Abstract: This paper proposes a declarative language for
knowledge representation (Ibn Rochd), and its environment of
exploitation (DeGSE). This DeGSE system was designed and
developed to facilitate Ibn Rochd writing applications. The system
was tested on several knowledge bases by ascending complexity,
culminating in a system for recognition of a plant or a tree, and
advisors to purchase a car, for pedagogical and academic guidance,
or for bank savings and credit. Finally, the limits of the language and
research perspectives are stated.
Abstract: The most reliable and accurate description of the actual behavior of a software system is its source code. However, not all questions about the system can be answered directly by resorting to this repository of information. What the reverse engineering methodology aims at is the extraction of abstract, goal-oriented “views" of the system, able to summarize relevant properties of the computation performed by the program. While concentrating on reverse engineering we had modeled the C++ files by designing the translator.
Abstract: This paper reviews various approaches that have been
used for the modeling and simulation of large-scale engineering
systems and determines their appropriateness in the development of a
RICS modeling and simulation tool. Bond graphs, linear graphs,
block diagrams, differential and difference equations, modeling
languages, cellular automata and agents are reviewed. This tool
should be based on linear graph representation and supports symbolic
programming, functional programming, the development of noncausal
models and the incorporation of decentralized approaches.
Abstract: This paper describes various stages of design and prototyping of a modular robot for use in various industrial applications. The major goal of current research has been to design and make different robotic joints at low cost capable of being assembled together in any given order for achieving various robot configurations. Five different types of joins were designed and manufactured where extensive research has been carried out on the design of each joint in order to achieve optimal strength, size, modularity, and price. This paper presents various stages of research and development undertaken to engineer these joints that include material selection, manufacturing, and strength analysis. The outcome of this research addresses the birth of a new generation of modular industrial robots with a wider range of applications and greater efficiency.
Abstract: Today, numerical simulation is a powerful tool to
solve various hydraulic engineering problems. The aim of this
research is numerical solutions of shallow water equations using
finite volume method for Simulations of dam break over wet and dry
bed. In order to solve Riemann problem, Roe-s approximate solver is
used. To evaluate numerical model, simulation was done in 1D and
2D states. In 1D state, two dam break test over dry bed (with and
without friction) were studied. The results showed that Structural
failure around the dam and damage to the downstream constructions
in bed without friction is more than friction bed. In 2D state, two
tests for wet and dry beds were done. Generally in wet bed case,
waves are propagated to canal sides but in dry bed it is not
significant. Therefore, damage to the storage facilities and
agricultural lands in wet bed case is more than in dry bed.
Abstract: In this paper we propose a method for vision systems
to consistently represent functional dependencies between different
visual routines along with relational short- and long-term knowledge
about the world. Here the visual routines are bound to visual properties
of objects stored in the memory of the system. Furthermore,
the functional dependencies between the visual routines are seen
as a graph also belonging to the object-s structure. This graph is
parsed in the course of acquiring a visual property of an object to
automatically resolve the dependencies of the bound visual routines.
Using this representation, the system is able to dynamically rearrange
the processing order while keeping its functionality. Additionally, the
system is able to estimate the overall computational costs of a certain
action. We will also show that the system can efficiently use that
structure to incorporate already acquired knowledge and thus reduce
the computational demand.
Abstract: Stainless steel has been employed in many
engineering applications ranging from pharmaceutical equipment to
piping in the nuclear reactors and storage to chemical products. In
this attempt, simulation of fatigue crack growth based on
experimental results of austenitic stainless steel 304L was presented
using AFGROW code when NASGRO mode laws adopted. Double
through crack at hole specimen is used in this investigation under
constant amplitude loading. Effect of mean stress is highlighted.
Results show that fatigue crack growth rate (FCGR) and fatigue life
were affected by maximum applied load and dimension of hole. An
equivalent of Paris law for this material was estimated.
Abstract: Although services play a crucial role in economy,
service did not gain as much importance as productivity management
in manufacturing. This paper presents key findings from literature
and practice. Based on an initial definition of complex services, seven
productivity concepts are briefly presented and assessed by relevant,
complex service specific criteria. Following the findings a complex
service productivity model is proposed. The novel model comprises
of all specific dimensions of service provision from both, the
provider-s as well as costumer-s perspective. A clear assignment of
identified value drivers and relationships between them is presented.
In order to verify the conceptual service productivity model a case
study from a project engineering department of a chemical plant
development and construction company is presented.
Abstract: Multiphase flow transport in porous medium is very common and significant in science and engineering applications. For example, in CO2 Storage and Enhanced Oil Recovery processes, CO2 has to be delivered to the pore spaces in reservoirs and aquifers. CO2 storage and enhance oil recovery are actually displacement processes, in which oil or water is displaced by CO2. This displacement is controlled by pore size, chemical and physical properties of pore surfaces and fluids, and also pore wettability. In this study, a technique was developed to measure the pressure profile for driving gas/liquid to displace water in pores. Through this pressure profile, the impact of pore size on the multiphase flow transport and displacement can be analyzed. The other rig developed can be used to measure the static and dynamic pore wettability and investigate the effects of pore size, surface tension, viscosity and chemical structure of liquids on pore wettability.
Abstract: Complex engineering design problems consist of
numerous factors of varying criticalities. Considering fundamental features of design and inferior details alike will result in an extensive
waste of time and effort. Design parameters should be introduced gradually as appropriate based on their significance relevant to the
problem context. This motivates the representation of design parameters at multiple levels of an abstraction hierarchy. However, developing abstraction hierarchies is an area that is not well
understood. Our research proposes a novel hierarchical abstraction methodology to plan effective engineering designs and processes. It
provides a theoretically sound foundation to represent, abstract and stratify engineering design parameters and tasks according to causality and criticality. The methodology creates abstraction
hierarchies in a recursive and bottom-up approach that guarantees no
backtracking across any of the abstraction levels. The methodology consists of three main phases, representation, abstraction, and layering to multiple hierarchical levels. The effectiveness of the
developed methodology is demonstrated by a design problem.
Abstract: Within the domain of Systems Engineering the need
to perform property aggregation to understand, analyze and manage
complex systems is unequivocal. This can be seen in numerous
domains such as capability analysis, Mission Essential Competencies
(MEC) and Critical Design Features (CDF). Furthermore, the need
to consider uncertainty propagation as well as the sensitivity of
related properties within such analysis is equally as important when
determining a set of critical properties within such a system.
This paper describes this property breakdown in a number of
domains within Systems Engineering and, within the area of CDFs,
emphasizes the importance of uncertainty analysis. As part of this, a
section of the paper describes possible techniques which may be used
within uncertainty propagation and in conclusion an example is
described utilizing one of the techniques for property and uncertainty
aggregation within an aircraft system to aid the determination of
Critical Design Features.
Abstract: Octree compression techniques have been used
for several years for compressing large three dimensional data
sets into homogeneous regions. This compression technique
is ideally suited to datasets which have similar values in
clusters. Oil engineers represent reservoirs as a three dimensional
grid where hydrocarbons occur naturally in clusters. This
research looks at the efficiency of storing these grids using
octree compression techniques where grid cells are broken
into active and inactive regions. Initial experiments yielded
high compression ratios as only active leaf nodes and their
ancestor, header nodes are stored as a bitstream to file on
disk. Savings in computational time and memory were possible
at decompression, as only active leaf nodes are sent to the
graphics card eliminating the need of reconstructing the original
matrix. This results in a more compact vertex table, which can
be loaded into the graphics card quicker and generating shorter
refresh delay times.
Abstract: In this study, effects of premixed and equivalence
ratios on CO and HC emissions of a dual fuel HCCI engine are
investigated. Tests were conducted on a single-cylinder engine with
compression ratio of 17.5. Premixed gasoline is provided by a
carburetor connected to intake manifold and equipped with a screw
to adjust premixed air-fuel ratio, and diesel fuel is injected directly
into the cylinder through an injector at pressure of 250 bars. A heater
placed at inlet manifold is used to control the intake charge
temperature. Optimal intake charge temperature results in better
HCCI combustion due to formation of a homogeneous mixture,
therefore, all tests were carried out over the optimum intake
temperature of 110-115 ºC. Timing of diesel fuel injection has a great
effect on stratification of in-cylinder charge and plays an important
role in HCCI combustion phasing. Experiments indicated 35 BTDC
as the optimum injection timing. Varying the coolant temperature in
a range of 40 to 70 ºC, better HCCI combustion was achieved at 50
ºC. Therefore, coolant temperature was maintained 50 ºC during all
tests. Simultaneous investigation of effective parameters on HCCI
combustion was conducted to determine optimum parameters
resulting in fast transition to HCCI combustion. One of the
advantages of the method studied in this study is feasibility of easy
and fast transition of typical diesel engine to a dual fuel HCCI
engine. Results show that increasing premixed ratio, while keeping
EGR rate constant, increases unburned hydrocarbon (UHC)
emissions due to quenching phenomena and trapping of premixed
fuel in crevices, but CO emission decreases due to increase in CO to
CO2 reactions.
Abstract: In this paper a new control strategy based on Brain
Emotional Learning (BEL) model has been introduced. A modified
BEL model has been proposed to increase the degree of freedom,
controlling capability, reliability and robustness, which can be
implemented in real engineering systems.
The performance of the proposed BEL controller has been
illustrated by applying it on different nonlinear uncertain systems,
showing very good adaptability and robustness, while maintaining
stability.
Abstract: Green Lean Total Quality Management (TQM)
System is a system comprises of Environmental Management System
(EMS) practices which is integrated to TQM with Lean
Manufacturing (LM) principles. The ultimate goal of this system is to
focus on achieving total customer satisfaction and environmental care by removing eight wastes available in any process in an
organization. A survey questionnaire was developed and distributed to 30 highly active automotive vendors in Malaysia and analyzed by
SPSS v.17. It was found out that some vendors have been practicing TQM and LM while some have started to implement EMS. This
study is only focusing on highly active companies that have been involved in MAJAICO Program and Proton Vendor Development
Program. This is the first study conducted to know the current status of TQM, LM and EMS practices in highly active automotive companies in Malaysia. It was found out that EMS has been
practiced by 16 companies out of 30. Within these 16 companies the
approach is more holistic and green. This is a preliminary study that combined 4 awards practices, ISO/TS16949, Toyota Production
System SAEJ4000, MAJAICO Lean Production System and EMS.
Abstract: The index of sustainable functionality (ISF) is an adaptive, multi-criteria technique that is used to measure sustainability; it is a concept that can be transposed to many regions throughout the world. An ISF application of the Southern Regional Organisation of Councils (SouthROC) in South East Queensland (SEQ) – the fastest growing region in Australia – indicated over a 25 year period an increase of over 10% level of functionality from 58.0% to 68.3%. The ISF of SouthROC utilised methodologies that derived from an expert panel based approach. The overall results attained an intermediate level of functionality which amounted to related concerns of economic progress and lack of social awareness. Within the region, a solid basis for future testing by way of measured changes and developed trends can be established. In this regard as management tool, the ISF record offers support for regional sustainability practice and decision making alike. This research adaptively analyses sustainability – a concept that is lacking throughout much of the academic literature and any reciprocal experimentation. This lack of knowledge base has been the emphasis of where future sustainability research can grow from and prove useful in rapidly growing regions. It is the intentions of this research to help further develop the notions of index-based quantitative sustainability.
Abstract: Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.
Abstract: The dynamic or complex modulus test is considered
to be a mechanistically based laboratory test to reliably characterize
the strength and load-resistance of Hot-Mix Asphalt (HMA) mixes
used in the construction of roads. The most common observation is
that the data collected from these tests are often noisy and somewhat
non-sinusoidal. This hampers accurate analysis of the data to obtain
engineering insight. The goal of the work presented in this paper is to
develop and compare automated evolutionary computational
techniques to filter test noise in the collection of data for the HMA
complex modulus test. The results showed that the Covariance
Matrix Adaptation-Evolutionary Strategy (CMA-ES) approach is
computationally efficient for filtering data obtained from the HMA
complex modulus test.
Abstract: Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This article presents the development of an ANN-based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data and historical electric load-related data using the data from the calendar years 2001, 2002, 2003, and 2004 for training. The model tested for one week at five different seasons, typically, winter, spring, summer, Ramadan and fall seasons, and the mean absolute average error for one hour-ahead load forecasting found 1.12%.
Abstract: As emails communications have no consistent
authentication procedure to ensure the authenticity, we present an
investigation analysis approach for detecting forged emails based on
Random Forests and Naïve Bays classifiers. Instead of investigating
the email headers, we use the body content to extract a unique writing
style for all the possible suspects. Our approach consists of four main
steps: (1) The cybercrime investigator extract different effective
features including structural, lexical, linguistic, and syntactic
evidence from previous emails for all the possible suspects, (2) The
extracted features vectors are normalized to increase the accuracy
rate. (3) The normalized features are then used to train the learning
engine, (4) upon receiving the anonymous email (M); we apply the
feature extraction process to produce a feature vector. Finally, using
the machine learning classifiers the email is assigned to one of the
suspects- whose writing style closely matches M. Experimental
results on real data sets show the improved performance of the
proposed method and the ability of identifying the authors with a
very limited number of features.