel in the strategy,the optimal power allocation is studied to maximize capacity.After analyzing and optimizing the capacity in abstract models,the study is extended to a non-fading and a Gaussian wireless channel model to satisfy the scenario of cellular system.Giving the relay nodes position or distribution of noise power,the obtained results can determine the optimal power allocation among the transmitter and relay nodes.At last,the simulation results show that the strategy and its optimal power allocation have a significant improvement on the performance.     

On the Range of the Common Cycle Time in a Road Network

In a coordinated road network, the optimal common cycle time is determined by evaluating the performance of the network in the given range of cycles. Normally, this range is determined by users’ experience. And a large range of common cycle time, e.g. \[0,0\] is chosen, which requires long computation time. This study considers that the optimal common cycle time ranges between the minimal and maximal value of intersections’ individual optimal cycle time. It is proved mathematically from the convexity condition, that the delay of the network and each individual intersection are convex functions of the cycle time according to Webster delay model. Finally, 2 000 random cases for the network composed of two intersections and of eight intersections are created to underline the proposed conclusions. The results of all cases confirm the validity, and show up to 90% improvement in computation time to compare with experience range. The signal optimization tool, Synchro, is also used to validate the conclusion by 50 random cases. The results confirm reliability further.     

Study on optimal design of vertical-shaft mechanical mixer based on numerical simulation of flow field

In the paper the three-dimensional flow fields are numerically simulated in the vertical-shaft mechanical mix tank of a water treatment plant by means of FLUENT software based on the method of Computational Fluid Dynamics (CFD). The influences of design parameters on flow fields and the mixing effect are analyzed. Firstly,the prediction capability of the turbulence model adopted in simulations is evaluated. And then,the mesh independence is checked up. Finally,the flow fields in various dimensionless blade diameters and dimensionless shaft spans are numerically simulated respectively. The results have shown that the numerical simulation method based on CFD is a feasible assistance for the optimal designs of mixers. Moreover,the optimal design of the blade diameter should take into account both the flow field and the power consumption. The optimization of the shaft span is to achieve a relatively even distribution of the flow field without any rupture. With the consideration of an optimal design,the dimensionless blade diameter and dimensionless shaft span should be 0.45 and 0.57 respectively in the case.     

Optimal operation of water distribution networks under local pipe failures

The optimal operation of water distribution networks under local pipe failures, such as water main breaks, was proposed. Based on a hydraulic analysis and a simulation of water distribution networks, a macroscopic model for a network under a local pipe failure was established by the statistical regression. After the operation objectives under a local pipe failure were determined, the optimal operation model was developed and solved by the genetic algorithm. The program was developed and examined by a city distribution network. The optimal operation alternative shows that the electricity cost is saved approximately 11%, the income of the water corporation is increased approximately 5%, and the pressure in the water distribution network is distributed evenly to ensure the network safe operation. Therefore, the proposed method for optimal operation under local pipe failure is feasible and cost-effective.     

Simulation platform of economical operation and dispatch for power plant based on float-coded genetic algorithm

This paper discusses a float-coded genetic algorithm and its application to the optimization of the power plant operation concerning the simulation problem of economical operation for power plant systems. The method proposed realizes the load optimization between generating units of power plants and their loads, solves the problem of influence of a unit plant pause spoilage and load variance on the optimal plant combination and load, and finally establishes a simulation platform for the power plant economical operation.     

Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm

Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution [3 275.3,3 603.7] can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.     

Design of quaternary logic circuits based on source-coupled logic

In order to improve the performance of arithmetic very large-scale integration ( VLSI) system,a novel structure of quaternary logic gates is proposed based on multiple-valued current mode ( MVCM) by using dynamic source-coupled logic ( SCL) . Its key components,the comparator and the output generator are both based on differential-pair circuit ( DPC) ,and the latter is constructed by using the structure of DPC trees. The pre-charge evaluates logic style makes a steady current flow cut off, thereby greatly saving the power dissipation. The combination of multiple-valued source-coupled logic and differential-pair circuit makes it lower power consumption and more compact. The performance is evaluated by HSPICE simulation with 0. 18 μm CMOS technology. The power dissipation,transistor numbers and delay are superior to corresponding binary CMOS implementation. Multiple-valued logic will be the potential solution for the high performance arithmetic VLSI system in the future.     

Energy control strategy for parallel hydrostatic transmission hybrid vehicles

Aimed at the relatively lower energy density and complicated coordinating operation between two power sources,a special energy control strategy is required to maximize the fuel saving potential.Then a new type of configuration for hydrostatic transmission hybrid vehicles(PHHV) and the selection criterion for important components are proposed.Based on the optimization of planet gear transmission ratio and the analysis of optimal energy distribution for the proposed PHHV on a representative urban driving cycle,a fuzzy torque control strategy and a braking energy regeneration strategy are designed and developed to realize the real-time control of energy for the proposed PHHV.Simulation results demonstrate that the energy control strategy effectively improves the fuel economy of PHHV.     

Modeling and characterization of novel magnetorheological(MR) cell with individual currents

Magnetorheological(MR) cell with multi-coil was designed to enlarge the range of controllable transmission torque by increasing the effective length. Individual input current was proposed to maximize its potential for reducing power consumption and generating large yield stress. Finite element analysis was performed to analyze magnetic field distribution, based on which a prototype MR cell was fabricated and tested to investigate the performance of various combinations of individual input currents. A good correlation was identified between experimental results and FEA predications. The results show that the power consumption can be reduced to 42.4%, maintaining large transmission torque, by distributing the total current(2 A) to three individual magnetic coils. In addition, optimal results of four input currents considering a multi-objective function are obtained by changing the weighting factor λ. The advantage of this design, such as lower power consumption and more control flexibility, makes it more competitive in engineering applications that require large energy consumption.     

Cross-Layer Adaptive Resource Allocation Algorithm with Diverse QoS Requirements for Single-Cell OFDMA Systems

The orthogonal frequency division multiple access( OFDMA) based communication system has been considered as the main trend of next-Generation communication system. But the existing resource allocation algorithm designed for such system is always with high complexity thus hard to be realized. To solve such problem with the constraints of spectrum efficiency and buffer state,a novel cross-layer resource allocation algorithm( RAA) is proposed in this paper. The goal of our RAA is to maximize the system throughput while satisfying several practical constraints,such as fairness among services,head of line( Ho L) delay and diverse quality of service( Qo S) requirements. Due to these constraints,finding the optimal solution becomes a NPhard problem. Therefore in this paper a novel method to solve such problem with acceptable complexity is proposed within following steps: firstly,based on the link state we formulate the ideal subchannel allocation strategy as a convex optimization problem,which can be efficiently solved by our proposed lagrange multiplier technique subchannel allocation( LMTSA) algorithm; secondly,according to the obtained channel allocation matrix,a power allocation algorithm based on the water-filling power allocation( WPA) idea is deployed to get the optimal power allocation matrix combining with adaptive modulation and coding( AMC); finally,through a greedy algorithm,the ultimate subchannel and power allocation matrix can be obtained based on iterative method. The simulation results illustrate that we can achieve the higher throughput and better Qo S performance than the widely-used maximum throughput( MT) algorithm and round robin( RR) algorithm.     

Particulate flow modelling in a spiral separator by using the Eulerian multi-fluid VOF approach

The Euler-Euler model is less effective in capturing the free surface of flow film in the spiral separator,and thus a Eulerian multi-fluid volume of fluid(VOF) model was first proposed to describe the particulate flow in spiral separators. In order to improve the applicability of the model in the high solid concentration system, the Bagnold effect was incorporated into the modelling framework. The capability of the proposed model in terms of predicting the flow film shape in a LD9 spiral separat...     

Traffic jam in signalized road network简

Traffic jam in large signalized road network presents a complex nature.In order to reveal the jam characteristics,two indexes,SVS（speed of virtual signal） and VOS（velocity of spillover）,were proposed respectively.SVS described the propagation of queue within a link while VOS reflected the spillover velocity of vehicle queue.Based on the two indexes,network jam simulation was carried out on a regular signalized road network.The simulation results show that：1） The propagation of traffic congestion on a signalized road network can be classified into two stages：virtual split driven stage and flow rate driven stage.The former stage is characterized by decreasing virtual split while the latter only depends on flow rate; 2） The jam propagation rate and direction are dependent on traffic demand distribution and other network parameters.The direction with higher demand gets more chance to be jammed.Our findings can serve as the basis of the prevention of the formation and propagation of network traffic jam.     

Study of a New Improved PSO-BP Neural Network Algorithm

In order to overcome shortcomings of traditional BP neural network, such as low study efficiency, slow convergence speed, easily trapped into local optimal solution, we proposed an improved BP neural network model based on adaptive particle swarm optimization （PSO） algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP （IPSO-BP） model, it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals, and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP, PSO-BP and linear PSO-BP （LPSO-BP） algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed, lower errors, higher diagnostic accuracy and learning ability.