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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Confirmatory factor analysis of the Test of Performance Strategies (TOPS) among adolescent athletes
The aim of the present study was to examine the factorial validity of the Test of Performance Strategies (TOPS; Thomas et al., 1999) among adolescent athletes using confirmatory factor analysis. The TOPS was designed to assess eight psychological strategies used in competition (i.e. activation, automaticity, emotional control, goal-setting, imagery, negative thinking, relaxation and self-talk,) and eight used in practice (the same strategies except negative thinking is replaced by attentional control). National-level athletes (n = 584) completed the 64-item TOPS during training camps. Fit indices provided partial support for the overall measurement model for the competition items (robust comparative fit index = 0.92, Tucker-Lewis index = 0.88, root mean square error of approximation = 0.05) but minimal support for the training items (robust comparative fit index = 0.86, Tucker-Lewis index = 0.81, root mean square error of approximation = 0.06). For the competition items, the automaticity, goal-setting, relaxation and self-talk scales showed good fit, whereas the activation, emotional control, imagery and negative thinking scales did not. For the practice items, the attentional control, emotional control, goal-setting, imagery and self-talk scales showed good fit, whereas the activation, automaticity and relaxation scales did not. Overall, it appears that the factorial validity of the TOPS for use with adolescents is questionable at present and further development is required
Search based software engineering: Trends, techniques and applications
© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.
This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E
Automated system for integration and display of physiological response data
The system analysis approach was applied in a study of physiological systems in both 1-g and weightlessness, for short and long term experiments. A whole body, algorithm developed as the first step in the construction of a total body simulation system is described and an advanced biomedical computer system concept including interactive display/command consoles is discussed. The documentation of the design specifications, design and development studies, and user's instructions (which include program listings) for these delivered end-terms; the reports on the results of many research and feasibility studies; and many subcontract reports are cited in the bibliography
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Response
Considerable delays often exist between the discovery of a vulnerability and
the issue of a patch. One way to mitigate this window of vulnerability is to
use a configuration workaround, which prevents the vulnerable code from being
executed at the cost of some lost functionality -- but only if one is
available. Since program configurations are not specifically designed to
mitigate software vulnerabilities, we find that they only cover 25.2% of
vulnerabilities.
To minimize patch delay vulnerabilities and address the limitations of
configuration workarounds, we propose Security Workarounds for Rapid Response
(SWRRs), which are designed to neutralize security vulnerabilities in a timely,
secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs
neutralize vulnerabilities by preventing vulnerable code from being executed at
the cost of some lost functionality. However, the key difference is that SWRRs
use existing error-handling code within programs, which enables them to be
mechanically inserted with minimal knowledge of the program and minimal
developer effort. This allows SWRRs to achieve high coverage while still being
fast and easy to deploy.
We have designed and implemented Talos, a system that mechanically
instruments SWRRs into a given program, and evaluate it on five popular Linux
server programs. We run exploits against 11 real-world software vulnerabilities
and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative
measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can
neutralize 75.1% of all potential vulnerabilities and incur a loss of
functionality similar to configuration workarounds in 71.3% of those cases. Our
overall conclusion is that automatically generated SWRRs can safely mitigate
2.1x more vulnerabilities, while only incurring a loss of functionality
comparable to that of traditional configuration workarounds.Comment: Published in Proceedings of the 37th IEEE Symposium on Security and
Privacy (Oakland 2016
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
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