Abstract: This paper proposes an improvement method of classification
efficiency in a classification model. The model is used
in a risk search system and extracts specific labels from articles
posted at bulletin board sites. The system can analyze the important
discussions composed of the articles. The improvement method
introduces ensemble learning methods that use multiple classification
models. Also, it introduces expressions related to the specific labels
into generation of word vectors. The paper applies the improvement
method to articles collected from three bulletin board sites selected
by users and verifies the effectiveness of the improvement method.
Abstract: With the explosive growth of information sources available on the World Wide Web, it has become increasingly difficult to identify the relevant pieces of information, since web pages are often cluttered with irrelevant content like advertisements, navigation-panels, copyright notices etc., surrounding the main content of the web page. Hence, tools for the mining of data regions, data records and data items need to be developed in order to provide value-added services. Currently available automatic techniques to mine data regions from web pages are still unsatisfactory because of their poor performance and tag-dependence. In this paper a novel method to extract data items from the web pages automatically is proposed. It comprises of two steps: (1) Identification and Extraction of the data regions based on visual clues information. (2) Identification of data records and extraction of data items from a data region. For step1, a novel and more effective method is proposed based on visual clues, which finds the data regions formed by all types of tags using visual clues. For step2 a more effective method namely, Extraction of Data Items from web Pages (EDIP), is adopted to mine data items. The EDIP technique is a list-based approach in which the list is a linear data structure. The proposed technique is able to mine the non-contiguous data records and can correctly identify data regions, irrespective of the type of tag in which it is bound. Our experimental results show that the proposed technique performs better than the existing techniques.
Abstract: This paper presents a conceptual model of agreement
options for negotiation support in multi-person decision on
optimizing high-rise building columns. The decision is complicated
since many parties involved in choosing a single alternative from a
set of solutions. There are different concern caused by differing
preferences, experiences, and background. Such building columns as
alternatives are referred to as agreement options which are
determined by identifying the possible decision maker group,
followed by determining the optimal solution for each group. The
group in this paper is based on three-decision makers preferences that
are designer, programmer, and construction manager. Decision
techniques applied to determine the relative value of the alternative
solutions for performing the function. Analytical Hierarchy Process
(AHP) was applied for decision process and game theory based agent
system for coalition formation. An n-person cooperative game is
represented by the set of all players. The proposed coalition
formation model enables each agent to select individually its allies or
coalition. It further emphasizes the importance of performance
evaluation in the design process and value-based decision.
Abstract: This paper describes a novel and effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Standard deviation of mean vectors of color distribution of rows and columns of images for CBIR". In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. This paper describes the approach that uses contents as feature vector for retrieval of similar images. There are several classes of features that are used to specify queries: colour, texture, shape, spatial layout. Colour features are often easily obtained directly from the pixel intensities. In this paper feature extraction is done for the texture descriptor that is 'variance' and 'Variance of Variances'. First standard deviation of each row and column mean is calculated for R, G, and B planes. These six values are obtained for one image which acts as a feature vector. Secondly we calculate variance of the row and column of R, G and B planes of an image. Then six standard deviations of these variance sequences are calculated to form a feature vector of dimension six. We applied our approach to a database of 300 BMP images. We have determined the capability of automatic indexing by analyzing image content: color and texture as features and by applying a similarity measure Euclidean distance.
Abstract: In an open real-time system environment, the coexistence of different kinds of real-time and non real-time applications makes the system scheduling mechanism face new requirements and challenges. One two-level scheduling scheme of the open real-time systems is introduced, and points out that hard and soft real-time applications are scheduled non-distinctively as the same type real-time applications, the Quality of Service (QoS) cannot be guaranteed. It has two flaws: The first, it can not differentiate scheduling priorities of hard and soft real-time applications, that is to say, it neglects characteristic differences between hard real-time applications and soft ones, so it does not suit a more complex real-time environment. The second, the worst case execution time of soft real-time applications cannot be predicted exactly, so it is not worth while to cost much spending in order to assure all soft real-time applications not to miss their deadlines, and doing that may cause resource wasting. In order to solve this problem, a novel two-level real-time scheduling mechanism (including scheduling profile and scheduling algorithm) which adds the process of dealing with soft real-time applications is proposed. Finally, we verify real-time scheduling mechanism from two aspects of theory and experiment. The results indicate that our scheduling mechanism can achieve the following objectives. (1) It can reflect the difference of priority when scheduling hard and soft real-time applications. (2) It can ensure schedulability of hard real-time applications, that is, their rate of missing deadline is 0. (3) The overall rate of missing deadline of soft real-time applications can be less than 1. (4) The deadline of a non-real-time application is not set, whereas the scheduling algorithm that server 0 S uses can avoid the “starvation" of jobs and increase QOS. By doing that, our scheduling mechanism is more compatible with different types of applications and it will be applied more widely.
Abstract: In this work a new platform for mobile-health systems is
presented. System target application is providing decision support to
rescue corps or military medical personnel in combat areas. Software
architecture relies on a distributed client-server system that manages a
wireless ad-hoc networks hierarchy in which several different types of
client operate. Each client is characterized for different hardware and
software requirements. Lower hierarchy levels rely in a network of
completely custom devices that store clinical information and patient
status and are designed to form an ad-hoc network operating in the
2.4 GHz ISM band and complying with the IEEE 802.15.4 standard
(ZigBee). Medical personnel may interact with such devices, that are
called MICs (Medical Information Carriers), by means of a PDA
(Personal Digital Assistant) or a MDA (Medical Digital Assistant),
and transmit the information stored in their local databases as well as
issue a service request to the upper hierarchy levels by using IEEE
802.11 a/b/g standard (WiFi). The server acts as a repository that
stores both medical evacuation forms and associated events (e.g., a
teleconsulting request). All the actors participating in the diagnostic
or evacuation process may access asynchronously to such repository
and update its content or generate new events. The designed system
pretends to optimise and improve information spreading and flow
among all the system components with the aim of improving both
diagnostic quality and evacuation process.
Abstract: In this note, the robust static output feedback
stabilisation of an induction machine is addressed. The machine is
described by a non homogenous bilinear model with structural
uncertainties, and the feedback gain is computed via an iterative LMI
(ILMI) algorithm.
Abstract: Hybrid algorithm is the hot issue in Computational
Intelligence (CI) study. From in-depth discussion on Simulation
Mechanism Based (SMB) classification method and composite patterns,
this paper presents the Mamdani model based Adaptive Neural
Fuzzy Inference System (M-ANFIS) and weight updating formula in
consideration with qualitative representation of inference consequent
parts in fuzzy neural networks. M-ANFIS model adopts Mamdani
fuzzy inference system which has advantages in consequent part.
Experiment results of applying M-ANFIS to evaluate traffic Level
of service show that M-ANFIS, as a new hybrid algorithm in computational
intelligence, has great advantages in non-linear modeling,
membership functions in consequent parts, scale of training data and
amount of adjusted parameters.
Abstract: The more recent satellite projects/programs makes
extensive usage of real – time embedded systems. 16 bit processors
which meet the Mil-Std-1750 standard architecture have been used in
on-board systems. Most of the Space Applications have been written
in ADA. From a futuristic point of view, 32 bit/ 64 bit processors are
needed in the area of spacecraft computing and therefore an effort is
desirable in the study and survey of 64 bit architectures for space
applications. This will also result in significant technology
development in terms of VLSI and software tools for ADA (as the
legacy code is in ADA).
There are several basic requirements for a special processor for
this purpose. They include Radiation Hardened (RadHard) devices,
very low power dissipation, compatibility with existing operational
systems, scalable architectures for higher computational needs,
reliability, higher memory and I/O bandwidth, predictability, realtime
operating system and manufacturability of such processors.
Further on, these may include selection of FPGA devices, selection
of EDA tool chains, design flow, partitioning of the design, pin
count, performance evaluation, timing analysis etc.
This project deals with a brief study of 32 and 64 bit processors
readily available in the market and designing/ fabricating a 64 bit
RISC processor named RISC MicroProcessor with added
functionalities of an extended double precision floating point unit
and a 32 bit signal processing unit acting as co-processors. In this
paper, we emphasize the ease and importance of using Open Core
(OpenSparc T1 Verilog RTL) and Open “Source" EDA tools such as
Icarus to develop FPGA based prototypes quickly. Commercial tools
such as Xilinx ISE for Synthesis are also used when appropriate.
Abstract: Existing work in temporal logic on representing the
execution of infinitely many transactions, uses linear-time temporal
logic (LTL) and only models two-step transactions. In this paper,
we use the comparatively efficient branching-time computational tree
logic CTL and extend the transaction model to a class of multistep
transactions, by introducing distinguished propositional variables
to represent the read and write steps of n multi-step transactions
accessing m data items infinitely many times. We prove that the
well known correspondence between acyclicity of conflict graphs
and serializability for finite schedules, extends to infinite schedules.
Furthermore, in the case of transactions accessing the same set of
data items in (possibly) different orders, serializability corresponds
to the absence of cycles of length two. This result is used to give an
efficient encoding of the serializability condition into CTL.
Abstract: The main aim of this research is to investigate a novel technique for implementing a more natural and intelligent conversation system. Conversation systems are designed to converse like a human as much as their intelligent allows. Sometimes, we can think that they are the embodiment of Turing-s vision. It usually to return a predetermined answer in a predetermined order, but conversations abound with uncertainties of various kinds. This research will focus on an integrated natural language processing approach. This approach includes an integrated knowledge-base construction module, a conversation understanding and generator module, and a state manager module. We discuss effectiveness of this approach based on an experiment.
Abstract: The dynamics of User Datagram Protocol (UDP) traffic
over Ethernet between two computers are analyzed using nonlinear
dynamics which shows that there are two clear regimes in the data
flow: free flow and saturated. The two most important variables
affecting this are the packet size and packet flow rate. However,
this transition is due to a transcritical bifurcation rather than phase
transition in models such as in vehicle traffic or theorized large-scale
computer network congestion. It is hoped this model will help lay
the groundwork for further research on the dynamics of networks,
especially computer networks.
Abstract: This paper proposes a novel solution for optimizing
the size and communication overhead of a distributed multiagent
system without compromising the performance. The proposed approach
addresses the challenges of scalability especially when the
multiagent system is large. A modified spectral clustering technique
is used to partition a large network into logically related clusters.
Agents are assigned to monitor dedicated clusters rather than monitor
each device or node. The proposed scalable multiagent system is
implemented using JADE (Java Agent Development Environment)
for a large power system. The performance of the proposed topologyindependent
decentralized multiagent system and the scalable multiagent
system is compared by comprehensively simulating different
fault scenarios. The time taken for reconfiguration, the overall computational
complexity, and the communication overhead incurred are
computed. The results of these simulations show that the proposed
scalable multiagent system uses fewer agents efficiently, makes faster
decisions to reconfigure when a fault occurs, and incurs significantly
less communication overhead.
Abstract: Self-organizing map (SOM) is a well known data
reduction technique used in data mining. It can reveal structure in
data sets through data visualization that is otherwise hard to detect
from raw data alone. However, interpretation through visual
inspection is prone to errors and can be very tedious. There are
several techniques for the automatic detection of clusters of code
vectors found by SOM, but they generally do not take into account
the distribution of code vectors; this may lead to unsatisfactory
clustering and poor definition of cluster boundaries, particularly
where the density of data points is low. In this paper, we propose the
use of an adaptive heuristic particle swarm optimization (PSO)
algorithm for finding cluster boundaries directly from the code
vectors obtained from SOM. The application of our method to
several standard data sets demonstrates its feasibility. PSO algorithm
utilizes a so-called U-matrix of SOM to determine cluster boundaries;
the results of this novel automatic method compare very favorably to
boundary detection through traditional algorithms namely k-means
and hierarchical based approach which are normally used to interpret
the output of SOM.
Abstract: In this paper, all variables are supposed to be integer
and positive. In this modern method, objective function is assumed to
be maximized or minimized but constraints are always explained like
less or equal to. In this method, choosing a dual combination of ideal
nonequivalent and omitting one of variables. With continuing this
act, finally, having one nonequivalent with (n-m+1) unknown
quantities in which final nonequivalent, m is counter for constraints,
n is counter for variables of decision.
Abstract: Understanding proteins functions is a major goal in
the post-genomic era. Proteins usually work in context of other
proteins and rarely function alone. Therefore, it is highly relevant to
study the interaction partners of a protein in order to understand its
function. Machine learning techniques have been widely applied to
predict protein-protein interactions. Kernel functions play an
important role for a successful machine learning technique. Choosing
the appropriate kernel function can lead to a better accuracy in a
binary classifier such as the support vector machines. In this paper,
we describe a Bayesian kernel for the support vector machine to
predict protein-protein interactions. The use of Bayesian kernel can
improve the classifier performance by incorporating the probability
characteristic of the available experimental protein-protein
interactions data that were compiled from different sources. In
addition, the probabilistic output from the Bayesian kernel can assist
biologists to conduct more research on the highly predicted
interactions. The results show that the accuracy of the classifier has
been improved using the Bayesian kernel compared to the standard
SVM kernels. These results imply that protein-protein interaction can
be predicted using Bayesian kernel with better accuracy compared to
the standard SVM kernels.
Abstract: The reliability of distributed systems and computer
networks have been modeled by a probabilistic network or a graph G.
Computing the residual connectedness reliability (RCR), denoted by
R(G), under the node fault model is very useful, but is an NP-hard
problem. Since it may need exponential time of the network size to
compute the exact value of R(G), it is important to calculate its tight
approximate value, especially its lower bound, at a moderate
calculation time. In this paper, we propose an efficient algorithm for
reliability lower bound of distributed systems with unreliable nodes.
We also applied our algorithm to several typical classes of networks
to evaluate the lower bounds and show the effectiveness of our
algorithm.
Abstract: Service-oriented systems have become popular and
presented many advantages in develop and maintain process. The
coupling is the most important attribute of services when they are
integrated into a system. In this paper, we propose a suite of metrics
to evaluate service-s quality according to its ability of coupling. We
use the coupling metrics to measure the maintainability, reliability,
testability, and reusability of services. Our proposed metrics are
operated in run-time which bring more exact results.
Abstract: With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.
Abstract: EPA (Ethernet for Plant Automation) resolves the nondeterministic problem of standard Ethernet and accomplishes real-time communication by means of micro-segment topology and deterministic scheduling mechanism. This paper studies the real-time performance of EPA periodic data transmission from theoretical and experimental perspective. By analyzing information transmission characteristics and EPA deterministic scheduling mechanism, 5 indicators including delivery time, time synchronization accuracy, data-sending time offset accuracy, utilization percentage of configured timeslice and non-RTE bandwidth that can be used to specify the real-time performance of EPA periodic data transmission are presented and investigated. On this basis, the test principles and test methods of the indicators are respectively studied and some formulas for real-time performance of EPA system are derived. Furthermore, an experiment platform is developed to test the indicators of EPA periodic data transmission in a micro-segment. According to the analysis and the experiment, the methods to improve the real-time performance of EPA periodic data transmission including optimizing network structure, studying self-adaptive adjustment method of timeslice and providing data-sending time offset accuracy for configuration are proposed.