Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

A novel distributed algorithm for power loss minimizing in Smart Grid

Because power generation of renewable resources are unstable and demands of the customers are time-varying, the supply power and demands of the customers are always unequal. To meet the demands of the customers, power is transmitted from primary power generation to secondary power generation. It will cause high power loss. To solve this problem, a distributed algorithm is proposed in this paper. By using the algorithm, the micro-grids are able to exchange power with their neighbors so as to minimize the total power losses of the smart grid. Moreover, communication overhead (bandwidth) is reduced, comparing with centralized algorithm. Through computer simulations, we demonstrate that the proposed algorithm can lead to near-optimal result for alleviating the average power loss per micro-grid and reduce the communication overhead significantly in contrast with the centralized approach.

Fog computing: a cloud to the ground support for smart things and machine-to-machine networks

Cloud services to smart things face latency and intermittent connectivity issues. Fog devices are positioned between cloud and smart devices. Their high speed Internet connection to the cloud, and physical proximity to users, enable real time applications and location based services, and mobility support. Cisco promoted fog computing concept in the areas of smart grid, connected vehicles and wireless sensor and actuator networks. This survey article expands this concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios. Our literature review identifies a handful number of articles. Cooperative data scheduling and adaptive traffic light problems in SDN based vehicular networks, and demand response management in macro station and micro-grid based smart grids are discussed. Security, privacy and trust issues, control information overhead and network control policies do not seem to be studied so far within the fog computing concept.

Environmental impacts of tariff and tax reforms under origin and destination principles

The impacts of a point-by-point tariff/tax reform on the environment under the origin-based or destination-based tax principle are examined. The policy reform under the origin-based principle can raise the optimal pollution tax and, hence, improve the environment when the consumption demand and pollution are strongly substitutable, whereas the reform under the destination-based principle lowers the optimal pollution tax and, hence, worsens the environment. Nonetheless, when the consumption demand and pollution exhibit weak substitutes or even complements, the tariff/tax reform results in less environmental deterioration under the destination-based principle.

Agent-based smart grid protection and security

The vision of a smart grid is to provide a modern, resilient, and secure electric power grid as it boasts up with a highly reliable and efficient environment through effective use of its information and communication technology (ICT). Generally, the control and operation of a smart grid which integrate the distributed energy resources (DERs) such as, wind power, solar power, energy storage, etc., largely depends on a complex network of computers, softwares, and communication infrastructure superimposed on its physical grid architecture facilitated with the deployment of intelligent decision support system applications. In recent years, multi-agent system (MAS) has been well investigated for wide area power system applications and specially gained a significant attention in smart grid protection and security due to its distributed characteristics. In this chapter, a MAS framework for smart grid protection relay coordination is proposed, which consists of a number of intelligent autonomous agents each of which are embedded with the protection relays. Each agent has its own thread of control that provides it with a capability to operate the circuit breakers (CBs) using the critical clearing time (CCT) information as well as communicate with each other through high speed communication network. Besides physical failure, since smart grid highly depends on communication infrastructure, it is vulnerable to several cyber threats on its information and communication channel. An attacker who has knowledge about a certain smart grid communication framework can easily compromise its appliances and components by corrupting the information which may destabilize a system results a widespread blackout. To mitigate such risk of cyber attacks, a few innovative counter measuring techniques are discussed in this chapter.

Development of high performance materials based on smart elastomer nanocomposites

 The present thesis explores the fabrication of technologically relevant nanocomposites out of a few elastomers and conducting fillers like carbon nanotubes, graphene and polyaniline. The developed materials have good applications in sensors, shape memory devices and capacitors. Different characterization methods reveal the influence of filler-elastomer interactions on the various properties of the obtained nanocomposites as well.

A multi-agent approach for enhancing transient stability of smart grids

Transient stability, an important issue to avoid the loss of synchronous operation in power systems, can be achieved through proper coordination and operation of protective devices within the critical clearing time (CCT). In view of this, the development of an intelligent decision support system is useful for providing better protection relay coordination. This paper presents an intelligent distributed agent-based scheme to enhance the transient stability of smart grids in light of CCT where a multi-agent framework (MAF) is developed and the agents are represented in such a way that they are equipped with protection relays (PRs). In addition to this, an algorithm is developed which assists the agents to make autonomous decision for controlling circuit breakers (CBs) independently. The proposed agents are responsible for the coordination of protection devices which is done through the precise detection and isolation of faults within the CCT. The agents also perform the duty of reclosing CBs after the clearance of faults. The performance of the proposed approach is demonstrated on a standard IEEE 39-bus test system by considering short-circuit faults at different locations under various load conditions. To further validate the suitability of the proposed scheme a benchmark 16-machine 68-bus power system is also considered. Simulation results show that MAF exhibits full flexibility to adapt the changes in system configurations and increase the stability margin for both test systems.

Functionalized mesoporous silica nanoparticles for smart agrochemical delivery system

The agrochemical delivery system has been built up based on mesoporous silica nanoparticles as carriers in a controllable fashion. Several peer reviewed papers have been published with this research work. This delivery system will benefit for the future agricultural application.

The use of kernel density estimation to examine associations between neighborhood destination intensity and walking and physical activity

BACKGROUND: Local destinations have previously been shown to be associated with higher levels of both physical activity and walking, but little is known about how the distribution of destinations is related to activity. Kernel density estimation is a spatial analysis technique that accounts for the location of features relative to each other. Using kernel density estimation, this study sought to investigate whether individuals who live near destinations (shops and service facilities) that are more intensely distributed rather than dispersed: 1) have higher odds of being sufficiently active; 2) engage in more frequent walking for transport and recreation. METHODS: The sample consisted of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Destinations within these areas were geocoded and kernel density estimates of destination intensity were created using kernels of 400m (meters), 800m and 1200m. Using multilevel logistic regression, the association between destination intensity (classified in quintiles Q1(least)-Q5(most)) and likelihood of: 1) being sufficiently active (compared to insufficiently active); 2) walking≥4/week (at least 4 times per week, compared to walking less), was estimated in models that were adjusted for potential confounders. RESULTS: For all kernel distances, there was a significantly greater likelihood of walking≥4/week, among respondents living in areas of greatest destinations intensity compared to areas with least destination intensity: 400m (Q4 OR 1.41 95%CI 1.02-1.96; Q5 OR 1.49 95%CI 1.06-2.09), 800m (Q4 OR 1.55, 95%CI 1.09-2.21; Q5, OR 1.71, 95%CI 1.18-2.48) and 1200m (Q4, OR 1.7, 95%CI 1.18-2.45; Q5, OR 1.86 95%CI 1.28-2.71). There was also evidence of associations between destination intensity and sufficient physical activity, however these associations were markedly attenuated when walking was included in the models. CONCLUSIONS: This study, conducted within urban Melbourne, found that those who lived in areas of greater destination intensity walked more frequently, and showed higher odds of being sufficiently physically active-an effect that was largely explained by levels of walking. The results suggest that increasing the intensity of destinations in areas where they are more dispersed; and or planning neighborhoods with greater destination intensity, may increase residents' likelihood of being sufficiently active for health.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A decentralised multi-agent approach to enhance the stability of smart microgrids with renewable energy

This paper presents the impact of large penetration of wind power on the transient stability through a dynamic evaluation of the critical clearing times (CCTs) by using intelligent agent-based approach. A decentralised multi-agent-based framework is developed, where agents represent a number of physical device models to form a complex infrastructure for computation and communication. They enable the dynamic flow of information and energy for the interaction between the physical processes and their activities. These agents dynamically adapt online measurements and use the CCT information for relay coordination to improve the transient stability of power systems. Simulations are carried out on a smart microgrid system for faults at increasing wind power penetration levels and the improvement in transient stability using the proposed agent-based framework is demonstrated.