Single phase inverter system using proportional resonant current control

This paper presents the harmonic reduction performance of proportional resonant (PR) current controller in single phase inverter system connected to nonlinear load. In the study, proportional resonant current controller and low pass filter is discussed to eliminate low order harmonics injection in single phase inverter system. The potential of nonlinear load in producing harmonics is showed and identified by developing a nonlinear load model using a full bridge rectifier circuit. The modelling and simulation is done in MATLAB Simulink while harmonic spectrum results are obtained using Fast Fourier Transfor. End result show PR current controller capability to overcome the injection of current harmonic problems thus improved the overall total harmonic distortion (THD)

Indicators for measuring satisfaction towards design quality of buildings

Design quality is an important component in measuring satisfaction towards total product quality (TPQ) of buildings, the product of construction projects. Design Quality Indicator (DQI), developed by the Construction Industry Council (CIC) in the UK looking at three quality fields, i.e. functionality, build quality, and impact of building in measuring the quality of design embodied in the buildings through feedback and perceptions of all stakeholders involved in the production and use of buildings. Design quality is always a major concern in the Malaysian construction industry. With inspiration from this DQI, this study was carried out to identify indicators for measuring the satisfaction towards design quality of buildings and to evaluate the suitability of the indicators for application in the context of Malaysian construction industry. Through literature survey, 32 indicators of design quality were identified and grouped into the three design quality fields. A questionnaire survey was carried out among Malaysian construction professionals (architects, engineers, quantity surveyors, contractors and developers) to assess the identified design quality indicators in terms of their relevance and significance in the context of construction industry in Malaysia. The survey reveals that access, natural lighting, access and use, structure element, landscape, finishes, location, external environment, urban and social integration and noise are among the design quality indicators that were perceived as the most important to be looked at. In overall, all the indicators are relevant for adoption in the Malaysian construction industry to measure the satisfaction towards design quality of buildings

Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks

This paper presents a structural health monitoring (SHM) method for in situ damage detection and localization in carbon fiber reinforced plates (CFRPs). The detection is achieved using the electromechanical impedance (EMI) technique employing piezoelectric transducers as high-frequency modal sensors. Numerical simulations based on the finite element method are carried out so as to simulate more than a hundred damage scenarios. Damage metrics are then used to quantify and detect changes between the electromechanical impedance spectrum of a pristine and damaged structure. The localization process relies on artificial neural networks (ANNs) whose inputs are derived from a principal component analysis of the damage metrics. It is shown that the resulting ANN can be used as a tool to predict the in-plane position of a single damage in a laminated composite plate

Cortical Dynamics of Contextually-Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation

How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.CELEST, an NSF Science of Learning Center (SBE-0354378); SyNAPSE program of Defense Advanced Research Projects Agency (HR0011-09-3-0001, HR0011-09-C-0011

Emergence of Object Segmentation in Perturbed Generative Models

We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Spotlight presentatio