Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

面向智慧小镇建设的机房电气管线多目标优化布置方法

常规的管线布置优化方法难以在优化过程中得到全局搜索的最优解,导致安全性能无法得到保障,因此面向智慧小镇建设设计一个新的机房电气管线多目标优化布置方法。设置电气管线约束条件,将电压均值、单位时间内电流量、电气管线损耗恢复能力作为目标函数。优化管线布置全局搜索,使用交叉操作的方式不断得到更优解。建立多目标优化电气管线模型,得到电气管线多目标优化的数学模型。通过实验数据可知,该管线布置方法在算法测试中优于常规的3种算法,且在安全性能的检测中只与标准最优值相差6.22×10⁴,3个常规方法与标准最优值的差距为6.813×10⁴、7.6×10⁴、8.32×10⁴,因此可知该多目标优化的管线布置方法可以得到更优解。     

考虑区域协调性的城际列车开行方案优化

针对城际列车开行方案没有有效匹配城市轨道交通运能的问题，提出一种考虑区域协调性的城际列车开行方案优化方法。首先，以旅客出行费用最小和铁路运输效益最大为优化目标，考虑城际列车载客能力、出发地目的地（Original Destination，OD）客流需求和通过能力等约束；然后，在此基础上增加运能匹配度的限制，构建了考虑区域协调性的城际列车开行方案多目标非线性规划模型，并设计改进的模拟退火算法求解模型；最后，以广深城际铁路为例并进行两组对比分析。实验结果表明:考虑区域协调性的列车开行方案可以使旅客出行总广义费用降低约4.06%，铁路部门的效益提高约9.58%，旅客和铁路的系统总成本降低约23.27%；与遗传算法相比，改进的模拟退火算法在求解质量与收敛速度上均有较大提高。所提模型和算法可充分兼顾旅客和铁路双方利益，能够为城际列车开行方案优化问题提供有效解决方法。     

基于NSGA-II算法的柔性流水车间优化调度模型的构建与应用

为解决造纸企业的高效优化排产问题，使机器利用率最高、减少产品切换次数以及满足客户对产品的时间需求，构建了以成本及最大完工时间最小化为优化目标的两阶段柔性流水车间调度优化模型，并通过一种快速非支配遗传算法（NSGA-II）来求解该模型。结果表明，与人工排产相比，NSGA-II得到的排产结果缩短了约6.5%的最大完工时间，降低了约4.7%的生产成本。     

基于改进人工蜂群算法的多目标绿色柔性作业车间调度研究

针对多目标绿色柔性作业车间调度问题（MGFJSP）的特点，提出从碳排放量、噪声和废弃物这3个指标来综合评定环境污染程度，建立了以最小化最大完成时间和环境污染程度为优化目标的MGFJSP模型，并提出了一种改进的人工蜂群算法来求解该模型。算法的具体改进包括：设计了一种三维向量的编码和对应解码方案，在跟随蜂搜索阶段引入一种有效的动态邻域搜索操作来提高算法的局部搜索能力，在侦查蜂阶段提出产生新食物源的策略用于增加种群的多样性。最后进行了实验研究与算法对比，以验证所建模型和所提算法的有效性。     

In Silico Study on Tumor-Size-Dependent Thermal Profiles inside an Anthropomorphic Female Breast Phantom Subjected to Multi-Dipole Antenna Array

Electromagnetic hyperthermia as a potent adjuvant for conventional cancer therapies can be considered valuable in modern oncology, as its task is to thermally destroy cancer cells exposed to high-frequency electromagnetic fields. Hyperthermia treatment planning based on computer in silico simulations has the potential to improve the localized heating of breast tissues through the use of the phased-array dipole applicators. Herein, we intended to improve our understanding of temperature estimation in an anatomically accurate female breast phantom embedded with a tumor, particularly when it is exposed to an eight-element dipole antenna matrix surrounding the breast tissues. The Maxwell equations coupled with the modified Pennes’ bioheat equation was solved in the modelled breast tissues using the finite-difference time-domain (FDTD) engine. The microwave (MW) applicators around the object were modelled with shortened half-wavelength dipole antennas operating at the same 1 GHz frequency, but with different input power and phases for the dipole sources. The total input power of an eight-dipole antenna matrix was set at 8 W so that the temperature in the breast tumor did not exceed 42 °C. Finding the optimal setting for each dipole antenna from the matrix was our primary objective. Such a procedure should form the basis of any successful hyperthermia treatment planning. We applied the algorithm of multi for multi-objective optimization for the power and phases for the dipole sources in terms of maximizing the specific absorption rate (SAR) parameter inside the breast tumor while minimizing this parameter in the healthy tissues. Electro-thermal simulations were performed for tumors of different radii to confirm the reliable operation of the given optimization procedure. In the next step, thermal profiles for tumors of various sizes were calculated for the optimal parameters of dipole sources. The computed results showed that larger tumors heated better than smaller tumors; however, the procedure worked well regardless of the tumor size. This verifies the effectiveness of the applied optimization method, regardless of the various stages of breast tumor development.     

计及风险规避的电力零售商平衡市场交易模型

由于新能源出力以及终端负荷需求的不确定性,电力零售商在日前市场的竞标电量与实时市场的购买电量之间存在不平衡而产生惩罚成本风险。引入用户侧可控负荷作为平衡资源参与市场交易,提出了一种风险规避程度指标,以此来度量交易前后电量偏差程度,以信息熵度量残差序列离散程度计算风险规避程度指标。以电力零售商运行收益、用户需求响应满意度以及风险规避程度最大为目标建立多目标风险规避模型,采用自适应权重粒子群算法进行模型求解。通过算例表明,所提出的模型从电网-电力零售商-用户多个角度去考虑电力零售商参与平衡市场交易策略,能够有效提高电力零售商的运行效益以及用户满意度,同时可以提高电力市场管理的可靠性与安全性。     

2种灰色模型在矿区地表沉陷预计中的适用性

目前矿区地表单点沉陷动态预计方法主要基于传统的水准测量数据,监测方法单一,成本高,观测点易破坏,不能保证地表形变信息的实时性,且采用灰色模型进行地表沉陷预计时只针对单一模型的应用,没有结合模型自身特点分析其适用性。以袁店二矿7221工作面为试验区域,采用合成孔径雷达差分干涉测量技术监测矿区地表沉陷量,分别建立了描述沉陷量与时间关系的GM(1,1)与灰色Verhulst模型进行地表沉陷量预计,实现了矿区地表沉陷监测与动态预计一体化。通过比较、分析GM(1,1)与灰色Verhulst模型对地表沉陷量的拟合及预计结果,得出了2种灰色模型在矿区地表沉陷预计中的适用性:在矿区开采沉陷开始至活跃前期,若地表单点沉陷量曲线呈近似单峰型,则宜采用GM(1,1)进行短期预计;当矿区地表沉陷进入衰退阶段,单点沉陷量曲线呈平底饱和状态,则宜采用灰色Verhulst模型进行中长期预计。     

分时电价下多目标绿色可重入混合流水车间调度

针对多目标绿色可重入混合流水车间调度问题（RHFSP）的特点，在机器分配和工序排序的基础上引入分时电价机制，构建了以最小化最大完工时间、总能耗成本和碳排放为目标的绿色调度优化模型，提出了一种改进的多目标文化基因算法（MOMA）来求解该问题，通过数值实验验证了所设计的MOMA算法的可行性。实验结果表明MOMA算法在非劣解的收敛性、多样性和支配性指标方面都显著优于多目标蚁狮优化算法(MOALO)、多目标粒子群优化算法（MOPSO）和带精英策略的非支配排序遗传算法（NSGA-Ⅱ），四种算法的分布性指标无显著差异。所提出的模型能够使企业有效避开高电价时段作业，合理转移用电负荷，达到降低总用电成本和碳排放的目的。