基于生物学优化肿瘤放射治疗计划的评估函数

目的:在基于生物学优化肿瘤放射治疗计划的评估函数提出以后,用评估函数选取合适的治疗计划,通过对治疗计划实施结果的评判验证其在临床上的可行性。方法:充分结合临床放疗医生经验与基于生物学优化肿瘤放射治疗计划的评估函数参数值来选择合适的治疗计划,选取鼻咽癌、食管癌和肺癌,对病例随机分组为比较组、超分割组以及适形调强(IMRT)组和容积调强(VMAT)组,然后从生存率、近期疗效、局控率以及近远期毒副作用等方面对病例治疗结果进行统计分析,评估治疗方案的可行性。结果:从近期疗效、局控率、生存率以及近期毒副作用来看,鼻咽癌组全程连续超分割高于其他对照组;对于食管癌后程加速超分割效果最好;对于肺癌是连续超分割组好于其他对照组。这种结果跟治疗前利用评估函数预判的结果一致,即利用评估函数所选取的治疗方案是有效的。结论:基于生物学优化肿瘤放射治疗计划的评估函数用于临床选取治疗方案方面是可行的。

基于生物学优化肿瘤放射治疗计划的评估函数研究

目的:从生物学优化肿瘤放射治疗计划的角度来提出可用于评估放射治疗方案可行性的评估函数。方法:在以细胞受到照射后的再修复、再群体化、细胞周期的再分布、肿瘤内乏氧细胞的再氧合,即"4R理论"[1]的基础上,分析生物效应剂量函数BED的变种公式,最后得出3个用于评估肿瘤放疗计划好坏的评估函数。再利用评估函数试验性的分析文献报道过的肿瘤放射治疗方案,并与对应的方案结果进行比对,理论上评价评估函数的可行性。结果:综合分析已有治疗方案参数与评估函数理论参数基本一致。结论:基于生物学优化肿瘤放射治疗计划的评估函数在临床上用来预判治疗方案是可行的。

Two-dimensional cross entropy multi-threshold image segmentation based on improved BBO algorithm

In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.

A Novel Real-time Optimization Methodology for Chemical Plants

In this paper, a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants. Taking advantage of the ad-hoc structure of PGQ which imitates biologic nature of flying wild geese, a chemical plant optimization problem can be re-formulated as a combination of a multi-layer PGQ and a PGQ-Objective according to the relationship among process variables involved in the objective and constraints. Subsequently, chemical plant RTO solutions are converted into coordination issues among PGQs which could be dealt with in a novel way. Accordingly, theoretical definitions, adjustment rule and implementing procedures associated with the approach are explicitly introduced together with corresponding enabling algorithms. Finally, an exemplary chemical plant is employed to demonstrate the feasibility and validity of the contribution.

Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.

Enhancing recovery of uranium column bioleaching by process optimization and kinetic modeling

This research aimed to enhance the column bioleaching recovery of uranium ore by Acidithiobacillus ferrooxidans.Seven factors were examined for their significance on bioleaching using a Plackett-Burman factorial design.Four significant variables([Fe2+]initial,pH,aeration rate and inoculation percent)were selected for the optimization studies.The effect of these variables on uranium bioleaching was studied using a central composite design(CCD).The optimal values of the variables for the maximum uranium bioleaching recovery(90.27±0.98)%were as follows:[Fe2+]initial=2.89g/L,aeration rate420mL/min,pH1.45and inoculation6%(v/v).[Fe2+]initial was found to be the most effective parameter.The maximum uranium recovery from the predicted models was92.01%.This value was in agreement with the actual experimental value.The analysis of bioleaching residue of uranium ore under optimum conditions confirmed the formation of K-jarosite on the surface of minerals.By using optimal conditions,uranium bioleaching recovery is increased at column and jarosite precipitation is minimized.The kinetic model showed that uranium recovery has a direct relation with ferric ion concentration.

Dolphin swarm algorithm

By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human＇s demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm＇ in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.

A hybrid biogeography-based optimization method for the inverse kinematics problem of an 8-DOF redundant humanoid manipulator

The redundant humanoid manipulator has characteristics of multiple degrees of freedom and complex joint structure, and it is not easy to obtain its inverse kinematics solution. The inverse kinematics problem of a humanoid manipulator can be formulated as an equivalent minimization problem, and thus it can be solved using some numerical optimization methods. Biogeography-based optimization （BBO） is a new biogeography inspired optimization algorithm, and it can be adopted to solve the inverse kinematics problem of a humanoid manipulator. The standard BBO algorithm that uses traditional migration and mutation operators suffers from slow convergence and prematurity. A hybrid biogeography-based optimization （HBBO） algorithm, which is based on BBO and differential evolution （DE）, is presented. In this hybrid algorithm, new habitats in the ecosystem are produced through a hybrid migration operator, that is, the BBO migration strategy and Did/best/I/bin differential strategy, to alleviate slow convergence at the later evolution stage of the algorithm. In addition, a Gaussian mutation operator is adopted to enhance the exploration ability and improve the diversity of the population. Based on these, an 8-DOF （degree of freedom） redundant humanoid manipulator is employed as an example. The end-effector error （position and orientation） and the ＇away limitation level＇ value of the 8-DOF humanoid manipulator constitute the fitness function of HBBO. The proposed HBBO algorithm has been used to solve the inverse kinematics problem of the 8-DOF redundant humanoid manipulator. Numerical simulation results demonstrate the effectiveness of this method.

Efficiency analysis and optimization of wireless power transfer system for freely moving biomedical implants

In the wireless power transfer system for freely moving biomedical implants,the receiving unit was generally inefficient for the reason that its design parameters including the receiving coil's dimension and receiving circuits' topology were always determined by experiments.In order to build the relationship between these parameters and the total transfer efficiency,this paper developed a novel efficiency model based on the impedance model of the coil and the circuit model of the receiving circuits.According to the design constraints,the optimal design parameters in the worst case were derived.The results indicate that the combination of the two-layered receiving coil and half-bridge rectifier has more advantages in size,efficiency and safety,which is preferred in the receiving unit.Additionally,when the load resistance increases,the optimal turn number of the receiving coil basically keeps constant and the corresponding transmitting current and total efficiency decrease.For 100 Ω load,the transmitting current and total efficiency in the worst case were measured to be 5.30 A and 1.45% respectively,which are much better than the published results.In general,our work provides an efficient method to determine the design parameters of the wireless power transfer system for freely moving biomedical implants.