Search results for: signal classification

Authors: Daniel Bulanda, Janusz A. Starzyk, Adrian Horzyk

Abstract:

The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms.

Keywords: characteristic points, electrocardiogram, ECG, machine learning, signal compression

Procedia PDF Downloads 158

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

Procedia PDF Downloads 203

Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

Abstract:

Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

Procedia PDF Downloads 152

Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

Abstract:

Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

Procedia PDF Downloads 175

Authors: Jiwon Lee, Chanho Jung, Si-Hwan Jang, Kyung-Ill Kim, Sanghyun Joo, Wook-Ho Son

Abstract:

Since the advances in digital imaging technologies have led to development of high quality digital devices, there are a lot of illegal copies of copyrighted video content on the internet. Thus, we propose a high-quality (HQ) video watermarking scheme that can prevent these illegal copies from spreading out. The proposed scheme is applied spatial and temporal gradient methods to improve the fidelity and detection performance. Also, the scheme duplicates the watermark signal temporally to alleviate the signal reduction caused by geometric and signal-processing distortions. Experimental results show that the proposed scheme achieves better performance than previously proposed schemes and it has high fidelity. The proposed scheme can be used in broadcast monitoring or traitor tracking applications which need fast detection process to prevent illegally recorded video content from spreading out.

Keywords: editing prevention technique, gradient method, luminance change, video watermarking

Procedia PDF Downloads 452

Authors: Zdenek Silar, Martin Dobrovolny

Abstract:

This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method.

Keywords: background estimation, direction of optical flow, K-means clustering, objects detection, railway crossing monitoring, velocity vectors

Procedia PDF Downloads 514

Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

Abstract:

This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

Procedia PDF Downloads 112

Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

Abstract:

An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

Procedia PDF Downloads 163

Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

Procedia PDF Downloads 413

Authors: M. Abdullah, Al-Wadud

Abstract:

Sensor-based activity recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.

Keywords: Naïve Bayesian, based classification, activity recognition, sensor data, object-usage model

Procedia PDF Downloads 318

Authors: Jianwei Ma, Diriba Gemechu

Abstract:

In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm.

Keywords: anisotropic total fractional order variation, fractional order bounded variation, seismic random noise attenuation, split Bregman algorithm

Procedia PDF Downloads 203

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

Procedia PDF Downloads 137

Authors: K. Badron, A. F. Ismail, A. L. Asnawi, N. F. A. Malik, S. Z. Abidin, S. Dzulkifly

Abstract:

The occurrences of precipitation, also commonly referred as rain, in the form of "convective" and "stratiform" have been identified to exist worldwide. In this study, the radar return echoes or known as reflectivity values acquired from radar scans have been exploited in the process of classifying the type of rain endured. The investigation use radar data from Malaysian Meteorology Department (MMD). It is possible to discriminate the types of rain experienced in tropical region by observing the vertical characteristics of the rain structure. .Heavy rain in tropical region profoundly affects radiowave signals, causing transmission interference and signal fading. Required wireless system fade margin depends on the type of rain. Information relating to the two mentioned types of rain is critical for the system engineers and researchers in their endeavour to improve the reliability of communication links. This paper highlights the quantification of percentage occurrences over one year period in 2009.

Keywords: stratiform, convective, tropical region, attenuation radar reflectivity

Procedia PDF Downloads 281

Authors: Ju-Hong Lee, Ching-Wei Liao

Abstract:

An adaptive array beamformer is designed for automatically preserving the desired signals while cancelling interference and noise. Providing robustness against model mismatches and tracking possible environment changes calls for robust adaptive beamforming techniques. The design criterion yields the well-known generalized sidelobe canceller (GSC) beamformer. In practice, the knowledge of the desired steering vector can be imprecise, which often occurs due to estimation errors in the DOA of the desired signal or imperfect array calibration. In these situations, the SOI is considered as interference, and the performance of the GSC beamformer is known to degrade. This undesired behavior results in a reduction of the array output signal-to-interference plus-noise-ratio (SINR). Therefore, it is worth developing robust techniques to deal with the problem due to local scattering environments. As to the implementation of adaptive beamforming, the required computational complexity is enormous when the array beamformer is equipped with massive antenna array sensors. To alleviate this difficulty, a generalized sidelobe canceller (GSC) with partially adaptivity for less adaptive degrees of freedom and faster adaptive response has been proposed in the literature. Unfortunately, it has been shown that the conventional GSC-based adaptive beamformers are usually very sensitive to the mismatch problems due to local scattering situations. In this paper, we present an effective GSC-based beamformer against the mismatch problems mentioned above. The proposed GSC-based array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. We utilize the predefined steering vector and a presumed angle tolerance range to carry out the required estimation for obtaining an appropriate steering vector. A matrix associated with the direction vector of signal sources is first created. Then projection matrices related to the matrix are generated and are utilized to iteratively estimate the actual direction vector of the desired signal. As a result, the quiescent weight vector and the required signal blocking matrix required for performing adaptive beamforming can be easily found. By utilizing the proposed GSC-based beamformer, we find that the performance degradation due to the considered local scattering environments can be effectively mitigated. To further enhance the beamforming performance, a signal subspace projection matrix is also introduced into the proposed GSC-based beamformer. Several computer simulation examples show that the proposed GSC-based beamformer outperforms the existing robust techniques.

Keywords: adaptive antenna beamforming, local scattering, signal blocking, steering mismatch

Procedia PDF Downloads 106

Authors: Takuro Kida, Yuichi Kida

Abstract:

We show a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detailed algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory and it is indicated that introducing conversations with feedback does not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, conditional optimization

Procedia PDF Downloads 148

Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi

Abstract:

In this paper, we present a power profiling model for smart grid consumers based on real time load data acquired smart meters. It profiles consumers’ power consumption behaviour using the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features be extracted. Two load types are defined and the related load patterns are extracted for classifying consumption behaviour by DTW. The classification methodology is discussed in detail. To evaluate the performance of the method, we analyze the time-series load data measured by a smart meter in a real case. The results verify the effectiveness of the proposed profiling method with 90.91% true positive rate for load type clustering in the best case.

Keywords: power profiling, user privacy, dynamic time warping, smart grid

Procedia PDF Downloads 142

Authors: Ahmed Mahmoud Ahmed Abouelmagd

Abstract:

The diversity is the usual remedy of the transmitted signal level variations (Fading phenomena) in radio frequency channels. Diversity techniques utilize two or more copies of a signal and combine those signals to combat fading. The basic concept of diversity is to transmit the signal via several independent diversity branches to get independent signal replicas via time – frequency - space - and polarization diversity domains. Coding and decoding processes can be an alternative remedy for fading phenomena, it cannot increase the channel capacity, but it can improve the error performance. In this paper we propose the use of replication decoding with BCH code class, and Viterbi decoding algorithm with convolution coding; as examples of coding and decoding processes. The results are compared to those obtained from two optimized selection space diversity techniques. The performance of Rayleigh fading channel, as the model considered for radio frequency channels, is evaluated for each case. The evaluation results show that the coding and decoding approaches, especially the BCH coding approach with replication decoding scheme, give better performance compared to that of selection space diversity optimization approaches. Also, an approach for combining the coding and decoding diversity as well as the space diversity is considered, the main disadvantage of this approach is its complexity but it yields good performance results.

Keywords: Rayleigh fading, diversity, BCH codes, Replication decoding, ‎convolution coding, viterbi decoding, space diversity

Procedia PDF Downloads 433

Authors: Serge Provost, Yishan Zang

Abstract:

Following a review of various approaches that are utilized for classifying the tail behavior of a distribution, an easily implementable methodology that relies on an arctangent transformation is presented. The classification criterion is actually based on the difference between two specific quantiles of the transformed distribution. The resulting categories enable one to classify distributional tails as distinctly short, short, nearly medium, medium, extended medium and somewhat long, providing that at least two moments exist. Distributions possessing a single moment are said to be long tailed while those failing to have any finite moments are classified as having an extremely long tail. Several illustrative examples will be presented.

Keywords: arctangent transformation, tail classification, heavy-tailed distributions, distributional moments

Procedia PDF Downloads 117

Authors: Aishrith Rao

Abstract:

COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

Procedia PDF Downloads 153

Authors: Saba Ebrahimi, Hedieh Ashrafi

Abstract:

Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

Procedia PDF Downloads 209

Authors: Nazmul Shahadat, Anthony S. Maida

Abstract:

In recent years, hypercomplex inspired neural networks improved deep CNN architectures due to their ability to share weights across input channels and thus improve cohesiveness of representations within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention ResNet with their quaternion variants that use cross-channel weight sharing to assess the effect on image classification. We expect the quaternion enhancements to produce improved feature maps with more interlinked representations. We experiment with the stem of the network, the bottleneck layer, and the fully connected backend by replacing them with quaternion versions. These modifications lead to novel architectures which yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.

Keywords: axial attention, representational networks, weight sharing, cross-channel correlations, quaternion-enhanced axial attention, deep networks

Procedia PDF Downloads 78

Authors: Gabriel Ugalahi, Dominic S. Nyitamen

Abstract:

This project seeks to improve Wi-Fi reception by utilizing the properties of directional microstrip patch antennae. Where there is a dense population of Wi-Fi signal, several signal sources transmitting on the same frequency band and indeed channel constitutes interference to each other. The time it takes for request to be received, resolved and response given between a user and the resource provider is increased considerably. By deploying a directional patch antenna with a narrow bandwidth, the range of frequency received is reduced and should help in limiting the reception of signal from unwanted sources. A rectangular microstrip patch antenna (RMPA) is designed to operate at the Industrial Scientific and Medical (ISM) band (2.4GHz) commonly used in Wi-Fi network deployment. The dimensions of the antenna are calculated and these dimensions are used to generate a model on Advanced Design System (ADS), a microwave simulator. Simulation results are then analyzed and necessary optimization is carried out to further enhance the radiation quality so as to achieve desired results. Impedance matching at 50Ω is also obtained by using the inset feed method. Final antenna dimensions obtained after simulation and optimization are then used to implement practical construction on an FR-4 double sided copper clad printed circuit board (PCB) through a chemical etching process using ferric chloride (Fe2Cl). Simulation results show an RMPA operating at a centre frequency of 2.4GHz with a bandwidth of 40MHz. A voltage standing wave ratio (VSWR) of 1.0725 is recorded on a return loss of -29.112dB at input port showing an appreciable match in impedance to a source of 50Ω. In addition, a gain of 3.23dBi and directivity of 6.4dBi is observed during far-field analysis. On deployment, signal reception from wireless devices is improved due to antenna gain. A test source with a received signal strength indication (RSSI) of -80dBm without antenna installed on the receiver was improved to an RSSI of -61dBm. In addition, the directional radiation property of the RMPA prioritizes signals by pointing in the direction of a preferred signal source thus, reducing interference from undesired signal sources. This was observed during testing as rotation of the antenna on its axis resulted to the gain of signal in-front of the patch and fading of signals away from the front.

Keywords: advanced design system (ADS), inset feed, received signal strength indicator (RSSI), rectangular microstrip patch antenna (RMPA), voltage standing wave ratio (VSWR), wireless fidelity (Wi-Fi)

Procedia PDF Downloads 214

Authors: P. Saravanakumar, N. Raja, M. Rameshkumar, D. Mohankumar, R. Sateeshkumar, B. Maheshwari

Abstract:

This research work is designed using Microsoft VB. Net as front end and MySQL as back end. The project deals with secure the user transaction in the ATM system. This application contains the option for sending the failed transaction details to the particular customer by using the SMS. When the customer withdraws the amount from the Bank ATM system, sometimes the amount will not be dispatched but the amount will be debited to the particular account. This application is used to avoid this type of problems in the ATM system. In this proposed system using IR technique to detect the dispatched amount. IR Transmitter and IR Receiver are placed in the path of cash dispatch. It is connected each other through the IR signal. When the customers withdraw the amount in the ATM system then the amount will be dispatched or not is monitored by IR Receiver. If the amount will be dispatched then the signal will be interrupted between the IR Receiver and the IR Transmitter. At that time, the monitoring system will be reduced their particular withdraw amount on their account. If the cash will not be dispatched, the signal will not be interrupted, at that time the particular withdraw amount will not be reduced their account. If the transaction completed successfully, the transaction details such as withdraw amount and current balance can be sent to the customer via the SMS. If the transaction fails, the transaction failed message can be send to the customer.

Keywords: ATM system, monitoring system, IR Transmitter, IR Receiver

Procedia PDF Downloads 304

Authors: Amritpal Singh Nafria, Rohit Sharma, Md. Shami Ansari

Abstract:

Up to now only five different equations of motion can be derived from velocity time graph without needing to know the normal and frictional forces acting at the point of contact. In this paper we obtained all possible requisite conditions to be considering an equation as an equation of motion. After that we classified equations of motion by considering two equations as fundamental kinematical equations of motion and other three as additional kinematical equations of motion. After deriving these five equations of motion, we examine the easiest way of solving a wide variety of useful numerical problems. At the end of the paper, we discussed the importance and educational benefits of classification of equations of motion.

Keywords: velocity-time graph, fundamental equations, additional equations, requisite conditions, importance and educational benefits

Procedia PDF Downloads 783

Authors: Jerzy Banski

Abstract:

Small towns represent a key element of settlement structure and serve a number of important functions associated with the servicing of rural areas that surround them. It is in light of this that scientific studies have paid considerable attention to the functional structure of centers of this kind, as well as the relationships with both surrounding rural areas and other urban centers. But a preliminary to such research has typically involved attempts at classifying the urban centers themselves, with this also assisting with the planning and shaping of development policy on different spatial scales. The purpose of the work is to test out the methods underpinning three different classifications of small urban centers, as well as to offer a preliminary interpretation of the outcomes obtained. Research took in 722 settlement units in Poland, granted town rights and populated by fewer than 20,000 inhabitants. A morphologically-based classification making reference to the database of topographic objects as regards land cover within the administrative boundaries of towns and cities was carried out, and it proved possible to distinguish the categories of “housing-estate”, industrial and R&R towns, as well as towns characterized by dichotomy. Equally, a functional/morphological approach taken with the same database allowed for the identification – via an alternative method – of three main categories of small towns (i.e., the monofunctional, multifunctional or oligo functional), which could then be described in far greater detail. A third, multi-criterion classification made simultaneous reference to the conditioning of a structural, a location-related, and an administrative hierarchy-related nature, allowing for distinctions to be drawn between small towns in 9 different categories. The results obtained allow for multifaceted analysis and interpretation of the geographical differentiation characterizing the distribution of Poland’s urban centers across space in the country.

Keywords: small towns, classification, local planning, Poland

Procedia PDF Downloads 80

Authors: Amal Abdelhadi

Abstract:

Consumer misbehavior evaluation can be markedly different based on a number of variables and different from one environment to another. Using three aberrant consumer behavior (ACB) scenarios (shoplifting, stealing from hotel rooms and software piracy) this study aimed to explore Libyan seller and consumers of ACB. Materials were collected by using a multi-method approach was employed (qualitative and quantitative approaches) in two fieldwork phases. In the phase stage, a qualitative data were collected from 26 Libyan sellers’ by face-to-face interviews. In the second stage, a consumer survey was used to collect quantitative data from 679 Libyan consumers. This study found that the consumer’s and seller’s evaluation of ACB are not always consistent. Further, ACB evaluations differed based on the form of ACB. Furthermore, the study found that not all consumer behaviors that were considered as bad behavior in other countries have the same evaluation in Libya; for example, software piracy. Therefore this study suggested a newly developed classification of ACB based on marketers’ and consumers’ views. This classification provides 9 ACB types within two dimensions (marketers’ and consumers’ views) and three degrees of behavior evaluation (good, acceptable and misbehavior).

Keywords: aberrant consumer behavior, Libya, multi-method approach, planned behavior theory

Procedia PDF Downloads 568

Authors: Yusuke Kuramitsu, Tomoaki Hashimoto, Hirokazu Tahara

Abstract:

The control problem of underactuated spacecrafts has attracted a considerable amount of interest. The control method for a spacecraft equipped with less than three control torques is useful when one of the three control torques had failed. On the other hand, the quantized control of systems is one of the important research topics in recent years. The random dither quantization method that transforms a given continuous signal to a discrete signal by adding artificial random noise to the continuous signal before quantization has also attracted a considerable amount of interest. The objective of this study is to develop the control method based on random dither quantization method for stabilizing the rotational motion of a rigid spacecraft with two control inputs. In this paper, the effectiveness of random dither quantization control method for the stabilization of rotational motion of spacecrafts with two torque inputs is verified by numerical simulations.

Keywords: spacecraft control, quantized control, nonlinear control, random dither method

Procedia PDF Downloads 176

Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

Abstract:

Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

Procedia PDF Downloads 449

Authors: Teja Sai Puligadda

Abstract:

Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

Procedia PDF Downloads 152

Authors: Wassana Naiyapo, Atichat Sangtong

Abstract:

The processes in software development by Object Oriented methodology have many stages those take time and high cost. The inconceivable error in system analysis process will affect to the design and the implementation process. The unexpected output causes the reason why we need to revise the previous process. The more rollback of each process takes more expense and delayed time. Therefore, the good test process from the early phase, the implemented software is efficient, reliable and also meet the user’s requirement. Unified Modelling Language (UML) is the tool which uses symbols to describe the work process in Object Oriented Analysis (OOA). This paper presents the approach for automatically generated UML use case diagram and test cases. UML use case diagram is generated from the event table and test cases are generated from use case specifications and Graphic User Interfaces (GUI). Test cases are derived from the Classification Tree Method (CTM) that classify data to a node present in the hierarchy structure. Moreover, this paper refers to the program that generates use case diagram and test cases. As the result, it can reduce work time and increase efficiency work.

Keywords: classification tree method, test case, UML use case diagram, use case specification

Procedia PDF Downloads 157