Querying XML data streams from wireless sensor networks: an evaluation of query engines

As the deployment of wireless sensor networks increase and their application domain widens, the opportunity for effective use of XML filtering and streaming query engines is ever more present. XML filtering engines aim to provide efficient real-time querying of streaming XML encoded data. This paper provides a detailed analysis of several such engines, focusing on the technology involved, their capabilities, their support for XPath and their performance. Our experimental evaluation identifies which filtering engine is best suited to process a given query based on its properties. Such metrics are important in establishing the best approach to filtering XML streams on-the-fly

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites

ANALISIS PENINGKATAN PRODUKSI DENGAN RANCANG BANGUN ALAT PEMOTONG PADA PROSES PACKING: rancangan bangun alat pemotong dalam peningkatan produksi

Improving company productivity and efficiency in empowering human resource assessment is fulfilled to achieve company goals. one factor that can be done is the development of more effective equipment. Plastic cutting tools in the product packaging process are still simple, less efficient and less effective. The value engineering method is used as a method for selecting an alternative modification to a modified cutting tool and analyze it. At the analysis stage, two alternative modifications were modified from the improvement engine, where the initial design is used as a benchmark in performance evaluation. The result of tool design is the reduction in the number of defective products by 20.68% to 3.1% and The result of the cutting tool design is that there is an average increase in production 246 products per day.Improving company productivity and efficiency in empowering human resource assessment is fulfilled to achieve company goals. one factor that can be done is the development of more effective equipment. Plastic cutting tools in the product packaging process are still simple, less efficient and less effective. The value engineering method is used as a method for selecting an alternative modification to a modified cutting tool and analyze it. At the analysis stage, two alternative modifications were modified from the improvement engine, where the initial design is used as a benchmark in performance evaluation. The result of tool design is the reduction in the number of defective products by 20.68% to 3.1% and The result of the cutting tool design is that there is an average increase in production 246 products per day

Analytical results for the multi-objective design of model-predictive control

In model-predictive control (MPC), achieving the best closed-loop performance under a given computational resource is the underlying design consideration. This paper analyzes the MPC design problem with control performance and required computational resource as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control performance and the computational resource separately -- often with the latter as a fixed constraint -- which requires the implementation hardware to be known a priori. The proposed approach focuses on the tuning of structural MPC parameters, namely sampling time and prediction horizon length, to produce a set of optimal choices available to the practitioner. The posed design problem is then analyzed to reveal key properties, including smoothness of the design objectives and parameter bounds, and establish certain validated guarantees. Founded on these properties, necessary and sufficient conditions for an effective and efficient solver are presented, leading to a specialized multi-objective optimizer for the MOD-MPC being proposed. Finally, two real-world control problems are used to illustrate the results of the design approach and importance of the developed conditions for an effective solver of the MOD-MPC problem

Using Active Learning and Team Competition to Teach Gas Turbine Cycle Design

An elective, Analysis and Design of Propulsion Systems, has been a traditional lecture course teaching gas turbine engines from a design perspective. This past fall semester additional active learning modules were introduced to make the course more interactive. Students formed teams of four and each team was designated a company. The task was to design a replacement engine for the B-52H which served as the basis for learning about gas turbine engine design. The companies picked a name, developed a logo, and wrote a mission statement. Competition was encouraged and the “companies” were tasked to eventually design the lowest cost, most efficient high bypass turbofan engine to replace the existing engine. A three part design project led to a final report on the engine design. To conclude the process, each team presented their engine as if they were a company trying to sell their product to a customer. The customer, the professor, picked an overall winner based on the information presented. Assessment of the course showed that the students appreciated the competitive environment giving them insight into how a gas turbine company, such as Rolls-Royce, GE, or Pratt & Whitney, might operate. In conclusion, the active learning modules and the design project were effective in challenging and exciting the students about the design of gas turbine engines. The company context for teams prepares students for what they might encounter in industry.Cockrell School of Engineerin

Advanced rotary engine studies

A review of rotary engine developments relevant to a stratified charge rotary aircraft engine is presented. Advantages in module size and weight, fuel efficiency, reliability, and multi-fuel capability are discussed along with developments in turbocharging, increased mean effective pressure, improved apex seal/trochoid wear surfacing materials, and high strength and temperature aluminum casting alloys. A carbureted prototype aircraft engine is also described

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201