Sustainability impact assessment for local energy supplies' development – The case of the alpine area of Lake Como,Italy

The development of distributed energy systems has important environmental, social and economic implications. Local decision-making processes must be guided by a careful evaluation of the sustainability of production chains and alternative choices. The aim of this study is to explore if and how an integrated assessment can quantify the extent to which bioenergy supply chain development contributes to rural development and energy policy objectives. We applied a Sustainability Impact Assessment (SIA) for local bioenergy development in the alpine area of Lake Como (Italy). We modeled the local bioenergy chain in 2008 and eleven scenarios considering different biomass utilizations, mechanization levels, combustion technologies, and subsidies schemes at 2020. We calculated economic, social and environmental indicators. We interpret and discuss the scenario analysis in order to support the bioenergy planning under the light of its implications for the different policy aims and concerns.     

Electromagnetism-like algorithms for optimized tool path planning in 5-axis flank machining

Optimization of tool path planning using metaheuristic algorithms such as ant colony systems (ACS) and particle swarm optimization (PSO) provides a feasible approach to reduce geometrical machining errors in 5-axis flank machining of ruled surfaces. The optimal solutions of these algorithms exhibit an unsatisfactory quality in a high-dimensional search space. In this study, various algorithms derived from the electromagnetism-like mechanism (EM) were applied. The test results of representative surfaces showed that all EM-based methods yield more effective optimal solutions than does PSO, despite a longer search time. A new EM-MSS (electromagnetism-like mechanism with move solution screening) algorithm produces the most favorable results by ensuring the continuous improvement of new searches. Incorporating an SPSA (simultaneous perturbation stochastic approximation) technique further improves the search results with effective initial solutions. This work enhances the practical values of tool path planning by providing a satisfactory machining quality.     

Bio-economic modelling for marine spatial planning application in North Sea shrimp and flatfish fisheries

Marine activities have been increasingly competing for space and reducing areas for fishing. The use of spatially explicit tools can assist the decision making process on defining the optimal location of closures for fishing due to these emerging activities.This paper presents the Spatial Integrated bio-economic Model for FISHeries (SIMFISH). In this model fishers behaviour is simulated based on optimal effort allocation. The added value of this model compared to other existing spatial management tools lies in the presence of (i) short and long term fishers behaviour (ii) spatial explicit stock and fleet dynamics and (iii) relatively low data requirements.As an illustration, SIMFISH is applied in this paper to estimate the impact of area closures in the North Sea. Overall area closures have a negative impact on the profitability of the fleets. This would be worsened with higher fuel prices and decreased stock productivity.     

Simplifying the powder metallurgy manufacturing process using soft computing tools

The tools of soft computing will aid the knowledge mining in predicting and classifying the properties of various parameters while designing the composite preforms in the manufacturing of Powder Metallurgy (P/M) Lab. In this paper, an integrated PRNET (PCA-Radial basis functional neural NET) model is proposed in different versions to select the relevant parameters for preparing composite preforms and to predict the deformation and strain hardening properties of Al–Fe composites. It reveals that the predictability of this model has been increased by 67.89% relatively from the conventional models. A new PR-filter is proposed by slightly modifying the conventional filters of RBFNN, which improves the power of PRNET even though raw data are highly non-linear, interrelated and noisy. Moreover, fixing the range of input parameters for classifying the properties of composite preforms can be automated by the Fuzzy logic. These types of models will avoid expensive experimentation and risky environment while preparing sintered composite preforms. Thus the manufacturing process of composites in P/M Lab will be simplified with minimum energy by the support of these soft-computing tools.     

The PAR(p) Interconfigurations model used by the Brazilian Electric Sector

This study discusses the characteristics of the Periodic Autoregressive model, PAR(p), which is used to generate synthetic series of inflow energies that serve as entries for computer platforms that implement the planning and expansion of the operations of the BES – the Brazilian Electric Sector (SEB – Sistema Elétrico Brasileiro). The methodology for the design of a generating plant is presented in addition to the fundamentals of the “PAR(p) Interconfigurations” Model, which is referred to as the Inflow Energy Generation Model (IEGM) in this study. The major contribution of this study is to provide the first scientific discussion of the representation of multiple configurations using the PAR(p) model. For this purpose, several topics related to the time series are discussed, such as the definition of the model order, the matter of stationarity and the need to address possible outliers. Finally, a case study is presented, wherein the results of the estimation and generation of the described model’s scenarios are demonstrated.     

适应于自动驾驶的计算结构与平台综述

信息与通信技术领域的不断发展,推动了自动驾驶汽车的巨大进步。自动驾驶汽车有效解决了用户对出行日益提高的安全、便捷、舒适、高效等个性化需求,有着巨大的商业前景和应用价值。首先从自动驾驶的定义出发,对自动驾驶汽车计算任务进行了分类总结,阐述了自动驾驶汽车集移动端、边缘节点和云端一体的计算结构。然后分类讨论了目前自动驾驶汽车移动端计算平台,呈现出异构多核的特点,并对当前主流的底层计算平台进行了对比分析。最后,从性能与成本和信息安全两个方面总结目前自动驾驶汽车计算结构和平台遇到的问题与挑战。     

基于Q-learning的不确定环境BDI Agent最优策略规划研究

BDI模型能够很好地解决在特定环境下的Agent的推理和决策问题,但在动态和不确定环境下缺少决策和学习的能力。强化学习解决了Agent在未知环境下的决策问题,却缺少BDI模型中的规则描述和逻辑推理。针对BDI在未知和动态环境下的策略规划问题,提出基于强化学习Q-learning算法来实现BDI Agent学习和规划的方法,并针对BDI的实现模型ASL的决策机制做出了改进,最后在ASL的仿真平台Jason上建立了迷宫的仿真,仿真实验表明,在加入Q-learning学习机制后的新的ASL系统中,Agent在不确定环境下依然可以完成任务。     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.