The Hydro-Modeling Platform (HydroMP) - Enabling Cloud-Based Environmental Modeling Using Software-As-A-Service (SaaS) Cloud Computing

Hydro-model has become important tool for water resources management, with higher demand in simulation precision and speed of decision support, models designed for sectoral application becoming outmoded, and original mode that massive schemes are run sequentially cannot meet the real-time requirements, especially with the computation increase by finer discretization granularity and broader research range. Water management organizations are increasingly looking for new generation tools that allow integration across domains, and can provide extensible computing resources to assist their decision making processes. In response to this need, a hydro-modeling platform(HydroMP) based on cloud computing is designed and implemented, which can deployed in distributed HPC Cluster and center HPC Cluster use a resources balancer to manage load balancing. This platform integrates multi models and computing resources (i.e. blade computer) dynamically to assure models integrated in platform get extensible computing capacity. A server, hosting HydroMP Web Service and interfaces, is connected to the HPC Cluster and Internet constituting the gateway for registered users. Any terminal (i.e. decision making system) can reference library and Web service of HydroMP in their systems. Massive modeling schemes can be submitted by different users simultaneously, and terminal can get simulation results from HydroMP real-time. Some key approaches and techniques are utilized including: i) a standard model component wrapper communicating with platform by named pipe have developed. OpenMI-compliant model-components can be integrated to this wrapper; ii) API and Event-Handler interface provided by HPC Server, task scheduler and calculation management table is employed to dispatch computing resource, while controlling multiple concurrent scheme submitting; iii) Interface array(i.e. SchemesSubmit, StatusInquiry, GetResult) in the Web Service is supplied to make terminal communicate with platform; iv) Oracle database is used to manage massive model data, results and model-components. This paper describes the details of design and implementation, and gives a case presentation platform application

A sensory-guided surgical micro-drill

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2010 The Authors.This article describes a surgical robotic device that is able to discriminate tissue interfaces and other controlling parameters ahead of the drill tip. The advantage in such a surgery is that the tissues at the interfaces can be preserved. A smart tool detects ahead of the tool point and is able to control the interaction with respect to the flexing tissue, to avoid penetration or to control the extent of protrusion with respect to the position of the tissue. For surgical procedures, where precision is required, the tool offers significant benefit. To interpret the drilling conditions and the conditions leading up to breakthrough at a tissue interface, a sensing scheme is used that discriminates between the variety of conditions posed in the drilling environment. The result is a fully autonomous system, which is able to respond to the tissue type, behaviour, and deflection in real-time. The system is also robust in terms of disturbances encountered in the operating theatre. The device is pragmatic. It is intuitive to use, efficient to set up, and uses standard drill bits. The micro-drill, which has been used to prepare cochleostomies in the theatre, was used to remove the bone tissue leaving the endosteal membrane intact. This has enabled the preservation of sterility and the drilling debris to be removed prior to the insertion of the electrode. It is expected that this technique will promote the preservation of hearing and reduce the possibility of complications. The article describes the device (including simulated drill progress and hardware set-up) and the stages leading up to its use in the theatre.Queen Elizabeth Hospital, Birmingham, U

Stealthy Deception Attacks Against SCADA Systems

SCADA protocols for Industrial Control Systems (ICS) are vulnerable to network attacks such as session hijacking. Hence, research focuses on network anomaly detection based on meta--data (message sizes, timing, command sequence), or on the state values of the physical process. In this work we present a class of semantic network-based attacks against SCADA systems that are undetectable by the above mentioned anomaly detection. After hijacking the communication channels between the Human Machine Interface (HMI) and Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a fake view of the industrial process, deceiving the human operator into taking manual actions. Our most advanced attack also manipulates the messages generated by the operator's actions, reversing their semantic meaning while causing the HMI to present a view that is consistent with the attempted human actions. The attacks are totaly stealthy because the message sizes and timing, the command sequences, and the data values of the ICS's state all remain legitimate. We implemented and tested several attack scenarios in the test lab of our local electric company, against a real HMI and real PLCs, separated by a commercial-grade firewall. We developed a real-time security assessment tool, that can simultaneously manipulate the communication to multiple PLCs and cause the HMI to display a coherent system--wide fake view. Our tool is configured with message-manipulating rules written in an ICS Attack Markup Language (IAML) we designed, which may be of independent interest. Our semantic attacks all successfully fooled the operator and brought the system to states of blackout and possible equipment damage

Spike-based control monitoring and analysis with Address Event Representation

Neuromorphic engineering tries to mimic biological information processing. Address-Event Representation (AER) is a neuromorphic communication protocol for spiking neurons between different chips. We present a new way to drive robotic platforms using spiking neurons. We have simulated spiking control models for DC motors, and developed a mobile robot (Eddie) controlled only by spikes. We apply AER to the robot control, monitoring and measuring the spike activity inside the robot. The mobile robot is controlled by the AER-Robot tool, and the AER information is sent to a PC using the USBAERmini2 interface.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Intelligent agent for formal modelling of temporal multi-agent systems

Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system