Abstract: Accelerated life cycle test is applied to various products or components in order to reduce the time of life cycle test in industry. It must be considered for many test conditions according to the product characteristics for the test and the selection of acceleration parameter is especially very important. We have carried out the general life cycle test and the accelerated life cycle test by applying the acceleration factor (AF) considering the characteristics of brushless DC (BLDC) motor for washing machine. The final purpose of this study is to verify the validity by analyzing the results of the general life cycle test and the accelerated life cycle test. It will make it possible to reduce the life test time through the reasonable accelerated life cycle test.
Abstract: The main purpose of the article is to make complex statistical analysis of tourist expenses of foreign visitors. We used mixed technique of selection that implies rules of random and proportional selection. Computer software SPSS was used to compute statistical data for corresponding analysis. Corresponding methodology of tourism statistics was implemented according to international standards. Important information was collected and grouped from the major Georgian airports. Techniques of statistical observation were prepared. A representative population of foreign visitors and a rule of selection of respondents were determined. We have a trend of growth of tourist numbers and share of tourists from post-soviet countries constantly increases. Level of satisfaction with tourist facilities and quality of service has grown, but still we have a problem of disparity between quality of service and prices. The design of tourist expenses of foreign visitors is diverse; competitiveness of tourist products of Georgian tourist companies is higher.
Abstract: In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.
Abstract: In this paper Artificial Neural Network (ANN) is used
to predict the solar irradiation in Djibouti for the first Time that
is useful to the integration of Concentrating Solar Power (CSP)
and sites selections for new or future solar plants as part of solar
energy development. An ANN algorithm was developed to establish
a forward/reverse correspondence between the latitude, longitude,
altitude and monthly solar irradiation. For this purpose the German
Aerospace Centre (DLR) data of eight Djibouti sites were used as
training and testing in a standard three layers network with the back
propagation algorithm of Lavenber-Marquardt. Results have shown a
very good agreement for the solar irradiation prediction in Djibouti
and proves that the proposed approach can be well used as an
efficient tool for prediction of solar irradiation by providing so helpful
information concerning sites selection, design and planning of solar
plants.
Abstract: There is numerous non-invasive blood glucose measurement technique developed by researchers, and near infrared (NIR) is the potential technique nowadays. However, there are some disagreements on the optimal wavelength range that is suitable to be used as the reference of the glucose substance in the blood. This paper focuses on the experimental data collection technique and also the analysis method used to analyze the data gained from the experiment. The selection of suitable linear and non-linear model structure is essential in prediction system, as the system developed need to be conceivably accurate.
Abstract: The aerial photogrammetry of shallow water bottoms has the potential to be an efficient high-resolution survey technique for shallow water topography, thanks to the advent of convenient UAV and automatic image processing techniques Structure-from-Motion (SfM) and Multi-View Stereo (MVS)). However, it suffers from the systematic overestimation of the bottom elevation, due to the light refraction at the air-water interface. In this study, we present an empirical method to correct for the effect of refraction after the usual SfM-MVS processing, using common software. The presented method utilizes the empirical relation between the measured true depth and the estimated apparent depth to generate an empirical correction factor. Furthermore, this correction factor was utilized to convert the apparent water depth into a refraction-corrected (real-scale) water depth. To examine its effectiveness, we applied the method to two river sites, and compared the RMS errors in the corrected bottom elevations with those obtained by three existing methods. The result shows that the presented method is more effective than the two existing methods: The method without applying correction factor and the method utilizes the refractive index of water (1.34) as correction factor. In comparison with the remaining existing method, which used the additive terms (offset) after calculating correction factor, the presented method performs well in Site 2 and worse in Site 1. However, we found this linear regression method to be unstable when the training data used for calibration are limited. It also suffers from a large negative bias in the correction factor when the apparent water depth estimated is affected by noise, according to our numerical experiment. Overall, the good accuracy of refraction correction method depends on various factors such as the locations, image acquisition, and GPS measurement conditions. The most effective method can be selected by using statistical selection (e.g. leave-one-out cross validation).
Abstract: Recently, collectable manufacturing data are rapidly
increasing. On the other hand, mega recall is getting serious as
a social problem. Under such circumstances, there are increasing
needs for preventing mega recalls by defect analysis such as
root cause analysis and abnormal detection utilizing manufacturing
data. However, the time to classify strings in manufacturing data
by traditional method is too long to meet requirement of quick
defect analysis. Therefore, we present String Length Distribution
Classification method (SLDC) to correctly classify strings in a short
time. This method learns character features, especially string length
distribution from Product ID, Machine ID in BOM and asset list.
By applying the proposal to strings in actual manufacturing data, we
verified that the classification time of strings can be reduced by 80%.
As a result, it can be estimated that the requirement of quick defect
analysis can be fulfilled.
Abstract: Gas condensing units with inner tubes heat exchangers represent third generation technology and differ from second generation heat and mass transfer units, which are fulfilled by passive filling material layer. The first one improves heat and mass transfer by increasing cooled contact surface of gas and condensate drops and film formed in inner tubes heat exchanger. This paper presents a selection of significant factors which influence the heat and mass transfer. Experimental planning is based on the research and analysis of main three independent variables; velocity of water and gas as well as density of spraying. Empirical mathematical models show that the coefficient of heat transfer is used as dependent parameter which depends on two independent variables; water and gas velocity. Empirical model is proved by the use of experimental data of two independent gas condensing units in Lithuania and Russia. Experimental data are processed by the use of heat transfer criteria-Kirpichov number. Results allow drawing the graphical nomogram for the calculation of heat and mass transfer conditions in the innovative and energy efficient gas cooling unit.
Abstract: Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.
Abstract: One of the biggest challenges in nonparametric
regression is the curse of dimensionality. Additive models are known
to overcome this problem by estimating only the individual additive
effects of each covariate. However, if the model is misspecified, the
accuracy of the estimator compared to the fully nonparametric one
is unknown. In this work the efficiency of completely nonparametric
regression estimators such as the Loess is compared to the estimators
that assume additivity in several situations, including additive and
non-additive regression scenarios. The comparison is done by
computing the oracle mean square error of the estimators with regards
to the true nonparametric regression function. Then, a backward
elimination selection procedure based on the Akaike Information
Criteria is proposed, which is computed from either the additive or
the nonparametric model. Simulations show that if the additive model
is misspecified, the percentage of time it fails to select important
variables can be higher than that of the fully nonparametric approach.
A dimension reduction step is included when nonparametric estimator
cannot be computed due to the curse of dimensionality. Finally, the
Boston housing dataset is analyzed using the proposed backward
elimination procedure and the selected variables are identified.
Abstract: Wireless sensor networks can be tiny, low cost, intelligent sensors connected with advanced communication systems. WSNs have pulled in significant consideration as a matter of fact that, industrial as well as medical solicitations employ these in monitoring targets, conservational observation, obstacle exposure, movement regulator etc. In these applications, sensor hubs are thickly sent in the unattended environment with little non-rechargeable batteries. This constraint requires energy-efficient systems to drag out the system lifetime. There are redundancies in data sent over the network. To overcome this, multiple virtual spine scheduling has been presented. Such networks problems are called Maximum Lifetime Backbone Scheduling (MLBS) problems. Though this sleep wake cycle reduces radio usage, improvement can be made in the path in which the group heads stay selected. Cluster head selection with emphasis on geometrical relation of the system will enhance the load sharing among the nodes. Also the data are analyzed to reduce redundant transmission. Multi-hop communication will facilitate lighter loads on the network.
Abstract: Cloud computing is ready to transform the structure of businesses and learning through supplying the real-time applications and provide an immediate help for small to medium sized businesses. The ability to run a hypervisor inside a virtual machine is important feature of virtualization and it is called nested virtualization. In today’s growing field of information technology, many of the virtualization models are available, that provide a convenient approach to implement, but decision for a single model selection is difficult. This paper explains the applications of operating system based virtualization in cloud computing with an appropriate/suitable model with their different specifications and user’s requirements. In the present paper, most popular models are selected, and the selection was based on container and hypervisor based virtualization. Selected models were compared with a wide range of user’s requirements as number of CPUs, memory size, nested virtualization supports, live migration and commercial supports, etc. and we identified a most suitable model of virtualization.
Abstract: With the development of HyperSpectral Imagery
(HSI) technology, the spectral resolution of HSI became denser,
which resulted in large number of spectral bands, high correlation
between neighboring, and high data redundancy. However, the
semantic interpretation is a challenging task for HSI analysis
due to the high dimensionality and the high correlation of the
different spectral bands. In fact, this work presents a dimensionality
reduction approach that allows to overcome the different issues
improving the semantic interpretation of HSI. Therefore, in order
to preserve the spatial information, the Tensor Locality Preserving
Projection (TLPP) has been applied to transform the original HSI.
In the second step, knowledge has been extracted based on the
adjacency graph to describe the different pixels. Based on the
transformation matrix using TLPP, a weighted matrix has been
constructed to rank the different spectral bands based on their
contribution score. Thus, the relevant bands have been adaptively
selected based on the weighted matrix. The performance of the
presented approach has been validated by implementing several
experiments, and the obtained results demonstrate the efficiency
of this approach compared to various existing dimensionality
reduction techniques. Also, according to the experimental results,
we can conclude that this approach can adaptively select the
relevant spectral improving the semantic interpretation of HSI.
Abstract: One good analysis method in seismic response analysis is direct time integration, which widely adopts Rayleigh damping. An approach is presented for selection of Rayleigh damping coefficients to be used in seismic analyses to produce a response that is consistent with Modal damping response. In the presented approach, the expression of the error of peak response, acquired through complete quadratic combination method, and Rayleigh damping coefficients was set up and then the coefficients were produced by minimizing the error. Two finite element modes of soil layers, excited by 28 seismic waves, were used to demonstrate the feasibility and validity.
Abstract: There are numerous nutritional supplements, such as multivitamins and nutrition drinks, in the market today. Many of these supplements are expensive and tend to be driven commercially by business decisions and big marketing budgets. Many of the costs are ultimately borne by the end user in the quest for keeping to a healthy lifestyle. This paper proposes a system with a list of ten determinants to gauge how to decide the value of various supplements. It suggests variables such as composition, safety, efficacy and bioavailability, as well as several other considerations. These guidelines can help to tackle many of the issues that people of all ages face in the way that they receive essential nutrients. The system also aims to promote and improve the safety and choice of foods and supplements. In so doing, the system aims to promote the individual’s or population’s control over their own health and reduce the growing health care burden on the society.
Abstract: This study presents a technique clarifying the effect of ambient air temperature and loads power factor changing from standard values on electric generator power rating. The study introduces an optimized technique for selecting the correct electric generator power rating for certain application and operating site ambient temperature. The de-rating factors due to the previous effects will be calculated to be applied on a generator to select its power rating accurately to avoid unsafe operation and save its lifetime. The information in this paper provides a simple, accurate, and general method for synchronous generator selection and eliminates common errors.
Abstract: This research aims at finding out the causes that led to wrong lexical selections in machine translation (MT) rather than categorizing lexical errors, which has been a main practice in error analysis. By manually examining and analyzing lexical errors outputted by a MT system, it suggests what knowledge would help the system reduce lexical errors.
Abstract: New sensors and technologies – such as microphones,
touchscreens or infrared sensors – are currently making their
appearance in the automotive sector, introducing new kinds of
Human-Machine Interfaces (HMIs). The interactions with such tools
might be cognitively expensive, thus unsuitable for driving tasks.
It could for instance be dangerous to use touchscreens with a
visual feedback while driving, as it distracts the driver’s visual
attention away from the road. Furthermore, new technologies in
car cockpits modify the interactions of the users with the central
system. In particular, touchscreens are preferred to arrays of buttons
for space improvement and design purposes. However, the buttons’
tactile feedback is no more available to the driver, which makes
such interfaces more difficult to manipulate while driving. Gestures
combined with an auditory feedback might therefore constitute an
interesting alternative to interact with the HMI. Indeed, gestures can
be performed without vision, which means that the driver’s visual
attention can be totally dedicated to the driving task. In fact, the
auditory feedback can both inform the driver with respect to the task
performed on the interface and on the performed gesture, which might
constitute a possible solution to the lack of tactile information. As
audition is a relatively unused sense in automotive contexts, gesture
sonification can contribute to reducing the cognitive load thanks
to the proposed multisensory exploitation. Our approach consists
in using a virtual object (VO) to sonify the consequences of the
gesture rather than the gesture itself. This approach is motivated
by an ecological point of view: Gestures do not make sound, but
their consequences do. In this experiment, the aim was to identify
efficient sound strategies, to transmit dynamic information of VOs to
users through sound. The swipe gesture was chosen for this purpose,
as it is commonly used in current and new interfaces. We chose
two VO parameters to sonify, the hand-VO distance and the VO
velocity. Two kinds of sound parameters can be chosen to sonify the
VO behavior: Spectral or temporal parameters. Pitch and brightness
were tested as spectral parameters, and amplitude modulation as a
temporal parameter. Performances showed a positive effect of sound
compared to a no-sound situation, revealing the usefulness of sounds
to accomplish the task.
Abstract: In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.
Abstract: The influence of injector attitude on wall heat flux plays an important role in predicting the start-up transient and also determining the combustion chamber wall durability of liquid rockets. In this paper comprehensive numerical studies have been carried out on an idealized liquid rocket combustion chamber to examine the transient wall heat flux during its start-up transient at different injector attitude. Numerical simulations have been carried out with the help of a validated 2d axisymmetric, double precision, pressure-based, transient, species transport, SST k-omega model with laminar finite rate model for governing turbulent-chemistry interaction for four cases with different jet intersection angles, viz., 0o, 30o, 45o, and 60o. We concluded that the jets intersection angle is having a bearing on the time and location of the maximum wall-heat flux zone of the liquid rocket combustion chamber during the start-up transient. We also concluded that the wall heat flux mapping in liquid rocket combustion chamber during the start-up transient is a meaningful objective for the chamber wall material selection and the lucrative design optimization of the combustion chamber for improving the payload capability of the rocket.