Joint Optimization of Resource Allocation and Trajectory Based on User Trajectory for UAV-Assisted Backscatter Communication System

The Backscatter communication has gained widespread attention from academia and industry in recent years. In this paper, A method of resource allocation and trajectory optimization is proposed for UAV-assisted backscatter communication based on user trajectory. This paper will establish an optimization problem of jointly optimizing the UAV trajectories, UAV transmission power and BD scheduling based on the large-scale channel state signals estimated in advance of the known user trajectories, taking into account the constraints of BD data and working energy consumption, to maximize the energy efficiency of the system. The problem is a non-convex optimization problem in fractional form, and there is nonlinear coupling between optimization variables.An iterative algorithm is proposed based on Dinkelbach algorithm, block coordinate descent method and continuous convex optimization technology. First, the objective function is converted into a non-fractional programming problem based on Dinkelbach method,and then the block coordinate descent method is used to decompose the original complex problem into three independent sub-problems. Finally, the successive convex approximation method is used to solve the trajectory optimization sub-problem. The simulation results show that the proposed scheme and algorithm have obvious energy efficiency gains compared with the comparison scheme.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Numerical Optimization by Finite Element Method of Stainless Steel/Glass-Epoxy Composite Bolted Joint under Tension and Compression

The aim of this study was to optimize the geometry and the design of metallic/composite single bolted joints subjected to tension-compression loading. For this purpose, it was necessary to evaluate the stress state in each component of the bolted join. The multi-material assembly was based on the principle of double lap bolted joint. It was composed of a symmetrical balanced woven glass-epoxy composite material plate fastened to two stainless sheets using a stainless pre-stressed bolt. In order to optimize the design and the geometry of the assembly, ten configurations were proposed and studied: a classical simple bolted joint, two joints with an insert (a BigHeadR insert and a stair one) embedded in the composite, two “waved” solutions, three symmetrical configurations composed of a succession of metallic and composites layers, without a sleeve, with one and with two sleeves, and two non-symmetrical constituted of metallic and composites layers associated with a stair-insert (one with a sleeve and one without). A tridimensional Finite Element Method (FEM) was used to model each configuration mentioned above. The FE models taked into account the different materials, the effects of contact between the different sheets of the assembly and the pre-stress in the bolt. The stress state was analyzed in the composite part. The concept of stress concentration factor was used in order to evaluate the stress increase in the highly stressed regions and to compare the ten configurations studied. For this purpose, three stress concentration factors were defined: one for a monotonic loading in tension, another for a monotonic loading in compression, and the third for a tension-compression cyclic loading. The results of the FEM computations showed that the use of alternative metallic and composite layers associated with two sleeves gived low values of stress concentration factors, smaller than 1.4. In this case, there was no contact between the bolt and the composite part and the most stressed region was not the vicinity of the hole but the end of the longest layers of the metallic inserts.

Joint Beamforming Optimization for Reconfigurable Intelligent Surface-Enabled MISO-OFDM Systems

In this paper,we investigate the reconfigurable intelligent surface(RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing(MISO-OFDM)system under frequency-selective channels,and propose a low-complexity alternating optimization(AO)based joint beamforming and RIS phase shifts optimization algorithm to maximize the achievable rate.First,with fixed RIS phase shifts,we devise the optimal closedform transmit beamforming vectors corresponding to different subcarriers.Then,with given active beamforming vectors,near-optimal RIS reflection coefficients can be determined efficiently leveraging fractional programming(FP)combined with manifold optimization(MO)or majorization-minimization(MM)framework.Additionally,we also propose a heuristic RIS phase shifts design approach based on the sum of subcarrier gain maximization(SSGM)criterion requiring lower complexity.Numerical results indicate that the proposed MO/MM algorithm can achieve almost the same rate as the upper bound achieved by the semidefinite relaxation(SDR)algorithm,and the proposed SSGM based scheme is only slightly inferior to the upper bound while has much lower complexity.These results demonstrate the effectiveness of the proposed algorithms.

An adaptive waveform-detection threshold joint optimization method for target tracking

The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association(MPDA)filter.The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance.The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function,while the optimization problem is solved through the genetic algorithm(GA).The detection probability,false alarm probability and measurement noise covariance are all considered together,which significantly improves the tracking performance of the joint detection and tracking system.Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method,which will reduce the tracking error.The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m,while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m.Similar error reduction occurs for the velocity error and acceleration error.

Joint optimization of maintenance inspection and spare provisioning for aircraft deteriorating parts

With the wide application of condition based maintenance(CBM) in aircraft maintenance practice, the joint optimization of maintenance and inventory management, which can take full advantage of CBM and reduce the aircraft operational cost, is receiving increasing attention. In order to optimize the inspection interval, maintenance decision and spare provisioning together for aircraft deteriorating parts, firstly, a joint inventory management strategy is presented, then, a joint optimization of maintenance inspection and spare provisioning for aircraft parts subject to the Wiener degradation process is proposed based on the strategy.Secondly, a combination of the genetic algorithm(GA) and the Monte Carol method is developed to minimize the total cost rate.Finally, a case study is conducted and the proposed joint optimization model is compared with the existing optimization model and the airline real case. The results demonstrate that the proposed model is more beneficial and effective. In addition, the sensitivity analysis of the proposed model shows that the lead time has higher influence on the optimal results than the urgent order cost and the corrective maintenance cost, which is consistent with the actual situation of aircraft maintenance practices and inventory management.

Joint Power-Trajectory-Scheduling Optimization in A Mobile UAV-Enabled Network via Alternating Iteration

This work focuses on an unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) system based on device-to-device(D2D) communication. In this system, the UAV exhibits caching,computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes.To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.

Performance Analysis of Sparse Array based Massive MIMO via Joint Convex Optimization

Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array.

Identification and preoperative optimization of risk factors to prevent periprosthetic joint infection

Despite significant improvements over the past several decades in diagnosis,treatment and prevention of periprosthetic joint infection(PJI),it still remains a major challenge following total joint arthroplasty.Given the devastating nature and accelerated incidence of PJI,prevention is the most important strategy to deal with this challenging problem and should start from identifying risk factors.Understanding and well-organized optimization of these risk factors in individuals before elective arthroplasty are essential to the ultimate success in reducing the incidence of PJI.Even though some risk factors such as demographic characteristics are seldom changeable,they allow more accurate expectation regarding individual risks of PJI and thus,make proper counseling for shared preoperative decision-making possible.Others that increase the risk of PJI,but are potentially modifiable should be optimized prior to elective arthroplasty.Although remarkable advances have been achieved in past decades,many questions regarding standardized practice to prevent this catastrophic complication remain unanswered.The current study provide a comprehensive knowledge regarding risk factors based on general principles to control surgical siteinfection by the review of current literature and also share own practice at our institution to provide practical and better understandings.

Specific Order and Bisection Search Aided Joint 3D Beamforming and RIS Reflecting Optimization

In this paper,a three-node transmission model is conceived,where the base station(BS)node leverages 3D beamforming,the reconfigurable intelligent surface(RIS)node can constructively reconfigure the wireless channel,the user node only has a single antenna due to a limited price.Maximization of its downlink spectral efficiency is a joint optimization problem of three variables,namely phase-shift matrixΦof RIS,tilt angleθand beamforming vector w used in BS 3D beamforming.We solve this problem by employing the alternating optimization(AO)algorithm.But,in each iteration,a specific optimization order of firstlyΦ,secondlyθand finally w is proposed,which facilitates the search of optimalθin the way of narrowing its trust region and enabling unimodal property over the narrowed trust region.It finally results in a better combination of{Φ,θ,w}.

An Approach of Distributed Joint Optimization for Cluster-based Wireless Sensor Networks

Wireless sensor networks (WSNs) are energyconstrained, so energy saving is one of the most important issues in typical applications. The clustered WSN topology is considered in this paper. To achieve the balance of energy consumption and utility of network resources, we explicitly model and factor the effect of power and rate. A novel joint optimization model is proposed with the protection for cluster head. By the mean of a choice of two appropriate sub-utility functions, the distributed iterative algorithm is obtained. The convergence of the proposed iterative algorithm is proved analytically. We consider general dual decomposition method to realize variable separation and distributed computation, which is practical in large-scale sensor networks. Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission, and validate the performance in terms of prolonging of network lifetime and improvement of throughput. © 2014 Chinese Association of Automation.

Propulsive Velocity Optimization of 3-Joint Fish Robot Using Genetic-Hill Climbing Algorithm

Underwater robot is a new research field which is emerging quickly in recent years.Previous researches in this field focus on Remotely Operated Vehicles(ROVs),Autonomous Underwater Vehicles(AUVs),underwater manipulators,etc.Fish robot, which is a new type of underwater biomimetic robot,has attracted great attention because of its silence in moving and energy efficiency compared to conventional propeller-oriented propulsive mechanism. However,most of researches on fish robots have been carried out via empirical or experimental approaches,not based on dynamic optimality.In this paper,we proposed an analytical optimization approach which can guarantee the maximum propulsive velocity of fish robot in the given parametric conditions.First,a dynamic model of 3-joint(4 links)carangiform fish robot is derived,using which the influences of parameters of input torque functions,such as amplitude,frequency and phase difference,on its velocity are investigated by simulation.Second,the maximum velocity of the fish robot is optimized by combining Genetic Algorithm(GA)and Hill Climbing Algorithm(HCA).GA is used to generate the initial optimal parameters of the input functions of the system.Then,the parameters are optimized again by HCA to ensure that the final set of parameters is the'near'global optimization.Finally,both simulations and primitive experiments are carried out to prove the feasibility of the proposed method.

Joint optimization of LORA and spares stocks considering corrective maintenance time

Level of repair analysis（LORA） is an important method of maintenance decision for establishing systems of operation and maintenance in the equipment development period. Currently, the research on equipment of repair level focuses on economic analysis models which are used to optimize costs and rarely considers the maintenance time required by the implementation of the maintenance program. In fact, as to the system requiring high mission complete success, the maintenance time is an important factor which has a great influence on the availability of equipment systems. Considering the relationship between the maintenance time and the spares stocks level, it is obvious that there are contradictions between the maintenance time and the cost. In order to balance these two factors, it is necessary to build an optimization LORA model. To this end, the maintenance time representing performance characteristic is introduced, and on the basis of spares stocks which is traditionally regarded as a decision variable, a decision variable of repair level is added, and a multi-echelon multiindenture（MEMI） optimization LORA model is built which takes the best cost-effectiveness ratio as the criterion, the expected number of backorder（EBO） as the objective function and the cost as the constraint. Besides, the paper designs a convex programming algorithm of multi-variable for the optimization model, provides solutions to the non-convex objective function and methods for improving the efficiency of the algorithm. The method provided in this paper is proved to be credible and effective according to the numerical example and the simulation result.

Topology Optimization and Fatigue Analysis of Temporomandibular Joint Prosthesis

Treatment of bone tumors in the mandible often involves extensive excavation of affected bone, followed by mandibular reconstruction. Prosthetic implants may be needed to restore jaw functionality. The challenges of making prosthetic bone implants include stress shielding and extending the mechanical life of the implant. We have developed a design algorithm to improve the efficiency of prosthesis design. A finite element model of the patient case is constructed from a computer tomography scan, and the computer implements topology optimization techniques to design the prosthesis with limited stress shielding affected by highly biomechanical compatibility. Topology optimization facilitates the design of low weight structures by automatically introducing holes into the structure. This is governed by engineering predetermined constraints to meet certain job specifications. Such a design will be tested for fatigue life before it is ready to be manufactured and used. Topology optimization can be performed as a design process to achieve a final design that takes stress shielding into consideration. The problem of stress shielding is solved by matching the stiffness of the orthopedic implant to the original bone that is being replaced. The material we used was titanium alloy (Ti-6Al-7Nb). Volume fraction of the orthodox implant was used (0.2872 for the studied case) as volume constraints. Compliance of the bulk bone was set as a further constraint to match the stiffness of the bone with the designed structure. Our results show a good life expectancy for the designed parts, with 12% higher life expectancy for stress-based topology optimization than for compliance-based topology optimization.

Spring-Joint Method for Topology Optimization of Planar Passive Compliant Mechanisms

There is seldom approach developed for the initial topology design of flexure-based compliant mechanisms. The most commonly-used approaches, which start with an existing rigid-body mechanism, do not consider the performances between different topologies. Moreover, they rely heavily on the rigid-body topology, therefore limit the diversity of compliant mechanisms topology. To obtain the optimal initial topology of such mechanisms directly from problem specifications without referencing to the existing mechanism topologies, a spring-joint method is presented for a restricted class of the serial passive flexure-based compliant mechanisms, which are the building blocks of parallel compliant mechanisms. The topology of the compliant mechanisms is represented by a serial spring-joint mechanism（SSJM） that is a traditional rigid-body mechanism with a torsional spring acting on each joint, and is described by position vectors of the spring-joints. A simplified compliance matrix, determined by the position vectors, is used to characterize the tip of the SSJM kinematically, and is optimized to ensure the desired freedoms of the compliant mechanisms during optimization. The topology optimization problem is formulated as finding out the optimal position of the spring-joints in a blank design domain with an objective function derived from the simplified compliance matrix. In design examples, syntheses of the compliant mechanisms with both single freedom and two decoupled freedoms are presented to illustrate the proposed method. The proposed method provides a new way for the initial design of flexure-based compliant mechanisms.

Bearing Failure Optimization of Composite Double-Lap Bolted Joints Based on a Three-Step Strategy Marked By Feasible Region Reduction and Model Decoupling

To minimize the mass and increase the bearing failure load of composite double-lap bolted joints,a three-step optimization strategy including feasible region reduction,optimization model decoupling and optimization was presented.In feasible region reduction,the dimensions of the feasible design region were reduced by selecting dominant design variables from numerous multilevel parameters by sensitivity analyses,and the feasible regions of variables were reduced by influence mechanism analyses.In model decoupling,the optimization model with a large number of variables was divided into various sub-models with fewer variables by variance analysis.In the third step,the optimization sub-models were solved one by one using a genetic algorithm,and the modified characteristic curve method was adopted as the failure prediction method.Based on the proposed optimization method,optimization of a double-lap single-bolt joint was performed using the ANSYS®code.The results show that the bearing failure load increased by 13.5%and that the mass decreased by 8.7%compared with those of the initial design of the joint,which validated the effectiveness of the three-step optimization strategy.

Metallographic structure,mechanical properties,and process parameter optimization of 5A06 joints formed by ultrasonic-assisted refill friction stir spot welding

Novel hybrid refill friction stir spot welding （RFSSW） assisted with ultrasonic oscillation was introduced to 5A06 aluminum alloy joints. The metallographic structure and mechanical properties of 5A06 aluminum alloy RFSSW joints formed without ultrasonic assistance and with lateral and longitudinal ultrasonic assistance were compared, and the ultrasonic-assisted RFSSW process parameters were opti- mized. The results show that compared with lateral ultrasonic oscillation, longitudinal ultrasonic oscillation strengthens the horizontal bond- ing ligament in the joint and has a stronger effect on the joint＇s shear strength. By contrast, lateral ultrasonic oscillation strengthens the ver- tical bonding ligament and is more effective in increasing the joint＇s tensile strength. The maximum shear strength of ultrasonic-assisted RFSSW 5A06 aluminum alloy joints is as high as 8761 N, and the maximum tensile strength is 3679 N when the joints are formed at a tool rotating speed of 2000 r/rain, a welding time of 3.5 s, a penetration depth of 0.2 mm, and an axial pressure of 11 kN.

Multi-objective optimization strategies using adjoint method and game theory in aerodynamics

There are currently three different game strategies originated in economics： （1） Cooperative games （Pareto front）, （2） Competitive games （Nash game） and （3） Hierarchical games （Stackelberg game）. Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi- criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

Joint optimization traffic signal control for an urban arterial road

This paper considers the optimal traffic signal setting for an urban arterial road. By introducing the concepts of synchronization rate and non-synchronization degree, a mathematical model is constructed and an optimization problem is posed. Then, a new iterative algorithm is developed to solve this optimal traffic control signal setting problem. Convergence properties for this iterative algorithm are established. Finally, a numerical example is solved to illustrate the effectiveness of the method.

Joint optimization scheduling for water conservancy projects incomplex river networks

In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.