A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

A frequency adaptive single-phase Phase-Locked Loop with harmonic rejection

An improved Phase-Locked Loop (PLL) for extracting phase and frequency of the fundamental component of a highly distorted grid voltage is presented. The structure of the single-phase PLL is based on the Synchronous Reference Frame (SRF) PLL and uses an All Pass Filter (APF) to generate the quadrature component from the single phase input voltage. In order to filter the harmonic content, a Moving Average Filter (MAF) is used, and performance is improved by designing a lead compensator and also a feed-forward compensator. The simulation results are compared to show the improved performance with feed-forward. In addition, the frequency dependency of MAF is dealt with by a proposed method for adaption to the frequency. This method changes the window size based on the frequency on a sample-by-sample basis. By using this method, the speed of resizing can be reduced in order to decrease the output ripples caused by window size variations.

Vagueness and software engineering

We do not commonly associate software engineering with philosophical debate. Indeed, software engineers ought to be concerned with building software systems and not settling philosophical questions. I attempt to show that software engineers do, in fact, take philosophical sides when designing software applications. In particular, I look at how the problem of vagueness arises in software engineering and argue that when software engineers solve it, they commit to philosophical views that they are seldom aware of. In the second part of the paper, I suggest a way of dealing with vague predicates without having to confront the problem of vagueness itself. The purpose of my paper is to highlight the currently prevalent disconnect between philosophy and software engineering. I claim that a better knowledge of the philosophical debate is important as it can have ramifications for crucial software design decisions. Better awareness of philosophical issues not only produces better software engineers, it also produces better engineered products.

Real-time price based home energy management scheduler

With the recent development of advanced metering infrastructure, real-time pricing (RTP) scheme is anticipated to be introduced in future retail electricity market. This paper proposes an algorithm for a home energy management scheduler (HEMS) to reduce the cost of energy consumption using RTP. The proposed algorithm works in three subsequent phases namely real-time monitoring (RTM), stochastic scheduling (STS) and real-time control (RTC). In RTM phase, characteristics of available controllable appliances are monitored in real-time and stored in HEMS. In STS phase, HEMS computes an optimal policy using stochastic dynamic programming (SDP) to select a set of appliances to be controlled with an objective of the total cost of energy consumption in a house. Finally, in RTC phase, HEMS initiates the control of the selected appliances. The proposed HEMS is unique as it intrinsically considers uncertainties in RTP and power consumption pattern of various appliances. In RTM phase, appliances are categorized according to their characteristics to ease the control process, thereby minimizing the number of control commands issued by HEMS. Simulation results validate the proposed method for HEMS.

Predicting reduced driver alertness on monotonous highways : a driving simulator study

Impaired driver alertness increases the likelihood of drivers’ making mistakes and reacting too late to unexpected events while driving. This is particularly a concern on monotonous roads, where a driver’s attention can decrease rapidly. While effective countermeasures do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behavior in real-time. The aim of this study is to predict drivers’ level of alertness through surrogate measures collected from in-vehicle sensors. Electroencephalographic activity is used as a reference to evaluate alertness. Based on a sample of 25 drivers, data was collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device. Various classification models were tested from linear regressions to Bayesians and data mining techniques. Results indicated that Neural Networks were the most efficient model in detecting lapses in alertness. Findings also show that reduced alertness can be predicted up to 5 minutes in advance with 90% accuracy, using surrogate measures such as time to line crossing, blink frequency and skin conductance level. Such a method could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring, in real-time, drivers' behavior on highways.

Dispatch strategy of PHEVs to mitigate selected patterns of seasonally varying outputs from renewable generation

Rapid development of plug-in hybrid electric vehicles (PHEVs) brings new challenges and opportunities to the power industry. A large number of idle PHEVs can potentially be employed to form a distributed energy storage system for supporting renewable generation. To reduce the negative effects of unsteady renewable generation outputs, a stochastic optimization-based dispatch model capable of handling uncertain outputs of PHEVs and renewable generation is formulated in this paper. The mathematical expectations, second-order original moments, and variances of wind and photovoltaic (PV) generation outputs are derived analytically. Incorporated all the derived uncertainties, a novel generation shifting objective is proposed. The cross-entropy (CE) method is employed to solve this optimal dispatch model. Multiple patterns of renewable generation depending on seasons and renewable market shares are investigated. The feasibility and efficiency of the developed optimal dispatch model, as well as the CE method, are demonstrated with a 33-node distribution system.

Passenger segmentation using smart card data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

A supervisory load-leveling approach to improve the voltage profile in distribution network

This paper suggests a supervisory control for storage units to provide load leveling in distribution networks. This approach coordinates storage units to charge during high generation and discharge during peak load times, while utilized to improve the network voltage profile indirectly. The aim of this control strategy is to establish power sharing on a pro rata basis for storage units. As a case study, a practical distribution network with 30 buses is simulated and the results are provided.

Heat maps for aggregating bioacoustic annotations

In our large library of annotated environmental recordings of animal vocalizations, searching annotations by label can return thousands of results. We propose a heat map of aggregated annotation time and frequency bounds, maintaining the shape of the annotations as they appear on the spectrogram. This compactly displays the distribution of annotation bounds for the user's query, and allows them to easily identify unusual annotations. Key to this is allowing zero values on the map to be differentiated from areas where there are single annotations.