Abstract: Anaerobic treatment has many advantages over other
biological method particularly when used to treat complex
wastewater such as petroleum refinery wastewater. In this study two
Up-flow Anaerobic Sludge Blanket (UASB) reactors were operated
in parallel to treat six volumetric organic loads (0.58, 1.21, 0.89,
2.34, 1.47 and 4.14 kg COD/m3·d) to evaluate the chemical oxygen
demand (COD) removal efficiency. The reactors were continuously
adapting to the changing of operation condition with increase in the
removal efficiency or slight decrease until the last load which was
more than two times the load, at which the reactor stressed and the
removal efficiency decreased to 75% with effluent concentration of
1746 mg COD/L. Other parameters were also monitored such as pH,
alkalinity, volatile fatty acid and gas production rate. The UASB
reactor was suitable to treat petroleum refinery wastewater and the
highest COD removal rate was 83% at 1215 kg/m3·d with COD
concentration about 356 mg/L in the effluent.
Abstract: This paper proposes an innovative methodology for
Acceptance Sampling by Variables, which is a particular category of
Statistical Quality Control dealing with the assurance of products
quality. Our contribution lies in the exploitation of machine learning
techniques to address the complexity and remedy the drawbacks of
existing approaches. More specifically, the proposed methodology
exploits Artificial Neural Networks (ANNs) to aid decision making
about the acceptance or rejection of an inspected sample. For any
type of inspection, ANNs are trained by data from corresponding
tables of a standard-s sampling plan schemes. Once trained, ANNs
can give closed-form solutions for any acceptance quality level and
sample size, thus leading to an automation of the reading of the
sampling plan tables, without any need of compromise with the
values of the specific standard chosen each time. The proposed
methodology provides enough flexibility to quality control engineers
during the inspection of their samples, allowing the consideration of
specific needs, while it also reduces the time and the cost required for
these inspections. Its applicability and advantages are demonstrated
through two numerical examples.
Abstract: In this paper, a direct method based on variable step
size Block Backward Differentiation Formula which is referred as
BBDF2 for solving second order Ordinary Differential Equations
(ODEs) is developed. The advantages of the BBDF2 method over the
corresponding sequential variable step variable order Backward
Differentiation Formula (BDFVS) when used to solve the same
problem as a first order system are pointed out. Numerical results are
given to validate the method.
Abstract: One of research issues in social network analysis is to
evaluate the position/importance of users in social networks. As the
information diffusion in social network is evolving, it seems difficult
to evaluate the importance of users using traditional approaches. In
this paper, we propose an evaluation approach for user importance
with fractal view in social networks. In this approach, the global importance
(Fractal Importance) and the local importance (Topological
Importance) of nodes are considered. The basic idea is that the bigger
the product of fractal importance and topological importance of a
node is, the more important of the node is. We devise the algorithm
called TFRank corresponding to the proposed approach. Finally, we
evaluate TFRank by experiments. Experimental results demonstrate
our TFRank has the high correlations with PageRank algorithm
and potential ranking algorithm, and it shows the effectiveness and
advantages of our approach.
Abstract: This study proposes a multi-response surface
optimization problem (MRSOP) for determining the proper choices
of a process parameter design (PPD) decision problem in a noisy
environment of a grease position process in an electronic industry.
The proposed models attempts to maximize dual process responses
on the mean of parts between failure on left and right processes. The
conventional modified simplex method and its hybridization of the
stochastic operator from the hunting search algorithm are applied to
determine the proper levels of controllable design parameters
affecting the quality performances. A numerical example
demonstrates the feasibility of applying the proposed model to the
PPD problem via two iterative methods. Its advantages are also
discussed. Numerical results demonstrate that the hybridization is
superior to the use of the conventional method. In this study, the
mean of parts between failure on left and right lines improve by
39.51%, approximately. All experimental data presented in this
research have been normalized to disguise actual performance
measures as raw data are considered to be confidential.
Abstract: System testing is actually done to the entire system
against the Functional Requirement Specification and/or the System
Requirement Specification. Moreover, it is an investigatory testing
phase, where the focus is to have almost a destructive attitude and
test not only the design, but also the behavior and even the believed
expectations of the customer. It is also intended to test up to and
beyond the bounds defined in the software/hardware requirements
specifications. In Motorola®, Automated Testing is one of the testing
methodologies uses by GSG-iSGT (Global Software Group - iDEN
TM
Subcriber Group-Test) to increase the testing volume, productivity
and reduce test cycle-time in iDEN
TM
phones testing. Testing is able
to produce more robust products before release to the market. In this
paper, iHopper is proposed as a tool to perform stress test on iDEN
TM
phonse. We will discuss the value that automation has brought to
iDEN
TM
Phone testing such as improving software quality in the
iDEN
TM
phone together with some metrics. We will also look into
the advantages of the proposed system and some discussion of the
future work as well.
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.
Abstract: Object-oriented programming is a wonderful way to
make programming of huge real life tasks much easier than by using
procedural languages. In order to teach those ideas to students, it
is important to find a good task that shows the advantages of OOprogramming
very naturally. This paper gives an example, the game
Battleship, which seems to work excellent for teaching the OO ideas
(using Java, [1], [2], [3], [4]).
A three-step task is presented for how to teach OO-programming
using just one example suitable to convey many of the OO ideas.
Observations are given at the end and conclusions about how the
whole teaching course worked out.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: This paper introduces a framework based on the collaboration of multi agent and hyper-heuristics to find a solution of the real single machine production problem. There are many techniques used to solve this problem. Each of it has its own advantages and disadvantages. By the collaboration of multi agent system and hyper-heuristics, we can get more optimal solution. The hyper-heuristics approach operates on a search space of heuristics rather than directly on a search space of solutions. The proposed framework consists of some agents, i.e. problem agent, trainer agent, algorithm agent (GPHH, GAHH, and SAHH), optimizer agent, and solver agent. Some low level heuristics used in this paper are MRT, SPT, LPT, EDD, LDD, and MON
Abstract: Recently, wireless sensor networks have been paid
more interest, are widely used in a lot of commercial and military
applications, and may be deployed in critical scenarios (e.g. when a
malfunctioning network results in danger to human life or great
financial loss). Such networks must be protected against human
intrusion by using the secret keys to encrypt the exchange messages
between communicating nodes. Both the symmetric and asymmetric
methods have their own drawbacks for use in key management. Thus,
we avoid the weakness of these two cryptosystems and make use of
their advantages to establish a secure environment by developing the
new method for encryption depending on the idea of code
conversion. The code conversion-s equations are used as the key for
designing the proposed system based on the basics of logic gate-s
principals. Using our security architecture, we show how to reduce
significant attacks on wireless sensor networks.
Abstract: The paper presents a computational tool developed for
the evaluation of technical and economic advantages of an innovative
cleaning and conditioning technology of fluidized bed steam/oxygen
gasifiers outlet product gas. This technology integrates into a single
unit the steam gasification of biomass and the hot gas cleaning and
conditioning system. Both components of the computational tool,
process flowsheet and economic evaluator, have been developed
under IPSEpro software. The economic model provides information
that can help potential users, especially small and medium size
enterprises acting in the regenerable energy field, to decide the
optimal scale of a plant and to better understand both potentiality and
limits of the system when applied to a wide range of conditions.
Abstract: In this paper, the implementation of a rule-based
intuitive reasoner is presented. The implementation included two
parts: the rule induction module and the intuitive reasoner. A large
weather database was acquired as the data source. Twelve weather
variables from those data were chosen as the “target variables"
whose values were predicted by the intuitive reasoner. A “complex"
situation was simulated by making only subsets of the data available
to the rule induction module. As a result, the rules induced were
based on incomplete information with variable levels of certainty.
The certainty level was modeled by a metric called "Strength of
Belief", which was assigned to each rule or datum as ancillary
information about the confidence in its accuracy. Two techniques
were employed to induce rules from the data subsets: decision tree
and multi-polynomial regression, respectively for the discrete and the
continuous type of target variables. The intuitive reasoner was tested
for its ability to use the induced rules to predict the classes of the
discrete target variables and the values of the continuous target
variables. The intuitive reasoner implemented two types of
reasoning: fast and broad where, by analogy to human thought, the
former corresponds to fast decision making and the latter to deeper
contemplation. . For reference, a weather data analysis approach
which had been applied on similar tasks was adopted to analyze the
complete database and create predictive models for the same 12
target variables. The values predicted by the intuitive reasoner and
the reference approach were compared with actual data. The intuitive
reasoner reached near-100% accuracy for two continuous target
variables. For the discrete target variables, the intuitive reasoner
predicted at least 70% as accurately as the reference reasoner. Since
the intuitive reasoner operated on rules derived from only about 10%
of the total data, it demonstrated the potential advantages in dealing
with sparse data sets as compared with conventional methods.
Abstract: One of the main advantages of the LO paradigm is to
allow the availability of good quality, shareable learning material
through the Web. The effectiveness of the retrieval process requires a
formal description of the resources (metadata) that closely fits the
user-s search criteria; in spite of the huge international efforts in this
field, educational metadata schemata often fail to fulfil this
requirement. This work aims to improve the situation, by the
definition of a metadata model capturing specific didactic features of
shareable learning resources. It classifies LOs into “teacher-oriented"
and “student-oriented" categories, in order to describe the role a LO
is to play when it is integrated into the educational process. This
article describes the model and a first experimental validation process
that has been carried out in a controlled environment.
Abstract: The flash memory has many advantages such as low power consumption, strong shock resistance, fast I/O and non-volatility. And it is increasingly used in the mobile storage device. The YAFFS, one of the NAND flash file system, is widely used in the embedded device. However, the existing YAFFS takes long time to mount the file system because it scans whole spare areas in all pages of NAND flash memory. In order to solve this problem, we propose a new content-based flash file system using a mounting time reduction technique. The proposed method only scans partial spare areas of some special pages by using content-based block management. The experimental results show that the proposed method reduces the average mounting time by 87.2% comparing with JFFS2 and 69.9% comparing with YAFFS.
Abstract: The spatial variation in plant species associated with intercropping is intended to reduce resource competition between species and increase yield potential. A field experiment was carried out on corn (Zea mays L.) and soybean (Glycine max L.) intercropping in a replacement series experiment with weed contamination consist of: weed free, infestation of redroot pigweed, infestation of jimsonweed and simultaneous infestation of redroot pigweed and jimsonweed in Karaj, Iran during 2007 growing season. The experimental design was a randomized complete block in factorial experiment with replicated thrice. Significant (P≤0.05) differences were observed in yield in intercropping. Corn yield was higher in intercropping, but soybean yield was significantly reduced by corn when intercropped. However, total productivity and land use efficiency were high under the intercropping system even in contamination of either species of weeds. Aggressivity of corn relative to soybean revealed the greater competitive ability of corn than soybean. Land equivalent ratio (LER) more than 1 in all treatments attributed to intercropping advantages and was highest in 50: 50 (corn/soybean) in weed free. These findings suggest that intercropping corn and soybean increase total productivity per unit area and improve land use efficiency. Considering the experimental findings, corn-soybean intercropping (50:50) may be recommended for yield advantage, more efficient utilization of resources, and weed suppression as a biological control.
Abstract: Many exist studies always use Markov decision
processes (MDPs) in modeling optimal route choice in
stochastic, time-varying networks. However, taking many
variable traffic data and transforming them into optimal route
decision is a computational challenge by employing MDPs in
real transportation networks. In this paper we model finite
horizon MDPs using directed hypergraphs. It is shown that the
problem of route choice in stochastic, time-varying networks
can be formulated as a minimum cost hyperpath problem, and
it also can be solved in linear time. We finally demonstrate the
significant computational advantages of the introduced
methods.
Abstract: Concept maps can be generated manually or
automatically. It is important to recognize differences of the two
types of concept maps. The automatically generated concept maps
are dynamic, interactive, and full of associations between the terms
on the maps and the underlying documents. Through a specific
concept mapping system, Visual Concept Explorer (VCE), this paper
discusses how automatically generated concept maps are different
from manually generated concept maps and how different
applications and learning opportunities might be created with the
automatically generated concept maps. The paper presents several
examples of learning strategies that take advantages of the
automatically generated concept maps for concept learning and
exploration.
Abstract: This paper present the harmonic elimination of hybrid
multilevel inverters (HMI) which could be increase the number of
output voltage level. Total Harmonic Distortion (THD) is one of the
most important requirements concerning performance indices.
Because of many numbers output levels of HMI, it had numerous
unknown variables of eliminate undesired individual harmonic and
THD nonlinear equations set. Optimized harmonic stepped waveform
(OHSW) is solving switching angles conventional method, but most
complicated for solving as added level. The artificial intelligent
techniques are deliberation to solve this problem. This paper presents
the Particle Swarm Optimization (PSO) technique for solving
switching angles to get minimum THD and eliminate undesired
individual harmonics of 15-levels hybrid multilevel inverters.
Consequently it had many variables and could eliminate numerous
harmonics. Both advantages including high level of inverter and
Particle Swarm Optimization (PSO) are used as powerful tools for
harmonics elimination.
Abstract: Urban disaster risks and vulnerabilities are great problems for Turkey. The annual loss of life and property through disaster in the world-s major metropolitan areas is increasing. Urban concentrations of the poor and less-informed in environmentally fragile locations suffer the impact of disaster disproportionately. Gecekondu (squatter) developments will compound the inherent risks associated with high-density environments, in appropriate technologies, and inadequate infrastructure. On the other hand, there are many geological disadvantages such as sitting on top of active tectonic plate boundaries, and why having avalanche, flood, and landslide and drought prone areas in Turkey. However, this natural formation is inevitable; the only way to survive in such a harsh geography is to be aware of importance of these natural events and to take political and physical measures. The main aim of this research is to bring up the magnitude of natural hazard risks in Izmir built-up zone, not being taken into consideration adequately. Because the dimensions of the peril are not taken seriously enough, the natural hazard risks, which are commonly well known, are not considered important or they are being forgotten after some time passes. Within this research, the magnitude of natural hazard risks for Izmir is being presented in the scope of concrete and local researches over Izmir risky areas.