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Industrial engineering applications in metrology: Job scheduling, calibration interval and average outgoing quality
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis research deals with the optimization of metrology and calibration problems. The optimization involved here is the application scientifically sound operations research techniques to help in solving the problem intended optimally or semi-optimally with a practical time frame. The research starts by exploring the subject of measurement science known as metrology. This involves defining all the constituents of metrology facilities along with their various components. The definitions include the SI units’ history and structure as well as their characteristics. After that, a comprehensive description of most of the operations and parameters encountered in metrology is presented. This involves all sources of uncertainties in most of the parameters that affect the measurements. From the background presented and using all the information within it; an identification of the most important and critical general problems is attempted. In this treatment a number of potential optimization problems are identified along with their description, problem statement definition, impact on the system and possible treatment method. After that, a detailed treatment of the scheduling problem, the calibration interval determination problem and the average outgoing quality problem is presented. The scheduling problem is formulated and modelled as a mixed integer program then solved using LINGO program. A heuristic algorithm is then developed to solve the problem near optimally but in much quicker time, and solution is packaged in a computer program. The calibration interval problem treatment deals with the determination of the optimal CI. Four methods are developed to deal with different cases. The cases considered are the reliability target case, the CI with call cost and failure cost of both first failure and all failures and the case of large number of similar TMDEs. The average out going quality (AOQ) treatment involves the development two methods to assess the AOQ of a calibration facility that uses a certain multistage inspection policy. The two methods are mathematically derived and verified using a simulation model that compares them with an actual failure rate of a virtual calibration facility
Shaping the future by engineering: 58th IWK, Ilmenau Scientific Colloquium, Technische Universität Ilmenau, 8 - 12 September 2014 ; programme
Druckausgabe erschienen im Universitätsverlag Ilmenau:
Shaping the future by engineering : 58th IWK, Ilmenau Scientific Colloquium, Technische Universität Ilmenau, 8 - 12 September 2014 ; programme / Department of Mechanical Engineering, Technische Universität Ilmenau. [Hrsg.: Peter Scharff. Red.: Andrea Schneider]
Ilmenau : Univ.-Verl. Ilmenau, 2014. - 155 S.
ISBN 978-3-86360-085-
Manufacturing Metrology
Metrology is the science of measurement, which can be divided into three overlapping activities: (1) the definition of units of measurement, (2) the realization of units of measurement, and (3) the traceability of measurement units. Manufacturing metrology originally implicates the measurement of components and inputs for a manufacturing process to assure they are within specification requirements. It can also be extended to indicate the performance measurement of manufacturing equipment. This Special Issue covers papers revealing novel measurement methodologies and instrumentations for manufacturing metrology from the conventional industry to the frontier of the advanced hi-tech industry. Twenty-five papers are included in this Special Issue. These published papers can be categorized into four main groups, as follows: Length measurement: covering new designs, from micro/nanogap measurement with laser triangulation sensors and laser interferometers to very-long-distance, newly developed mode-locked femtosecond lasers. Surface profile and form measurements: covering technologies with new confocal sensors and imagine sensors: in situ and on-machine measurements. Angle measurements: these include a new 2D precision level design, a review of angle measurement with mode-locked femtosecond lasers, and multi-axis machine tool squareness measurement. Other laboratory systems: these include a water cooling temperature control system and a computer-aided inspection framework for CMM performance evaluation
The e-value and the Full Bayesian Significance Test: Logical Properties and Philosophical Consequences
This article gives a conceptual review of the e-value, ev(H|X) – the epistemic value of hypothesis H given observations X. This statistical significance measure was developed in order to allow logically coherent and consistent tests of hypotheses, including sharp or precise hypotheses, via the Full Bayesian Significance Test (FBST).
Arguments of analysis allow a full characterization of this statistical test by its logical or compositional properties, showing a mutual complementarity between results of mathematical statistics and the logical desiderata lying at the foundations of this theory
Advanced sensors technology survey
This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed
WEATHER LORE VALIDATION TOOL USING FUZZY COGNITIVE MAPS BASED ON COMPUTER VISION
Published ThesisThe creation of scientific weather forecasts is troubled by many technological challenges (Stern
& Easterling, 1999) while their utilization is generally dismal. Consequently, the majority of
small-scale farmers in Africa continue to consult some forms of weather lore to reach various
cropping decisions (Baliscan, 2001). Weather lore is a body of informal folklore (Enock, 2013),
associated with the prediction of the weather, and based on indigenous knowledge and human
observation of the environment. As such, it tends to be more holistic, and more localized to the
farmers’ context. However, weather lore has limitations; for instance, it has an inability to offer
forecasts beyond a season. Different types of weather lore exist, utilizing almost all available
human senses (feel, smell, sight and hearing). Out of all the types of weather lore in existence, it
is the visual or observed weather lore that is mostly used by indigenous societies, to come up
with weather predictions.
On the other hand, meteorologists continue to treat this knowledge as superstition, partly because
there is no means to scientifically evaluate and validate it. The visualization and characterization
of visual sky objects (such as moon, clouds, stars, and rainbows) in forecasting weather are
significant subjects of research. To realize the integration of visual weather lore in modern
weather forecasting systems, there is a need to represent and scientifically substantiate this form
of knowledge. This research was aimed at developing a method for verifying visual weather lore that is used by
traditional communities to predict weather conditions. To realize this verification, fuzzy
cognitive mapping was used to model and represent causal relationships between selected visual
weather lore concepts and weather conditions. The traditional knowledge used to produce these
maps was attained through case studies of two communities (in Kenya and South Africa).These
case studies were aimed at understanding the weather lore domain as well as the causal effects
between metrological and visual weather lore. In this study, common astronomical weather lore
factors related to cloud physics were identified as: bright stars, dispersed clouds, dry weather,
dull stars, feathery clouds, gathering clouds, grey clouds, high clouds, layered clouds, low
clouds, stars, medium clouds, and rounded clouds. Relationships between the concepts were also
identified and formally represented using fuzzy cognitive maps. On implementing the verification tool, machine vision was used to recognize sky objects
captured using a sky camera, while pattern recognition was employed in benchmarking and
scoring the objects. A wireless weather station was used to capture real-time weather parameters.
The visualization tool was then designed and realized in a form of software artefact, which
integrated both computer vision and fuzzy cognitive mapping for experimenting visual weather
lore, and verification using various statistical forecast skills and metrics. The tool consists of four
main sub-components: (1) Machine vision that recognizes sky objects using support vector
machine classifiers using shape-based feature descriptors; (2) Pattern recognition–to benchmark
and score objects using pixel orientations, Euclidean distance, canny and grey-level concurrence
matrix; (3) Fuzzy cognitive mapping that was used to represent knowledge (i.e. active hebbian
learning algorithm was used to learn until convergence); and (4) A statistical computing
component was used for verifications and forecast skills including brier score and contingency
tables for deterministic forecasts.
Rigorous evaluation of the verification tool was carried out using independent (not used in the
training and testing phases) real-time images from Bloemfontein, South Africa, and Voi-Kenya.
The real-time images were captured using a sky camera with GPS location services. The results
of the implementation were tested for the selected weather conditions (for example, rain, heat, cold, and dry conditions), and found to be acceptable (the verified prediction accuracies were
over 80%). The recommendation in this study is to apply the implemented method for processing
tasks, towards verifying all other types of visual weather lore. In addition, the use of the method
developed also requires the implementation of modules for processing and verifying other types
of weather lore, such as sounds, and symbols of nature. Since time immemorial, from Australia to Asia, Africa to Latin America, local communities have
continued to rely on weather lore observations to predict seasonal weather as well as its effects
on their livelihoods (Alcock, 2014). This is mainly based on many years of personal experiences
in observing weather conditions. However, when it comes to predictions for longer lead-times
(i.e. over a season), weather lore is uncertain (Hornidge & Antweiler, 2012). This uncertainty has
partly contributed to the current status where meteorologists and other scientists continue to treat
weather lore as superstition (United-Nations, 2004), and not capable of predicting weather.
One of the problems in testing the confidence in weather lore in predicting weather is due to
wide varieties of weather lore that are found in the details of indigenous sayings, which are
tightly coupled to locality and pattern variations(Oviedo et al., 2008). This traditional knowledge
is entrenched within the day-to-day socio-economic activities of the communities using it and is
not globally available for comparison and validation (Huntington, Callaghan, Fox, & Krupnik,
2004). Further, this knowledge is based on local experience that lacks benchmarking techniques;
so that harmonizing and integrating it within the science-based weather forecasting systems is a
daunting task (Hornidge & Antweiler, 2012). It is partly for this reason that the question of
validation of weather lore has not yet been substantially investigated. Sufficient expanded
processes of gathering weather observations, combined with comparison and validation, can produce some useful information. Since forecasting weather accurately is a challenge even with
the latest supercomputers (BBC News Magazine, 2013), validated weather lore can be useful if it
is incorporated into modern weather prediction systems.
Validation of traditional knowledge is a necessary step in the management of building integrated
knowledge-based systems. Traditional knowledge incorporated into knowledge-based systems
has to be verified for enhancing systems’ reliability. Weather lore knowledge exists in different
forms as identified by traditional communities; hence it needs to be tied together for comparison
and validation. The development of a weather lore validation tool that can integrate a framework
for acquiring weather data and methods of representing the weather lore in verifiable forms can
be a significant step in the validation of weather lore against actual weather records using
conventional weather-observing instruments. The success of validating weather lore could
stimulate the opportunity for integrating acceptable weather lore with modern systems of weather prediction to improve actionable information for decision making that relies on seasonal weather
prediction.
In this study a hybrid method is developed that includes computer vision and fuzzy cognitive
mapping techniques for verifying visual weather lore. The verification tool was designed with
forecasting based on mimicking visual perception, and fuzzy thinking based on the cognitive
knowledge of humans. The method provides meaning to humanly perceivable sky objects so that
computers can understand, interpret, and approximate visual weather outcomes.
Questionnaires were administered in two case study locations (KwaZulu-Natal province in South
Africa, and Taita-Taveta County in Kenya), between the months of March and July 2015. The
two case studies were conducted by interviewing respondents on how visual astronomical and
meteorological weather concepts cause weather outcomes. The two case studies were used to
identify causal effects of visual astronomical and meteorological objects to weather conditions.
This was followed by finding variations and comparisons, between the visual weather lore
knowledge in the two case studies. The results from the two case studies were aggregated in
terms of seasonal knowledge. The causal links between visual weather concepts were
investigated using these two case studies; results were compared and aggregated to build up
common knowledge. The joint averages of the majority of responses from the case studies were determined for each set of interacting concepts.
The modelling of the weather lore verification tool consists of input, processing components and
output. The input data to the system are sky image scenes and actual weather observations from
wireless weather sensors. The image recognition component performs three sub-tasks, including:
detection of objects (concepts) from image scenes, extraction of detected objects, and
approximation of the presence of the concepts by comparing extracted objects to ideal objects.
The prediction process involves the use of approximated concepts generated in the recognition
component to simulate scenarios using the knowledge represented in the fuzzy cognitive maps.
The verification component evaluates the variation between the predictions and actual weather
observations to determine prediction errors and accuracy.
To evaluate the tool, daily system simulations were run to predict and record probabilities of
weather outcomes (i.e. rain, heat index/hotness, dry, cold index). Weather observations were
captured periodically using a wireless weather station. This process was repeated several times until there was sufficient data to use for the verification process. To match the range of the
predicted weather outcomes, the actual weather observations (measurement) were transformed
and normalized to a range [0, 1].In the verification process, comparisons were made between the
actual observations and weather outcome prediction values by computing residuals (error values)
from the observations. The error values and the squared error were used to compute the Mean
Squared Error (MSE), and the Root Mean Squared Error (RMSE), for each predicted weather
outcome.
Finally, the validity of the visual weather lore verification model was assessed using data from a
different geographical location. Actual data in the form of daily sky scenes and weather
parameters were acquired from Voi, Kenya, from December 2015 to January 2016.The results on
the use of hybrid techniques for verification of weather lore is expected to provide an incentive
in integrating indigenous knowledge on weather with modern numerical weather prediction
systems for accurate and downscaled weather forecasts
Systems Engineering: Availability and Reliability
Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling
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