Search results for: static posture recognition.

Authors: Svitov David, Alyamkin Sergey

Abstract:

The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax.

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Authors: Morteza Zahedi, Philippe Dreuw, Thomas Deselaers, Hermann Ney

Abstract:

Sign language is used by the deaf and hard of hearing people for communication. Automatic sign language recognition is a challenging research area since sign language often is the only way of communication for the deaf people. Sign language includes different components of visual actions made by the signer using the hands, the face, and the torso, to convey his/her meaning. To use different aspects of signs, we combine the different groups of features which have been extracted from the image frames recorded directly by a stationary camera. We combine the features in two levels by employing three techniques. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, or by concatenating feature groups over time and using LDA to choose the most discriminant elements. At the model level, a late fusion of differently trained models can be carried out by a log-linear model combination. In this paper, we investigate these three combination techniques in an automatic sign language recognition system and show that the recognition rate can be significantly improved.

Keywords: American sign language, appearance-based features, Feature combination, Sign language recognition

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Authors: Yan Ma, ShunBao Li

Abstract:

A new approach is adopted in this paper based on Turk and Pentland-s eigenface method. It was found that the probability density function of the distance between the projection vector of the input face image and the average projection vector of the subject in the face database, follows Rayleigh distribution. In order to decrease the false acceptance rate and increase the recognition rate, the input face image has been recognized using two thresholds including the acceptance threshold and the rejection threshold. We also find out that the value of two thresholds will be close to each other as number of trials increases. During the training, in order to reduce the number of trials, the projection vectors for each subject has been averaged. The recognition experiments using the proposed algorithm show that the recognition rate achieves to 92.875% whilst the average number of judgment is only 2.56 times.

Keywords: Eigenface, Face Recognition, Threshold, Rayleigh Distribution, Feature Extraction

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Authors: Mahmoud Elmezain, Samar El-shinawy

Abstract:

Many approaches to pattern recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features. Generative and discriminative models have very different characteristics, as well as complementary strengths and weaknesses. In this paper, we study these models to recognize the patterns of alphabet characters (A-Z) and numbers (0-9). To handle isolated pattern, generative model as Hidden Markov Model (HMM) and discriminative models like Conditional Random Field (CRF), Hidden Conditional Random Field (HCRF) and Latent-Dynamic Conditional Random Field (LDCRF) with different number of window size are applied on extracted pattern features. The gesture recognition rate is improved initially as the window size increase, but degrades as window size increase further. Experimental results show that the LDCRF is the best in terms of results than CRF, HCRF and HMM at window size equal 4. Additionally, our results show that; an overall recognition rates are 91.52%, 95.28%, 96.94% and 98.05% for CRF, HCRF, HMM and LDCRF respectively.

Keywords: Statistical Pattern Recognition, Generative Model, Discriminative Model, Human Computer Interaction.

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Authors: Shagufta Tabassum, V. P. Pawar

Abstract:

The study of static dielectric properties in a binary mixture of 1,2 dichloroethane (DE) and n,n dimethylformamide (DMF) polar liquids has been carried out in the frequency range of 10 MHz to 30 GHz for 11 different concentration using time domain reflectometry technique at 10ºC temperature. The dielectric relaxation study of solute-solvent mixture at microwave frequencies gives information regarding the creation of monomers and multimers as well as interaction between the molecules of the binary mixture. The least squares fit method is used to determine the values of dielectric parameters such as static dielectric constant (ε₀), dielectric constant at high frequency (ε_∞) and relaxation time (τ).

Keywords: Excess parameters, relaxation time, static dielectric constant, time domain reflectometry.

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Authors: S. Sowmyayani

Abstract:

The advancement of computer-aided design helps the medical force and security force. Some applications include biometric recognition, elderly fall detection, face recognition, cancer recognition, tumor recognition, etc. This paper deals with different machine learning algorithms that are more generically used for any health care system. The most focused problems are classification and regression. With the rise of big data, machine learning has become particularly important for solving problems. Machine learning uses two types of techniques: supervised learning and unsupervised learning. The former trains a model on known input and output data and predicts future outputs. Classification and regression are supervised learning techniques. Unsupervised learning finds hidden patterns in input data. Clustering is one such unsupervised learning technique. The above-mentioned models are discussed briefly in this paper.

Keywords: Supervised learning, unsupervised learning, regression, neural network.

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Authors: Y. Deng, X. Li, C. Kwan, B. Raj, R. Stern

Abstract:

The current speech interfaces in many military applications may be adequate for native speakers. However, the recognition rate drops quite a lot for non-native speakers (people with foreign accents). This is mainly because the nonnative speakers have large temporal and intra-phoneme variations when they pronounce the same words. This problem is also complicated by the presence of large environmental noise such as tank noise, helicopter noise, etc. In this paper, we proposed a novel continuous acoustic feature adaptation algorithm for on-line accent and environmental adaptation. Implemented by incremental singular value decomposition (SVD), the algorithm captures local acoustic variation and runs in real-time. This feature-based adaptation method is then integrated with conventional model-based maximum likelihood linear regression (MLLR) algorithm. Extensive experiments have been performed on the NATO non-native speech corpus with baseline acoustic model trained on native American English. The proposed feature-based adaptation algorithm improved the average recognition accuracy by 15%, while the MLLR model based adaptation achieved 11% improvement. The corresponding word error rate (WER) reduction was 25.8% and 2.73%, as compared to that without adaptation. The combined adaptation achieved overall recognition accuracy improvement of 29.5%, and WER reduction of 31.8%, as compared to that without adaptation.

Keywords: speaker adaptation; environment adaptation; robust speech recognition; SVD; non-native speech recognition

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Authors: Khampheth Bounnady, Boontee Kruatrachue, Somkiat Wangsiripitak

Abstract:

This paper proposed high level feature for online Lao handwritten recognition. This feature must be high level enough so that the feature is not change when characters are written by different persons at different speed and different proportion (shorter or longer stroke, head, tail, loop, curve). In this high level feature, a character is divided in to sequence of curve segments where a segment start where curve reverse rotation (counter clockwise and clockwise). In each segment, following features are gathered cumulative change in direction of curve (- for clockwise), cumulative curve length, cumulative length of left to right, right to left, top to bottom and bottom to top ( cumulative change in X and Y axis of segment). This feature is simple yet robust for high accuracy recognition. The feature can be gather from parsing the original time sampling sequence X, Y point of the pen location without re-sampling. We also experiment on other segmentation point such as the maximum curvature point which was widely used by other researcher. Experiments results show that the recognition rates are at 94.62% in comparing to using maximum curvature point 75.07%. This is due to a lot of variations of turning points in handwritten.

Keywords: Handwritten feature, chain code, Lao handwritten recognition.

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Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim

Abstract:

Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.

Keywords: Control chart pattern recognition, Multi-resolution wavelet analysis, Bi-directional Kohonen network, Back-propagation network, Feature extraction.

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Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd Zaizu Ilyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

Abstract:

This paper presents two techniques, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapped on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non- Gaussian in the feature space and by using combination of several Gaussian functions that has different statistical properties, the best feature representation can be modelled using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculated GMM components. The method is tested using FERET datasets and is able to achieved 92% recognition rates.

Keywords: Local features modelling, face recognition system, Gaussian mixture models.

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Authors: Dong-Hyun Kim, Jin-Woong Choi, Hyeong-Yeol Kim, Sun-Kyu Park

Abstract:

This study is to evaluate the behavior of integral and segmental specimens through static and cyclic tests. Integral specimens were made with the same size to be compared with segmental specimens that were made by connected precast members. To evaluate its bending performance and serviceability, 1 integral and 3 segmental specimens were tested under static load. And 1 integral and 2 segmental specimens were tested under cyclic load, respectively. Different load ranges were considered in the cyclic tests to evaluate the safety and serviceability. The test results showed that under static loading, segmental specimens had about 94% of the integral specimen's maximum moment, averagely. Under cyclic loading, the segmental specimens showed that had enough safety in the range of higher than service load and enough serviceability. In conclusion, the maximum crack width (0.16mm) satisfied the allowable crack width (0.30mm) in the range of service load.

Keywords: Modular bridge, Transverse connection, Precast concrete, Static and cyclic test.

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Authors: R. Walha, F. Drira, H. El-Abed, A. M. Alimi

Abstract:

The Automatic Speech Recognition (ASR) applied to Arabic language is a challenging task. This is mainly related to the language specificities which make the researchers facing multiple difficulties such as the insufficient linguistic resources and the very limited number of available transcribed Arabic speech corpora. In this paper, we are interested in the development of a HMM-based ASR system for Standard Arabic (SA) language. Our fundamental research goal is to select the most appropriate acoustic parameters describing each audio frame, acoustic models and speech recognition unit. To achieve this purpose, we analyze the effect of varying frame windowing (size and period), acoustic parameter number resulting from features extraction methods traditionally used in ASR, speech recognition unit, Gaussian number per HMM state and number of embedded re-estimations of the Baum-Welch Algorithm. To evaluate the proposed ASR system, a multi-speaker SA connected-digits corpus is collected, transcribed and used throughout all experiments. A further evaluation is conducted on a speaker-independent continue SA speech corpus. The phonemes recognition rate is 94.02% which is relatively high when comparing it with another ASR system evaluated on the same corpus.

Keywords: ASR, HMM, acoustical analysis, acoustic modeling, Standard Arabic language

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Authors: Dejan Stantic, Jun Jo

Abstract:

There have been significant improvements in automatic voice recognition technology. However, existing systems still face difficulties, particularly when used by non-native speakers with accents. In this paper we address a problem of identifying the English accented speech of speakers from different backgrounds. Once an accent is identified the speech recognition software can utilise training set from appropriate accent and therefore improve the efficiency and accuracy of the speech recognition system. We introduced the Q factor, which is defined by the sum of relationships between frequencies of the formants. Four different accents were considered and experimented for this research. A scoring method was introduced in order to effectively analyse accents. The proposed concept indicates that the accent could be identified by analysing their formants.

Keywords: Accent Identification, Formants, Q Factor.

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

Abstract:

Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: Communication signal, feature extraction, holder coefficient, improved cloud model.

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Authors: Ayşe Onat, Ferruh Yildiz, Mesut Gündüz

Abstract:

In this study, an OCR system for segmentation, feature extraction and recognition of Ottoman Scripts has been developed using handwritten characters. Detection of handwritten characters written by humans is a difficult process. Segmentation and feature extraction stages are based on geometrical feature analysis, followed by the chain code transformation of the main strokes of each character. The output of segmentation is well-defined segments that can be fed into any classification approach. The classes of main strokes are identified through left-right Hidden Markov Model (HMM).

Keywords: Chain Code, HMM, Ottoman Script Recognition, OCR

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Authors: Velappa Ganapathy, Kok Leong Liew

Abstract:

Advancement in Artificial Intelligence has lead to the developments of various “smart" devices. Character recognition device is one of such smart devices that acquire partial human intelligence with the ability to capture and recognize various characters in different languages. Firstly multiscale neural training with modifications in the input training vectors is adopted in this paper to acquire its advantage in training higher resolution character images. Secondly selective thresholding using minimum distance technique is proposed to be used to increase the level of accuracy of character recognition. A simulator program (a GUI) is designed in such a way that the characters can be located on any spot on the blank paper in which the characters are written. The results show that such methods with moderate level of training epochs can produce accuracies of at least 85% and more for handwritten upper case English characters and numerals.

Keywords: Character recognition, multiscale, backpropagation, neural network, minimum distance technique.

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Authors: Khaled E. A. Negm

Abstract:

A neuron can emit spikes in an irregular time basis and by averaging over a certain time window one would ignore a lot of information. It is known that in the context of fast information processing there is no sufficient time to sample an average firing rate of the spiking neurons. The present work shows that the spiking neurons are capable of computing the radial basis functions by storing the relevant information in the neurons' delays. One of the fundamental findings of the this research also is that when using overlapping receptive fields to encode the data patterns it increases the network-s clustering capacity. The clustering algorithm that is discussed here is interesting from computer science and neuroscience point of view as well as from a perspective.

Keywords: Temporal Neurons, RBF Recognition, Perturbation, On Line Recognition.

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Authors: C. J. W. Habets, D. J. Peters, J. G. de Gijt, A. V. Metrikine, S. N. Jonkman

Abstract:

Conventional seismic designs of quay walls in ports are mostly based on pseudo-static analysis. A more advanced alternative is the Performance-Based Design (PBD) method, which evaluates permanent deformations and amounts of (repairable) damage under seismic loading. The aim of this study is to investigate the suitability of this method for anchored sheet pile quay walls that were not purposely designed for seismic loads. A research methodology is developed in which pseudo-static, permanent-displacement and finite element analysis are employed, calibrated with an experimental reference case that considers a typical anchored sheet pile wall. A reduction factor that accounts for deformation behaviour is determined for pseudo-static analysis. A model to apply traditional permanent displacement analysis on anchored sheet pile walls is proposed. Dynamic analysis is successfully carried out. From the research it is concluded that PBD evaluation can effectively be used for seismic analysis and design of this type of structure.

Keywords: Anchored sheet pile quay wall, simplified dynamic analysis, performance-based design, pseudo-static analysis.

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Authors: M. Vatsa, R. Singh, A. Noore

Abstract:

This paper presents a novel iris recognition system using 1D log polar Gabor wavelet and Euler numbers. 1D log polar Gabor wavelet is used to extract the textural features, and Euler numbers are used to extract topological features of the iris. The proposed decision strategy uses these features to authenticate an individual-s identity while maintaining a low false rejection rate. The algorithm was tested on CASIA iris image database and found to perform better than existing approaches with an overall accuracy of 99.93%.

Keywords: Iris recognition, textural features, topological features.

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Authors: Essam Al Daoud

Abstract:

To improve the classification rate of the face recognition, features combination and a novel non-linear kernel are proposed. The feature vector concatenates three different radius of local binary patterns and Gabor wavelet features. Gabor features are the mean, standard deviation and the skew of each scaling and orientation parameter. The aim of the new kernel is to incorporate the power of the kernel methods with the optimal balance between the features. To verify the effectiveness of the proposed method, numerous methods are tested by using four datasets, which are consisting of various emotions, orientations, configuration, expressions and lighting conditions. Empirical results show the superiority of the proposed technique when compared to other methods.

Keywords: Face recognition, Gabor wavelet, LBP, Non-linearkerner

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

Abstract:

Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: Speech recognition, acoustic features, Mel Frequency Cepstral Coefficients.

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Authors: Mohd Fikri Azli bin Abdullah, Ali Fahmi Perwira Negara, Md. Shohel Sayeed, Deok-Jai Choi, Kalaiarasi Sonai Muthu

Abstract:

Rapid advancement in computing technology brings computers and humans to be seamlessly integrated in future. The emergence of smartphone has driven computing era towards ubiquitous and pervasive computing. Recognizing human activity has garnered a lot of interest and has raised significant researches- concerns in identifying contextual information useful to human activity recognition. Not only unobtrusive to users in daily life, smartphone has embedded built-in sensors that capable to sense contextual information of its users supported with wide range capability of network connections. In this paper, we will discuss the classification algorithms used in smartphone-based human activity. Existing technologies pertaining to smartphone-based researches in human activity recognition will be highlighted and discussed. Our paper will also present our findings and opinions to formulate improvement ideas in current researches- trends. Understanding research trends will enable researchers to have clearer research direction and common vision on latest smartphone-based human activity recognition area.

Keywords: Classification algorithms, Human Activity Recognition (HAR), Smartphones

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

Abstract:

In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, Neural networks, Local cost computation.

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Authors: Jong Han Joo, Jeong Hun Lee, Young Sun Kim, Jae Young Kang, Seung Ho Choi

Abstract:

In this study, we propose a novel technique for acoustic echo suppression (AES) during speech recognition under barge-in conditions. Conventional AES methods based on spectral subtraction apply fixed weights to the estimated echo path transfer function (EPTF) at the current signal segment and to the EPTF estimated until the previous time interval. However, the effects of echo path changes should be considered for eliminating the undesired echoes. We describe a new approach that adaptively updates weight parameters in response to abrupt changes in the acoustic environment due to background noises or double-talk. Furthermore, we devised a voice activity detector and an initial time-delay estimator for barge-in speech recognition in communication networks. The initial time delay is estimated using log-spectral distance measure, as well as cross-correlation coefficients. The experimental results show that the developed techniques can be successfully applied in barge-in speech recognition systems.

Keywords: Acoustic echo suppression, barge-in, speech recognition, echo path transfer function, initial delay estimator, voice activity detector.

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Authors: Abdelwahed Echchatbi, Adil Rizki, Ali Haddi, Nabil Mrani, Noureddine Elalami

Abstract:

In this note, the robust static output feedback stabilisation of an induction machine is addressed. The machine is described by a non homogenous bilinear model with structural uncertainties, and the feedback gain is computed via an iterative LMI (ILMI) algorithm.

Keywords: Induction machine, Static output feedback, robust stabilisation.

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Authors: Toshihiko Sasama, Kentaro Kishida, Kazunori Sugahara, Hiroshi Masuyama

Abstract:

A mobile ad hoc network is a network of mobile nodes without any notion of centralized administration. In such a network, each mobile node behaves not only as a host which runs applications but also as a router to forward packets on behalf of others. Clustering has been applied to routing protocols to achieve efficient communications. A CH network expresses the connected relationship among cluster-heads. This paper discusses the methods for constructing a CH network, and produces the following results: (1) The required running costs of 3 traditional methods for constructing a CH network are not so different from each other in the static circumstance, or in the dynamic circumstance. Their running costs in the static circumstance do not differ from their costs in the dynamic circumstance. Meanwhile, although the routing costs required for the above 3 methods are not so different in the static circumstance, the costs are considerably different from each other in the dynamic circumstance. Their routing costs in the static circumstance are also very different from their costs in the dynamic circumstance, and the former is one tenths of the latter. The routing cost in the dynamic circumstance is mostly the cost for re-routing. (2) On the strength of the above results, we discuss new 2 methods regarding whether they are tolerable or not in the dynamic circumstance, that is, whether the times of re-routing are small or not. These new methods are revised methods that are based on the traditional methods. We recommended the method which produces the smallest routing cost in the dynamic circumstance, therefore producing the smallest total cost.

Keywords: cluster, mobile ad hoc network, re-routing cost, simulation

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Authors: Mehrdad Nouri Khajavi, Sajjad Ahoui Ghazvin, Ghader Rezazadeh, Mohammad Fathalilou

Abstract:

In this paper static and dynamic response of a varactor of a micro-phase shifter to DC, step DC and AC voltages have been studied. By presenting a mathematical modeling Galerkin-based step by step linearization method (SSLM) and Galerkin-based reduced order model have been used to solve the governing static and dynamic equations, respectively. The calculated static and dynamic pull-in voltages have been validated by previous experimental and theoretical results and a good agreement has been achieved. Then the frequency response and phase diagram of the system has been studied. It has been shown that applying the DC voltage shifts down the phase diagram and frequency response. Also increasing the damping ratio shifts up the phase diagram.

Keywords: MEMS, Phase Shifter, Pull-in Voltage, PhaseDiagram

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

Abstract:

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: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

Abstract:

The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than Optical Character Recognition (OCR) results.

Keywords: Biological pathway, image understanding, gene name recognition, object detection, Siamese network, Visual Geometry Group.

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Authors: Mohamed Moustafa Mahmoud Sedky

Abstract:

In Line start permanent magnet synchronous motor,  eccentricity is a common fault that can make it necessary to remove  the motor from the production line. However, because the motor may  be inaccessible, diagnosing the fault is not easy. This paper presents  an FEM that identifies different models, static eccentricity, dynamic  eccentricity, and mixed eccentricity, at no load and full load. The  method overcomes the difficulty of applying FEMs to transient  behavior. It simulates motor speed, torque and flux density  distribution along the air gap for SE,DE, and ME. This paper  represents the various effects of different eccentricitiestypes on the  transient performance.

Keywords: Line Start Permanent magnet, synchronous machine, Static Eccentricity, Dynamic Eccentricity, Mixed Eccentricity.

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