Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

An industrial case study on variability handling in large enterprise software systems

ContextEnterprise software systems (e.g., enterprise resource planning software) are often deployed in different contexts (e.g., different organizations or different business units or branches of one organization). However, even though organizations, business units or branches have the same or similar business goals, they may differ in how they achieve these goals. Thus, many enterprise software systems are subject to variability and adapted depending on the context in which they are used.ObjectiveOur goal is to provide a snapshot of variability in large scale enterprise software systems. We aim at understanding the types of variability that occur in large industrial enterprise software systems. Furthermore, we aim at identifying how variability is handled in such systems.MethodWe performed an exploratory case study in two large software organizations, involving two large enterprise software systems. Data were collected through interviews and document analysis. Data were analyzed following a grounded theory approach.ResultsWe identified seven types of variability (e.g., functionality, infrastructure) and eight mechanisms to handle variability (e.g., add-ons, code switches).ConclusionsWe provide generic types for classifying variability in enterprise software systems, and reusable mechanisms for handling such variability. Some variability types and handling mechanisms for enterprise software systems found in the real world extend existing concepts and theories. Others confirm findings from previous research literature on variability in software in general and are therefore not specific to enterprise software systems. Our findings also offer a theoretical foundation for describing variability handling in practice. Future work needs to provide more evaluations of the theoretical foundations, and refine variability handling mechanisms into more detailed practices.     

Dynamic equipment workspace generation for improving earthwork safety using real-time location system

Earthwork equipment accounts for a large proportion of the fatalities on construction sites. According to the U.S. Bureau of Labor Statistics, in the period between 1992 and 2002, struck by vehicles and struck by objects (e.g., vehicle parts, vehicle loads, or falling vehicles) were identified as the causes of 30% and 24% of fatal equipment-related accidents on excavations sites, respectively. It is therefore of a paramount importance to improve the safety of construction sites by increasing the peripheral awareness of the operators of earthwork equipment. Several research works have investigated numerous collision avoidance systems that exploit real-time location systems and proximity measurements to mitigate the risk of accidents on excavation sites. However, these systems often detect collisions based on using the workspaces that only account for the geometry and the degrees of freedom of the equipment, and thus disregard the state-dependent characteristics of equipment. This results in reserving a large space for every piece of equipment, which reduces the applicability of these systems in congested sites. Therefore, this paper proposes a novel method for generating dynamic equipment workspaces based on the continuous monitoring of a spectrum of equipment-related information, i.e., the current pose/state of the equipment, and the speed characteristics of each movement. This method uses the required operation stoppage time to determine how much space needs to be reserved for each piece of equipment. A case study is conducted to validate the proposed method. It is shown that the proposed method has a strong potential in capturing the hazardous areas around the equipment and triggering warnings in view of the impending movements of various pieces of equipment. Also, the proposed method proved to have potential applications in actual projects in congested sites where space is limited.     

Regressor selection for ozone prediction

Being able to predict high concentrations of tropospheric ozone is important because of its negative impact on human health. In this paper eight regressor-selection methods are utilised in a case study for ozone prediction in the city of Nova Gorica, Slovenia. The comparison of the selected methods proved to be useful for building models that successfully predict the ozone concentrations for the treated case. Different regressors are selected for different models, with different methods based on the validation procedure’s cost functions. Namely, for the model to predict the maximum daily ozone concentration, ten regressors are selected; for the average concentration of ozone between 8.00 and 20.00 h, fifteen regressors are selected; and for the average daily concentration, ten regressors are selected. The result of the study is a regressor selection that is specific for a particular geographical location. Moreover, the study reveals that regressor selection, as well as the obtained models, differ depending on the kind of averaging interval of the ozone concentration.     

共网数字集群系统应用问题的研究

现今国内的数字集群网络发展存在局限性、差异性，还远没有达到“网”状分布的程度，因此，共网数字集群发展空间巨大、前景广阔。通过列举共网数字集群系统在龙嘉国际机场和长春雷锋车队的成功应用案例，探讨了我国共网数字集群系统应用的可行性和必要性。     

Using a Discrete-Event System Specifications (DEVS) for designing a Modelica compiler

We introduce a new architecture for the design of a tool for modeling and simulation of continuous and hybrid systems. The environment includes a compiler based on Modelica, a modular and a causal standard specification language for physical systems modeling (the tool supports models composed using certain component classes defined in the Modelica Standard Library, and the instantiation, parameterization and connection of these MSL components are described using a subset of Modelica). Models are defined in Modelica and are translated into DEVS models. DEVS theory (originally defined for modeling and simulation of discrete event systems) was extended in order to permit defining these of models. The different steps in the compiling process are show, including how to model these dynamic systems under the discrete event abstraction, including examples of model simulation with their execution results.     

Agent-based modeling of energy technology adoption: Empirical integration of social,behavioral, economic,and environmental factors

Agent-based modeling (ABM) techniques for studying human-technical systems face two important challenges. First, agent behavioral rules are often ad hoc, making it difficult to assess the implications of these models within the larger theoretical context. Second, the lack of relevant empirical data precludes many models from being appropriately initialized and validated, limiting the value of such models for exploring emergent properties or for policy evaluation. To address these issues, in this paper we present a theoretically-based and empirically-driven agent-based model of technology adoption, with an application to residential solar photovoltaic (PV). Using household-level resolution for demographic, attitudinal, social network, and environmental variables, the integrated ABM framework we develop is applied to real-world data covering 2004–2013 for a residential solar PV program at the city scale. Two applications of the model focusing on rebate program design are also presented.     

Self-adaptation of mobile systems driven by the Common Variability Language

The execution context in which pervasive systems or mobile computing run changes continually. Hence, applications for these systems require support for self-adaptation to the continual context changes. Most of the approaches for self-adaptive systems implement a reconfiguration service that receives as input the list of all possible configurations and the plans to switch between them. In this paper we present an alternative approach for the automatic generation of application configurations and the reconfiguration plans at runtime. With our approach, the generated configurations are optimal as regards different criteria, such as functionality or resource consumption (e.g. battery or memory). This is achieved by: (1) modelling architectural variability at design-time using the Common Variability Language (CVL), and (2) using a genetic algorithm that finds nearly-optimal configurations at run-time using the information provided by the variability model. We also specify a case study and we use it to evaluate our approach, showing that it is efficient and suitable for devices with scarce resources.     

Cheese fortification using saturated monoglyceride self‐assembly structures as carrier of omega‐3 fatty acids

The purpose of this research was to study the capacity of emulsions containing saturated monoglyceride self‐assembly structures to deliver omega‐3 fatty acids in fresh soft cheese. To this aim, fortified emulsions containing different ratios of milk, saturated monoglycerides (MGs) and cod liver oil were added to milk before cheese‐making. These emulsions were characterised by distinct microstructural features observed by polarised light microscopy and apparent viscosity values. The omega‐3 delivery performance of MG emulsions highlighted that this strategy allowed a good retention of the omega‐3‐rich oil in the curd (up to 75%). The fortified cheeses showed yield value and fat content higher than those of control samples. The enriched cheese showed hardness and cohesiveness obtained by texture profile analysis similar to those of the unfortified product. Only a slight decrease in gumminess was detected in fortified cheese.