Abstract: Industrial robots become useless without end-effectors
that for many instances are in the form of friction grippers.
Commonly friction grippers apply frictional forces to different
objects on the basis of programmers- experiences. This puts a
limitation on the effectiveness of gripping force that may result in
damaging the object. This paper describes various stages of design
and development of a low cost sensor-based robotic gripper that
would facilitate the task of applying right gripping forces to different
objects. The gripper is also equipped with range sensors in order to
avoid collisions of the gripper with objects. It is a fully functional
automated pick and place gripper which can be used in many
industrial applications. Yet it can also be altered or further developed
in order to suit a larger number of industrial activities. The current
design of gripper could lead to designing completely automated robot
grippers able to improve the efficiency and productivity of industrial
robots.
Abstract: Question answering (QA) aims at retrieving precise information from a large collection of documents. Most of the Question Answering systems composed of three main modules: question processing, document processing and answer processing. Question processing module plays an important role in QA systems to reformulate questions. Moreover answer processing module is an emerging topic in QA systems, where these systems are often required to rank and validate candidate answers. These techniques aiming at finding short and precise answers are often based on the semantic relations and co-occurrence keywords. This paper discussed about a new model for question answering which improved two main modules, question processing and answer processing which both affect on the evaluation of the system operations. There are two important components which are the bases of the question processing. First component is question classification that specifies types of question and answer. Second one is reformulation which converts the user's question into an understandable question by QA system in a specific domain. The objective of an Answer Validation task is thus to judge the correctness of an answer returned by a QA system, according to the text snippet given to support it. For validating answers we apply candidate answer filtering, candidate answer ranking and also it has a final validation section by user voting. Also this paper described new architecture of question and answer processing modules with modeling, implementing and evaluating the system. The system differs from most question answering systems in its answer validation model. This module makes it more suitable to find exact answer. Results show that, from total 50 asked questions, evaluation of the model, show 92% improving the decision of the system.
Abstract: This paper describes the design concepts and
implementation of a 5-Joint mechanical arm for a rescue robot named
CEO Mission II. The multi-joint arm is a five degree of freedom
mechanical arm with a four bar linkage, which can be stretched to
125 cm. long. It is controlled by a teleoperator via the user-friendly
control and monitoring GUI program. With Inverse Kinematics
principle, we developed the method to control the servo angles of all
arm joints to get the desired tip position. By clicking the determined
tip position or dragging the tip of the mechanical arm on the
computer screen to the desired target point, the robot will compute
and move its multi-joint arm to the pose as seen on the GUI screen.
The angles of each joint are calculated and sent to all joint servos
simultaneously in order to move the mechanical arm to the desired
pose at once. The operator can also use a joystick to control the
movement of this mechanical arm and the locomotion of the robot.
Many sensors are installed at the tip of this mechanical arm for
surveillance from the high level and getting the vital signs of victims
easier and faster in the urban search and rescue tasks. It works very
effectively and easy to control. This mechanical arm and its software
were developed as a part of the CEO Mission II Rescue Robot that
won the First Runner Up award and the Best Technique award from
the Thailand Rescue Robot Championship 2006. It is a low cost,
simple, but functioning 5-Jiont mechanical arm which is built from
scratch, and controlled via wireless LAN 802.11b/g. This 5-Jiont
mechanical arm hardware concept and its software can also be used
as the basic mechatronics to many real applications.
Abstract: Detection of player identity is challenging task in sport video content analysis. In case of soccer video player number recognition is effective and precise solution. Jersey numbers can be considered as scene text and difficulties in localization and recognition appear due to variations in orientation, size, illumination, motion etc. This paper proposed new method for player number localization and recognition. By observing hue, saturation and value for 50 different jersey examples we noticed that most often combination of low and high saturated pixels is used to separate number and jersey region. Image segmentation method based on this observation is introduced. Then, novel method for player number localization based on internal contours is proposed. False number candidates are filtered using area and aspect ratio. Before OCR processing extracted numbers are enhanced using image smoothing and rotation normalization.
Abstract: Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data, both density based and partitioning clustering are combined for outlier detection. In this scheme partitioning clustering is also used to assign weights to attributes depending upon their respective relevance and weights are adaptive. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.
Abstract: The transition to sustainable development requires
considerable investments from stakeholders, both financial and
immaterial. However, accounting for such investments often poses a
challenge, as ventures with intangible or non-financial returns remain
oblivious to conventional accounting techniques and risk assessment.
That such investments may significantly contribute to the welfare of
those affected may act as a driving force behind attempting to bridge
this gap. This gains crucial importance as investments must be also
backed by governments and administrations; entities whose budget
depends on taxpayers- contributions and whose tasks are based on
securing the welfare of their citizens. Besides economic welfare,
citizens also require social and environmental wellbeing too.
However, administrations must also safeguard that welfare is
guaranteed not only to present, but to future generations too. With
already strained budgets and the requirement of sustainable
development, governments on all levels face the double challenge of
making both of these ends meet.
Abstract: Research has suggested that implicit learning tasks
may rely on episodic processing to generate above chance
performance on the standard classification tasks. The current
research examines the invariant features task (McGeorge and Burton,
1990) and argues that such episodic processing is indeed important.
The results of the experiment suggest that both rejection and
similarity strategies are used by participants in this task to
simultaneously reject unfamiliar items and to accept (falsely) familiar
items. Primarily these decisions are based on the presence of low or
high frequency goal based features of the stimuli presented in the
incidental learning phase. It is proposed that a goal based analysis of
the incidental learning task provides a simple step in understanding
which features of the episodic processing are most important for
explaining the match between incidental, implicit learning and test
performance.
Abstract: The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.
Abstract: The increasing demand for higher data rates in wireless communication systems has led to the more effective and efficient use of all allocated frequency bands. In order to use the whole bandwidth at maximum efficiency, one needs to have RF power amplifiers with a higher linear level and memory-less performance. This is considered to be a major challenge to circuit designers. In this thesis the linearity and memory are studied and examined via the behavior of the intermodulation distortion (IMD). A major source of the in-band distortion can be shown to be influenced by the out-of-band impedances presented at either the input or the output of the device, especially those impedances terminated the low frequency (IF) components. Thus, in order to regulate the in-band distortion, the out of-band distortion must be controllable. These investigations are performed on a 12W LDMOS device characterised at 2.1 GHz within a purpose built, high-power measurement system.
Abstract: The task of strategic information technology
management is to focus on adapting technology to ensure
competitiveness. A key factor for success in this sector is awareness
and readiness to deploy new technologies and exploit the services
they offer. Recently, the need for more flexible and dynamic user
interfaces (UIs) has been recognized, especially in mobile
applications. An ongoing research project (MOP), initiated by TUT
in Finland, is looking at how mobile device UIs can be adapted for
different needs and contexts. It focuses on examining the possibilities
to develop adapter software for solving the challenges related to the
UI and its flexibility in mobile devices. This approach has great
potential for enhancing information transfer in mobile devices, and
consequently for improving information management. The
technology presented here could be one of the key emerging
technologies in the information technology sector in relation to
mobile devices and telecommunications.
Abstract: With the necessity of increased processing capacity with less energy consumption; power aware multiprocessor system has gained more attention in the recent future. One of the additional challenges that is to be solved in a multi-processor system when compared to uni-processor system is job allocation. This paper presents a novel task dependent job allocation algorithm: Energy centric- Allocation (Ec-A) and Rate Monotonic (RM) scheduling to minimize energy consumption in a multiprocessor system. A simulation analysis is carried out to verify the performance increase with reduction in energy consumption and required number of processors in the system.
Abstract: Classifying biomedical literature is a difficult and
challenging task, especially when a large number of biomedical
articles should be organized into a hierarchical structure. In this paper,
we present an approach for classifying a collection of biomedical text
abstracts downloaded from Medline database with the help of
ontology alignment. To accomplish our goal, we construct two types
of hierarchies, the OHSUMED disease hierarchy and the Medline
abstract disease hierarchies from the OHSUMED dataset and the
Medline abstracts, respectively. Then, we enrich the OHSUMED
disease hierarchy before adapting it to ontology alignment process for
finding probable concepts or categories. Subsequently, we compute
the cosine similarity between the vector in probable concepts (in the
“enriched" OHSUMED disease hierarchy) and the vector in Medline
abstract disease hierarchies. Finally, we assign category to the new
Medline abstracts based on the similarity score. The results obtained
from the experiments show the performance of our proposed approach
for hierarchical classification is slightly better than the performance of
the multi-class flat classification.
Abstract: Today, building automation is advancing from simple
monitoring and control tasks of lightning and heating towards more
and more complex applications that require a dynamic perception
and interpretation of different scenes occurring in a building. Current
approaches cannot handle these newly upcoming demands. In this
article, a bionically inspired approach for multimodal, dynamic scene
perception and interpretation is presented, which is based on neuroscientific
and neuro-psychological research findings about the perceptual
system of the human brain. This approach bases on data from diverse
sensory modalities being processed in a so-called neuro-symbolic
network. With its parallel structure and with its basic elements being
information processing and storing units at the same time, a very
efficient method for scene perception is provided overcoming the
problems and bottlenecks of classical dynamic scene interpretation
systems.
Abstract: One major difficulty that faces developers of
concurrent and distributed software is analysis for concurrency based
faults like deadlocks. Petri nets are used extensively in the
verification of correctness of concurrent programs. ECATNets [2] are
a category of algebraic Petri nets based on a sound combination of
algebraic abstract types and high-level Petri nets. ECATNets have
'sound' and 'complete' semantics because of their integration in
rewriting logic [12] and its programming language Maude [13].
Rewriting logic is considered as one of very powerful logics in terms
of description, verification and programming of concurrent systems.
We proposed in [4] a method for translating Ada-95 tasking
programs to ECATNets formalism (Ada-ECATNet). In this paper,
we show that ECATNets formalism provides a more compact
translation for Ada programs compared to the other approaches based
on simple Petri nets or Colored Petri nets (CPNs). Such translation
doesn-t reduce only the size of program, but reduces also the number
of program states. We show also, how this compact Ada-ECATNet
may be reduced again by applying reduction rules on it. This double
reduction of Ada-ECATNet permits a considerable minimization of
the memory space and run time of corresponding Maude program.
Abstract: Assembly line balancing is a very important issue in
mass production systems due to production cost. Although many
studies have been done on this topic, but because assembly line
balancing problems are so complex they are categorized as NP-hard
problems and researchers strongly recommend using heuristic
methods. This paper presents a new heuristic approach called the
critical task method (CTM) for solving U-shape assembly line
balancing problems. The performance of the proposed heuristic
method is tested by solving a number of test problems and comparing
them with 12 other heuristics available in the literature to confirm the
superior performance of the proposed heuristic. Furthermore, to
prove the efficiency of the proposed CTM, the objectives are
increased to minimize the number of workstation (or equivalently
maximize line efficiency), and minimizing the smoothness index.
Finally, it is proven that the proposed heuristic is more efficient than
the others to solve the U-shape assembly line balancing problem.
Abstract: The cumulative conformance count (CCC) charts are
widespread in process monitoring of high-yield manufacturing.
Recently, it is found the use of variable sampling interval (VSI)
scheme could further enhance the efficiency of the standard CCC
charts. The average time to signal (ATS) a shift in defect rate has
become traditional measure of efficiency of a chart with the VSI
scheme. Determining the ATS is frequently a difficult and tedious
task. A simple method based on a finite Markov Chain approach for
modeling the ATS is developed. In addition, numerical results are
given.
Abstract: In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results.
Abstract: Markov games can be effectively used to design
controllers for nonlinear systems. The paper presents two novel
controller design algorithms by incorporating ideas from gametheory
literature that address safety and consistency issues of the
'learned' control strategy. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. We generate
an optimal control policy for the agent (controller) via a simple
Linear Program enabling the controller to learn about the unknown
environment. The controller is facing an unknown environment and
in our formulation this environment corresponds to the behavior rules
of the noise modeled as the opponent. Proposed approaches aim to
achieve 'safe-consistent' and 'safe-universally consistent' controller
behavior by hybridizing 'min-max', 'fictitious play' and 'cautious
fictitious play' approaches drawn from game theory. We empirically
evaluate the approaches on a simulated Inverted Pendulum swing-up
task and compare its performance against standard Q learning.
Abstract: The way music is interpreted by the human brain is a very interesting topic, but also an intricate one. Although this domain has been studied for over a century, many gray areas remain in the understanding of music. Recent advances have enabled us to perform accurate measurements of the time taken by the human brain to interpret and assimilate a sound. Cognitive computing provides tools and development environments that facilitate human cognition simulation. ACT-R is a cognitive architecture which offers an environment for implementing human cognitive tasks. This project combines our understanding of the music interpretation by a human listener and the ACT-R cognitive architecture to build SINGER, a computerized simulation for listening and recalling songs. The results are similar to human experimental data. Simulation results also show how it is easier to remember short melodies than long melodies which require more trials to be recalled correctly.
Abstract: This paper presents a design of source encoding
calculator software which applies the two famous algorithms in the
field of information theory- the Shannon-Fano and the Huffman
schemes. This design helps to easily realize the algorithms without
going into a cumbersome, tedious and prone to error manual
mechanism of encoding the signals during the transmission. The
work describes the design of the software, how it works, comparison
with related works, its efficiency, its usefulness in the field of
information technology studies and the future prospects of the
software to engineers, students, technicians and alike. The designed
“Encodia" software has been developed, tested and found to meet the
intended requirements. It is expected that this application will help
students and teaching staff in their daily doing of information theory
related tasks. The process is ongoing to modify this tool so that it can
also be more intensely useful in research activities on source coding.