FRAMEWORKS FOR GENERALIZATION CONSTRAINTS AND OPERATIONS BASED ON OBJECT-ORIENTED DATA STRUCTURE IN DATABASE GENERALIZATION

The constraints and the operations play an important role in database generalization.They guide and govern database generalization.The constraints are translation of the required conditions that should take into account not only the objects and relationships among objects but also spatial data schema (classification and aggregation hierarchy) associated with the final existing database.The operations perform the actions of generalization in support of data reduction in the database.The constraints in database generalization are still lack of research.There is still the lack of frameworks to express the constraints and the operations on the basis of object_oriented data structure in database generalization.This paper focuses on the frameworks for generalization operations and constraints on the basis of object_oriented data structure in database generalization.The constraints as the attributes of the object and the operations as the methods of the object can be encapsulated in classes.They have the inheritance and polymorphism property.So the framework of the constraints and the operations which are based on object_oriented data structure can be easily understood and implemented.The constraint and the operations based on object_oriented database are proposed based on object_oriented database.The frameworks for generalization operations,constraints and relations among objects based on object_oriented data structure in database generalization are designed.The categorical database generalization is concentrated on in this paper.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power U+0028 CCHP U+0029 with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution U+0028 TOPSIS U+0029 method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits. © 2014 Chinese Association of Automation.

Optimal Irrigation Planning and Operation of Multi Objective Reservoir Using Fuzzy Logic

In the present study the MOFLP models have been developed for the optimal cropping pattern planning which maximizes the four objectives such as Net Benefits (NB), Crop Production (CP), Employment Generation (EG) and Manure Utilization (MU) under conflicting situation and also, for maximization of Releases for Irrigation (RI) and Releases for Power (RP) simultaneously under uncertainty by considering the fuzziness in the objective functions. The developed models have been applied using the LINGO 13 (Language for Interactive General Optimization) optimization software to the case study of the Jayakwadi Project Stage-II across Sindhphana River, in the State of Maharashtra India. The various constraints have been taken into consideration like sowing area, affinity to crop, labour availability, manure availability, water availability for optimal cropping pattern planning. Similarly constraints to find the optimal reservoir operating policy are releases for power and turbine capacity, irrigation demand, reservoir storage capacity, reservoir storage continuity. The level of satisfaction for a compromised solution of optimal cropping pattern planning for four conflicting objectives under fuzzy environment is worked out to be λ = 0.68. The MOFLP compromised solution provides NB = 1088.46 (Million Rupees), CP = 241003 (Tons), EG = 23.13 (Million Man days) and MU = 111454.70 (Tons) respectively. The compromised solution for optimal operation of multi objective reservoir yields the level of satisfaction (λ) = 0.533 for maximizing the releases for irrigation and power simultaneously by satisfying the constraint of the system under consideration. The compromised solution provides the optimal releases, i.e. RI = 348.670 Mm3 and RP = 234.285 Mm3 respectively.

Objective Evaluations of the Manner and Effect for Artificial Hail Suppression Operation in the Middle Region of Inner Mongolia

[ Objective] The research aimed to conduct objective evaluation on manner and effect of the artificial hail suppression operation in central Inner Mongolia. [ Method] Depending on analyses of the radar observation data, hail shooting data and artificial hail suppression operation data in the middle region of Inner Mongolia （ represented by Huhhot） from 1990 to 2007, and combining capability of the artificial hail suppression operation tool （＂ 37＂ anti-aircraft gun）, nucleation rate of the catalyst and suitable catalyst volume in hail cloud per unit volume, objective evaluation of the manner and effect for artificial hail suppression operation in the area was obtained. [ Result] It was exacter to distinguish shape and intensity of the hail cloud, but it was uncertain to distinguish height of the hail cloud, when selecting operation target cloud. Operation height was still lower, and perfect seeding height couldn＇t be attained. Operation occasion was controlled well, but there still were phenomena of the late operation. Shell amounts of the operation were few obviously, needing to increase shell amount or to improve nucleation rate of the catalyst. [ Conclsion] Syntheti- cally, manner of the artificial hail suppression operation in central Inner Mongolia was suitable, and total efficiency was about 85%.

Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study

Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.

Optimization on Water Resource System Operation Policy during Drought

The mixed linear programming model is commonly recognized to be an effective means for searching optimal reservoir operation policy in water resources system. In this paper a multi-objective mixed integer linear programming model is set up to obtain the optimal operation policy of multi-reservoir water supply system during drought, which is able to consider the operation rule of reservoir-group system within longer-term successive drought periods, according to the basic connotation of indexes expressing the water-supply risk of reservoir during drought, that is, reliability, resilience and vulnerability of reservoir water supply, and mathematical programming principles. The model-solving procedures, particularly, the decomposition-adjustment algorithm, are proposed based on characteristics of the model structure. The principle of model-solving technique is to decompose the complex system into several smaller sub-systems on which some ease-solving mathematical models may be established. The objective of this optimization model aims at maximizing the reliability of water supply and minimizing the maximum water-shortage of single time-period within water- supply system during drought. The multi-objective mixed integer linear programming model and proposed solving procedures are applied to a case study of reservoir-group water-supply system in Huanghe-Huaihe River Basin, China. The desired water-shortage distribution within the system operation term and the maximum shortage of single time-period are achieved. The results of case study verifies that the lighter water-shortage distributed evenly among several time-periods can avoid the calamities resulted from severe water shortage concentrated on a few time-periods during drought.

Development operators on 4D moving object databases

In this paper we propose four dimensional (4D) operators, which can be used to deal with sequential changes of topological relationships between 4D moving objects and we call them 4D development operators. In contrast to the existing operators, we can apply the operators to real applications on 4D moving objects. We also propose a new approach to define them. The approach is based on a dimension separated method, which considers x y coordinates and z coordinates separately. In order to show the applicability of our operators, we show the algorithms for the proposed operators and development graph between 4D moving objects.

基于改进YOLOv5s的小目标检测算法

针对当前主流目标检测算法对图像中远距离小目标产生的漏检、误检等问题,提出一种改进YOLOv5s的小目标检测算法。在模型训练过程中,通过引入Focal-EIOU定位损失函数,加强边界框的定位精度;在骨干网络中,通过添加小目标检测层,提高小目标的检测精度;在Neck结构中,通过优化上采样算子和添加注意力机制,加强小目标的特征信息。实验结果表明,改进后的算法在VisDrone数据集上与YOLOv5s算法相比,mAP@small提高了3.2%,且检测速度满足实时性的要求,能够很好地应用于小目标检测任务中。

基于客观结构化临床考试的情景模拟式教学在手术室专科护士培训中的应用

目的探讨基于客观结构化临床考试(objective structured clinical examination,OSCE)的情景模拟式教学在手术室专科护士培训中的应用效果。方法选取参加郑州大学第一附属医院手术室专科护士培训的78名学员为研究对象。按照培训时间先后顺序,将2021年4月-6月参加培训的40名学员作为对照组,将2022年4月-6月参加培训的38名学员作为观察组。对照组采用常规情景模拟式教学,观察组采用基于OSCE的情景模拟式教学,比较2组操作考核成绩、临床思维能力。结果观察组操作考核中患者核查交接、手术体位安置、职业暴露防护的考核成绩均好于对照组(t=4.616,P<0.001;t=3.004,P=0.004;t=3.574,P=0.001),观察组临床思维能力得分高于对照组(t=7.309,P<0.001)。结论将基于OSCE的情景模拟式教学应用于手术室专科护士培训,有助于提高其操作技能和临床思维能力,值得临床推广应用。

低碳视角下集成式纺织服装闭环供应链运作决策

纺织服装产业的蓬勃发展,不仅有效带动了社会经济的快速发展,同时也造成了废旧服装数量的日益剧增。为有效提高废旧服装回收效率及其利用率,兼顾供应链整体运营与生态环境的目标,文章设计分析了一个由供应商、制造商和零售商组成的三级纺织服装闭环供应链网络,其中零售商集成废旧服装的统一回收,制造商集成废旧服装的统一分类及再利用废旧服装的加工处理。鉴于服装回收处理过程中碳排放量高且分布不均这一现实问题,引入碳配额机制,以供应链整体收益和成本为目标,建立一个双目标决策优化模型,并对模型设计了约束求解方法。最后通过数值实验说明了本文所建模型的有效性和相应运营决策方案的合理最优性。相关研究结果表明:废旧纺织服装的回收处理尽管需要一定的运营成本,但从长期运营视角来看,其能够给供应链整体带来较大的潜在收益。零售商和制造商的集成化运营模式不仅可有效的降低供应链的整体运营成本,同时亦可有效提升供应链整体的协同运作效率。合理的碳配额机制可有效激励相关成员企业去绿色低碳运营,进而提升自身竞争力和可持续发展能力。相关研究成果不仅具有一定的理论价值,同时对政府和服装产业制定合理的碳配额机制,设计构建纺织服装闭环供应链网络具有一定的指导和借鉴意义。

基于改进NSGA-Ⅱ算法的梯级水库多目标优化调度

针对在时间步长较小、计算时段数目较多时,传统智能优化算法在求解梯级水库联合优化调度问题上效率低甚至无可行解的问题,提出了一种改进NSGA-Ⅱ算法。该算法基于NSGA-Ⅱ算法框架,引入参考目标值、潜力目标值、偏移度以及变异引导算子来优化种群进化过程,强化迭代中的种群质量,使获得的解集更加接近真实的Pareto前沿。福建省金溪流域梯级水库多目标优化调度实例验证结果表明,改进NSGA-Ⅱ算法相对其他算法运算效率更高,优化结果更好,具有较好的实用性。

基于改进YOLOv7-tiny的高空作业人员安防装备检测算法

针对现有高空作业人员安防装备检测算法参数量较大且检测目标相对单一,难以适应复杂的高空作业场景等问题,提出一种基于改进YOLOv7-tiny的高空作业人员安防装备检测算法。首先,将主干网络重新设计为更轻量的YOLOv7-FasterNet,并调整空间金字塔池化结构,实现模型参数量的压缩;其次,在ELAN-L模块中扩展梯度传输路径的分支,解决了模型压缩造成的通道信息缺失问题,提升了特征信息的提取能力;最后,将网络中下采样部分替换为Involution模块,降低参数冗余,增强网络对全局的捕获能力。实验结果表明,改进的YOLOv7-tiny算法能够更好地适应复杂高空作业场景,在开源数据集上具备良好的性能。该算法的平均检测精度达到94.7%,较原模型提升1.5%,参数量较原模型下降11.6%,实验结果验证了算法改进措施的有效性。

非法经营罪中证券业务的限定与出罪

司法实务中对非法经营罪中的证券业务存在宽泛解读的倾向。要厘清非法经营罪中证券业务的内涵与外延,关键在于:一是将证券业务型非法经营罪的法益内容明确化,在遵循形式判断的基础上,进一步借助实质判断来进行限定;二是在双重违法性的判断中以刑事违法为核心来展开,重视刑法对于行政违法判断的制约与限定。在考察是否属于非法经营罪所规制的证券业务时,需要把握四个要点:相应业务有无采取或变相采取证券化的方式,是否属于涉众型的证券业务,是否属于核心的证券业务,以及上下游行为是否构成其他证券犯罪。从出罪的角度而言,以下情形不能成立证券业务型非法经营罪:为未采取证券形式的P2P借贷提供信息中介服务的;为非上市公司的股权买卖提供中介服务,不能同时满足将股份拆细、以股票的形式与向不特定公众出售的要件的;从事证券投资咨询与财务顾问等非核心领域的证券业务的;上下游环节的相关主体从事的是合法证券业务的。

提升光储充电站运行效率的多目标优化配置策略

光储充电站的运行效率直接影响到其经济效益及电网侧的电能质量。针对在进行容量配置时对运行效率考虑不足会导致非必要的电能损耗,文中提出一种提升光储充电站运行效率的多目标优化配置策略。通过分析光储充电站变换器与内源线路功率损耗对于运行效率的影响,提出充电站的运行效率评估指标与计算方法,并讨论光储充电站运行效率对其容量配置的影响。建立以充电站经济效益、运行效率、电网侧峰谷供电功率补偿能力最佳为优化目标的多目标容量优化配置策略。针对优化目标特性,提出一种改进二代非支配排序遗传算法得到优化策略求解方法。选取中国西南地区某典型光储充电站运营场景,通过算例验证了优化策略的有效性与优越性。

基于ASP-SAC算法的列车自动驾驶速度控制

随着经济建设的绿色转型以及人工智能的快速发展,城市轨道交通已成为居民日常出行的重要方式,在保障安全性、高效性和准点性的前提下,列车运行的节能性和舒适性需求也越来越被关注。合理的运行策略能够有效实现多种目标需求下的列车自动驾驶速度控制,强化学习作为一种智能决策方法,能够有效解决这一控制问题。首先,通过综合分析技术、安全性和乘客体验等方面的因素,基于专家经验动作划分和状态信息熵将软演员-评论家(SAC)改进为动作状态经验优先软演员-评论家(ASP-SAC)方法,用于研究列车自动驾驶速度控制问题。其次,将问题马尔可夫形式化,搭建了列车运行环境,确定了状态空间、动作空间以及基于目标控制的奖励函数。最后,以北京地铁亦庄线的一段区间数据为例进行试验,对ASP-SAC方法进行验证并与其他一些算法在相同环境下进行性能优劣比较。研究结果表明:该方法对于多目标控制需求下的列车自动驾驶速度控制问题具有可行性,与未改进前相比算法效率提高22.73%,与PPO算法相比提高29.17%,改进效果良好。同时,列车运行时在安全性、舒适性无误的情况下,准时性、精确性和节能性都强于SAC、DQN、PPO以及PID算法,其中能耗分别减少3.64%、5.62%、4.38%、7.35%,控制效果良好。此外,该方法亦具备鲁棒性,在列车自动驾驶速度控制方面具有一定的优越性和可参考性。

基于视觉图像的飞行区大型目标监视方法改进研究

机场飞行区现使用的场面监视方法存在着定位偏差较大、不稳定、易跳变、皆为点源定位等问题。针对这些问题,设计了基于视觉图像的飞行区监视方法,实现快速准确的目标检测和轮廓定位,使飞行区监视更加稳定精确。提出了一种基于MobileNetV3和YOLOv5的网络模型(以下称为MobileNetV3-YOLOv5),即在YOLOv5的主干中使用MobileNetV3,来提高对目标的检测速度和准确度;提出了一种基于优化特征点提取的改进定向快速旋转简报(Oriented FAST and Rotated BRIEF,ORB)算法,将图像分割成多个区域,分别提取每个区域的特征点,从而提高目标识别框内区域的特征点识别数量,再进行特征点聚类筛选,最后根据识别目标类型采用最小包围盒进行轮廓划分,得到目标的轮廓定位。试验结果表明:MobileNetV3-YOLOv5方法对比原始YOLOv5模型,在识别目标准确率方面提升5百分点,在效率方面提升14张/s;同时在0~60 m的范围内,轮廓估计误差仅为2.9%;体现了所提出的监视方法的有效性,可以提升飞行区监视定位准确性和运行安全性。

基于改进YOLOv5的井场作业风险智能识别与分类

为精确识别井场安全生产过程中存在的不规范行为,降低井场事故发生率,提出了一种改进YOLOv5井场作业风险智能分类识别方法。首先,使用labelimg工具标注出这些不规范的行为;其次,以YOLOv5为基础网络,通过在基础网络结构中替换损失函数和增加注意力机制模块来改善目标检测问题。用SIoU Loss损失函数替换CIoU Loss损失函数,对距离损失进行重新定义,有效减少了回归自由度,加速了网络收敛速度,提高了回归精度。通过增加注意力机制模块,网络能够对输入图像进行自主学习并关注其中的重要信息,以提升模型的鲁棒性和泛化能力。实验证明,改进的YOLOv5算法可以对井场作业中的不规范进行有效的分类和识别。与原有YOLOv5方法相比精度提高了3%,改进方法在保证正确率的前提下,具有较高的预测精度和较好的实时性,获得了较满意的效果。

一回路自然循环优化研究

鉴于对提升反应堆的运行效率和安全性能的需求,本文建立了反应堆一回路自然循环计算模型,并运用混合多目标遗传算法对其进行优化设计,旨在减少压力容器的高度并提升自然循环冷却剂流量。研究结果显示,在追求压力容器高度最小化的方案中,总高度降低了19.21%,相应的冷却剂流量减少了9.64%;而在最大化冷却剂流量的方案中,压力容器总高度增加了12.25%,冷却剂流量得到了29.26%的显著提升。

黄河中下游水资源多目标利益对古贤水库运行的响应

为量化未来古贤水库参与黄河中下游梯级水库群联合运行对供水、发电、调水调沙、生态多目标关键利益及非关键利益的影响,设置2030水平年有古贤水库及无古贤水库两种情景,基于梯级水库群多目标协同优化调度理论构建黄河中下游梯级水库群多目标协同优化调度模型,通过求解模型分析了径流减少及未来综合需水增加的背景下,黄河中下游多目标利益对古贤水库参与联合运行的响应关系。结果表明:与无古贤水库情景相比,有古贤水库情景下水库群调度系统总协同度略微增加,供水与发电子系统有序度有所增加,农业关键期、非关键期多年平均缺水量减少,水库群多年平均发电量增加;除调沙频率略微下降外,有古贤水库情景下输沙、生态目标关键利益及非关键利益均有所提升。