Formal REA model at operational level

Despite a lot of attention gained by the Resource-Event-Agent (REA) framework among researchers in enterprise modeling, it still lacks comprehensive formal description. Most of the formalization approaches to REA use only UML or other graphical representation. This paper aims to define REA ontology at operational level using formal logic tools. The general approach to formal logic description of REA was motivated by LTAP introduced by Ito, Hagihara and Yonezaki. After basic REA concepts are presented, semantics and logical language LREA are defined including axioms for the REA operational level. Future research is shortly described in conclusion.REA framework; formal models; modal logic

JEERP: Energy Aware Enterprise Resource Planning

Ever increasing energy costs, and saving requirements, especially in enterprise contexts, are pushing the limits of Enterprise Resource Planning to better account energy, with component-level asset granularity. Using an application-oriented approach we discuss the different aspects involved in designing Energy Aware ERPs and we show a prototypical open source implementation based on the Dog Domotic Gateway and the Oratio ER

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Modeling the Financial Behavior of Population (I) – Conceptual Assignations

The paper shows a few conceptual assignations concerning the financial behavior of the population. Thus, there are defined attached predicates (the inclusion into the economic behavior category, the existence of a monetary factor, the action for goods and non-autonomous flows, as well as for goods and nominal flows), the sources that generate the financial behavior (acquisition of real goods and services, acquisition of financing sources, acquisition of saving sources), the process of non-autonomous financial flow formation and finally, there are identified categories of nominal flows attached to the financial behavior of the population.logic model, financial behavior, sustainability

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Static analysis of energy consumption for LLVM IR programs

Energy models can be constructed by characterizing the energy consumed by executing each instruction in a processor's instruction set. This can be used to determine how much energy is required to execute a sequence of assembly instructions, without the need to instrument or measure hardware. However, statically analyzing low-level program structures is hard, and the gap between the high-level program structure and the low-level energy models needs to be bridged. We have developed techniques for performing a static analysis on the intermediate compiler representations of a program. Specifically, we target LLVM IR, a representation used by modern compilers, including Clang. Using these techniques we can automatically infer an estimate of the energy consumed when running a function under different platforms, using different compilers. One of the challenges in doing so is that of determining an energy cost of executing LLVM IR program segments, for which we have developed two different approaches. When this information is used in conjunction with our analysis, we are able to infer energy formulae that characterize the energy consumption for a particular program. This approach can be applied to any languages targeting the LLVM toolchain, including C and XC or architectures such as ARM Cortex-M or XMOS xCORE, with a focus towards embedded platforms. Our techniques are validated on these platforms by comparing the static analysis results to the physical measurements taken from the hardware. Static energy consumption estimation enables energy-aware software development, without requiring hardware knowledge

AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture

CPU-FPGA heterogeneous architectures are attracting ever-increasing attention in an attempt to advance computational capabilities and energy efficiency in today's datacenters. These architectures provide programmers with the ability to reprogram the FPGAs for flexible acceleration of many workloads. Nonetheless, this advantage is often overshadowed by the poor programmability of FPGAs whose programming is conventionally a RTL design practice. Although recent advances in high-level synthesis (HLS) significantly improve the FPGA programmability, it still leaves programmers facing the challenge of identifying the optimal design configuration in a tremendous design space. This paper aims to address this challenge and pave the path from software programs towards high-quality FPGA accelerators. Specifically, we first propose the composable, parallel and pipeline (CPP) microarchitecture as a template of accelerator designs. Such a well-defined template is able to support efficient accelerator designs for a broad class of computation kernels, and more importantly, drastically reduce the design space. Also, we introduce an analytical model to capture the performance and resource trade-offs among different design configurations of the CPP microarchitecture, which lays the foundation for fast design space exploration. On top of the CPP microarchitecture and its analytical model, we develop the AutoAccel framework to make the entire accelerator generation automated. AutoAccel accepts a software program as an input and performs a series of code transformations based on the result of the analytical-model-based design space exploration to construct the desired CPP microarchitecture. Our experiments show that the AutoAccel-generated accelerators outperform their corresponding software implementations by an average of 72x for a broad class of computation kernels

Energy and the Global Economy

This article describes the contribution economists can make in uncovering energy choices capable of reducing carbon emissions on a global scale. All production and consumption activities involve the use of energy, and economists possess theoretical and analytic frameworks relating production and consumption in individual economies with international trade among them. Current challenges include deepening collaboration with physical scientists and engineers by according primacy to the formulation of scenarios and to the representation of physical stocks and flows of resources as factor inputs. Energy scenarios are discussed in terms of technological options, distinguishing those options that are already known but not yet widely applied from ones that still require research breakthroughs. Scenarios about household lifestyles and consumption in the areas of diet, housing and mobility are also discussed, distinguishing those that could already be initiated by households from those that would require changes in the built environment. Models and databases of the global economy have existed since the 1970s, and one was first used to analyze energy scenarios in the early 1990s based on the recommendations of the Brundtland Report of 1987. Relevant areas of progress since that time are described both in modeling the global economy and in compiling a global economic and environmental database. The paper concludes with a few examples of recent applications of a particular global economic model to analyzing energy scenarios to demonstrate both the progress that has been made and the nature of some of the challenges still to be faced.