Hammerstein-Wiener nonlinear model based predictive control for relative QoS performance and resource management of software systems

Runtime management of Quality of Service (QoS) performance and resource provisioning is a vital issue in shared resource software environments. A useful performance management technique for such software systems is the relative guarantee feedback control scheme. The existing approaches for this class of control systems are mainly based on off-line linear or on-line model identification and control techniques, which tend to have performance issues in the presence of nonlinearities induced by this scheme. Instead of using such modeling techniques, this paper proposes a new approach for QoS performance management and resource provisioning by using an off-line identification of Hammerstein and Wiener nonlinear block structural model. Using the characteristic structure of the nonlinear model, a predictive feedback controller based on a gain schedule technique is incorporated in the design to achieve the performance objectives. The proposed approach is validated using experiments based on a prototype, demonstrating superior runtime QoS performance management and resource provisioning in a complex software system.     

Urban Walkability Design Using Virtual Population Simulation

We present a system to generate a procedural environment that produces a desired crowd behaviour. Instead of altering the behavioural parameters of the crowd itself, we automatically alter the environment to yield such desired crowd behaviour. This novel inverse approach is useful both to crowd simulation in virtual environments and to urban crowd planning applications. Our approach tightly integrates and extends a space discretization crowd simulator with inverse procedural modelling. We extend crowd simulation by goal exploration (i.e. agents are initially unaware of the goal locations), variable‐appealing sign usage and several acceleration schemes. We use Markov chain Monte Carlo to quickly explore the solution space and yield interactive design. We have applied our method to a variety of virtual and real‐world locations, yielding one order of magnitude faster crowd simulation performance over related methods and several fold improvement of crowd indicators.     

On identification of Hammerstein and Wiener model with application to virtualised software system

This paper proposes a system identification method for estimating virtualised software system dynamics within the framework of a Hammerstein–Wiener model. Building on the authors’ previous work in identification and control of the software systems, the approach utilises frequency sampling filter structure to describe the linear dynamics and B-spline curve functions for the inverse static output nonlinearity. Furthermore, the issue on parameter selection for B-spline model approximation of scatter data is addressed by using a data clustering method. An experimental test-bed of virtualised software system is established to generate real observational data which are used to confirm the performance of the proposed approach. The identification results have shown that the model efficacy is increased with the proposed approach because the dimension of the nonlinear model can be significantly reduced while maintaining the desired accuracy.     

Applications of RF aperture-array spatially-bandpass 2-D IIR filters in sub-Nyquist spectrum sensing,wideband doppler radar and radio astronomy beamforming

The application of two-dimensional (2-D) infinite impulse response (IIR) spatially-bandpass (SBP) filters as a digital beamformer for a wide spectrum of practical applications spanning wireless cognitive radio communications, doppler radar, and radio astronomy instrumentation is discussed. The paper starts with an introduction of the recently proposed 2-D SBP filter. The first application is a spectrum sensing scheme for dynamic spectrum access based cognitive radios. A 2-D IIR SBP filter is used in conjunction with a sub-Nyquist wideband signal reconstruction technique to achieve aperture-array directional spectrum sensing using sub-Nyquist sparse sampling based on the recently reported Eldar algorithm. The second application is related to wideband pulse and continuous-wave frequency modulated Doppler radar sensing. The SBP filter is integrated with a wideband radar back-end connected to an electronically-steerable aperture antenna. A a low-complexity directional localization algorithm is presented, which estimates the range and angle of a target scatterer with a signal to interference ratio improvement of 10 dB. We also present applications of 2-D IIR SBP in the fields of classification and remote sensing of unmanned aerial vehicles. Finally, a digital aperture-array wideband beamforming model using the 2-D IIR SBP filters is presented for radio telescope systems based on dense aperture arrays and time-domain beamforming. A well-known example is the study of pulsar astrophysics using a highly-directional aperture antenna system. The 2-D IIR SBP beamformer is simulated as the digital backend of the time-domain beamforming system with array signals synthesized using measured time-domain signatures from the Crab pulsar obtained from the GAVRT. The SBP filter shows a gain of 12.3 dB with an order of magnitude lower circuit complexity compared to traditional phased-array digital beamformers. To obtain comparable levels of SINR improvement, the wideband phased-array beamformers require 48-point FFTs per antenna. Assuming the optimum three real-multiplications per complex multiplication for the Gauss algorithm, it is discovered that the proposed 2-D IIR SBP beamformers are more than 97 % lower in digital multiplier complexity compared to traditional FIR phased-array FFT-beamformers.     

Actual and perceived causes of flood risk: climate versus anthropogenic effects in a wet zone catchment in Sri Lanka

The Kalu Ganga Basin in Sri Lanka is generally flooded once a year. A network of low-lying lands acts as natural retention and storage that captures floodwater, minimizing damage. An increase in the flood frequency has been observed in recent years. It is commonly perceived that this increase is caused by a rise in the frequency and severity of ‘very wet’ precipitation events. We conclude that land-use changes may have played a larger role in generating floods.     

Energy rating system for climate conscious operation of multi-unit residential buildings

Residential sector accounts for 17% of domestic energy use and 14% of the greenhouse gas emissions in Canada. Improving the energy performance of the Canadian residential building stock is vital in achieving climate action goals. A comprehensive review of popular building energy rating systems noted their lack of life cycle thinking. The scope of the popular rating systems is limited to operational performance, ignoring the condition of the assets. This paper proposes a condition assessment framework for the energy system of existing multi-unit residential buildings (MURB), which combines the concepts of asset rating and operational rating. Indicators were identified to define energy, environmental, economic, and asset performance of MURB. A life cycle assessment was conducted to quantify the overall environmental impacts of energy technologies from point of generation. A fuzzy logic-based approach was used to overcome the challenges due to data uncertainty and impreciseness. A case study was conducted for a residential building at UBC Okanagan, Canada. The weights at indicator level and category level were defined based on stakeholder consultation, while also considering different decision scenarios. A fuzzy rule-based approach was used to combine the different performance categories to obtain an overall condition rating. The results indicate that the performance of the case study building can be rated as “good” under operational, asset, and overall categories. The findings of this research can be used to improve the asset and operational management strategies in existing buildings and can inform the key stakeholders during the operational phase of the buildings.     

Instrumented model slopes to investigate the effects of slope inclination on rainfall-induced landslides

Rainfall infiltration is considered as one of the most significant factors triggering slope instability as a number of slope failure occurrences have been documented during or immediately after a rainfall. The rainfall-induced slope instability is governed by a complex interaction of topographical, hydrological and geological conditions of the slopes. Hence slope inclination is vital in determining slope stability under rainfall. Although studies have been carried out to investigate the mechanism of rainfall-induced slope failure, limited compelling experimental studies have been conducted on the factors influencing the initiation of slope failure. In this study, instrumented model slopes were subjected to artificial rainfalls to investigate the effects of the slope inclination on slope stability, and a validated numerical model was developed using the test results from the instrumented model slopes. The outcomes of the study prove that the slopes become more susceptible to sudden collapse during rainfall as the slope angle increases. Further, the results highlight that when the slope inclination is 1.2 times greater than the friction angle of the soil, the failure is initiated by the loss of soil suction, and when it is smaller than or equal to 1.2 times the friction angle of the soil, the failure is initiated by the positive pore water pressure developed at the toe of the slope.     

De Novo Design of Polyhedral Protein Assemblies: Before and After the AI Revolution

Self-assembling polyhedral protein biomaterials have gained attention as engineering targets owing to their naturally evolved sophisticated functions, ranging from protecting macromolecules from the environment to spatially controlling biochemical reactions. Precise computational design of de novo protein polyhedra is possible through two main types of approaches: methods from first principles, using physical and geometrical rules, and more recent data-driven methods based on artificial intelligence (AI), including deep learning (DL). Here, we retrospect first principle- and AI-based approaches for designing finite polyhedral protein assemblies, as well as advances in the structure prediction of such assemblies. We further highlight the possible applications of these materials and explore how the presented approaches can be combined to overcome current challenges and to advance the design of functional protein-based biomaterials.