Abstract: This paper will consider the problem of sequential
mining patterns embedded in a database by handling the time
constraints as defined in the GSP algorithm (level wise algorithms).
We will compare two previous approaches GTC and PSP, that
resumes the general principles of GSP. Furthermore this paper will
discuss PG-hybrid algorithm, that using PSP and GTC. The results
show that PSP and GTC are more efficient than GSP. On the other
hand, the GTC algorithm performs better than PSP. The PG-hybrid
algorithm use PSP algorithm for the two first passes on the database,
and GTC approach for the following scans. Experiments show that
the hybrid approach is very efficient for short, frequent sequences.
Abstract: In aircraft design, the jump from the conceptual to
preliminary design stage introduces a level of complexity which
cannot be realistically handled by a single optimiser, be that a
human (chief engineer) or an algorithm. The design process is often
partitioned along disciplinary lines, with each discipline given a level
of autonomy. This introduces a number of challenges including, but
not limited to: coupling of design variables; coordinating disciplinary
teams; handling of large amounts of analysis data; reaching an
acceptable design within time constraints. A number of classical
Multidisciplinary Design Optimisation (MDO) architectures exist in
academia specifically designed to address these challenges. Their
limited use in the industrial aircraft design process has inspired
the authors of this paper to develop an alternative strategy based
on well established ideas from Decision Support Systems. The
proposed rule based architecture sacrifices possibly elusive guarantees
of convergence for an attractive return in simplicity. The method
is demonstrated on analytical and aircraft design test cases and its
performance is compared to a number of classical distributed MDO
architectures.
Abstract: Nowadays, manufactures are encountered with production of different version of products due to quality, cost and time constraints. On the other hand, Additive Manufacturing (AM) as a production method based on CAD model disrupts the design and manufacturing cycle with new parameters. To consider these issues, the researchers utilized Design For Manufacturing (DFM) approach for AM but until now there is no integrated approach for design and manufacturing of product through the AM. So, this paper aims to provide a general methodology for managing the different production issues, as well as, support the interoperability with AM process and different Product Life Cycle Management tools. The problem is that the models of System Engineering which is used for managing complex systems cannot support the product evolution and its impact on the product life cycle. Therefore, it seems necessary to provide a general methodology for managing the product’s diversities which is created by using AM. This methodology must consider manufacture and assembly during product design as early as possible in the design stage. The latest approach of DFM, as a methodology to analyze the system comprehensively, integrates manufacturing constraints in the numerical model in upstream. So, DFM for AM is used to import the characteristics of AM into the design and manufacturing process of a hybrid product to manage the criteria coming from AM. Also, the research presents an integrated design method in order to take into account the knowledge of layers manufacturing technologies. For this purpose, the interface model based on the skin and skeleton concepts is provided, the usage and manufacturing skins are used to show the functional surface of the product. Also, the material flow and link between the skins are demonstrated by usage and manufacturing skeletons. Therefore, this integrated approach is a helpful methodology for designer and manufacturer in different decisions like material and process selection as well as, evaluation of product manufacturability.
Abstract: Batch production plants provide a wide range of
scheduling problems. In pharmaceutical industries a batch process
is usually described by a recipe, consisting of an ordering of tasks
to produce the desired product. In this research work we focused
on pharmaceutical production processes requiring the culture of
a microorganism population (i.e. bacteria, yeasts or antibiotics).
Several sources of uncertainty may influence the yield of the culture
processes, including (i) low performance and quality of the cultured
microorganism population or (ii) microbial contamination. For
these reasons, robustness is a valuable property for the considered
application context. In particular, a robust schedule will not collapse
immediately when a cell of microorganisms has to be thrown away
due to a microbial contamination. Indeed, a robust schedule should
change locally in small proportions and the overall performance
measure (i.e. makespan, lateness) should change a little if at all.
In this research work we formulated a constraint programming
optimization (COP) model for the robust planning of antibiotics
production. We developed a discrete-time model with a multi-criteria
objective, ordering the different criteria and performing a
lexicographic optimization. A feasible solution of the proposed
COP model is a schedule of a given set of tasks onto available
resources. The schedule has to satisfy tasks precedence constraints,
resource capacity constraints and time constraints. In particular
time constraints model tasks duedates and resource availability
time windows constraints. To improve the schedule robustness, we
modeled the concept of (a, b) super-solutions, where (a, b) are input
parameters of the COP model. An (a, b) super-solution is one in
which if a variables (i.e. the completion times of a culture tasks)
lose their values (i.e. cultures are contaminated), the solution can be
repaired by assigning these variables values with a new values (i.e.
the completion times of a backup culture tasks) and at most b other
variables (i.e. delaying the completion of at most b other tasks).
The efficiency and applicability of the proposed model is
demonstrated by solving instances taken from a real-life
pharmaceutical company. Computational results showed that
the determined super-solutions are near-optimal.
Abstract: This work addresses the problem of optimizing
completely batch water-using network with multiple contaminants
where the flow change caused by mass transfer is taken into
consideration for the first time. A mathematical technique for
optimizing water-using network is proposed based on
source-tank-sink superstructure. The task is to obtain the freshwater
usage, recycle assignments among water-using units, wastewater
discharge and a steady water-using network configuration by
following steps. Firstly, operating sequences of water-using units are
determined by time constraints. Next, superstructure is simplified by
eliminating the reuse and recycle from water-using units with
maximum concentration of key contaminants. Then, the non-linear
programming model is solved by GAMS (General Algebra Model
System) for minimum freshwater usage, maximum water recycle and
minimum wastewater discharge. Finally, numbers of operating periods
are calculated to acquire the steady network configuration. A case
study is solved to illustrate the applicability of the proposed approach.
Abstract: This paper presents Qmulus- a Cloud Based GPS
Model. Qmulus is designed to compute the best possible route which
would lead the driver to the specified destination in the shortest time
while taking into account real-time constraints. Intelligence
incorporated to Qmulus-s design makes it capable of generating and
assigning priorities to a list of optimal routes through customizable
dynamic updates. The goal of this design is to minimize travel and
cost overheads, maintain reliability and consistency, and implement
scalability and flexibility. The model proposed focuses on
reducing the bridge between a Client Application and a Cloud
service so as to render seamless operations. Qmulus-s system
model is closely integrated and its concept has the potential to be
extended into several other integrated applications making it capable
of adapting to different media and resources.
Abstract: The network of delivering commodities has been an important design problem in our daily lives and many transportation applications. The delivery performance is evaluated based on the system reliability of delivering commodities from a source node to a sink node in the network. The system reliability is thus maximized to find the optimal routing. However, the design problem is not simple because (1) each path segment has randomly distributed attributes; (2) there are multiple commodities that consume various path capacities; (3) the optimal routing must successfully complete the delivery process within the allowable time constraints. In this paper, we want to focus on the design optimization of the Multi-State Flow Network (MSFN) for multiple commodities. We propose an efficient approach to evaluate the system reliability in the MSFN with respect to randomly distributed path attributes and find the optimal routing subject to the allowable time constraints. The delivery rates, also known as delivery currents, of the path segments are evaluated and the minimal-current arcs are eliminated to reduce the complexity of the MSFN. Accordingly, the correct optimal routing is found and the worst-case reliability is evaluated. It has been shown that the reliability of the optimal routing is at least higher than worst-case measure. Two benchmark examples are utilized to demonstrate the proposed method. The comparisons between the original and the reduced networks show that the proposed method is very efficient.
Abstract: This paper proposes a scheduling scheme using feedback
control to reduce the response time of aperiodic tasks with soft
real-time constraints. We design an algorithm based on the proposed
scheduling scheme and Total Bandwidth Server (TBS) that is a
conventional server technique for scheduling aperiodic tasks. We then
describe the feedback controller of the algorithm and give the control
parameter tuning methods. The simulation study demonstrates that the
algorithm can reduce the mean response time up to 26% compared
to TBS in exchange for slight deadline misses.
Abstract: Embedded systems need to respect stringent real
time constraints. Various hardware components included in such
systems such as cache memories exhibit variability and therefore
affect execution time. Indeed, a cache memory access from an
embedded microprocessor might result in a cache hit where the
data is available or a cache miss and the data need to be fetched
with an additional delay from an external memory. It is therefore
highly desirable to predict future memory accesses during
execution in order to appropriately prefetch data without incurring
delays. In this paper, we evaluate the potential of several artificial
neural networks for the prediction of instruction memory
addresses. Neural network have the potential to tackle the nonlinear
behavior observed in memory accesses during program
execution and their demonstrated numerous hardware
implementation emphasize this choice over traditional forecasting
techniques for their inclusion in embedded systems. However,
embedded applications execute millions of instructions and
therefore millions of addresses to be predicted. This very
challenging problem of neural network based prediction of large
time series is approached in this paper by evaluating various neural
network architectures based on the recurrent neural network
paradigm with pre-processing based on the Self Organizing Map
(SOM) classification technique.
Abstract: In this study, we propose a network architecture for
providing secure access to information resources of enterprise
network from remote locations in a wireless fashion. Our proposed
architecture offers a very promising solution for organizations which
are in need of a secure, flexible and cost-effective remote access
methodology. Security of the proposed architecture is based on
Virtual Private Network technology and a special role based access
control mechanism with location and time constraints. The flexibility
mainly comes from the use of Internet as the communication medium
and cost-effectiveness is due to the possibility of in-house
implementation of the proposed architecture.