3 research outputs found
Depth-based Outlier Detection for Grouped Smart Meters: a Functional Data Analysis Toolbox
Smart metering infrastructures collect data almost continuously in the form
of fine-grained long time series. These massive time series often have common
daily patterns that are repeated between similar days or seasons and shared
between grouped meters. Within this context, we propose a method to highlight
individuals with abnormal daily dependency patterns, which we term evolution
outliers. To this end, we approach the problem from the standpoint of
Functional Data Analysis (FDA), by treating each daily record as a function or
curve. We then focus on the morphological aspects of the observed curves, such
as daily magnitude, daily shape, derivatives, and inter-day evolution. The
proposed method for evolution outliers relies on the concept of functional
depth, which has been a cornerstone in the literature of FDA to build shape and
magnitude outlier detection methods. In conjunction with our evolution outlier
proposal, these methods provide an outlier detection toolbox for smart meter
data that covers a wide palette of functional outliers classes. We illustrate
the outlier identification ability of this toolbox using actual smart metering
data corresponding to photovoltaic energy generation and circuit voltage
records
Verification Method for Area Optimization of Mixed - Polarity Reed - Muller Logic Circuits
Area minimization of mixed-polarity Reed-Muller (MPRM) logic circuits is an important step in logic synthesis. While previous studies are mainly based on various artificial intelligence algorithms and not comparable with the results from the mainstream electronics design automation (EDA) tool. Furthermore, it is hard to verify the superiority of intelligence algorithms to the EDA tool on area optimization. To address these problems, a multi-step novel verification method was proposed. First, a hybrid simulated annealing (SA) and discrete particle swarm optimization (DPSO) approach (SADPSO) was applied to optimize the area of the MPRM logic circuit. Second, a Design Compiler (DC) algorithm was used to optimize the area of the same MPRM logic circuit under certain settings and constraints. Finally, the area optimization results of the two algorithms were compared based on MCNC benchmark circuits. Results demonstrate that the SADPSO algorithm outperforms the DC algorithm in the area optimization for MPRM logic circuits. The SADPSO algorithm saves approximately 9.1% equivalent logic gates compared with the DC algorithm. Our proposed verification method illustrates the efficacy of the intelligence algorithm in area optimization compared with DC algorithm. Conclusions in this study provide guidance for the improvement of EDA tools in relation to the area optimization of combinational logic circuits