Anti-interference beam pattern design based on second-order cone programming optimization

When signal-to-interference ratio is low, the energy of strong interference leaked from the side lobe of beam pattern will infect the detection of weak target. Therefore, the beam pattern needs to be optimized. The existing Dolph-Chebyshev weighting method can get the lowest side lobe level under given main lobe width, but for the other non-uniform circular array and nonlinear array, the low side lobe pattern needs to be designed specially. The second order cone programming optimization （SOCP） algorithm proposed in the paper transforms the optimization of the beam pattern into a standard convex optimization problem. Thus there is a paradigm to follow for any array formation, which not only achieves the purpose of Dolph-Chebyshev weighting, but also solves the problem of the increased side lobe when the signal is at end fire direction The simulation proves that the SOCP algorithm can detect the weak target better than the conventional beam forming.

Strategic mining options optimization:Open pit mining, underground mining or both

Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming(MILP) optimization framework. The MILP formulation maximizes the Net Present Value(NPV) of the reserve when extracted with(i) open pit mining,(ii) underground mining, and(iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.

Fuzzy linear model for production optimization of mining systems with multiple entities

Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

Modeling and optimization for oil well production scheduling

In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.

Automatic Generation of Very Efficient Programs by Generalized Partial Computation

Generalized Partial Computation (GPC) is a program transformation method utilizing partial information about input data, properties of auxiliary functions and the logical structure of a source program. GPC uses both an inference engine such as a theorem prover and a classical partial evaluator to optimize programs. Therefore, GPC is more powerful than classical partial evaluators but harder to implement and control. We have implemented an experimental GPC system called WSDFU (Waseda Simplify Distribute Fold Unfold). This paper discusses the power of the program transformation system, its theorem prover and future works.

Vehicle path tracking by integrated chassis control

The control problem of trajectory based path following for passenger vehicles is studied. Comprehensive nonlinear vehicle model is utilized for simulation vehicle response during various maneuvers in MATLAB/Simulink. In order to follow desired path, a driver model is developed to enhance closed loop driver/vehicle model. Then, linear quadratic regulator(LQR) controller is developed which regulates direct yaw moment and corrective steering angle on wheels. Particle swam optimization(PSO) method is utilized to optimize the LQR controller for various dynamic conditions. Simulation results indicate that, over various maneuvers, side slip angle and lateral acceleration can be reduced by 10% and 15%, respectively, which sustain the vehicle stable. Also, anti-lock brake system is designed for longitudinal dynamics of vehicle to achieve desired slip during braking and accelerating. Proposed comprehensive controller demonstrates that vehicle steerability can increase by about 15% during severe braking by preventing wheel from locking and reducing stopping distance.

Supply-based optimal scheduling of oil product pipelines

Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil pro- duct pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes＇ sequence. The ant colony optimization algo- rithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.

Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and Direct Optimization Programming

Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering(GC) and direct optimization programming(DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction(NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

Optimal programming management of ventricular tachycardia storm in ICD patients

Ventricular tachycardia storm （VTS） is defined as a life-threatening syndrome of three or more separate episodes of ventricular tachycardia （VT） leading to implantable cardioverter defibrillator （ICD） therapy within 24 hours. Patients with VTS have poor outcomes and require immediate medical attention. ICD shocks have been shown to be associated with increased mortality in several studies. Optimal programming in minimization of ICD shocks may decrease mortality. Large controlled trials showed that long detection time and high heart rate detection threshold reduced ICD shock burden without an increase in syncope or death. As a fundamental therapy of ICD, anti- tachycardia pacing （ATP） can terminate most slow VT with a low risk of acceleration. For fast VT, burst pacing is more effective and less likely to result in acceleration than ramp pacing. One algorithm of optimal programming management during a VTS is presented in the review.

Optimal Investment Strategy in Safe-region on Consumption and Portfolio Problem

This paper investigates an optimal investment strategy on consumption and portfolio problem, in which the investor must withdraw funds continuously at a given rate. By analyzing the evolving process of wealth, we give the definition of safe-region for investment. Moreover, in order to obtain the target wealth as quickly as possible, using Bellman dynamic programming principle, we get the optimal investment strategy and corresponding necessary expected time. At last we give some numerical computations for a set of different parameters.

Approximate Solutions for a Class of Fractional-Order Model of HIV Infection via Linear Programming Problem

In this paper, we provide a new approach to solve approximately a system of fractional differential equations (FDEs). We extend this approach for approximately solving a fractional-order differential equation model of HIV infection of CD4+T cells with therapy effect. The fractional derivative in our approach is in the sense of Riemann-Liouville. To solve the problem, we reduce the system of FDE to a discrete optimization problem. By obtaining the optimal solutions of new problem by minimization the total errors, we obtain the approximate solution of the original problem. The numerical solutions obtained from the proposed approach indicate that our approximation is easy to implement and accurate when it is applied to a systems of FDEs.

建设用地规划方案的碳排放核算与低碳优化方法

Built-up area(BUA)significantly contributes to global greenhouse gas emissions,making strategic spatial planning crucial for carbon emission control.Given the diverse land use patterns and carbon emission sources in BUAs,this study proposed a land-based strategy system for carbon emission assessment and optimization.A three-step method was devised to create a planner-friendly tool for implementing the system,which involves carbon emission intensity calculation based on current land use,spatial illustration of carbon emission intensities based on land use planning,and planning program optimization and emission reduction effect assessment.The method was applied to the central urban area of Changxing County(Zhejiang)in China.The results showed that the structures and emission intensities of urban land use substantially influenced the overall carbon emissions in the central urban area.Our comprehensive land use optimization strategies reduced the overall carbon emissions of the central urban area by 36.9%when compared to the original planning program.The Monte Carlo simulation indicated that land use structure optimization and emission intensity control measures could reduce carbon emission rate by 5.20%to 18.28%,and 18.44%to 31.67%,respectively.The results underlined the importance of making specific adjustments to land use structure and implementing intensity control measures for effective carbon reduction.In conclusion,this study offers methods and insights for urban planners in creating sustainable and low-carbon urban spaces.

MILP Optimization Model for Assessing the Participation of Distributed Residential PV-battery Systems in Ancillary Services Market

A novel non-linear stochastic method based on a Mixed-Integer Linear Programming(MILP)optimization model is proposed to optimally manage a high number of photovoltaic(PV)-battery systems for the provision of up and down regulation in the ancillary services market.This method,considers both the technical constraints of the power system,and those of the equipment used by all the prosumers.This allows an aggregator of many residential prosumers endowed with photovoltaic(PV)-battery systems to evaluate the baseline of the aggregate by minimizing the costs related to the electrical energy absorbed from the grid and then to assess the up and down flexibility curves with relative offer prices.As confirmed by simulation results carried out considering different realistic case studies,the method can effectively be used by an aggregator to evaluate the economic impact of its participation in the ancillary services market,both for the aggregator and for its prosumers.

Structure splitting framework for data-layout optimization

To improve data cache performance, optimizing program data layout by data reorganization has become an important method of decreasing the impact of increasing gap of speed between processor and memory. In this article, a structure splitting framework with an analysis model named structure field relation graph （SFRG） is presented to optimize program data layout. The SFRG can be used to quantify relationship between fields. It helps to find an optimal layout for structure as well as the optimal program data layout. And the data cache performance is improved through SFRG-based structure splitting. Experiments show that this framework is effective in optimizing program data layout and improving the performance of data cache and whole program.

Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm

Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach.

Parallel power system restoration planning using heuristic initialization and discrete evolutionary programming

This paper proposes a sectionalizing planning for parallel power system restoration after a complete system blackout.Parallel restoration is conducted in order to reduce the total restoration process time.Physical and operation knowledge of the system,operating personnel experience,and computer simulation are combined in this planning to improve the system restoration and serve as a guidance for system operators/planners.Sectionalizing planning is obtained using discrete evolutionary programming optimization method assisted by heuristic initialization and graph theory approach.Set of transmission lines that should not be restored during parallel restoration process(cut set)is determined in order to sectionalize the system into subsystems or islands.Each island with almost similar restoration time is set as an objective function so as to speed up the resynchronization of the islands.Restoration operation and constraints(black start generator availability,load-generation balance and maintaining acceptable voltage magnitude within each island)is also takeninto account in the course of this planning.The method is validated using the IEEE 39-bus and 118-bus system.Promising results in terms of restoration time was compared to other methods reported in the literature.

SIMPLE COMPUTING OF THE CUSTOMER LIFETIME VALUE:A FIXED LOCAL-OPTIMAL POLICY APPROACH

In this paper,we present a new method for finding a fixed local-optimal policy for computing the customer lifetime value.The method is developed for a class of ergodic controllable finite Markov chains.We propose an approach based on a non-converging state-value function that fluctuates（increases and decreases） between states of the dynamic process.We prove that it is possible to represent that function in a recursive format using a one-step-ahead fixed-optimal policy.Then,we provide an analytical formula for the numerical realization of the fixed local-optimal strategy.We also present a second approach based on linear programming,to solve the same problem,that implement the c-variable method for making the problem computationally tractable.At the end,we show that these two approaches are related：after a finite number of iterations our proposed approach converges to same result as the linear programming method.We also present a non-traditional approach for ergodicity verification.The validity of the proposed methods is successfully demonstrated theoretically and,by simulated credit-card marketing experiments computing the customer lifetime value for both an optimization and a game theory approach.

A Termination Condition of Unfolding Loop for Generalized Partial Computation

The unfolding problem of loop has always been a difficult problem on the partial computation and Generalized Partial Computation( GPC ) of imperative language. This paper makes use of Data Flow Analysis( DFA ) technique to present an efficient termination condition of unfolding loop for partial evaluation or generalized partial evaluation, and this termination condition can solve the problem very well.