Adaptive SOA Infrastructure Based on Variability Management

In order to exploit the adaptability of a SOA infrastructure, it becomes necessary to provide platform mechanisms that support a mapping of the variability in the applications to the variability provided by the infrastructure. The approach focuses on the configuration of the needed infrastructure mechanisms including support for the derivation of the infrastructure variability model.

Multi-wavelength switchable fibre ring laser based on polarisation selective tilted fibre gratings capable of strain and temperature sensing

Using three fibre gratings with excessively tilted structures in the cavity, we have experimentally demonstrated a multiwavelength switchable erbium-doped fibre ring laser system. The three tilted gratings act as in-fibre polariser and polarisation dependent loss filters to induce the polarisation hole burning effect in the cavity for the operation of the laser at single, double, triple and quadruple wavelengths. The laser system has demonstrated good stability under room temperature conditions and also achieved a high degree of polarization (~30dB), high optical signal to noise ratio (up to 63dB) and high side mode suppression (~50dB). The system has also been investigated for temperature and strain sensing by subjecting the seeding fibre Bragg gratings (FBG) to temperature and strain variations. Since the loss band of the polarisation dependent loss filter is broader than the bandwidth of the seeding FBG, the laser output shifts in wavelength with the applied temperature and strain. The fibre ring laser has shown good responses to the temperature and strain, providing sensitivities of approximately 11.7 pm/°C and 0.85pm/µe respectively.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Adaptive E-learning Content Design and Delivery Based on Learning Styles and Knowledge Level

ACM Computing Classification System (1998): K.3.1, K.3.2.

Highly sensitive liquid-level sensor based on dual-wavelength double-ring fiber laser assisted by beat frequency interrogation

A highly sensitive liquid-level sensor based on dual-wavelength single-longitudinal-mode fiber laser is proposed and demonstrated. The laser is formed by exploiting two parallel arranged phase-shift fiber Bragg gratings (ps-FBGs), acting as ultra-narrow bandwidth filters, into a doublering resonators. By beating the dual-wavelength lasing output, a stable microwave signal with frequency stability better than 5 MHz is obtained. The generated beat frequency varies with the change of dual-wavelength spacing. Based on this characteristic, with one ps-FBG serving as the sensing element and the other one acting as the reference element, a highly sensitive liquid level sensor is realized by monitoring the beat frequency shift of the laser. The sensor head is directly bonded to a float which can transfer buoyancy into axial strain on the fiber without introducing other elastic elements. The experimental results show that an ultra-high liquidlevel sensitivity of 2.12 × 107 MHz/m within the measurement range of 1.5 mm is achieved. The sensor presents multiple merits including ultra-high sensitivity, thermal insensitive, good reliability and stability. © 2012 Optical Society of America.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.

Rules for the governance of coastal and marine ecosystem services: An evaluative framework based on the IAD framework

There is an increasing need for a comprehensive institutional understanding pertaining to ecosystem services (ESs) in coastal and marine fields. This paper develops a systematic framework to inform coastal and marine governance about the integration of ES concepts. First, as a theoretical basis, we analyze the generic rules that are part of the Institutional Analysis and Development (IAD) framework. Second, by an extensive literature review, we formulate a set of ES-specific rules and develop an evaluative framework for coastal and marine governance. Third, we examine this evaluative framework in a specific action situation, namely coastal strategic planning concerning Qingdao, China. Results from the literature review and the case study reveal that when designing ES-specific rules for coastal and marine governance, there are several socio-spatial and economic aspects that should be taken into account: (1) conceive of stakeholders as ES users, (2) capture the effect of ecological scaling, (3) understand ES interactions and clarify indirect impacts and causalities, (4) account for ES values, and (5) draw on economic choices for use rights to deal with ES issues.

A low overhead error confinement method based on application statistical characteristics

Reliability has emerged as a critical design constraint especially in memories. Designers are going to great lengths to guarantee fault free operation of the underlying silicon by adopting redundancy-based techniques, which essentially try to detect and correct every single error. However, such techniques come at a cost of large area, power and performance overheads which making many researchers to doubt their efficiency especially for error resilient systems where 100% accuracy is not always required. In this paper, we present an alternative method focusing on the confinement of the resulting output error induced by any reliability issues. By focusing on memory faults, rather than correcting every single error the proposed method exploits the statistical characteristics of any target application and replaces any erroneous data with the best available estimate of that data. To realize the proposed method a RISC processor is augmented with custom instructions and special-purpose functional units. We apply the method on the proposed enhanced processor by studying the statistical characteristics of the various algorithms involved in a popular multimedia application. Our experimental results show that in contrast to state-of-the-art fault tolerance approaches, we are able to reduce runtime and area overhead by 71.3% and 83.3% respectively.

Short-term wind power forecast based on cluster analysis and artificial neural networks

[EN]In this paper an architecture for an estimator of short-term wind farm power is proposed. The estimator is made up of a Linear Machine classifier and a set of k Multilayer Perceptrons, training each one for a specific subspace of the input space. The splitting of the input dataset into the k clusters is done using a k-means technique, obtaining the equivalent Linear Machine classifier from the cluster centroids...

Design of Streaming Media Panoramic Video System Based on WebGL

2016 is the outbreak year of the virtual reality industry. In the field of virtual reality, 3D surveying plays an important role. Nowadays, 3D surveying technology has received increasing attention. This project aims to establish and optimize a WebGL three-dimensional broadcast platform combined with streaming media technology. It takes streaming media server and panoramic video broadcast in browser as the application background. Simultaneously, it discusses about the architecture from streaming media server to panoramic media player and analyzing relevant theory problem. This paper focuses on the debugging of streaming media platform, the structure of WebGL player environment, different types of ball model analysis, and the 3D mapping technology. The main work contains the following points: Initially, relay on Easy Darwin open source streaming media server, built a streaming service platform. It can realize the transmission from RTSP stream to streaming media server, and forwards HLS slice video to clients; Then, wrote a WebGL panoramic video player based on Three.js lib with JQuery browser playback controls. Set up a HTML5 panoramic video player; Next, analyzed the latitude and longitude sphere model which from Three.js library according to WebGL rendering method. Pointed out the drawbacks of this model and the breakthrough point of improvement; After that, on the basis of Schneider transform principle, established the Schneider sphere projection model, and converted the output OBJ file to JS file for media player reading. Finally implemented real time panoramic video high precision playing without plugin; At last, I summarized the whole project. Put forward the direction of future optimization and extensible market.

Multi-purpose adaptable business tier components based on Call Level Interfaces

Call Level Interfaces (CLI) play a key role in business tiers of relational and on some NoSQL database applications whenever a fine tune control between application tiers and the host databases is a key requirement. Unfortunately, in spite of this significant advantage, CLI are low level API, this way not addressing high level architectural requirements. Among the examples we emphasize two situations: a) the need to decouple or not to decouple the development process of business tiers from the development process of application tiers and b) the need to automatically adapt business tiers to new business and/or security needs at runtime. To tackle these CLI drawbacks, and simultaneously keep their advantages, this paper proposes an architecture relying on CLI from which multi-purpose business tiers components are built, herein referred to as Adaptable Business Tier Components (ABTC). Beyond the reference architecture, this paper presents a proof of concept based on Java and Java Database Connectivity (an example of CLI).

ABTC: multi-purpose adaptable business tier components based on Call Level Interfaces

Call Level Interfaces (CLI) are low level API that play a key role in database applications whenever a fine tune control between application tiers and the host databases is a key requirement. Unfortunately, in spite of this significant advantage, CLI were not designed to address organizational requirements and contextual runtime requirements. Among the examples we emphasize the need to decouple or not to decouple the development process of business tiers from the development process of application tiers and also the need to automatically adapt to new business and/or security needs at runtime. To tackle these CLI drawbacks, and simultaneously keep their advantages, this paper proposes an architecture relying on CLI from which multi-purpose business tiers components are built, herein referred to as Adaptable Business Tier Components (ABTC). This paper presents the reference architecture for those components and a proof of concept based on Java and Java Database Connectivity (an example of CLI).

Transition to Product Service Systems: methodology based on scenarios identification, modelling and evaluation

Part 4: Transition Towards Product-Service Systems

A Distributed Support based on Kubeflow to Ease MLOps of Federated Learning Algorithms

The main objective of my thesis work is to exploit the Google native and open-source platform Kubeflow, specifically using Kubeflow pipelines, to execute a Federated Learning scalable ML process in a 5G-like and simplified test architecture hosting a Kubernetes cluster and apply the largely adopted FedAVG algorithm and FedProx its optimization empowered by the ML platform ‘s abilities to ease the development and production cycle of this specific FL process. FL algorithms are more are and more promising and adopted both in Cloud application development and 5G communication enhancement through data coming from the monitoring of the underlying telco infrastructure and execution of training and data aggregation at edge nodes to optimize the global model of the algorithm ( that could be used for example for resource provisioning to reach an agreed QoS for the underlying network slice) and after a study and a research over the available papers and scientific articles related to FL with the help of the CTTC that suggests me to study and use Kubeflow to bear the algorithm we found out that this approach for the whole FL cycle deployment was not documented and may be interesting to investigate more in depth. This study may lead to prove the efficiency of the Kubeflow platform itself for this need of development of new FL algorithms that will support new Applications and especially test the FedAVG algorithm performances in a simulated client to cloud communication using a MNIST dataset for FL as benchmark.

Design and Implementation of Single phase 230V to Three Phase Inverter Based on the Embedded System STM-32 Applied to motor control

In the field of Power Electronics, several types of motor control systems have been developed using STM microcontroller and power boards. In both industrial power applications and domestic appliances, power electronic inverters are widely used. Inverters are used to control the torque, speed, and position of the rotor in AC motor drives. An inverter delivers constant-voltage and constant-frequency power in uninterruptible power sources. Because inverter power supplies have a high-power consumption and low transfer efficiency rate, a three-phase sine wave AC power supply was created using the embedded system STM32, which has low power consumption and efficient speed. It has the capacity of output frequency of 50 Hz and the RMS of line voltage. STM32 embedded based Inverter is a power supply that integrates, reduced, and optimized the power electronics application that require hardware system, software, and application solution, including power architecture, techniques, and tools, approaches capable of performance on devices and equipment. Power inverters are currently used and implemented in green energy power system with low energy system such as sensors or microcontroller to perform the operating function of motors and pumps. STM based power inverter is efficient, less cost and reliable. My thesis work was based on STM motor drives and control system which can be implemented in a gas analyser for operating the pumps and motors. It has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes and improved structural reliability. The present research was designed to use STM Inverter board on low power MCU such as NUCLEO with some practical examples such as Blinking LED, and PWM. Then we have implemented a three phase Inverter model with Steval-IPM08B board, which converter single phase 230V AC input to three phase 380 V AC output, the output will be useful for operating the induction motor.