Abstract: Gauteng, as the province with the greatest industrial and population density, the economic hub of South Africa also generates the greatest amount of waste, both general and hazardous. Therefore the province has a significant need to develop and apply appropriate integrated waste management policies that ensure that waste is recognised as a serious problem and is managed in an effective integrated manner to preserve both the present and future human health and environment. This paper reflects on Gauteng-s waste outlook in particular the province-s General Waste Minimisation Plan and its Integrated Waste Management Policy. The paper also looks at general waste generation, recyclable waste streams as well as recycling and separation at source initiatives in the province. Both the quantity and nature of solid waste differs considerably across the socio-economic spectrum. People in informal settlements generate an average of 0.16 kg per person per day whereas 2 kg per day is not unusual in affluent areas. For example the amount of waste generated in Johannesburg is approximately 1.2 kg per person per day.
Abstract: A novel robust audio watermarking scheme is
proposed in this paper. In the proposed scheme, the host audio signals
are segmented into frames. Two consecutive frames are assessed if
they are suitable to represent a watermark bit. If so, frequency
transform is performed on these two frames. The compressionexpansion
technique is adopted to generate distortion over the two
frames. The distortion is used to represent one watermark bit.
Psychoacoustic model is applied to calculate local auditory mask to
ensure that the distortion is not audible. The watermarking schemes
using mono and stereo audio signals are designed differently. The
correlation-based detection method is used to detect the distortion
and extract embedded watermark bits. The experimental results show
that the quality degradation caused by the embedded watermarks is
perceptually transparent and the proposed schemes are very robust
against different types of attacks.
Abstract: Due to the excess of a vehicle operation through its life, some elements may face failure and deteriorate with time. This leads us to carry out maintenance, repair, tune up or full overhaul. After a certain period, the vehicle elements deteriorations increase with time which causes a very high increase of doing the maintenance operations and their costs. However, the logic decision at this point is to replace the current vehicle by a new one with minimum failure and maximum income. The importance of studying vehicle replacement problems come from the increase of stopping days due to many deteriorations in the vehicle parts. These deteriorations increase year after year causing an increase of operating costs and decrease the vehicle income. Vehicle replacement aims to determine the optimum time to keep, maintain, overhaul, renew and replace vehicles. This leads to an improvement in vehicle income, total operating costs, maintenance cost, fuel and oil costs, ton-kilometers, vehicle and engine performance, vehicle noise, vibration, and pollution. The aim of this paper is to find the optimum replacement policies of Kuwait Passenger Transport Company (KPTCP) fleet of busses. The objective of these policies is to maximize the busses pure profits. The dynamic programming (D.P.) technique is used to generate the busses optimal replacement policies
Abstract: In this paper an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.
Abstract: Fiber optic sensor technology offers the possibility of
sensing different parameters like strain, temperature, pressure in
harsh environment and remote locations. these kinds of sensors
modulates some features of the light wave in an optical fiber such an
intensity and phase or use optical fiber as a medium for transmitting
the measurement information.
The advantages of fiber optic sensors in contrast to conventional
electrical ones make them popular in different applications and now a
day they consider as a key component in improving industrial
processes, quality control systems, medical diagnostics, and
preventing and controlling general process abnormalities.
This paper is an introduction to fiber optic sensor technology and
some of the applications that make this branch of optic technology,
which is still in its early infancy, an interesting field.
Abstract: The main thrust of this paper is to assess the level of disclosure in the annual reports of non-financial Greek firms and to empirically investigate the hypothesized impact of several firm characteristics on the extent of mandatory disclosure. A disclosure checklist consisting of 100 mandatory items was developed to assess the level of disclosure in the 2009 annual reports of 43 Greek companies listed at the Athens stock exchange. The association between the level of disclosure and some firm characteristics was examined using multiple linear regression analysis. The study reveals that Greek companies on general have responded adequately to the mandatory disclosure requirements of the regulatory bodies. The findings also indicate that firm size was significant positively associated with the level of disclosure. The remaining variables such as age, profitability, liquidity, and board composition were found to be insignificant in explaining the variation of mandatory disclosures. The outcome of this study is undoubtedly of great concern to the investment community at large to assist in evaluating the extent of mandatory disclosure by Greek firms and explaining the variation of disclosure in light of firm-specific characteristics.
Abstract: In this paper a new Genetic Algorithm based on a heuristic operator and Centre of Mass selection operator (CMGA) is designed for the unbounded knapsack problem(UKP), which is NP-Hard combinatorial optimization problem. The proposed genetic algorithm is based on a heuristic operator, which utilizes problem specific knowledge. This center of mass operator when combined with other Genetic Operators forms a competitive algorithm to the existing ones. Computational results show that the proposed algorithm is capable of obtaining high quality solutions for problems of standard randomly generated knapsack instances. Comparative study of CMGA with simple GA in terms of results for unbounded knapsack instances of size up to 200 show the superiority of CMGA. Thus CMGA is an efficient tool of solving UKP and this algorithm is competitive with other Genetic Algorithms also.
Abstract: In this paper test generation methods and appropriate fault models for testing and analysis of embedded systems described as (extended) finite state machines ((E)FSMs) are presented. Compared to simple FSMs, EFSMs specify not only the control flow but also the data flow. Thus, we define a two-level fault model to cover both aspects. The goal of this paper is to reuse well-known FSM-based test generation methods for automation of embedded system testing. These methods have been widely used in testing and validation of protocols and communicating systems. In particular, (E)FSMs-based specification and testing is more advantageous because (E)FSMs support the formal semantic of already standardised formal description techniques (FDTs) despite of their popularity in the design of hardware and software systems.
Abstract: In this paper, linear multistep technique using power
series as the basis function is used to develop the block methods
which are suitable for generating direct solution of the special second
order ordinary differential equations with associated initial or
boundary conditions. The continuous hybrid formulations enable us
to differentiate and evaluate at some grids and off – grid points to
obtain two different four discrete schemes, each of order (5,5,5,5)T,
which were used in block form for parallel or sequential solutions of
the problems. The computational burden and computer time wastage
involved in the usual reduction of second order problem into system
of first order equations are avoided by this approach. Furthermore, a
stability analysis and efficiency of the block methods are tested on
linear and non-linear ordinary differential equations and the results
obtained compared favorably with the exact solution.
Abstract: Number of documents being created increases at an
increasing pace while most of them being in already known topics
and little of them introducing new concepts. This fact has started a
new era in information retrieval discipline where the requirements
have their own specialties. That is digging into topics and concepts
and finding out subtopics or relations between topics. Up to now IR
researches were interested in retrieving documents about a general
topic or clustering documents under generic subjects. However these
conventional approaches can-t go deep into content of documents
which makes it difficult for people to reach to right documents they
were searching. So we need new ways of mining document sets
where the critic point is to know much about the contents of the
documents. As a solution we are proposing to enhance LSI, one of
the proven IR techniques by supporting its vector space with n-gram
forms of words. Positive results we have obtained are shown in two
different application area of IR domain; querying a document
database, clustering documents in the document database.
Abstract: Let n ≥ 3 be an integer and p be a prime odd number. Let us consider Gp(n) the subgroup of (Z/nZ)* defined by : Gp(n) = {x ∈ (Z/nZ)* / xp = 1}. In this paper, we give an algorithm that computes a generating set of this subgroup.
Abstract: This Paper presents a particle swarm optimization (PSO) method for determining the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The brushless DC motor is modelled in Simulink and the PSO algorithm is implemented in MATLAB. Comparing with Genetic Algorithm (GA) and Linear quadratic regulator (LQR) method, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a linear brushless DC motor.
Abstract: A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion method, the JPDM method dose not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRLB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.
Abstract: A considerable amount of energy is consumed during
transmission and reception of messages in a wireless mesh network
(WMN). Reducing per-node transmission power would greatly
increase the network lifetime via power conservation in addition to
increasing the network capacity via better spatial bandwidth reuse. In
this work, the problem of topology control in a hybrid WMN of
heterogeneous wireless devices with varying maximum transmission
ranges is considered. A localized distributed topology control
algorithm is presented which calculates the optimal transmission
power so that (1) network connectivity is maintained (2) node
transmission power is reduced to cover only the nearest neighbours
(3) networks lifetime is extended. Simulations and analysis of results
are carried out in the NS-2 environment to demonstrate the
correctness and effectiveness of the proposed algorithm.
Abstract: Functioning of a biometric system in large part
depends on the performance of the similarity measure function.
Frequently a generalized similarity distance measure function such as
Euclidian distance or Mahalanobis distance is applied to the task of
matching biometric feature vectors. However, often accuracy of a
biometric system can be greatly improved by designing a customized
matching algorithm optimized for a particular biometric application.
In this paper we propose a tailored similarity measure function for
behavioral biometric systems based on the expert knowledge of the
feature level data in the domain. We compare performance of a
proposed matching algorithm to that of other well known similarity
distance functions and demonstrate its superiority with respect to the
chosen domain.
Abstract: Logic based methods for learning from structured data
is limited w.r.t. handling large search spaces, preventing large-sized
substructures from being considered by the resulting classifiers. A
novel approach to learning from structured data is introduced that
employs a structure transformation method, called finger printing, for
addressing these limitations. The method, which generates features
corresponding to arbitrarily complex substructures, is implemented in
a system, called DIFFER. The method is demonstrated to perform
comparably to an existing state-of-art method on some benchmark
data sets without requiring restrictions on the search space.
Furthermore, learning from the union of features generated by finger
printing and the previous method outperforms learning from each
individual set of features on all benchmark data sets, demonstrating
the benefit of developing complementary, rather than competing,
methods for structure classification.
Abstract: The distillation process in the general sense is a
relatively simple technique from the standpoints of its principles.
When dedicating distillation to water treatment and specifically
producing fresh water from sea, ocean and/ briny waters it is
interesting to notice that distillation has no limitations or domains of
applicability regarding the nature or the type of the feedstock water.
This is not the case however for other techniques that are
technologically quite complex, necessitate bigger capital investments
and are limited in their usability. In a previous paper we have
explored some of the effects of temperature on yield. In this paper,
we continue building onto that knowledge base and focus on the
effects of several additional engineering and design variables on
productivity.
Abstract: Well-developed strategic marketing planning is the essential
prerequisite for establishment of the right and unique competitive
advantage. Typical market, however, is a heterogeneous
and decentralized structure with natural involvement of individual
or group subjectivity and irrationality. These features cannot be
fully expressed with one-shot rigorous formal models based on,
e.g. mathematics, statistics or empirical formulas. We present an
innovative solution, extending the domain of agent based computational
economics towards the concept of hybrid modeling in service
provider and consumer market such as telecommunications. The
behavior of the market is described by two classes of agents -
consumer and service provider agents - whose internal dynamics
are fundamentally different. Customers are rather free multi-state
structures, adjusting behavior and preferences quickly in accordance
with time and changing environment. Producers, on the contrary,
are traditionally structured companies with comparable internal processes
and specific managerial policies. Their business momentum is
higher and immediate reaction possibilities limited. This limitation
underlines importance of proper strategic planning as the main
process advising managers in time whether to continue with more
or less the same business or whether to consider the need for future
structural changes that would ensure retention of existing customers
or acquisition of new ones.
Abstract: A generic and extendible Multi-Agent Data Mining
(MADM) framework, MADMF (the Multi-Agent Data Mining
Framework) is described. The central feature of the framework is that
it avoids the use of agreed meta-language formats by supporting a
framework of wrappers.
The advantage offered is that the framework is easily extendible,
so that further data agents and mining agents can simply be added to
the framework. A demonstration MADMF framework is currently
available. The paper includes details of the MADMF architecture and
the wrapper principle incorporated into it. A full description and
evaluation of the framework-s operation is provided by considering
two MADM scenarios.
Abstract: In this paper, we propose a new method to distinguish
between arousal and relaxation states by using multiple features
acquired from a photoplethysmogram (PPG) and support vector
machine (SVM). To induce arousal and relaxation states in subjects, 2
kinds of sound stimuli are used, and their corresponding biosignals are
obtained using the PPG sensor. Two features–pulse to pulse interval
(PPI) and pulse amplitude (PA)–are extracted from acquired PPG
data, and a nonlinear classification between arousal and relaxation is
performed using SVM.
This methodology has several advantages when compared with
previous similar studies. Firstly, we extracted 2 separate features from
PPG, i.e., PPI and PA. Secondly, in order to improve the classification
accuracy, SVM-based nonlinear classification was performed.
Thirdly, to solve classification problems caused by generalized
features of whole subjects, we defined each threshold according to
individual features.
Experimental results showed that the average classification
accuracy was 74.67%. Also, the proposed method showed the better
identification performance than the single feature based methods.
From this result, we confirmed that arousal and relaxation can be
classified using SVM and PPG features.