Abstract: People detection from images has a variety of applications such as video surveillance and driver assistance system, but is still a challenging task and more difficult in crowded environments such as shopping malls in which occlusion of lower parts of human body often occurs. Lack of the full-body information requires more effective features than common features such as HOG. In this paper, new features are introduced that exploits global self-symmetry (GSS) characteristic in head-shoulder patterns. The features encode the similarity or difference of color histograms and oriented gradient histograms between two vertically symmetric blocks. The domain-specific features are rapid to compute from the integral images in Viola-Jones cascade-of-rejecters framework. The proposed features are evaluated with our own head-shoulder dataset that, in part, consists of a well-known INRIA pedestrian dataset. Experimental results show that the GSS features are effective in reduction of false alarmsmarginally and the gradient GSS features are preferred more often than the color GSS ones in the feature selection.
Abstract: In the paper we discuss the influence of the route
flexibility degree, the open rate of operations and the production type
coefficient on makespan. The flexible job-open shop scheduling
problem FJOSP (an extension of the classical job shop scheduling) is
analyzed. For the analysis of the production process we used a
hybrid heuristic of the GRASP (greedy randomized adaptive search
procedure) with simulated annealing algorithm. Experiments with
different levels of factors have been considered and compared. The
GRASP+SA algorithm has been tested and illustrated with results for
the serial route and the parallel one.
Abstract: We present a BeeBot, Binus Multi-client Intelligent Telepresence Robot, a custom-build robot system specifically designed for teleconference with multiple person using omni directional actuator. The robot is controlled using a computer networks, so the manager/supervisor can direct the robot to the intended person to start a discussion/inspection. People tracking and autonomous navigation are intelligent features of this robot. We build a web application for controlling the multi-client telepresence robot and open-source teleconference system used. Experimental result presented and we evaluated its performance.
Abstract: It is well known that the channel capacity of Multiple-
Input-Multiple-Output (MIMO) system increases as the number of
antenna pairs between transmitter and receiver increases but it suffers
from multiple expensive RF chains. To reduce the cost of RF chains,
Antenna Selection (AS) method can offer a good tradeoff between
expense and performance. In a transmitting AS system, Channel
State Information (CSI) feedback is necessarily required to choose
the best subset of antennas in which the effects of delays and errors
occurred in feedback channels are the most dominant factors
degrading the performance of the AS method. This paper presents the
concept of AS method using CSI from channel reciprocity instead of
feedback method. Reciprocity technique can easily archive CSI by
utilizing a reverse channel where the forward and reverse channels
are symmetrically considered in time, frequency and location. In this
work, the capacity performance of MIMO system when using AS
method at transmitter with reciprocity channels is investigated by
own developing Testbed. The obtained results show that reciprocity
technique offers capacity close to a system with a perfect CSI and
gains a higher capacity than a system without AS method from 0.9 to
2.2 bps/Hz at SNR 10 dB.
Abstract: In this paper, we combine a probabilistic neural method with radial-bias functions in order to construct the lithofacies of the wells DF01, DF02 and DF03 situated in the Triassic province of Algeria (Sahara). Lithofacies is a crucial problem in reservoir characterization. Our objective is to facilitate the experts' work in geological domain and to allow them to obtain quickly the structure and the nature of lands around the drilling. This study intends to design a tool that helps automatic deduction from numerical data. We used a probabilistic formalism to enhance the classification process initiated by a Self-Organized Map procedure. Our system gives lithofacies, from well-log data, of the concerned reservoir wells in an aspect easy to read by a geology expert who identifies the potential for oil production at a given source and so forms the basis for estimating the financial returns and economic benefits.
Abstract: The “conveyor belt" as a product represents a
complex high performance component with a wide range of different
applications. Further development of these highly complex
components demands an integration of new technologies and new
enhanced materials. In this context nanostructured fillers appear to
have a more promising effect on the performance of the conveyor
belt composite than conventional micro-scaled fillers.
Within the project “DotTrans" nanostructured fillers, for example
silicon dioxide, are used to optimize performance parameters of
conveyor belt systems. The objective of the project includes
operating parameters like energy consumption or friction
characteristics as well as adaptive parameters like cut or wear
resistance.
Abstract: It has been defined that the “network is the system".
This implies providing levels of service, reliability, predictability and
availability that are commensurate with or better than those that
individual computers provide today. To provide this requires
integrated network management for interconnected networks of
heterogeneous devices covering both the local campus. In this paper
we are addressing a framework to effectively deal with this issue. It
consists of components and interactions between them which are
required to perform the service fault management. A real-world
scenario is used to derive the requirements which have been applied
to the component identification. An analysis of existing frameworks
and approaches with respect to their applicability to the framework is
also carried out.
Abstract: Creativity is often based on an unorthodox
recombination of knowledge; in fact: 80% of all innovations use
given knowledge and put it into a new combination. Cross-industry
innovations follow this way of thinking and bring together problems
and solution ideas from different industries. Therefore analogies and
search strategies have to be developed. Taking this path, the
questions where to search, what to search and how to search have to
be answered. Afterwards, the gathered information can be used
within a planned search process. Identified solution ideas have to be
assessed and analyzed in detail for the success promising adaption
planning.
Abstract: Face and facial expressions play essential roles in
interpersonal communication. Most of the current works on the facial
expression recognition attempt to recognize a small set of the
prototypic expressions such as happy, surprise, anger, sad, disgust
and fear. However the most of the human emotions are
communicated by changes in one or two of discrete features. In this
paper, we develop a facial expressions synthesis system, based on the
facial characteristic points (FCP's) tracking in the frontal image
sequences. Selected FCP's are automatically tracked using a crosscorrelation
based optical flow. The proposed synthesis system uses a
simple deformable facial features model with a few set of control
points that can be tracked in original facial image sequences.
Abstract: Ever increasing capacities of contemporary storage devices
inspire the vision to accumulate (personal) information without
the need of deleting old data over a long time-span. Hence the target
of SemanticLIFE project is to create a Personal Information Management
system for a human lifetime data. One of the most important
characteristics of the system is its dedication to retrieve information
in a very efficient way. By adopting user demands regarding the
reduction of ambiguities, our approach aims at a user-oriented and
yet powerful enough system with a satisfactory query performance.
We introduce the query system of SemanticLIFE, the Virtual Query
System, which uses emerging Semantic Web technologies to fulfill
users- requirements.
Abstract: In this paper, the effect of receive and/or transmit
antenna spacing on the performance (BER vs. SNR) of multipleantenna
systems is determined by using an RCS (Radar Cross
Section) channel model. In this physical model, the scatterers
existing in the propagation environment are modeled by their RCS so
that the correlation of the receive signal complex amplitudes, i.e.,
both magnitude and phase, can be estimated. The proposed RCS
channel model is then compared with classical models.
Abstract: In IETF RFC 2002, Mobile-IP was developed to
enable Laptobs to maintain Internet connectivity while moving
between subnets. However, the packet loss that comes from
switching subnets arises because network connectivity is lost while
the mobile host registers with the foreign agent and this encounters
large end-to-end packet delays. The criterion to initiate a simple and
fast full-duplex connection between the home agent and foreign
agent, to reduce the roaming duration, is a very important issue to be
considered by a work in this paper. State-transition Petri-Nets of the
modeling scenario-based CIA: communication inter-agents procedure
as an extension to the basic Mobile-IP registration process was
designed and manipulated to describe the system in discrete events.
The heuristic of configuration file during practical Setup session for
registration parameters, on Cisco platform Router-1760 using IOS
12.3 (15)T and TFTP server S/W is created. Finally, stand-alone
performance simulations from Simulink Matlab, within each subnet
and also between subnets, are illustrated for reporting better end-toend
packet delays. Results verified the effectiveness of our Mathcad
analytical manipulation and experimental implementation. It showed
lower values of end-to-end packet delay for Mobile-IP using CIA
procedure-based early registration. Furthermore, it reported packets
flow between subnets to improve losses between subnets.
Abstract: Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.
Abstract: The presented work is motivated by a French law
regarding nuclear waste management. A new conceptual Accelerator
Driven System (ADS) designed for the Minor Actinides (MA)
transmutation has been assessed by numerical simulation. The
MUltiple Spallation Target (MUST) ADS combines high thermal power (up to 1.4 GWth) and high specific power. A 30 mA and 1
GeV proton beam is divided into three secondary beams transmitted on three liquid lead-bismuth spallation targets. Neutron and thermalhydraulic
simulations have been performed with the code MURE, based on the Monte-Carlo transport code MCNPX. A methodology has been developed to define characteristic of the MUST ADS concept according to a specific transmutation scenario. The reference
scenario is based on a MA flux (neptunium, americium and curium)
providing from European Fast Reactor (EPR) and a plutonium multireprocessing
strategy is accounted for. The MUST ADS reference
concept is a sodium cooled fast reactor. The MA fuel at equilibrium is mixed with MgO inert matrix to limit the core reactivity and
improve the fuel thermal conductivity. The fuel is irradiated over five
years. Five years of cooling and two years for the fuel fabrication are
taken into account. The MUST ADS reference concept burns about 50% of the initial MA inventory during a complete cycle. In term of
mass, up to 570 kg/year are transmuted in one concept. The methodology to design the MUST ADS and to calculate fuel
composition at equilibrium is precisely described in the paper. A detailed fuel evolution analysis is performed and the reference scenario is compared to a scenario where only americium transmutation is performed.
Abstract: At present, it is very common to find renewable
energy resources, especially wind power, connected to distribution
systems. The impact of this wind power on voltage distribution levels
has been addressed in the literature. The majority of this works deals
with the determination of the maximum active and reactive power
that is possible to be connected on a system load bus, until the
voltage at that bus reaches the voltage collapse point. It is done by the
traditional methods of PV curves reported in many references.
Theoretical expression of maximum power limited by voltage
stability transfer through a grid is formulated using an exact
representation of distribution line with ABCD parameters. The
expression is used to plot PV curves at various power factors of a
radial system. Limited values of reactive power can be obtained. This
paper presents a method to study the relationship between the active
power and voltage (PV) at the load bus to identify the voltage
stability limit. It is a foundation to build a permitted working
operation region in complying with the voltage stability limit at the
point of common coupling (PCC) connected wind farm.
Abstract: Careful design and selection of daylighting systems can greatly help in reducing not only artificial lighting use, but also decrease cooling energy consumption and, therefore, potential for downsizing air-conditioning systems. This paper aims to evaluate the energy performance of two types of top-light daylighting systems due to the integration of daylight together with artificial lighting in an existing examinaton hall in University Kebangsaan Malaysia, based on a hot and humid climate. Computer simulation models have been created for building case study (base case) and the two types of toplight daylighting designs for building energy performance evaluation using the VisualDOE 4.0 building energy simulation program. The finding revealed that daylighting through top-light systems is a very beneficial design strategy in reducing annual lighting energy consumption and the overall total annual energy consumption.
Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Abstract: Isobaric vapor-liquid equilibrium measurements are reported for the binary mixtures of n-Butylamine and Triethylamine with Cumene at 97.3 kPa. The measurements have been performed using a vapor recirculating type (modified Othmer's) equilibrium still. The binary mixture of n-Butylamine + Cumene shows positive deviation from ideality. Triethylamine + Cumene mixture shows negligible deviation from ideality. None of the systems form an azeotrope. The activity coefficients have been calculated taking into consideration the vapor phase nonideality. The data satisfy the thermodynamic consistency test of Herington. The activity coefficients have been satisfactorily correlated by means of the Margules, NRTL, and Black equations. The activity coefficient values obtained by the UNIFAC model are also reported.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: With a surge of stream processing applications novel
techniques are required for generation and analysis of association
rules in streams. The traditional rule mining solutions cannot handle
streams because they generally require multiple passes over the data
and do not guarantee the results in a predictable, small time. Though
researchers have been proposing algorithms for generation of rules
from streams, there has not been much focus on their analysis.
We propose Association rule profiling, a user centric process for
analyzing association rules and attaching suitable profiles to them
depending on their changing frequency behavior over a previous
snapshot of time in a data stream.
Association rule profiles provide insights into the changing nature
of associations and can be used to characterize the associations. We
discuss importance of characteristics such as predictability of
linkages present in the data and propose metric to quantify it. We
also show how association rule profiles can aid in generation of user
specific, more understandable and actionable rules.
The framework is implemented as SUPAR: System for Usercentric
Profiling of Association Rules in streaming data. The
proposed system offers following capabilities:
i) Continuous monitoring of frequency of streaming item-sets
and detection of significant changes therein for association rule
profiling.
ii) Computation of metrics for quantifying predictability of
associations present in the data.
iii) User-centric control of the characterization process: user
can control the framework through a) constraint specification and b)
non-interesting rule elimination.