Abstract: Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.
Abstract: E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).
Abstract: Nowadays, due to diverse electric products and complex electromagnetic environment, the localization and troubleshooting of the electromagnetic radiation source is urgent and necessary especially on the condition of far field. However, based on the existing DOA positioning method, the system or devices are complex, bulky and expensive. To address this issue, this paper proposes a single antenna radiation source localization method. A single antenna moves to form a virtual antenna array combined with DOA and MUSIC algorithm to position accurately, meanwhile reducing the cost and simplify the equipment. As shown in the results of simulations and experiments, the virtual antenna array DOA estimation modeling is correct and its positioning is credible.
Abstract: Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.
Abstract: Power failure resulting from tower collapse due to violent seismic events might bring enormous and inestimable losses. The Chi-Chi earthquake, for example, strongly struck Taiwan and caused huge damage to the power system on September 21, 1999. Nearly 10% of extra high voltage (EHV) transmission towers were damaged in the earthquake. Therefore, seismic hazards of EHV transmission towers should be monitored and evaluated. The ultimate goal of this study is to establish a damage level assessment model for EHV transmission towers. The data of earthquakes provided by Taiwan Central Weather Bureau serve as a reference and then lay the foundation for earthquake simulations and analyses afterward. Some parameters related to the damage level of each point of an EHV tower are simulated and analyzed by the data from monitoring stations once an earthquake occurs. Through the Fourier transform, the seismic wave is then analyzed and transformed into different wave frequencies, and the data would be shown through a response spectrum. With this method, the seismic frequency which damages EHV towers the most is clearly identified. An estimation model is built to determine the damage level caused by a future seismic event. Finally, instead of relying on visual observation done by inspectors, the proposed model can provide a power company with the damage information of a transmission tower. Using the model, manpower required by visual observation can be reduced, and the accuracy of the damage level estimation can be substantially improved. Such a model is greatly useful for health and construction monitoring because of the advantages of long-term evaluation of structural characteristics and long-term damage detection.
Abstract: Oil palm or Elaeis guineensis is considered as the golden crop in Malaysia. But oil palm industry in this country is now facing with the most devastating disease called as Ganoderma Basal Stem Rot disease. The objective of this paper is to analyze the economic loss due to this disease. There were three commercial oil palm sites selected for collecting the required data for economic analysis. Yield parameter used to measure the loss was the total weight of fresh fruit bunch in six months. The predictors include disease severity, change in disease severity, number of infected neighbor palms, age of palm, planting generation, topography, and first order interaction variables. The estimation model of yield loss was identified by using backward elimination based regression method. Diagnostic checking was conducted on the residual of the best yield loss model. The value of mean absolute percentage error (MAPE) was used to measure the forecast performance of the model. The best yield loss model was then used to estimate the economic loss by using the current monthly price of fresh fruit bunch at mill gate.
Abstract: This paper aimed to introduce the solution of concrete
slump recovery using chemical admixture type-F (superplasticizer,
naphthalene base) to the practice in order to solve unusable concrete
problem due to concrete loss its slump, especially for those tropical
countries that have faster slump loss rate. In the other hand, randomly
adding superplasticizer into concrete can cause concrete to segregate.
Therefore, this paper also develops the estimation model used to
calculate amount of second dose of superplasticizer need for concrete
slump recovery. Fresh properties of ordinary Portland cement
concrete with volumetric ratio of paste to void between aggregate
(paste content) of 1.1-1.3 with water-cement ratio zone of 0.30 to
0.67 and initial superplasticizer (naphthalene base) of 0.25%-1.6%
were tested for initial slump and slump loss for every 30 minutes for
one and half hour by slump cone test. Those concretes with slump
loss range from 10% to 90% were re-dosed and successfully
recovered back to its initial slump. Slump after re-dosed was tested
by slump cone test. From the result, it has been concluded that, slump
loss was slower for those mix with high initial dose of
superplasticizer due to addition of superplasticizer will disturb
cement hydration. The required second dose of superplasticizer was
affected by two major parameters, which were water-cement ratio
and paste content, where lower water-cement ratio and paste content
cause an increase in require second dose of superplasticizer. The
amount of second dose of superplasticizer is higher as the solid
content within the system is increase, solid can be either from cement
particles or aggregate. The data was analyzed to form an equation use
to estimate the amount of second dosage requirement of
superplasticizer to recovery slump to its original.
Abstract: The global demand for long-tailed macaques for
medical experimentation has continued to increase. Fulfillment of
Indonesian export demands has been mostly from natural habitats,
based on a harvesting quota. This quota has been determined
according to the total catch for a given year, and not based on
consideration of any demographic parameters or physical
environmental factors with regard to the animal; hence threatening
the sustainability of the various populations. It is therefore necessary
to formulate a method for calculating a sustainable harvesting quota,
based on population parameters in natural habitats. Considering the
possibility of variations in habitat characteristics and population
parameters, a time series observation of demographic and
physical/biotic parameters, in various habitats, was performed on 13
groups of long-tailed macaques, distributed throughout the West
Java, Lampung and Yogyakarta areas of Indonesia. These provinces
were selected for comparison of the influence of human/tourism
activities. Data on population parameters that was collected included
data on life expectancy according to age class, numbers of
individuals by sex and age class, and ‘ratio of infants to reproductive
females’. The estimation of population growth was based on a
population dynamic growth model: the Leslie matrix. The harvesting
quota was calculated as being the difference between the actual
population size and the MVP (minimum viable population) for each
sex and age class. Observation indicated that there were variations within group size
(24–106 individuals), gender (sex) ratio (1:1 to 1:1.3), life expectancy
value (0.30 to 0.93), and ‘ratio of infants to reproductive females’
(0.23 to 1.56). Results of subsequent calculations showed that
sustainable harvesting quotas for each studied group of long-tailed
macaques, ranged from 29 to 110 individuals. An estimation model
of the MVP for each age class was formulated as Log Y = 0.315 +
0.884 Log Ni (number of individual on ith age class). This study also
found that life expectancy for the juvenile age class was affected by
the humidity under tree stands, and dietary plants’ density at sapling,
pole and tree stages (equation: Y=2.296 – 1.535 RH + 0.002 Kpcg –
0.002 Ktg – 0.001 Kphn, R2 = 89.6% with a significance value of
0.001). By contrast, for the sub-adult-adult age class, life expectancy
was significantly affected by slope (equation: Y=0.377 = 0.012 Kml,
R2 = 50.4%, with significance level of 0.007). The infant-toreproductive-
female ratio was affected by humidity under tree stands,
and dietary plant density at sapling and pole stages (equation: Y = -
1.432 + 2.172 RH – 0.004 Kpcg + 0.003 Ktg, R2 = 82.0% with
significance level of 0.001). This research confirmed the importance
of population parameters in determining the minimum viable
population, and that MVP varied according to habitat characteristics
(especially food availability). It would be difficult therefore, to
formulate a general mathematical equation model for determining a
harvesting quota for the species as a whole.
Abstract: Today’s VLSI networks demands for high speed. And
in this work the compact form mathematical model for current mode
signalling in VLSI interconnects is presented.RLC interconnect line
is modelled using characteristic impedance of transmission line and
inductive effect. The on-chip inductance effect is dominant at lower
technology node is emulated into an equivalent resistance. First order
transfer function is designed using finite difference equation, Laplace
transform and by applying the boundary conditions at the source and
load termination. It has been observed that the dominant pole
determines system response and delay in the proposed model. The
novel proposed current mode model shows superior performance as
compared to voltage mode signalling. Analysis shows that current
mode signalling in VLSI interconnects provides 2.8 times better
delay performance than voltage mode. Secondly the damping factor
of a lumped RLC circuit is shown to be a useful figure of merit.
Abstract: Fuzzy inference method based approach to the
forming of modular intellectual system of assessment the quality of
communication services is proposed. Developed under this approach
the basic fuzzy estimation model takes into account the
recommendations of the International Telecommunication Union in
respect of the operation of packet switching networks based on IPprotocol.
To implement the main features and functions of the fuzzy
control system of quality telecommunication services it is used
multilayer feedforward neural network.
Abstract: Software Effort Estimation is the process of estimating the effort required to develop software. By estimating the effort, the cost and schedule required to estimate the software can be determined. Accurate Estimate helps the developer to allocate the resource accordingly in order to avoid cost overrun and schedule overrun. Several methods are available in order to estimate the effort among which soft computing based method plays a prominent role. Software cost estimation deals with lot of uncertainty among all soft computing methods neural network is good in handling uncertainty. In this paper Radial Basis Function Network is compared with the back propagation network and the results are validated using six data sets and it is found that RBFN is best suitable to estimate the effort. The Results are validated using two tests the error test and the statistical test.
Abstract: Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector autoregressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is Neural networks using Nelson-Siegel estimation of yield curves.
Abstract: In this study, we analyzed the factors that affect research funds using linear regression analysis to increase the effectiveness of investments in national research projects. We collected 7,916 items of data on research projects that were in the process of being finished or were completed between 2010 and 2011. Data pre-processing and visualization were performed to derive statistically significant results. We identified factors that affected funding using analysis of fit distributions and estimated increasing or decreasing tendencies based on these factors.
Abstract: Accurate software cost estimates are critical to both
developers and customers. They can be used for generating request
for proposals, contract negotiations, scheduling, monitoring and
control. The exact relationship between the attributes of the effort
estimation is difficult to establish. A neural network is good at
discovering relationships and pattern in the data. So, in this paper a
comparative analysis among existing Halstead Model, Walston-Felix
Model, Bailey-Basili Model, Doty Model and Neural Network
Based Model is performed. Neural Network has outperformed the
other considered models. Hence, we proposed Neural Network
system as a soft computing approach to model the effort estimation
of the software systems.
Abstract: Virtual touch screen using camera is an ordinary screen which uses a camera to imitate the touch screen by taking a picture of an indicator, e.g., finger, which is laid on the screen, converting the indicator tip position on the picture to the position on the screen, and moving the cursor on the screen to that position. In fact, the indicator is not laid on the screen directly, but it is intervened by the cover at some intervals. In spite of this gap, if the eye-indicator-camera angle is not large, the mapping from the indicator tip positions on the image to the corresponding cursor positions on the screen is not difficult and could be done with a little error. However, the larger the angle is, the bigger the error in the mapping occurs. This paper proposes cursor position estimation model for virtual touch screen using camera which could eliminate this kind of error. The proposed model (i) moves the on-screen pilot cursor to the screen position which locates on the screen at the position just behind the indicator tip when the indicator tip has been looked from the camera position, and then (ii) converts that pilot cursor position to the desirable cursor position (the position on the screen when it has been looked from the user-s eye through the indicator tip) by using the bilinear transformation. Simulation results show the correctness of the estimated cursor position by using the proposed model.
Abstract: Design for cost (DFC) is a method that reduces life
cycle cost (LCC) from the angle of designers. Multiple domain
features mapping (MDFM) methodology was given in DFC. Using
MDFM, we can use design features to estimate the LCC. From the
angle of DFC, the design features of family cars were obtained, such
as all dimensions, engine power and emission volume. At the
conceptual design stage, cars- LCC were estimated using back
propagation (BP) artificial neural networks (ANN) method and
case-based reasoning (CBR). Hamming space was used to measure the
similarity among cases in CBR method. Levenberg-Marquardt (LM)
algorithm and genetic algorithm (GA) were used in ANN. The
differences of LCC estimation model between CBR and artificial
neural networks (ANN) were provided. ANN and CBR separately
each method has its shortcomings. By combining ANN and CBR
improved results accuracy was obtained. Firstly, using ANN selected
some design features that affect LCC. Then using LCC estimation
results of ANN could raise the accuracy of LCC estimation in CBR
method. Thirdly, using ANN estimate LCC errors and correct errors in
CBR-s estimation results if the accuracy is not enough accurate.
Finally, economically family cars and sport utility vehicle (SUV) was
given as LCC estimation cases using this hybrid approach combining
ANN and CBR.
Abstract: Software development is moving towards agility with use cases and scenarios being used for requirements stories. Estimates of software costs are becoming even more important than before as effects of delays is much larger in successive short releases context of agile development. Thus, this paper reports on the development of new linear use case based software cost estimation model applicable in the very early stages of software development being based on simple metric. Evaluation showed that accuracy of estimates varies between 43% and 55% of actual effort of historical test projects. These results outperformed those of wellknown models when applied in the same context. Further work is being carried out to improve the performance of the proposed model when considering the effect of non-functional requirements.
Abstract: Various models have been derived by studying large number of completed software projects from various organizations and applications to explore how project sizes mapped into project effort. But, still there is a need to prediction accuracy of the models. As Neuro-fuzzy based system is able to approximate the non-linear function with more precision. So, Neuro-Fuzzy system is used as a soft computing approach to generate model by formulating the relationship based on its training. In this paper, Neuro-Fuzzy technique is used for software estimation modeling of on NASA software project data and performance of the developed models are compared with the Halstead, Walston-Felix, Bailey-Basili and Doty Models mentioned in the literature.
Abstract: This paper is concerned with the application of the vision control algorithm for robot's point placement task in discontinuous trajectory caused by obstacle. The presented vision control algorithm consists of four models, which are the robot kinematic model, vision system model, parameters estimation model, and robot joint angle estimation model.When the robot moves toward a target along discontinuous trajectory, several types of obstacles appear in two obstacle regions. Then, this study is to investigate how these changes will affect the presented vision control algorithm.Thus, the practicality of the vision control algorithm is demonstrated experimentally by performing the robot's point placement task in discontinuous trajectory by obstacle.
Abstract: Effective evaluation of software development effort is an important aspect of successful project management. Based on a large database with 4106 projects ever developed, this study statistically examines the factors that influence development effort. The factors found to be significant for effort are project size, average number of developers that worked on the project, type of development, development language, development platform, and the use of rapid application development. Among these factors, project size is the most critical cost driver. Unsurprisingly, this study found that the use of CASE tools does not necessarily reduce development effort, which adds support to the claim that the use of tools is subtle. As many of the current estimation models are rarely or unsuccessfully used, this study proposes a parsimonious parametric model for the prediction of effort which is both simple and more accurate than previous models.