Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Research shows that, in learning science and engineering, guided project work leads to deeper understanding of theoretical concepts (as well as acquisition of hands-on skills) than the classical approach of textbook reading and attending lectures. In an approach to education based on project work, the role of the teacher is to create a stimulating learning environment and to supervise the students in accomplishing their objectives. The main challenge is to come up with projects that are engaging, diverse, and feasible in view of limited time and resources. In this paper, we describe such a signal processing project. The task is to improve the speed and accuracy characteristics of a sensor by real-time signal processing. It turns out that this is an application of Kalman filtering however the students need to identify a model of the sensor and implement the Kalman filter on a digital signal processor. The project consists of three main tasks: 1) mathematical formalization of the problem, 2) development of solution methods, and 3) implementation and testing of the methods. The testing is done on an inexpensive laboratory setup, using the Lego Mindstorms educational kit in combination with a temperature sensor. The learning outcomes are understanding of model representations, system identification, and state estimation as well as implementation in Matlab and C of real-time signal processing algorithms. Possible extensions are adaptive signal processing, multiple sensors data fusion, and nonconstant measured value estimation.
This article introduces transactive multi-agent systems as a promising solution for mitigating climate change, focusing on two specific applications: transactive energy systems with increasing renewable penetration and carbon permit trading systems. However, the implementation of transactive multi-agent systems faces social acceptance challenges, particularly in resource pricing. We identify three main issues in this regard: socially unacceptable pricing that leads to price spikes, locational pricing that results in resource allocation inequity, and price fluctuations that complicate system planning and operation. To address these challenges, we propose three social shaping approaches that adjust resource prices in accordance with social norms. We use the concept of competitive equilibrium as an efficient method for market clearing. We highlight its connections with other pricing mechanisms in the literature, including game theory and auction-based approaches. Notably, competitive equilibrium is equivalent to Nash equilibrium in a generalized game. Moreover, auction-based pricing tends to converge toward competitive equilibrium under minimal rationality assumptions.
There is no abstract per se, since the instructions to authors tell me not to. Here is a segment from the introduction. The purpose of this paper is twofold: to reveal the finesse of control system design beyond hoovering up indiscriminate uncurated data and passing it to the all-purpose “learning” algorithm; and, to study in some detail the combination of thought processes behind feedback control design in practice, including the links to control theory and to empirical science. The hope then is that we can differentiate between control applications amenable to benefit from machine learning and those inherently unsuited to it. The objectives include appreciating the logical distinction between control theory and control practice, the role of deliberately designed experiments and data in feedback design, and the development of a philosophical basis for control design in practice. Elements from the philosophy of science, notably deductive, experiment-design and inductive steps, are examined in principle and then with the benefit of applied industrial examples.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
