High-Performance Piezoelectric Energy Harvesters and Their Applications

https://wiki.dmt-nexus.me/w/images/1/1c/hppd_-_flashbacks.pdf

Hallucinogen persisting perception disorder: what do we know after 50 years?

In this paper, we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation, in and of itself, is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost- and time-intensive. Thus, much work has been put into finding methods which allow a reduction in involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented, conversational, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then present the evaluation methods regarding that class.

/pdf/survey-on-evaluation-methods-for-dialogue-systems-2jg1pygand.pdf

Survey on evaluation methods for dialogue systems

Recently, energy harvesting from human motion has attracted substantial research into its ability to replace conventional batteries for smart electronics. Human motion exhibits excellent potential to provide sustainable and clean energy for powering low-powered electronics, such as portable instruments and wearable devices. This review article reports on the piezoelectric, electromagnetic, and triboelectric energy harvesting technologies that can effectively scavenge biomechanical energy from human motion such as, walking, stretching, and human limb movement, as well as from small displacements (e.g., heartbeat, respiration, and muscle movement) inside the human body. Furthermore, various recent designs and configurations of human motion energy harvesters are presented according to their working mechanisms, device compositions, and performances. In order to provide insight into future research prospects, the paper also discusses the limitations, issues, and challenges of piezoelectric, electromagnetic, and triboelectric energy harvesting technologies for the development of smart electronics.

A Review of Human-Powered Energy Harvesting for Smart Electronics: Recent Progress and Challenges

Bistable structures, a class of highly geometrically nonlinear morphing structures, have two stable configurations that remain in their respective equilibrium positions without the continuous application of an external force. This paper aims to summarise, review, and assess the literature about the theoretical studies, driving methods, numerical simulations, experimental investigations, and fields of application of the structures. The theoretical models are separated into anisotropic and isotropic composites based on the properties of the material. The generalised methods to study the bistability of structures with various properties are presented. The driving methods used to trigger the snap of one equilibrium structure to the other include mechanical force, piezoelectric actuation, shape memory alloy actuation, thermal actuation, and magnetic actuation. Numerical simulations of the curing process during manufacture and of the morphing process, which includes the snap-through and snap-back of the bistable structure, are reviewed. Moreover, the experimental investigations of structures with unsymmetric and antisymmetric layups are also presented. Bistable composite laminates and their structures have been used in aerospace, bionics, energy harvesting, and other fields due to their unusual behaviour and morphing potential. This paper also highlights the remaining challenges and possible future work on bistable composite transition, from phenomenon to potential applications.

Bistable morphing composite structures: A review

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope—i.e., queries that do not fall into any of the system’s supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.

/pdf/an-evaluation-dataset-for-intent-classification-and-out-of-2b6pqfhx77.pdf

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

The elastic instabilities associated with buckling in bistable structures have been harnessed toward energy-based and motion-based applications, with significant research toward energy harvesting a...

https://journals.sagepub.com/doi/pdf/10.1177/1045389X18770895

A multifunctional bistable laminate: Snap-through morphing enabled by broadband energy harvesting:

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope---i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.

Andrew Lee

Papers

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

A multifunctional bistable laminate: Snap-through morphing enabled by broadband energy harvesting:

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction.

Electromechanical modelling of a bistable plate with Macro Fiber Composites under nonlinear vibrations

A piezoelectrically generated bistable laminate for morphing