Search results for: classifiers accuracy

Authors: Yanqin Chen, Chao Jiang, Chongdu Cho

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This research presents the three-dimensional mechanical characteristics of a commercial gas diffusion layer by experiment and simulation results. Although the mechanical performance of gas diffusion layers has attracted much attention, its reliability and accuracy are still a major challenge. With the help of simulation analysis methods, it is beneficial to the gas diffusion layer’s extensive commercial development and the overall stress analysis of proton electrolyte membrane fuel cells during its pre-production design period. Therefore, in this paper, a three-dimensional constitutive model of a commercial gas diffusion layer, including its material stiffness matrix parameters, is developed and coded, in the user-defined material model of a commercial finite element method software for simulation. Then, the model is validated by comparing experimental results as well as simulation outcomes. As a result, both the experimental data and simulation results show a good agreement with each other, with high accuracy.

Keywords: Gas diffusion layer, proton electrolyte membrane fuel cell, stiffness matrix, three-dimensional mechanical characteristics, user-defined material model.

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Authors: Ruxandra Barbulescu, Daniel Ioan, Gabriela Ciuprina

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The saltatory conduction is the way the action potential is transmitted along a myelinated axon. The potential diffuses along the myelinated compartments and it is regenerated in the Ranvier nodes due to the ion channels allowing the flow across the membrane. For an efficient simulation of populations of neurons, it is important to use reduced order models both for myelinated compartments and for Ranvier nodes and to have control over their accuracy and inner parameters. The paper presents a reduced order model of this neural system which allows an efficient simulation method for the saltatory conduction in myelinated axons. This model is obtained by concatenating reduced order linear models of 1D myelinated compartments and nonlinear 0D models of Ranvier nodes. The models for the myelinated compartments are selected from a series of spatially distributed models developed and hierarchized according to their modeling errors. The extracted model described by a nonlinear PDE of hyperbolic type is able to reproduce the saltatory conduction with acceptable accuracy and takes into account the finite propagation speed of potential. Finally, this model is again reduced in order to make it suitable for the inclusion in large-scale neural circuits.

Keywords: Saltatory conduction, action potential, myelinated compartments, nonlinear, Ranvier nodes, reduced order models, POD.

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Authors: Rafał Urbaniak, Robert Kłosowiak, Michał Ciałkowski, Jarosław Bartoszewicz

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The Chair of Thermal Engineering at Poznan University of Technology has been conducted research works on the possibilities of using carbon nanostructures in energy and mechanics applications for a couple of years. Those studies have provided results in a form of co-operation with foreign research centres, numerous publications and patent applications. Authors of this paper have studied the influence of multi-walled carbon nanostructures on changes in static friction arising when steel surfaces were moved. Tests were made using the original test stand consisting of automatically controlled inclined plane driven by precise stepper motors. Computer program created in the LabView environment was responsible for monitoring of the stand operation, accuracy of measurements and archiving the obtained results. Such a solution enabled to obtain high accuracy and repeatability of all conducted experiments. Tests and analysis of the obtained results allowed us to determine how additional layers of carbon nanostructures influenced on changes of static friction coefficients. At the same time, we analyzed the potential possibilities of applying nanostructures under consideration in mechanics.

Keywords: Carbon nanotubes, static friction, dynamic friction.

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Authors: D. Subedi, S. Pradhan

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Current transformers are an integral part of power system because it provides a proportional safe amount of current for protection and measurement applications. However, when the power system experiences an abnormal situation leading to huge current flow, then this huge current is proportionally injected to the protection and metering circuit. Since the protection and metering equipment’s are designed to withstand only certain amount of current with respect to time, these high currents pose a risk to man and equipment. Therefore, during such instances, the CT saturation characteristics have a huge influence on the safety of both man and equipment and on the reliability of the protection and metering system. This paper shows the effect of burden on the Accuracy Limiting factor/ Instrument security factor of current transformers and the change in saturation characteristics of the CT’s. The response of the CT to varying levels of overcurrent at different connected burden will be captured using the data acquisition software LabVIEW. Analysis is done on the real time data gathered using LabVIEW. Variation of current transformer saturation characteristics with changes in burden will be discussed.

Keywords: Accuracy limiting factor, burden, current transformer, instrument security factor, saturation characteristics.

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Authors: Bangbo Hu

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This paper discusses applications of a revolutionary information technology, Geographic Information Systems (GIS), in the field of the history of cartography by examples, including assessing accuracy of early maps, establishing a database of places and historical administrative units in history, integrating early maps in GIS or digital images, and analyzing social, political, and economic information related to production of early maps. GIS provides a new mean to evaluate the accuracy of early maps. Four basic steps using GIS for this type of study are discussed. In addition, several historical geographical information systems are introduced. These include China Historical Geographic Information Systems (CHGIS), the United States National Historical Geographic Information System (NHGIS), and the Great Britain Historical Geographical Information System. GIS also provides digital means to display and analyze the spatial information on the early maps or to layer them with modern spatial data. How GIS relational data structure may be used to analyze social, political, and economic information related to production of early maps is also discussed in this paper. Through discussion on these examples, this paper reveals value of GIS applications in this field.

Keywords: Cartography, GIS, history, maps.

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Authors: M. Abdul-Rani, K. K. Chong, A. F. M. Hani, Y. B. Yap, A. Jamil

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Imaging is defined as the process of obtaining geometric images either two dimensional or three dimensional by scanning or digitizing the existing objects or products. In this research, it applied to retrieve 3D information of the human skin surface in medical application. This research focuses on analyzing and determining volume of leg ulcers using imaging devices. Volume determination is one of the important criteria in clinical assessment of leg ulcer. The volume and size of the leg ulcer wound will give the indication on responding to treatment whether healing or worsening. Different imaging techniques are expected to give different result (and accuracies) in generating data and images. Midpoint projection algorithm was used to reconstruct the cavity to solid model and compute the volume. Misinterpretation of the results can affect the treatment efficacy. The objectives of this paper is to compare the accuracy between two 3D data acquisition method, which is laser triangulation and structured light methods, It was shown that using models with known volume, that structured-light-based 3D technique produces better accuracy compared with laser triangulation data acquisition method for leg ulcer volume determination.

Keywords: Imaging, Laser Triangulation, Structured Light, Volume Determination.

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Authors: Saeid Gharechelou, Ryutaro Tateishi

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Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimated or suggested combined use of optical and SAR data for improved accuracy, however finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specializd in developing SAR based technique with the target of rapid and accurate geospatial reporting. Should considers that limited time available in post-disaster situation offering quick computation exclusively based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, co-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though a higher accuracy obtained from the optical data then integration by optical-SAR data. Limitation of cloud-free images when urgently needed for earthquak evevent are and is not assured, thus further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channelling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.

Keywords: Sentinel-1A data, Landsat-8, earthquake damage, InSAR, rapid monitoring, 2015-Nepal earthquake.

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Authors: Somnath Dey, Debasis Samanta

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Iris-based biometric system is gaining its importance in several applications. However, processing of iris biometric is a challenging and time consuming task. Detection of iris part in an eye image poses a number of challenges such as, inferior image quality, occlusion of eyelids and eyelashes etc. Due to these problems it is not possible to achieve 100% accuracy rate in any iris-based biometric authentication systems. Further, iris detection is a computationally intensive task in the overall iris biometric processing. In this paper, we address these two problems and propose a technique to localize iris part efficiently and accurately. We propose scaling and color level transform followed by thresholding, finding pupil boundary points for pupil boundary detection and dilation, thresholding, vertical edge detection and removal of unnecessary edges present in the eye images for iris boundary detection. Scaling reduces the search space significantly and intensity level transform is helpful for image thresholding. Experimental results show that our approach is comparable with the existing approaches. Following our approach it is possible to detect iris part with 95-99% accuracy as substantiated by our experiments on CASIA Ver-3.0, ICE 2005, UBIRIS, Bath and MMU iris image databases.

Keywords: Iris recognition, iris localization, biometrics, image processing.

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Authors: Suresh Shirgave, Prakash Kulkarni, José Borges

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The continuous growth in the size of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills and more sophisticated tools to help the Web user to find the desired information. In order to make Web more user friendly, it is necessary to provide personalized services and recommendations to the Web user. For discovering interesting and frequent navigation patterns from Web server logs many Web usage mining techniques have been applied. The recommendation accuracy of usage based techniques can be improved by integrating Web site content and site structure in the personalization process.

Herein, we propose semantically enriched Web Usage Mining method for Personalization (SWUMP), an extension to solely usage based technique. This approach is a combination of the fields of Web Usage Mining and Semantic Web. In the proposed method, we envisage enriching the undirected graph derived from usage data with rich semantic information extracted from the Web pages and the Web site structure. The experimental results show that the SWUMP generates accurate recommendations and is able to achieve 10-20% better accuracy than the solely usage based model. The SWUMP addresses the new item problem inherent to solely usage based techniques.

Keywords: Prediction, Recommendation, Semantic Web Usage Mining, Web Usage Mining.

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Authors: Negar Riazifar, Nigel G. Stocks

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This paper proposes strategies in level crossing (LC) sampling and reconstruction that provide high fidelity signal reconstruction for speech signals; these strategies circumvent the problem of exponentially increasing number of samples as the bit-depth is increased and hence are highly efficient. Specifically, the results indicate that the distribution of the intervals between samples is one of the key factors in the quality of signal reconstruction; including samples with short intervals does not improve the accuracy of the signal reconstruction, whilst samples with large intervals lead to numerical instability. The proposed sampling method, termed reduced conventional level crossing (RCLC) sampling, exploits redundancy between samples to improve the efficiency of the sampling without compromising performance. A reconstruction technique is also proposed that enhances the numerical stability through linear interpolation of samples separated by large intervals. Interpolation is demonstrated to improve the accuracy of the signal reconstruction in addition to the numerical stability. We further demonstrate that the RCLC and interpolation methods can give useful levels of signal recovery even if the average sampling rate is less than the Nyquist rate.

Keywords: Level crossing sampling, numerical stability, speech processing, trigonometric polynomial.

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Authors: Agus Harjoko, Sri Hartati, Henry Dwiyasa

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There have been numerous implementations of security system using biometric, especially for identification and verification cases. An example of pattern used in biometric is the iris pattern in human eye. The iris pattern is considered unique for each person. The use of iris pattern poses problems in encoding the human iris. In this research, an efficient iris recognition method is proposed. In the proposed method the iris segmentation is based on the observation that the pupil has lower intensity than the iris, and the iris has lower intensity than the sclera. By detecting the boundary between the pupil and the iris and the boundary between the iris and the sclera, the iris area can be separated from pupil and sclera. A step is taken to reduce the effect of eyelashes and specular reflection of pupil. Then the four levels Coiflet wavelet transform is applied to the extracted iris image. The modified Hamming distance is employed to measure the similarity between two irises. This research yields the identification success rate of 84.25% for the CASIA version 1.0 database. The method gives an accuracy of 77.78% for the left eyes of MMU 1 database and 86.67% for the right eyes. The time required for the encoding process, from the segmentation until the iris code is generated, is 0.7096 seconds. These results show that the accuracy and speed of the method is better than many other methods.

Keywords: Biometric, iris recognition, wavelet transform.

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

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Cosmic showers, from their places of origin in space, after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of EAS and similar High Energy Particle Showers involve a plethora of experimental setups with certain constraints for which soft-computational tools like Artificial Neural Network (ANN)s can be adopted. The optimality of ANN classifiers can be enhanced further by the use of Multiple Classifier System (MCS) and certain data - dimension reduction techniques. This work describes the performance of certain data dimension reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Self Organizing Map (SOM) approximators for application with an MCS formed using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN). The data inputs are obtained from an array of detectors placed in a circular arrangement resembling a practical detector grid which have a higher dimension and greater correlation among themselves. The PCA, ICA and SOM blocks reduce the correlation and generate a form suitable for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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Authors: Maha Hammami, Olfa Benouda Sioud

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This study investigates the reliability of management earnings forecasts with reference to these two ingredients: verifiability and neutrality. Specifically, we examine the biasedness (or accuracy) of management earnings forecasts and company specific characteristics that can be associated with accuracy. Based on sample of 102 IPO prospectuses published for admission on NYSE Euronext Paris from 2002 to 2010, we found that these forecasts are on average optimistic and two of the five test variables, earnings variability and financial leverage are significant in explaining ex post bias. Acknowledging the possibility that the bias is the result of the managers’ forecasting behavior, we then examine whether managers decide to under-predict, over-predict or forecast accurately for self-serving purposes. Explicitly, we examine the role of financial distress, operating performance, ownership by insiders and the economy state in influencing managers’ forecasting preferences. We find that managers of distressed firms seem to over-predict future earnings. We also find that when managers are given more stock options, they tend to under-predict future earnings. Finally, we conclude that the management earnings forecasts are affected by an intentional bias due to managers’ forecasting preferences.

Keywords: Intentional bias, Management earnings forecasts, neutrality, verifiability.

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Authors: M. A. S. Fahim, J. Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realization often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: Air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter.

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Authors: A. K. Al-Othman, K. M. EL-Nagger

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Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED). Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using one system. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED).

Keywords: Security Constrained Economic Dispatch, Direct Search method, optimization.

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Authors: E. Irankhah, M. Zarif, E. Mazrooei Rad, K. Ghandehari

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Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.

Keywords: Alzheimer's disease, image and signal processing, medial temporal atrophy, LOO Cycle.

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Authors: Seyed Ali Jafari, Mohammad Ghomi Avili, Emad Benhelal

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In this study, a mathematical model was proposed and the accuracy of this model was assessed to predict the growth of Pseudomonas aeruginosa and rhamnolipid production under nitrogen limiting (sodium nitrate) fed-batch fermentation. All of the parameters used in this model were achieved individually without using any data from the literature. The overall growth kinetic of the strain was evaluated using a dual-parallel substrate Monod equation which was described by several batch experimental data. Fed-batch data under different glycerol (as the sole carbon source, C/N=10) concentrations and feed flow rates were used to describe the proposed fed-batch model and other parameters. In order to verify the accuracy of the proposed model several verification experiments were performed in a vast range of initial glycerol concentrations. While the results showed an acceptable prediction for rhamnolipid production (less than 10% error), in case of biomass prediction the errors were less than 23%. It was also found that the rhamnolipid production by P. aeruginosa was more sensitive at low glycerol concentrations. Based on the findings of this work, it was concluded that the proposed model could effectively be employed for rhamnolipid production by this strain under fed-batch fermentation on up to 80 g l- 1 glycerol.

Keywords: Fed-batch culture, glycerol, kinetic parameters, modelling, Pseudomonas aeruginosa, rhamnolipid.

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Authors: Zuraidasahana Zulkarnain, Mohd Shafry Mohd Rahim, Nor Anita Fairos Ismail, Mohd Azhar M. Arsad

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Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.

Keywords: biometrics, euclidean classifier, feature extraction, offline signature verification, VOTING-based classifier

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Authors: M. Gooroochurn, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs

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This paper presents the automated methods employed for extracting craniofacial landmarks in white light images as part of a registration framework designed to support three neurosurgical procedures. The intraoperative space is characterised by white light stereo imaging while the preoperative plan is performed on CT scans. The registration aims at aligning these two modalities to provide a calibrated environment to enable image-guided solutions. The neurosurgical procedures can then be carried out by mapping the entry and target points from CT space onto the patient-s space. The registration basis adopted consists of natural landmarks (eye corner and ear tragus). A 5mm accuracy is deemed sufficient for these three procedures and the validity of the selected registration basis in achieving this accuracy has been assessed by simulation studies. The registration protocol is briefly described, followed by a presentation of the automated techniques developed for the extraction of the craniofacial features and results obtained from tests on the AR and FERET databases. Since the three targeted neurosurgical procedures are routinely used for head injury management, the effect of bruised/swollen faces on the automated algorithms is assessed. A user-interactive method is proposed to deal with such unpredictable circumstances.

Keywords: Face Processing, Craniofacial Feature Extraction, Preoperative to Intraoperative Registration, Registration Basis.

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Authors: Cheng Zhang, James Marco, Walid Allafi, Truong Q. Dinh, W. D. Widanage

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Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The presented method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the presented CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the presented method for online SOC estimation is also verified on test data.

Keywords: Equivalent circuit model, continuous time domain estimation, linear integral filter method, parameter and SOC estimation, recursive least square.

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Authors: Apoorva Vinod, Archana Mathur, Snehanshu Saha

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Though there exists a plethora of Activation Functions (AFs) used in single and multiple hidden layer Neural Networks (NN), their behavior always raised curiosity, whether used in combination or singly. The popular AFs – Sigmoid, ReLU, and Tanh – have performed prominently well for shallow and deep architectures. Most of the time, AFs are used singly in multi-layered NN, and, to the best of our knowledge, their performance is never studied and analyzed deeply when used in combination. In this manuscript, we experiment on multi-layered NN architecture (both on shallow and deep architectures; Convolutional NN and VGG16) and investigate how well the network responds to using two different AFs (Sigmoid-Tanh, Tanh-ReLU, ReLU-Sigmoid) used alternately against a traditional, single (Sigmoid-Sigmoid, Tanh-Tanh, ReLU-ReLU) combination. Our results show that on using two different AFs, the network achieves better accuracy, substantially lower loss, and faster convergence on 4 computer vision (CV) and 15 Non-CV (NCV) datasets. When using different AFs, not only was the accuracy greater by 6-7%, but we also accomplished convergence twice as fast. We present a case study to investigate the probability of networks suffering vanishing and exploding gradients when using two different AFs. Additionally, we theoretically showed that a composition of two or more AFs satisfies Universal Approximation Theorem (UAT).

Keywords: Activation Function, Universal Approximation function, Neural Networks, convergence.

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Authors: Linyu Wang, Furui Huo, Jianhong Xiang

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The Doppler shift generated by high-speed movement and multipath effects in the channel are the main reasons for the generation of a time-frequency doubly-selective (DS) channel. There is severe inter-carrier interference (ICI) in the DS channel. Channel estimation for an orthogonal frequency division multiplexing (OFDM) system over a DS channel is very difficult. The simultaneous orthogonal matching pursuit (SOMP) algorithm under distributed compressive sensing theory (DCS-SOMP) has been used in channel estimation for OFDM systems over DS channels. However, the reconstruction accuracy of the DCS-SOMP algorithm is not high enough in the low Signal-to-Noise Ratio (SNR) stage. To solve this problem, in this paper, we propose an improved DCS-SOMP algorithm based on the inner product difference comparison operation (DCS-DCSOMP). The reconstruction accuracy is improved by increasing the number of candidate indexes and designing the comparison conditions of inner product difference. We combine the DCS-DCSOMP algorithm with the basis expansion model (BEM) to reduce the complexity of channel estimation. Simulation results show the effectiveness of the proposed algorithm and its advantages over other algorithms.

Keywords: OFDM, doubly selective, channel estimation, compressed sensing

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Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran

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In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.

Keywords: Parameterization, response surface, structure optimization, pressure hull.

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Authors: Khairul Jauhari, Achmad Widodo, Ismoyo Haryanto

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In this article, the radial displacement error correction capability of a high precision spindle grinding caused by unbalance force was investigated. The spindle shaft is considered as a flexible rotor mounted on two sets of angular contact ball bearing. Finite element methods (FEM) have been adopted for obtaining the equation of motion of the spindle. In this paper, firstly, natural frequencies, critical frequencies, and amplitude of the unbalance response caused by residual unbalance are determined in order to investigate the spindle behaviors. Furthermore, an optimization design algorithm is employed to minimize radial displacement of the spindle which considers dimension of the spindle shaft, the dynamic characteristics of the bearings, critical frequencies and amplitude of the unbalance response, and computes optimum spindle diameters and stiffness and damping of the bearings. Numerical simulation results show that by optimizing the spindle diameters, and stiffness and damping in the bearings, radial displacement of the spindle can be reduced. A spindle about 4 μm radial displacement error can be compensated with 2 μm accuracy. This certainly can improve the accuracy of the product of machining.

Keywords: Error correction, High precision grinding, Optimization, Radial displacement, Spindle.

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Authors: Jian Shi, Baoguo Yu, Haonan Jia, Meng Liu, Ping Huang

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Adapting to the flexibility of war, a large number of guided weapons launch from aircraft. Therefore, the inertial navigation system loaded in the weapon needs to undergo an alignment process in the air. This article proposes the following methods to the problem of inaccurate modeling of the system under large misalignment angles, the accuracy reduction of filtering caused by outliers, and the noise changes in GPS signals: first, considering the large misalignment errors of Strapdown Inertial Navigation System (SINS)/GPS, a more accurate model is made rather than to make a small-angle approximation, and the Unscented Kalman Filter (UKF) algorithms are used to estimate the state; then, taking into account the impact of GPS noise changes on the fine alignment algorithm, the innovation adaptive filtering algorithm is introduced to estimate the GPS’s noise in real-time; at the same time, in order to improve the anti-interference ability of the air fine alignment algorithm, a robust filtering algorithm based on outlier detection is combined with the air fine alignment algorithm to improve the robustness of the algorithm. The algorithm can improve the alignment accuracy and robustness under interference conditions, which is verified by simulation.

Keywords: Air alignment, fine alignment, inertial navigation system, integrated navigation system, UKF.

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, Fuzzy c means, Liver segmentation.

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Authors: Atena Sajedin, Shokoufeh Zakernejad, Soheil Faridi, Mehrdad Javadi, Reza Ebrahimpour

Abstract:

This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study.

Keywords: ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform

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Authors: A. F. Ali, D. M. Shawky

Abstract:

Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.

Keywords: Bioinformatics, Artificial Neural Networks, Protein Sequence Analysis, Feature Extraction.

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Authors: Hyun-Koo Kim, Ju H. Park, Ho-Youl Jung

Abstract:

This paper presents an effective traffic lights recognition method at the daytime. First, Potential Traffic Lights Detector (PTLD) use whole color source of YCbCr channel image and make each binary image of green and red traffic lights. After PTLD step, Shape Filter (SF) use to remove noise such as traffic sign, street tree, vehicle, and building. At this time, noise removal properties consist of information of blobs of binary image; length, area, area of boundary box, etc. Finally, after an intermediate association step witch goal is to define relevant candidates region from the previously detected traffic lights, Adaptive Multi-class Classifier (AMC) is executed. The classification method uses Haar-like feature and Adaboost algorithm. For simulation, we are implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and rural roads. Through the test, we are compared with our method and standard object-recognition learning processes and proved that it reached up to 94 % of detection rate which is better than the results achieved with cascade classifiers. Computation time of our proposed method is 15 ms.

Keywords: Traffic Light Detection, Multi-class Classification, Driving Assistance System, Haar-like Feature, Color SegmentationMethod, Shape Filter

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

Abstract:

The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an Artificial Neural Network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study include granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R2), Root Mean Square Error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: National development, granite, profitability assessment, ANN models.

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