Modeling Memory Processes and Performance Benchmarks of AWACS Weapons Director Teams

2006 ◽  
Author(s):  
Verlin B. Hinsz
Keyword(s):  
Memory Processes ◽  
Performance Benchmarks ◽  
And Performance

Understanding Impacts of Technology, Process Changes, and Organizational Controls to Improve the Cost of Cash in Retail Stores

2007 ◽  
Vol 51 (16) ◽  
pp. 977-980
Author(s):  
Mark Hoffman ◽  
Charles Cash
Keyword(s):  
Systems Approach ◽  
Flow Process ◽  
Process Improvements ◽  
Organizational Controls ◽  
Repetitive Tasks ◽  
Technology Process ◽  
Performance Benchmarks ◽  
And Performance ◽  
The Cost ◽  
And Task

The emergence of self checkout systems and transaction kiosks present an opportunity for retailers to redesign the cash management process and identify more efficient and automated solutions to improve performances within each step of the system. International and domestic retail companies from various industry segments are taking different views of opportunities to reduce the cost of cash. A socio-technical systems approach provides a foundation for anchoring predictions in process improvements and task automation and a context to identify impacts of step improvements in the Cash Flow Process. This paper provides an overview of a framework, findings from cash office operations studies of domestic and international retailers, and performance benchmarks for repetitive tasks of till balancing and till re-sets to help evaluate proposed changes in store cash office operations.

Songbird subthalamic neurons project to dopaminergic midbrain and exhibit singing-related activity

2021 ◽  
Author(s):  
Anindita Das ◽  
Jesse H. Goldberg
Keyword(s):  
Brain Regions ◽  
Skill Learning ◽  
Song Learning ◽  
Dopamine Neurons ◽  
Neural Recordings ◽  
Performance Benchmarks ◽  
Performance Error ◽  
Song Production ◽  
And Performance ◽  
Subthalamic Neurons

Skill learning requires motor output to be evaluated against internal performance benchmarks. In songbirds, ventral tegmental area (VTA) dopamine neurons (DA) signal performance errors important for learning, but it remains unclear which brain regions project to VTA and how these inputs may contribute to DA error signaling. Here we find that the songbird subthalamic nucleus (STN) projects to VTA and that STN micro-stimulation can excite VTA neurons. We also discover that STN receives inputs from motor cortical, auditory cortical and ventral pallidal brain regions previously implicated in song evaluation. In the first neural recordings from songbird STN, we discover that the activity of most STN neurons is associated with body movements and not singing, but a small fraction of neurons exhibits precise song timing and performance error signals. Our results place the STN in a pathway important for song learning, but not song production, and expand the territories of songbird brain potentially associated with song learning.

Image Quality and Performance Benchmarks in Vehicle and Hospital Mammography

2020 ◽  
Vol 20 (3) ◽  
pp. e358-e365
Author(s):  
Chen-Yu Chien ◽  
El-Wui Loh ◽  
Yen-Kuang Lin ◽  
Tsai-Wei Huang ◽  
Yuan-Hung Wang ◽  
...  
Keyword(s):  
Image Quality ◽  
Performance Benchmarks ◽  
And Performance

Environmental best practice and performance benchmarks among EMAS-certified organizations: An empirical study

2020 ◽  
Vol 80 ◽  
pp. 106315 ◽  
Author(s):  
Iñaki Heras-Saizarbitoria ◽  
Olivier Boiral ◽  
María García ◽  
Erlantz Allur
Keyword(s):  
Empirical Study ◽  
Best Practice ◽  
Performance Benchmarks ◽  
And Performance

scholarly journals Performance of local level planning policies for reducing greenhouse gases: insight from new buildings in Scotland

2020 ◽  
Vol 31 (4) ◽  
pp. 985-1002
Author(s):  
Vincent Onyango ◽  
Neil Burford
Keyword(s):  
Greenhouse Gases ◽  
Local Level ◽  
Local Planning ◽  
Content Type ◽  
Policy Performance ◽  
Ghg Reduction ◽  
Performance Benchmarks ◽  
And Performance ◽  
New Buildings ◽  
Planning Policies

PurposeThe purpose of the study is to assess performance of local level planning policies that required new buildings to avoid a specified and rising proportion of projected greenhouse gases (GHGs) from their use; it is calculated based on the approved design and plans for the specific development and through the installation and operation of low and zero-carbon generating technologies (LZCGTs).Design/methodology/approachData were extracted from a random sample of 911 new builds from 403 planning applications and related documents, across five Scottish local planning authorities (LPAs) who adopted GHG reduction policies. The data included GHG reduction, LZCGT installation and performance, use of plan designs to meet GHG reductions and exemptions from the GHG policies. Descriptive statistics using SPSS software, complimented by qualitative responses from questionnaires, helped to explain observed performance.FindingsThe policies performed poorly, at the level of delivering low-hanging fruits, with significant room for improvement. Design-led opportunities in the GHG policies were not actively pursued; most LZCGT installation was exempted from GHG policies and the policies were poor in targeting the relationship between building unit size, GHG emission and reductions.Research limitations/implicationsThe source documents, where the data came from, had varying quality and completeness and some LPAs are over-represented in the data. The study applied limited criteria to evaluate policy performance.Practical implicationsAreas for policymakers to further focus on when exploring how to enhance role and performance of LZCGT are highlighted, including practical suggestions.Originality/valueOne of the few studies assessing policy performance and distilling lessons, from early adopters of GHG policies at local level planning, offer performance benchmarks and raise points of concern for policymakers.

scholarly journals Voice adaptation by color-encoded frame matching as a multi-objective optimization problem for future games

2022 ◽  
Author(s):  
Mads Midtlyng ◽  
Yuji Sato ◽  
Hiroshi Hosobe
Keyword(s):  
Speech Processing ◽  
Optimization Problem ◽  
Probabilistic Models ◽  
Temporal Structure ◽  
Processing Technique ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Performance Benchmarks ◽  
And Performance ◽  
Online Video Games

AbstractVoice adaptation is an interactive speech processing technique that allows the speaker to transmit with a chosen target voice. We propose a novel method that is intended for dynamic scenarios, such as online video games, where the source speaker’s and target speaker’s data are nonaligned. This would yield massive improvements to immersion and experience by fully becoming a character, and address privacy concerns to protect against harassment by disguising the voice. With unaligned data, traditional methods, e.g., probabilistic models become inaccurate, while recent methods such as deep neural networks (DNN) require too substantial preparation work. Common methods require multiple subjects to be trained in parallel, which constraints practicality in productive environments. Our proposal trains a subject nonparallel into a voice profile used against any unknown source speaker. Prosodic data such as pitch, power and temporal structure are encoded into RGBA-colored frames used in a multi-objective optimization problem to adjust interrelated features based on color likeness. Finally, frames are smoothed and adjusted before output. The method was evaluated using Mean Opinion Score, ABX, MUSHRA, Single Ease Questions and performance benchmarks using two voice profiles of varying sizes and lastly discussion regarding game implementation. Results show improved adaptation quality, especially in a larger voice profile, and audience is positive about using such technology in future games.

Technologies for Big Data

2013 ◽  
pp. 1-22 ◽  
Author(s):  
Kapil Bakshi
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Design Considerations ◽  
Massively Parallel Processing ◽  
Performance Benchmarks ◽  
Technical Architecture ◽  
Big Data Technologies ◽  
And Performance ◽  
Technical Operation

This chapter provides a review and analysis of several key Big Data technologies. Currently, there are many Big Data technologies in development and implementation; hence, a comprehensive review of all of these technologies is beyond the scope of this chapter. This chapter focuses on the most popularly accepted technologies. The key Big Data technologies to be discussed include: Map-Reduce, NOSQL technology, MPP (Massively Parallel Processing), and In Memory Databases technologies. For each of these Big Data technologies, the following subtopics are discussed: the history and genesis of the Big Data technologies, problem set that this technology solves for Big Data analytics, the details of the technologies, including components, technical architecture, and theory of operations. This is followed by technical operation and infrastructure (compute, storage, and network), design considerations, and performance benchmarks. Finally, this chapter provides an integrated approach to the above-mentioned Big Data technologies.

scholarly journals Adaptive Control for Multi-Shaft with Web Materials Linkage Systems

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 76
Author(s):  
Van Trong Dang ◽  
Duc Thinh Le ◽  
Van-Anh Nguyen-Thi ◽  
Danh Huy Nguyen ◽  
Thi Ly Tong ◽  
...  
Keyword(s):  
Disturbance Observer ◽  
Tracking Error ◽  
Variable Structure ◽  
High Gain ◽  
Adaptive Performance ◽  
Performance Benchmarks ◽  
And Performance ◽  
The Moment ◽  
Fuzzy Disturbance Observer ◽  
Estimate System

In this paper, a fuzzy disturbance observer and a high-gain disturbance observer based on a variable structure controller are applied to deal with imprecise multi-shaft with web materials linkage systems taking into account the variation of the moment of inertia. Specifically, a high-gain disturbance observer and an adaptive fuzzy algorithm are separately applied to estimate system uncertainties and external disturbances. The high-gain disturbance observer is designed with auxiliary variables to avoid the amplification of the measurement disturbance, and the fuzzy disturbance observer has the advantage that it does not depend on model information. The convergence properties of the tracking error are analytically proven using Lyapunov’s theory. The obtained numerical results demonstrate the validity and the adaptive performance of the proposed control law in case the system is exposed to uncertainties and disturbances. Important remarks on the design process and performance benchmarks of the two observers are also demonstrated.

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