Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In another context, many studies have been carried out aimed at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We study in this paper the natural convergence between these two approaches and we propose an integration architecture, dealing with machine tool and machining control, that enables the exploitation of distributed smart sensors and actuators in the decisional system.     

CAD modeling for the components made of multi heterogeneous materials and smart materials

There appear more critical requirements for special functions of components/products in various areas, which can be satisfied only by using heterogeneous materials and/or smart materials. The heterogeneous materials include composite materials, functionally graded materials, and heterogeneous materials with a periodic microstructure. To design and manufacture the components made of these materials, the computer models for representing them need first to be built so that further analysis, optimization and manufacturing can be implemented based on the models. This paper develops such a modeling method, which can be implemented by employing the functions of current CAD graphic software and obtain the model that includes all the material information (about periodic microstructures, constituent composition, inclusions, and embedded parts) along with geometry information in 3D solid modeling without the problem arising from too much data.     

A novel Volt-VAR Optimization engine for smart distribution networks utilizing Vehicle to Grid dispatch

In recent years, Smart Grid technologies such as Advanced Metering, Pervasive Control, Automation and Distribution Management have created numerous control and optimization opportunities and challenges for smart distribution networks. Availability of Co-Gen loads and/or Electric Vehicles (EVs) enable these technologies to inject reactive power into the grid by changing their inverter’s operating mode without considerable impact on their active power operation. This feature has created considerable opportunity for distribution network planners to explore if EVs could be used in the distribution network as reliable VAR suppliers. It may be possible for network operators to employ some EVs as VAR suppliers for future distribution grids. This paper proposes an innovative Smart Grid-based Volt-VAR Optimization (VVO) engine, capable of minimizing system power loss cost as well as the operating cost of switched Capacitor Banks, while optimizing the system voltage using an improved Genetic Algorithm (GA) with two levels of mutation and two levels of crossover. The paper studies the impact of EVs with different charging and penetration levels on VVO in different operating scenarios. Furthermore, the paper demonstrates how a typical VVO engine could benefit from V2G’s reactive power support. In order to assess V2G impacts on VVO and test the applicability of the proposed VVO, revised IEEE-123 Node Test Feeder in presence of various load types is used as case study.     

Dynamic phase balancing in the smart distribution networks

A distribution system might be unbalanced due to various reasons which can cause some major problems. Re-phasing strategy is the most effective method for phase balancing performed manually in distribution networks. Dynamic phase balancing is the main objective of this paper that means re-phasing performance at any time of day when unbalance is more than permissible limit. It is possible only in smart distribution networks by installing the automatic re-phasing switches at busbars. Automatic re-phasing eliminates the customer service interruption cost and labour cost available in the manual re-phasing operation. There are two basic operations in the optimal dynamic phase balancing: detecting the best time for re-phasing and considering the load variations in real distribution networks. This paper consists of two scenarios. First scenario is switches installation at all nodes of feeder and second scenario is switches installation at the limited strategic nodes. First scenario focuses on search space reduction by employing a sensitivity analysis method which improves the convergence along with time saving. In order to solve the proposed optimization problem, the modified shuffled frog leaping algorithm (MSFLA) is implemented. Simulation studies greatly demonstrate the benefits achieved from this operation by reducing power loss cost and improving network phase balancing.     

IoT-enabled emergency information supply chain architecture for elderly people: The Australian context

The effective delivery of emergency information to elderly people is a challenging task. Failure to deliver appropriate information can have an adverse impact on the well-being of the elderly people. This paper addresses this challenge and proposes an IoT-enabled information architecture driven approach, which is called “Resalert”. Resalert offers IoT-enabled emergency information supply chain architecture pattern, IoT device architecture and system architecture. The applicability of the Resalert is evaluated by the means of an example scenario, a portable Raspberry Pi based system prototype and user evaluation. The results of this research indicate that the proposed approach seems useful to the effective delivery of emergency information to elderly people.     

Integration of Cloud computing and Internet of Things: A survey

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms–both proprietary and open source–and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet.     

谈综合医院的安全性评价体系

分析了现有医院进行安全性评价的重要意义,论述了现有医院及新建医院项目的安全性评价内容,并探讨了建立安全医院评价标准的意义及应涵盖的主要内容,对提高医院的应急救治能力具有重要意义。     

Modeling patterns of activities using activity curves

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.     

Current trends in redox polymers for energy and medicine

Polymers with redox properties are electroactive macromolecules containing localized sites or groups that can be oxidized (loss of electrons) and reduced (gain of electrons). This review highlights trends in the chemistry, characterization and application of polymers with redox properties. In the first part, we overview the synthetic advances in the design of innovative redox polymers. Special attention is given to state-of-art techniques for the characterization of redox polymers and their important properties are also explained. The last part is devoted to the redox polymers applied in energy and medicine. First, the main redox polymers investigated in energy technologies such as batteries, supercapacitors, solar cells, biofuel cells or thermoelectric cells are reviewed. Second, the emerging applications of redox polymers in medicine technologies such as drug delivery, biosensors, actuators or smart surfaces are explained in detail.     

Enhanced soft dielectric composite generators: the role of ceramic fillers

A notable issue in the field of dielectric elastomers is the characterization of composite materials with improved electromechanical coupling destined for mechanical-to-electrostatic energy converters. To this aim, random composites, where ceramic fillers with high dielectric constant are dispersed in a silicone matrix, represent an interesting option. Currently, the most promising reinforcing materials to be immersed in a silicone matrix, already tested for soft dielectric actuators, are lead magnesium niobate–lead titanate (PMN–PT) and lead zirconate-titanate (PZT). To estimate the performance improvement entailed by the composite device with respect to the homogeneous matrix, a typical four-phase cycle is considered in the model, where nominal load and electric charge are alternately held constant. Different materials are being studied: a composite consisting of a matrix in poly-dimethyl-siloxane (PDMS) reinforced with PMN–PT, assuming a filler concentration of 10% in volume and a PDMS–PZT composite with a 1% volume fraction of the ceramic component. In comparison with pure PDMS, the PDMS–10%PMN–PT allows an increase of more than 60% in the harvested energy per unit volume, while the PDMS–1%PZT composite, entailing a minor improvement, here in the range 23.5–37.4%, exhibits a better performance in terms of generated energy per unit weight. These results provide a guide for the choice and design of materials suitable for the realization of enhanced energy harvesters.