In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.
The main objective is to design a proportional controller of a robot manipulator using the fuzzy cerebellar model articulation controller based on Takagi-Sugeno (T-S) framework with a compensator. The controller and compensator apply in visual servoing, including system identification of image and kinematic Jacobians. The proposed approach is basically as a function of the visual error and extent from the error with respect to desire visual feature. This approach leads to enormous reduction on computational expense compared to the imagebased approaches of model inverse kinematics. The design of the controller architecture will make it possible to implement in general case. Proportional control variable are learned offline with the help of FCMAC-T-S model, and online compensator scheme has been proposed for adapting possible uncertainties in the unknown system and environment.Stimulation results have shown that visual servoing for tracking static target can be achieved using the proposed controller with compensator.
A potential technology by silicon interposer enables high bandwidth and low power application processing devices of the future, because the demand of smart mobile products are driving for higher logic-to-memory bandwidth (BW) over 30 GB/s with lower power consumption and ultra-memory capacity. This paper presents a 2.5D-IC structure with silicon interposer to demonstrate electrical performances including signal integrity (SI) and power integrity (PI) by using WideIO memory interface. Of course, the accuracy of TSV has demonstrated by measurement as well.
Keywords-Through silicon via (TSV), 2.5D-IC, WideIO, signal ntegrity (SI), power integrity (PI)I.
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