Abstract: In this paper, we are concerned with the design and
its simulation studies of a modified extremum seeking control for
nonlinear systems. A standard extremum seeking control has a simple
structure, but it takes a long time to reach an optimal operating point.
We consider a modification of the standard extremum seeking control
which is aimed to reach the optimal operating point more speedily
than the standard one. In the modification, PD acceleration term
is added before an integrator making a principal control, so that it
enables the objects to be regulated to the optimal point smoothly. This
proposed method is applied to Monod and Williams-Otto models to
investigate its effectiveness. Numerical simulation results show that
this modified method can improve the time response to the optimal
operating point more speedily than the standard one.
Abstract: A person-to-person information sharing is easily realized
by P2P networks in which servers are not essential. Leakage
of information, which are caused by malicious accesses for P2P
networks, has become a new social issues. To prevent information
leakage, it is necessary to detect and block traffics of P2P software.
Since some P2P softwares can spoof port numbers, it is difficult to
detect the traffics sent from P2P softwares by using port numbers.
It is more difficult to devise effective countermeasures for detecting
the software because their protocol are not public.
In this paper, a discriminating method of network applications
based on communication characteristics of application messages
without port numbers is proposed. The proposed method is based
on an assumption that there can be some rules about time intervals
to transmit messages in application layer and the number of necessary
packets to send one message. By extracting the rule from network
traffic, the proposed method can discriminate applications without
port numbers.
Abstract: The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Abstract: An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous region and diminish the edge-blurring effect and hence the name adaptive spatial finite mixture model. The proposed approach is compared with the spatially variant finite mixture model for pixel labeling. The experimental results with synthetic and Berkeley dataset demonstrate that the proposed method is effective in improving the segmentation and it can be employed in different practical image content understanding applications.
Abstract: This paper proposes a method that predicts attractive
evaluation objects. In the learning phase, the method inductively
acquires trend rules from complex sequential data. The data is
composed of two types of data. One is numerical sequential data.
Each evaluation object has respective numerical sequential data. The
other is text sequential data. Each evaluation object is described in
texts. The trend rules represent changes of numerical values related
to evaluation objects. In the prediction phase, the method applies
new text sequential data to the trend rules and evaluates which
evaluation objects are attractive. This paper verifies the effect of the
proposed method by using stock price sequences and news headline
sequences. In these sequences, each stock brand corresponds to an
evaluation object. This paper discusses validity of predicted attractive
evaluation objects, the process time of each phase, and the possibility
of application tasks.
Abstract: Recently the use of data mining to scientific bibliographic data bases has been implemented to analyze the pathways of the knowledge or the core scientific relevances of a laureated novel or a country. This specific case of data mining has been named citation mining, and it is the integration of citation bibliometrics and text mining. In this paper we present an improved WEB implementation of statistical physics algorithms to perform the text mining component of citation mining. In particular we use an entropic like distance between the compression of text as an indicator of the similarity between them. Finally, we have included the recently proposed index h to characterize the scientific production. We have used this web implementation to identify users, applications and impact of the Mexican scientific institutions located in the State of Morelos.
Abstract: User-based Collaborative filtering (CF), one of the
most prevailing and efficient recommendation techniques, provides
personalized recommendations to users based on the opinions of other
users. Although the CF technique has been successfully applied in
various applications, it suffers from serious sparsity problems. The
cloud-model approach addresses the sparsity problems by
constructing the user-s global preference represented by a cloud
eigenvector. The user-based CF approach works well with dense
datasets while the cloud-model CF approach has a greater
performance when the dataset is sparse. In this paper, we present a
hybrid approach that integrates the predictions from both the
user-based CF and the cloud-model CF approaches. The experimental
results show that the proposed hybrid approach can ameliorate the
sparsity problem and provide an improved prediction quality.
Abstract: Construction projects generally take place in
uncontrolled and dynamic environments where construction waste is
a serious environmental problem in many large cities. The total
amount of waste and carbon dioxide emissions from transportation
vehicles are still out of control due to increasing construction
projects, massive urban development projects and the lack of
effective tools for minimizing adverse environmental impacts in
construction. This research is about utilization of the integrated
applications of automated advanced tracking and data storage
technologies in the area of environmental management to monitor
and control adverse environmental impacts such as construction
waste and carbon dioxide emissions. Radio Frequency Identification
(RFID) integrated with the Global Position System (GPS) provides
an opportunity to uniquely identify materials, components, and
equipments and to locate and track them using minimal or no worker
input. The transmission of data to the central database will be carried
out with the help of Global System for Mobile Communications
(GSM).
Abstract: Model Predictive Control (MPC) is increasingly being
proposed for real time applications and embedded systems. However
comparing to PID controller, the implementation of the MPC in
miniaturized devices like Field Programmable Gate Arrays (FPGA)
and microcontrollers has historically been very small scale due to its
complexity in implementation and its computation time requirement.
At the same time, such embedded technologies have become an
enabler for future manufacturing enterprises as well as a transformer
of organizations and markets. Recently, advances in microelectronics
and software allow such technique to be implemented in embedded
systems. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and
applied control technique in the industrial engineering. In fact in
this paper, we propose an efficient framework for implementation
of Generalized Predictive Control (GPC) in the performed STM32
microcontroller. The STM32 keil starter kit based on a JTAG interface
and the STM32 board was used to implement the proposed GPC
firmware. Besides the GPC, the PID anti windup algorithm was
also implemented using Keil development tools designed for ARM
processor-based microcontroller devices and working with C/Cµ
langage. A performances comparison study was done between both
firmwares. This performances study show good execution speed and
low computational burden. These results encourage to develop simple
predictive algorithms to be programmed in industrial standard hardware.
The main features of the proposed framework are illustrated
through two examples and compared with the anti windup PID
controller.
Abstract: The application of a simple microcontroller to deal
with a three variable input and a single output fuzzy logic controller,
with Proportional – Integral – Derivative (PID) response control
built-in has been tested for an automatic voltage regulator. The
fuzzifiers are based on fixed range of the variables of output voltage.
The control output is used to control the wiper motor of the auto
transformer to adjust the voltage, using fuzzy logic principles, so that
the voltage is stabilized. In this report, the author will demonstrate
how fuzzy logic might provide elegant and efficient solutions in the
design of multivariable control based on experimental results rather
than on mathematical models.
Abstract: Image enhancement is the most important challenging preprocessing for almost all applications of Image Processing. By now, various methods such as Median filter, α-trimmed mean filter, etc. have been suggested. It was proved that the α-trimmed mean filter is the modification of median and mean filters. On the other hand, ε-filters have shown excellent performance in suppressing noise. In spite of their simplicity, they achieve good results. However, conventional ε-filter is based on moving average. In this paper, we suggested a new ε-filter which utilizes α-trimmed mean. We argue that this new method gives better outcomes compared to previous ones and the experimental results confirmed this claim.
Abstract: A computer model of Quantum Theory (QT) has been
developed by the author. Major goal of the computer model was
support and demonstration of an as large as possible scope of QT.
This includes simulations for the major QT (Gedanken-) experiments
such as, for example, the famous double-slit experiment.
Besides the anticipated difficulties with (1) transforming exacting
mathematics into a computer program, two further types of problems
showed up, namely (2) areas where QT provides a complete mathematical
formalism, but when it comes to concrete applications the
equations are not solvable at all, or only with extremely high effort;
(3) QT rules which are formulated in natural language and which do
not seem to be translatable to precise mathematical expressions, nor
to a computer program.
The paper lists problems in all three categories and describes also
the possible solutions or circumventions developed for the computer
model.
Abstract: Face recognition is a technique to automatically
identify or verify individuals. It receives great attention in
identification, authentication, security and many more applications.
Diverse methods had been proposed for this purpose and also a lot of
comparative studies were performed. However, researchers could not
reach unified conclusion. In this paper, we are reporting an extensive
quantitative accuracy analysis of four most widely used face
recognition algorithms: Principal Component Analysis (PCA),
Independent Component Analysis (ICA), Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM) using AT&T,
Sheffield and Bangladeshi people face databases under diverse
situations such as illumination, alignment and pose variations.
Abstract: Does open ended creative technology give positive impact in learning design? Although there are many researchers had examined on the impact of technology on design education but there are very few conclusive researches done on the impact of open ended used of software to learning design. This paper sought to investigate a group of student-s experience on relatively wider range of software application within the context of design project. A typography design project was used to create a learning environment with the aim of inculcate design skills into the learners and increase their creative problem-solving and critical thinking skills. The methods used in this study were questionnaire survey and personal observation which will be focus on the individual and group response during the completion of the task.
Abstract: In this work, bending fatigue life of notched
specimens with various notch geometries and dimensions is
investigated by experiment and Manson-Caffin theoretical method. In
this theoretical method, fatigue life of notched specimens is
calculated using the fatigue life obtained from the experiments for
plain specimens (without notch). Three notch geometries including
∪-shape, ∨-shape and C -shape notches are considered in this
investigation. The experiments are conducted on a rotary bending
Moore machine. The specimens are made of a low carbon steel alloy,
which has wide application in industry. The stress- life curves are
captured for all notched specimen by experiment. The results indicate
that Manson-Caffin analytical method cannot adequately predict
the fatigue life of notched specimen. However, it seems that the
difference between the experiments and Manson-Caffin predictions
can be compensated by a proportional factor.
Abstract: In this work a new method for low complexity
image coding is presented, that permits different settings and great
scalability in the generation of the final bit stream. This coding
presents a continuous-tone still image compression system that
groups loss and lossless compression making use of finite arithmetic
reversible transforms. Both transformation in the space of color and
wavelet transformation are reversible. The transformed coefficients
are coded by means of a coding system in depending on a
subdivision into smaller components (CFDS) similar to the bit
importance codification. The subcomponents so obtained are
reordered by means of a highly configure alignment system
depending on the application that makes possible the re-configure of
the elements of the image and obtaining different importance levels
from which the bit stream will be generated. The subcomponents of
each importance level are coded using a variable length entropy
coding system (VBLm) that permits the generation of an embedded
bit stream. This bit stream supposes itself a bit stream that codes a
compressed still image. However, the use of a packing system on the
bit stream after the VBLm allows the realization of a final highly
scalable bit stream from a basic image level and one or several
improvement levels.
Abstract: This research proposes a Preemptive Possibilistic
Linear Programming (PPLP) approach for solving multiobjective
Aggregate Production Planning (APP) problem with interval demand
and imprecise unit price and related operating costs. The proposed
approach attempts to maximize profit and minimize changes of
workforce. It transforms the total profit objective that has imprecise
information to three crisp objective functions, which are maximizing
the most possible value of profit, minimizing the risk of obtaining the
lower profit and maximizing the opportunity of obtaining the higher
profit. The change of workforce level objective is also converted.
Then, the problem is solved according to objective priorities. It is
easier than simultaneously solve the multiobjective problem as
performed in existing approach. Possible range of interval demand is
also used to increase flexibility of obtaining the better production
plan. A practical application of an electronic company is illustrated to
show the effectiveness of the proposed model.
Abstract: The theory of Groebner Bases, which has recently been
honored with the ACM Paris Kanellakis Theory and Practice Award,
has become a crucial building block to computer algebra, and is
widely used in science, engineering, and computer science. It is wellknown
that Groebner bases computation is EXP-SPACE in a general
setting. In this paper, we give an algorithm to show that Groebner
bases computation is P-SPACE in Boolean rings. We also show that
with this discovery, the Groebner bases method can theoretically be
as efficient as other methods for automated verification of hardware
and software. Additionally, many useful and interesting properties of
Groebner bases including the ability to efficiently convert the bases
for different orders of variables making Groebner bases a promising
method in automated verification.
Abstract: Failure modes and effects analysis (FMEA) is an effective technique for preventing potential problems and actions needed to error cause removal. On the other hand, the oil producing companies paly a critical role in the oil industry of Iran as a developing country out of which, Sepahan Oil Co. has a considerable contribution. The aim of this research is to show how FMEA could be applied and improve the quality of products at Sepahan Oil Co. For this purpose, the four liter production line of the company has been selected for investigation. The findings imply that the application of FMEA has reduced the scraps from 50000 ppm to 5000 ppm and has resulted in a 0.92 percent decrease of the oil waste.
Abstract: This paper presents the design and implements the prototype of an intelligent data processing framework in ubiquitous sensor networks. Much focus is put on how to handle the sensor data stream as well as the interoperability between the low-level sensor data and application clients. Our framework first addresses systematic middleware which mitigates the interaction between the application layer and low-level sensors, for the sake of analyzing a great volume of sensor data by filtering and integrating to create value-added context information. Then, an agent-based architecture is proposed for real-time data distribution to efficiently forward a specific event to the appropriate application registered in the directory service via the open interface. The prototype implementation demonstrates that our framework can host a sophisticated application on the ubiquitous sensor network and it can autonomously evolve to new middleware, taking advantages of promising technologies such as software agents, XML, cloud computing, and the like.