Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.     

An Advanced Universal Power Quality Conditioning System and MPPT method for grid integration of photovoltaic systems

A photovoltaic system usually produces power quality pollutions to the interfaced power systems without a proper functioning of active filters. At present, available active filters are unable to fully suppress power grid harmonics and distortions, worsening power quality conditions within the interfaced system. This paper presents an Advanced Universal Power Quality Conditioning System (AUPQS) to interface photovoltaic systems to a power grid. In the interfaced system, an improved series active filter is designed to generate output voltages to compensate all the source voltage deficiencies and to highly suppress the grid-end current harmonics and distortions, even under unbalanced non-linear load conditions. Also, an independent single-phase inverter is suggested at the load side instead of the source side to regulate the DC-link voltage. Hence, the distortions and harmonics generated by this single-phase inverter are absorbed by the shunt filter of the AUPQS. Finally, a hybrid maximum power point tracking (MPPT) approach is proposed for effective operation of photovoltaic systems even under sudden light intensity changes and solar cells temperature variations. The overall interfaced system is designed and developed using Matlab/Simulink software. The effectiveness of the proposed AUPQS and MPPT method is further validated by simulation results.     

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.     

Modeling Smart Grid neighborhoods with the ENERsip ontology

The electricity consumption in the buildings sector has been steadily increasing during the last decade, up to the point that energy efficiency in this sector has become a major problem for governments, utilities, customers, and the environment. The foreseen high penetration of distributed micro-generation facilities based on renewables can help to reduce the environmental footprint of buildings and households, although the complexity of managing effectively the electric grid increases dramatically under these conditions. The IEEE 2030 standard for interoperability in the Smart Grid remarks upon the importance of well-defined data models in such complex scenarios and puts emphasis on the benefits of ontologies and OWL (Web Ontology Language) for this purpose. This paper presents an OWL-based ontology that formally defines the vocabulary and taxonomy and captures the engineering and business semantics of this domain of knowledge (i.e., energy efficiency in the so-called nZEN – nearly Zero-Energy Neighborhoods). This ontology has been defined under the scope of the EU (European Union) research project ENERsip. The paper also highlights the main benefits the ontology brought to all the phases of the project life cycle, as well as how future work can make the most out of it.     

Pumped-hydro in Bendigo: Room for wider reform?

As the world adjusts to a rapidly changing climate, interest in renewable energy technologies has increased. One disadvantage of renewables is that they’re only intermittently reliable; when the sun doesn’t shine, or the wind stops blowing the energy generation capacity of these infrastructures tapers off. In light of these changes there has been renewed interest in the creation of Pumped Hydro-Electricity Storage (‘PHES’), which acts as a ‘battery’ storing excess electricity generated when consumption of energy is at its lowest. In addition new technologies allow for legacy infrastructures, that would otherwise be a liability for the community, to now be used in a productive way for projects like PHES. Although there are many exciting opportunities the Bendigo PHES project presents, there is a dearth of regulatory issues for a small-medium scale project like this. This paper will explore the way in which such a project would fit within the national electricity market, as well as the limitations and barriers to implementation it might face before making some brief recommendations on changes that would benefit other such schemes in the future.     

Malaysia’s electricity market structure in transition

The Malaysian electricity industry evolved from a vertically integrated utility into a single-buyer model, which considered a precursor to deregulation and a competitive market. This study considers electricity sector reform in Malaysia and proposes a market structure for the industry. We focus on the single-buyer model and the pool model for the analysis with the recommendation on reform stages required to ensure a smooth and successful market transition. A smooth transition from regulated market into deregulated market will ensure market stability and sustainability of the energy market. It will also provide enough time for the market participants to adapt and transit from regulated to deregulated energy market.     

从城市空间形态的性能指标看方格网城市

从古至今,以方格网的方法构筑城市空间结构和布局所形成的方格网城市是城市形态的一种基本模式。格网城市在世界各大文明圈的城市中都能寻找到原型,具有相当的普遍性。本文拟从凯文·林奇定义的对城市空间形态性能进行评价的五个基本指标入手,分析方格网城市形态存在的内在原因、合理性、灵活性和适应性,以期对我国当前的城市建设有一定的借鉴作用。     

Reliably integrating variable renewables: Moving grid flexibility resources from models to results

At least eight kinds of demand- and supply-side grid flexibility resources can complement and firm variable renewables (wind and photovoltaics) at generally lower cost than fossil-fuel backup or bulk electrical storage, supporting largely if not wholly renewable electricity without a battery revolution. Validating dozens of simulation studies, at least 10 nations with modest or no hydropower now reliably use many times the US variable and total renewable fraction with attractive economics and improved reliability.     

胀力心轴的创新与运用

随着汽车变速齿轮的精度要求的不断提高,与其相应的制造工艺、工装也在不断创新。介绍了一款在磨齿加工工序中,利用账力心轴夹具夹紧行程小这一特征,对账力心轴夹具的弹簧套与刚性心轴的工作锥度进行了创新型的改进,使原来弹簧套工作时,撑紧工件内孔的弹性夹紧力沿内孔轴向逐渐变化而趋于相等。由生产实践证实:使用该夹具进行工件定位,其定心精度大大提高,保证了齿轮磨齿加工后的高精度;工件的装夹方便、快捷,提高了生产效率,并有利于工件装夹实现自动化。     

土地适宜性评价数据处理方法探讨

土地适宜性评价是规划前期的重要环节,规划行业已广泛采用地理信息系统(GIS)作为评价分析工具。GIS提高了分析精度与处理效率,分析成果便于可视化,鼓励了多方参与。随着规划行业实际应用的探索与进展,评价因素趋于多样化,数据趋于复杂化,这对空间数据的处理提出了挑战。本文结合案例针对传统的矢量与栅格数据的处理方法的局限性进行讨论,进而对研究工作中提出的"伪栅格"的阶段性改进方案进行了优劣势分析。为进一步发挥矢量、栅格模型各自的优势,本项研究又提出了矢量、栅格互为补充的处理方法。该方法可以减少误差,提高效率,使约束条件、评价权重的调整变得简单、灵活,也可增强专题地图的显示效率。