Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization

In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.

Aircraft attitude estimation of MEMS sensor based on modified particle filter

The non-linearity problem of aircraft system could not be overcome by using the MEMS sensor only.In order to improve the accuracy of aerial vehicle attitude,an aircraft attitude estimation of the MEMS sensor based on modified particle filter is proposed.The aircraft attitude is optimized by the conjugate gradient method,and the drift error of gyroscope is reduced.Moreover,the particle weight is updated by the observed value to obtain an optimized state estimate.Finally,the conjugate gradient method and the modified particle filter are weightily combined to determine the optimal weighting factor.The attitude estimation is carried out with STM32 and MEMS sensor as the core to design system.The experimental results show that the static and dynamic attitude estimation performances of the aircraft are improved.The performances are well,the attitude data is relatively stable,and the tracking characteristics are better.Moreover,it has better robustness and stability.

Modified unscented particle filter for nonlinear Bayesian tracking

A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conventional unscented particle filter （UPF） confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.

Combustion Properties of Metal Particles as Components of Modified Double-Base Propellants

Metal particles such as aluminum（ Al）,magnesium（ Mg）,boron（ B） and nickel（ Ni）,as well as Mg/Al alloy（ Mg/Al = 3/4） are currently the most widely used ingredients in modified doublebase propellants. In this contribution,the combustion properties of the metal species are studied by means of the high-speed photography technique and the non-contact wavelet-based measurement of flame temperature distribution. The combustion process of the Al,Mg and Mg/Al samples shows both gas phase reaction and surface oxidation,which yield volatile and nonvolatile products,corresponding to the oxide and suboxide respectively. However,the combustion of B and Ni shows only gas phase reaction,due to their high melting point as well as high enthalpy of vaporization. In addition to the experiments,a hypothetical combustion model has been proposed to clarify the combustion characteristics of metal species in modified double-base propellants.

Robust airfoil optimization based on improved particle swarm optimization method

A robust airfoil optimization platform is constructed based on the modified particle swarm optimization method （i.e., the second-order oscillating particle swarm method）, which consists of an efficient optimization algorithm, a precise aerodynamic analysis program, a high accuracy surrogate model, and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared with the standard particle swarm method. First, the particle velocity is represented by the combination of the particle position and the variation of position, which makes the particle swarm algorithm a second-order precision method with respect to the particle po- sition. Second, for the sake of adding diversity to the swarm and enlarging the parameter searching domain to improve the global convergence performance of the algorithm, an oscillating term is introduced to the update formula of the particle velocity. At last, tak- ing two airfoils as examples, the aerodynamic shapes are optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils is greatly improved in a broad design range.

Coverage Optimization of LTE Networks Based on Antenna Tilt Adjusting Considering Network Load

In this paper,we investigate the coverage optimization for LTE networks considering the network load. The network coverage is defined as the number of served users of evolved Node B(eNB)which is determined by e NBs' antenna tilt angles(ATA). The coverage is optimized by optimizing the number of served users based on the Modified Particle Swarm Optimization(MPSO)algorithm. Simulation results show that both the number of served users by each e NB and the system throughput are significantly increased. As well,the average load and the bandwidth efficiency of the network are improved.

Structural optimization strategy of pipe isolation tool by dynamic plugging process analysis

During the pipeline plugging process,both the pipeline and the pipe isolation tool(PIT)will be greatly damaged,due to the violent vibration of the flow field.In this study,it was proposed for the first time to reduce the vibration of the flow field during the plugging process by optimizing the surface structure of the PIT.Firstly,the central composite design(CCD)was used to obtain the optimization schemes,and the drag coefficient and pressure coefficient were proposed to evaluate the degree of flow field changes.Secondly,a series of computational fluid dynamics(CFD)simulations were performed to obtain the drag coefficient and pressure coefficient during dynamic plugging.And the mathematical model of drag coefficient and pressure coefficient with the surface structure of the PIT were established respectively.Then,a modified particle swarm optimization(PSO)was applied to predict the optimal value of the surface structure of the PIT.Finally,an experimental rig was built to verify the effectiveness of the optimization.The results showed that the improved method could reduce the flow field vibration by 49.56%.This study provides a reference for the design of the PIT surface structure for flow field vibration technology.

A CFD Model for Fluid Dynamics in a Gas-fluidised Bed

A modified particle bed model derived from the two-fluid momentum balance equations was employed to predict the gas-fluidised bed behaviour. Additional terms are included in both the fluid and the particle momentum balance equations to take into account the effect of the dispersed solid phase. This model has been extended to two-dimensional formulations and has been implemented in the commercial code CFX 4.3. The model correctly simulates the homogeneous fluidisation of Geldart Group A and the bubbling fluidisation of Geldart Group B in gas-solid fluidised beds.

Application of MPSO technique to find optimal location and parameter setting of TCSC

A new mothod was presented to find the optimal location and parameter setting of Thyristor Controlled Series Compensator (TCSC) to maxmize the transfer capability.Firstly the sensitivity of the transfer capability with respect was described to the line's reactance was described to find the more sensitive lines for installing TCSC,however,the line which has the most sesitivity value is always not the best line for installing TCSC.For solving this problem,the more sensitive m lines were selected as the alternative line group of installing TCSC,and then modified particle swarm optimization (MPSO) was used to find out the optimal location and the optimal parameter settings of TCSC.Particle swarm optimization (PSO) algorithm can results premature convergence.For solving this problem,population entropy and cellular automata were introduced to it.Simulation results of IEEE 30-bus system proved the effectiveness of the method and its application values.

Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

Wireless Sensor Networks(WSN)are commonly used to observe and monitor precise environments.WSNs consist of a large number of inexpensive sensor nodes that have been separated and distributed in different environments.The base station received the amount of data collected by the numerous sensors.The current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large scale.The Trust-Based Adaptive Acknowledgement(TRAACK)Intrusion-Detection System for Wireless Sensor Networks(WSN)is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization(MPSO)has been proposed to predict knot confidence.Simulations were run for non-malicious networks(0%malicious)and different percentages of malicious nodes were discussed.The findings suggest that the proposed method TRAACK Modified Particle Swarm Optimization(MPSO)packet delivery rate outperforms TRAACKPSO by 3.3%with 0%malicious nodes.Similarly,the packet delivery rate of TRAACKMPSO is 30%malicious,3.5%better than TRAACKPSO in WSN.

Fabrication of highly water-repelling paper by surface coating with stearic acid modified calcium carbonate particles and reactive biopolymers

Cellulose paper is the most attractive green packaging material due to its recyclability, renewability, sustainability and biodegradability. In some applications, paper with a high level of water resistance is desirable to meet specific requirements in modern packaging fields. This research aimed to develop a water-repelling paper with cost-effective and nontoxic materials. Commercial precipitated calcium carbonate (PCC) particles were modified by stearic acid (SA) and incorporated with soybean oil-based binder as a water repelling coating agent. The water-repelling efficiency of the coated paper was highly dependent on the ratio of SA / PCC as well as the binder content in the coating formula. PCC particles modified with 12wt% SA were efficient in increasing the water contact angle (WCA) of the coated paper to 146° at a coating weight of 5 g/m2. The binder for the coating was synthesized with acrylated epoxidized soybean oil (AESO) through Michael addition reaction. The triglyceride structure in the polymer chain imparted good bio-degradability to the binder polymer. It was found that surface modification of PCC with stearic acid played an important role in improving the WCA of paper. A super hydrophobic paper with a WCA of 162° was fabricated with a coating formula of 60% SA-modified PCC and 40 wt% AESO-binder.

Preparation and characterization of Pd/Fe bimetallic nanoparticles immobilized on Al_2O_3/PVDF membrane:Parameter optimization and dechlorination of dichloroacetic acid

Using a liquid-solid phase inversion method, a hybrid matrix poly（vinylidene fluoride）（PVDF） membrane was prepared with alumina（Al2O3） nanoparticle addition. Pd/Fe nanoparticles（NPs） were successfully immobilized on the Al2O3/PVDF membrane, which was characterized by Scanning Electron Microscopy（SEM） and Transmission Electron Microscopy（TEM）. The micrographs showed that the Pd/Fe NPs were dispersed homogeneously. Several important experimental parameters were optimized, including the mechanical properties, contact angle and surface area of Al2O3/PVDF composite membranes with different Al2O3 contents. At the same time, the ferrous ion concentration and the effect of hydrophilization were studied. The results showed that the modified Al2O3/PVDF membrane functioned well as a support. The Al2O3/PVDF membrane with immobilized Pd/Fe NPs exhibited high efficiency in terms of dichloroacetic acid（DCAA） dechlorination. Additionally, a reaction pathway for DCAA dechlorination by Pd/Fe NPs immobilized on the Al2O3/PVDF membrane system was proposed.

Multi-component system maintenance optimization of a rail transit train based on opportunistic correlations

ln order to deal with the problems of insufficient or excessive maintenance in the current maintenance activities of China transit trains,this paper develops a novel multi-component system maintenance optimization approach based on an opportunistic correlation model.Based on the minimal reliability and failure rate change rule of each train component,the novel proposed maintenance optimization benefits from an improved opportunistic maintenance model with system structure correlation,fault correlation and reliability correlation under imperfect maintenance.Then,different maintenance modes can be determined by a proposed mainte-nance factor under the different conditions of components.Specifically,the reliability threshold of each component is also considered to optimize the maintenance cost by the system reliability and operational availability of the train.Furthermore,as the mentioned problem belongs to the NP-Hard optimization problems,a modified particle swarm optimization(PSO)with the improvement of inertia weight is proposed to cope with the optimization problem.Based on a specific case under the practical recorded failure data,the analysis shows that the proposed model and approach can effectively cut the maintenance cost.

Near Optimal PID Controllers for the Biped Robot While Walking on Uneven Terrains

The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation （PID） controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization （MCIWO） and particle swarm optimization （PSO） algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.