Search results for: sesimic data processing

Authors: Vineetha Bettaiah, Heggere S. Ranganath

Abstract:

This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity.

Keywords: data mining, dimensionality reduction, piecewise linear representation, time series representation

Procedia PDF Downloads 251

Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

Abstract:

In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

Procedia PDF Downloads 362

Authors: Martin Pellon Consunji

Abstract:

Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

Procedia PDF Downloads 97

Authors: K. S. Vedasingha, K. K. M. T. Perera, K. I. Hathurusinghe, H. W. I. Akalanka, Nelum Chathuranga Amarasena, Nalaka R. Dissanayake

Abstract:

Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method among all. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways and caused damages to not only precious lives but also to the economy of the countries. There are some major issues which need to be addressed in railways of South Asian countries since they fall under the developing category. The goal of this research is to minimize the influencing aspect of railway level crossing accidents by developing the “railway process automation system”, as there are high-risk areas that are prone to accidents, and safety at these places is of utmost significance. This paper describes the implementation methodology and the success of the study. The main purpose of the system is to ensure human safety by using the Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. And it is possible to do the above-mentioned process through a decision-making system by using past data. The specialty is both processes working parallel. As usual, if the system fails to close the railway gate due to technical or a network failure, the proposed system can identify the current location and close the railway gate through a decision-making system, which is a revolutionary feature. The proposed system introduces further two features to reduce the causes of railway accidents. Railway track crack detection and motion detection are those features which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype, and it is tested with real-world scenarios to gain the above 90% of accuracy.

Keywords: crack detection, decision-making, image processing, Internet of Things, motion detection, prototype, sensors

Procedia PDF Downloads 156

Authors: Touila Ahmed, Elie Louis, Hamza Gharbi

Abstract:

State of the art question answering models have recently shown impressive performance especially in a zero-shot setting. This approach is particularly useful when confronted with a highly diverse domain such as the legal field, in which it is increasingly difficult to have a dataset covering every notion and concept. In this work, we propose a flexible generative question answering approach to contract analysis as well as a weakly supervised procedure to leverage unannotated data and boost our models’ performance in general, and their zero-shot performance in particular.

Keywords: question answering, contract analysis, zero-shot, natural language processing, generative models, self-supervision

Procedia PDF Downloads 154

Authors: Rahele Mesbah, Nic Van Der Wee, Manja Koenders, Erik Giltay, Albert Van Hemert, Max De Leeuw

Abstract:

Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression.

Keywords: cognitive functioning, fMRI analysis, bipolar disorder, fronto-limbic network

Procedia PDF Downloads 432

Authors: A. Şahinoğlu, R. Gürbüz, A. Güllü, M. Karhan

Abstract:

This study in production machines, it is aimed that machine will automatically perceive cutting data and alter cutting parameters. The two most important parameters have to be checked in machine control unit are progress feed rate and speeds. These parameters are aimed to be controlled by sounds of machine. Optimum sound’s features introduced to computer. During process, real time data is received and converted by Matlab software. Data is converted into numerical values. According to them progress and speeds decreases/increases at a certain rate and thus optimum sound is acquired. Cutting process is made in respect of optimum cutting parameters. During chip remove progress, features of cutting tools, kind of cut material, cutting parameters and used machine; affects on various parameters. Instead of required parameters need to be measured such as temperature, vibration, and tool wear that emerged during cutting process; detailed analysis of the sound emerged during cutting process will provide detection of various data that included in the cutting process by the much more easy and economic way. The relation between cutting parameters and sound is being identified.

Keywords: cutting process, sound processing, intelligent late, sound analysis

Procedia PDF Downloads 308

Authors: Jun-Yong Lee, Hyoung-Gook Kim

Abstract:

In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing

Procedia PDF Downloads 400

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

Procedia PDF Downloads 236

Authors: Usama Shahid, Muhammad Zunnurain Hussain

Abstract:

This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

Procedia PDF Downloads 63

Authors: Kumar Sambhav Pandey, Anamika Singh

Abstract:

The continuous researches in the field of computer architecture basically aims at accelerating the computational speed and to gain enhanced performance. In this era, the superscalar, sub-scalar concept has not gained enough attention for improving the computation performance. In this paper, we have presented a sub-scalar approach to utilize the parallelism present with in the data while processing. The main idea is to split the data into individual smaller entities and these entities are processed with a defined known set of instructions. This sub-scalar approach to the MIPS architecture can bring out significant improvement in the computational speedup. MIPS-I is the basic design taken in consideration for the development of sub-scalar MIPS64 for increasing the instruction level parallelism (ILP) and resource utilization.

Keywords: dataword, MIPS, processor, sub-scalar

Procedia PDF Downloads 515

Authors: Min L. Stewart, Patrick Johnston

Abstract:

Predictive coding has been proposed as a general explanatory framework for understanding the neural mechanisms of perception. As such, an underweighting of perceptual priors has been hypothesised to underpin a range of differences in inferential and sensory processing in autism spectrum disorders. However, empirical evidence to support this has not been well established. The present study uses an electroencephalography paradigm involving changes of facial identity and person category (actors etc.) to explore how levels of autistic traits (AT) affect predictive coding at multiple stages in the visual processing hierarchy. The study uses a rapid serial presentation of faces, with hierarchically structured sequences involving both periodic and aperiodic repetitions of different stimulus attributes (i.e., person identity and person category) in order to induce contextual expectations relating to these attributes. It investigates two main predictions: (1) significantly larger and late neural responses to change of expected visual sequences in high-relative to low-AT, and (2) significantly reduced neural responses to violations of contextually induced expectation in high- relative to low-AT. Preliminary frequency analysis data comparing high and low-AT show greater and later event-related-potentials (ERPs) in occipitotemporal areas and prefrontal areas in high-AT than in low-AT for periodic changes of facial identity and person category but smaller ERPs over the same areas in response to aperiodic changes of identity and category. The research advances our understanding of how abnormalities in predictive coding might underpin aberrant perceptual experience in autism spectrum. This is the first stage of a research project that will inform clinical practitioners in developing better diagnostic tests and interventions for people with autism.

Keywords: hierarchical visual processing, face processing, perceptual hierarchy, prediction error, predictive coding

Procedia PDF Downloads 91

Authors: Shepherd Manhokwe, Sheron Shoko, Cuthbert Zvidzai

Abstract:

In the present study, the interactive effects of temperature and cultured bacteria on the performance of a biological treatment system of yeast processing wastewater were investigated. The main objective of this study was to investigate and optimize the operating parameters that reduce organic load and colour. Experiments were conducted based on a Central Composite Design (CCD) and analysed using Response Surface Methodology (RSM). Three dependent parameters were either directly measured or calculated as response. These parameters were total Chemical Oxygen Demand (COD) removal, colour reduction and total solids. COD removal efficiency of 26 % and decolourization efficiency of 44 % were recorded for the wastewater treatment. The optimized conditions for the biological treatment were found to be at 20 g/l cultured bacteria and 25 °C for COD reduction. For colour reduction optimum conditions were temperature of 30.35°C and bacterial formulation of 20g/l. Biological treatment of baker’s yeast processing effluent is a suitable process for the removal of organic load and colour from wastewater, especially when the operating parameters are optimized.

Keywords: COD reduction, optimisation, response surface methodology, yeast processing wastewater

Procedia PDF Downloads 313

Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

Abstract:

Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

Procedia PDF Downloads 87

Authors: Dina K. Badawy, Dina M. Ibrahim, Alsayed A. Sallam

Abstract:

Distributed database is a collection of logically related databases that cooperate in a transparent manner. Query processing uses a communication network for transmitting data between sites. It refers to one of the challenges in the database world. The development of sophisticated query optimization technology is the reason for the commercial success of database systems, which complexity and cost increase with increasing number of relations in the query. Mariposa, query trading and query trading with processing task-trading strategies developed for autonomous distributed database systems, but they cause high optimization cost because of involvement of all nodes in generating an optimal plan. In this paper, we proposed a modification on the autonomous strategy K-QTPT that make the seller’s nodes with the lowest cost have gradually high priorities to reduce the optimization time. We implement our proposed strategy and present the results and analysis based on those results.

Keywords: autonomous strategies, distributed database systems, high priority, query optimization

Procedia PDF Downloads 500

Authors: Boris Barbolyas, Kristina Buckova, Tomas Volensky, Cyril Belavy, Ladislav Dedik

Abstract:

Background: Based on statokinezigram, the human balance control is often studied. Approach to human postural reaction analysis is based on a combination of stabilometry output signal with retroreflective marker data signal processing, analysis, and understanding, in this study. The study shows another original application of Method of Developed Statokinesigram Trajectory (MDST), too. Methods: In this study, the participants maintained quiet bipedal standing for 10 s on stabilometry platform. Consequently, bilateral vibration stimuli to Achilles tendons in 20 s interval was applied. Vibration stimuli caused that human postural system took the new pseudo-steady state. Vibration frequencies were 20, 60 and 80 Hz. Participant's body segments - head, shoulders, hips, knees, ankles and little fingers were marked by 12 retroreflective markers. Markers positions were scanned by six cameras system BTS SMART DX. Registration of their postural reaction lasted 60 s. Sampling frequency was 100 Hz. For measured data processing were used Method of Developed Statokinesigram Trajectory. Regression analysis of developed statokinesigram trajectory (DST) data and retroreflective marker developed trajectory (DMT) data were used to find out which marker trajectories most correlate with stabilometry platform output signals. Scaling coefficients (λ) between DST and DMT by linear regression analysis were evaluated, too. Results: Scaling coefficients for marker trajectories were identified for all body segments. Head markers trajectories reached maximal value and ankle markers trajectories had a minimal value of scaling coefficient. Hips, knees and ankles markers were approximately symmetrical in the meaning of scaling coefficient. Notable differences of scaling coefficient were detected in head and shoulders markers trajectories which were not symmetrical. The model of postural system behavior was identified by MDST. Conclusion: Value of scaling factor identifies which body segment is predisposed to postural instability. Hypothetically, if statokinesigram represents overall human postural system response to vibration stimuli, then markers data represented particular postural responses. It can be assumed that cumulative sum of particular marker postural responses is equal to statokinesigram.

Keywords: center of pressure (CoP), method of developed statokinesigram trajectory (MDST), model of postural system behavior, retroreflective marker data

Procedia PDF Downloads 320

Authors: Mehdi Brahimi, Kamal Medjaher, Noureddine Zerhouni, Mohammed Leouatni

Abstract:

The Prognostics and Health Management is a system engineering discipline which provides solutions and models to the implantation of a predictive maintenance. The approach is based on extracting useful information from monitoring data to assess the “health” state of an industrial equipment or an asset. In this paper, we examine multiple extracted features from Pantograph-Catenary contact force in order to select the most relevant ones to achieve a diagnostics function. The feature extraction methodology is based on simulation data generated thanks to a Pantograph-Catenary simulation software called INPAC and measurement data. The feature extraction method is based on both statistical and signal processing analyses. The feature selection method is based on statistical criteria.

Keywords: catenary/pantograph interaction, diagnostics, Prognostics and Health Management (PHM), quality of current collection

Procedia PDF Downloads 272

Authors: Mohammad Nehal Hasnine, Md Kamrul Hasan Chayon, Md Mobasswer Rahman

Abstract:

Organizational tendencies towards computer-based information processing have been observed noticeably in the third-world countries. Many enterprises are taking major initiatives towards computerized working environment because of massive benefits of computer-based information processing. However, designing and developing information resource management software for small and mid-size enterprises under budget costs and strict deadline is always challenging for software engineers. Therefore, we introduced an approach to design mid-size enterprise software by using the Waterfall model, which is one of the SDLC (Software Development Life Cycles), in a cost effective way. To fulfill research objectives, in this study, we developed mid-sized enterprise software named “BSK Management System” that assists enterprise software clients with information resource management and perform complex organizational tasks. Waterfall model phases have been applied to ensure that all functions, user requirements, strategic goals, and objectives are met. In addition, Rich Picture, Structured English, and Data Dictionary have been implemented and investigated properly in engineering manner. Furthermore, an assessment survey with 20 participants has been conducted to investigate the usability and performance of the proposed software. The survey results indicated that our system featured simple interfaces, easy operation and maintenance, quick processing, and reliable and accurate transactions.

Keywords: end-user application development, enterprise software design, information resource management, usability

Procedia PDF Downloads 415

Authors: Hoda A. Abdel Hafez

Abstract:

Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: mining big data, big data, machine learning, telecommunication

Procedia PDF Downloads 375

Authors: Ufuk Tosun, Reza Aghazadeh, Mehmet Bülent Özer

Abstract:

This study puts forward a method to analyze the sloshing characteristics of liquid in a tuned sloshing absorber system by using image processing tools. Tuned sloshing vibration absorbers have recently attracted researchers’ attention as a seismic load damper in constructions due to its practical and logistical convenience. The absorber is liquid which sloshes and applies a force in opposite phase to the motion of structure. Experimentally characterization of the sloshing behavior can be utilized as means of verifying the results of numerical analysis. It can also be used to identify the accuracy of assumptions related to the motion of the liquid. There are extensive theoretical and experimental studies in the literature related to the dynamical and structural behavior of tuned sloshing dampers. In most of these works there are efforts to estimate the sloshing behavior of the liquid such as free surface motion and total force applied by liquid to the wall of container. For these purposes the use of sensors such as load cells and ultrasonic sensors are prevalent in experimental works. Load cells are only capable of measuring the force and requires conducting tests both with and without liquid to obtain pure sloshing force. Ultrasonic level sensors give point-wise measurements and hence they are not applicable to measure the whole free surface motion. Furthermore, in the case of liquid splashing it may give incorrect data. In this work a method for evaluating the sloshing wave height by using camera records and image processing techniques is presented. In this method the motion of the liquid and its container, made of a transparent material, is recorded by a high speed camera which is aligned to the free surface of the liquid. The video captured by the camera is processed frame by frame by using MATLAB Image Processing toolbox. The process starts with cropping the desired region. By recognizing the regions containing liquid and eliminating noise and liquid splashing, the final picture depicting the free surface of liquid is achieved. This picture then is used to obtain the height of the liquid through the length of container. This process is verified by ultrasonic sensors that measured fluid height on the surface of liquid.

Keywords: fluid structure interaction, image processing, sloshing, tuned liquid damper

Procedia PDF Downloads 324

Authors: Chayanika Hazarika Bordoloi

Abstract:

This paper is an attempt to work on a highly inflectional language called Assamese. This is also one of the national languages of India and very little has been achieved in terms of computational research. Building a language processing tool for a natural language is not very smooth as the standard and language representation change at various levels. This paper presents inflectional suffixes of Assamese verbs and how the statistical tools, along with linguistic features, can improve the tagging accuracy. Conditional random fields (CRF tool) was used to automatically tag and train the text data; however, accuracy was improved after linguistic featured were fed into the training data. Assamese is a highly inflectional language; hence, it is challenging to standardizing its morphology. Inflectional suffixes are used as a feature of the text data. In order to analyze the inflections of Assamese word forms, a list of suffixes is prepared. This list comprises suffixes, comprising of all possible suffixes that various categories can take is prepared. Assamese words can be classified into inflected classes (noun, pronoun, adjective and verb) and un-inflected classes (adverb and particle). The corpus used for this morphological analysis has huge tokens. The corpus is a mixed corpus and it has given satisfactory accuracy. The accuracy rate of the tagger has gradually improved with the modified training data.

Keywords: CRF, morphology, tagging, tagset

Procedia PDF Downloads 174

Authors: Miroslav Dumbrovsky, Lucie Larisova

Abstract:

The infiltration characteristic of the soil surface is one of the major factors that determines the potential soil degradation risk. The physical, chemical and biological characteristic of soil is changed by the processing of soil. The infiltration soil ability has an important role in soil and water conservation. The subject of the contribution is the evaluation of the influence of the conventional tillage and reduced tillage technology on soil surface crusting processing and infiltration properties of the soil in the North Bohemia region. Field experimental work at the area was carried out in the years 2013-2016 on Cambisol district medium-heavy clayey soil. The research was conducted on sloping erosion-endangered blocks of compacted arable land. The areas were chosen each year in the way that one of the experimental areas was handled by conventional tillage technologies and the other by reduced tillage technologies. Intact soil samples were taken into Kopecký´s cylinders in the three landscape positions, at a depth of 10 cm (representing topsoil) and 30 cm (representing subsoil). The cumulative infiltration was measured using a mini-disc infiltrometer near the consumption points. The Zhang method (1997), which provides an estimate of the unsaturated hydraulic conductivity K(h), was used for the evaluation of the infiltration tests of the mini-disc infiltrometer. The soil profile processed by conventional tillage showed a higher degree of compaction and soil crusting processing. The bulk density was between 1.10–1.67 g.cm⁻³, compared to the land processed by the reduced tillage technology, where the values were between 0.80–1.29 g.cm⁻³. Unsaturated hydraulic conductivity values were about one-third higher within the reduced tillage technology soil processing.

Keywords: soil crusting processing, unsaturated hydraulic conductivity, cumulative infiltration, bulk density, porosity

Procedia PDF Downloads 216

Authors: Namugenyi Ephrance Eunice, Julianne Sansa Otim, Marco Zennaro, Stephen D. Wolthusen

Abstract:

The Internet of Things (IoT) is a network of connected data processing devices, mechanical and digital machinery, items, animals, or people that may send data across a network without requiring human-to-human or human-to-computer interaction. Each component has sensors that can pick up on specific phenomena, as well as processing software and other technologies that can link to and communicate with other systems and/or devices over the Internet or other communication networks and exchange data with them. IoT is increasingly being used in fields other than consumer electronics, such as public safety, emergency response, industrial automation, autonomous vehicles, the Internet of Medical Things (IoMT), and general environmental monitoring. Consumer-based IoT applications, like smart home gadgets and wearables, are also becoming more prevalent. This paper presents the main IoT deployment areas for environmental monitoring in developing regions and the backhaul options suitable for them. A detailed review of each of the list of papers selected for the study is included in section III of this document. The study includes an overview of existing IoT deployments, the underlying communication architectures, protocols, and technologies that support them. This overview shows that Low Power Wireless Area Networks (LPWANs), as summarized in Table 1, are very well suited for monitoring environment architectures designed for remote locations. LoRa technology, particularly the LoRaWAN protocol, has an advantage over other technologies due to its low power consumption, adaptability, and suitable communication range. The prevailing challenges of the different architectures are discussed and summarized in Table 3 of the IV section, where the main problem is the obstruction of communication paths by buildings, trees, hills, etc.

Keywords: communication technologies, environmental monitoring, Internet of Things, IoT deployment challenges

Procedia PDF Downloads 61

Authors: Hala Zaghloul, Taymoor Nazmy

Abstract:

One of the great challenges of the 21st century is to build a robot that can perceive and act within its environment and communicate with people, while also exhibiting the cognitive capabilities that lead to performance like that of people. The Pulse Coupled Neural Network, PCNN, is a relative new ANN model that derived from a neural mammal model with a great potential in the area of image processing as well as target recognition, feature extraction, speech recognition, combinatorial optimization, compressed encoding. PCNN has unique feature among other types of neural network, which make it a candid to be an important approach for perceiving in cognitive systems. This work show and emphasis on the potentials of PCNN to perform different tasks related to image processing. The main drawback or the obstacle that prevent the direct implementation of such technique, is the need to find away to control the PCNN parameters toward perform a specific task. This paper will evaluate the performance of PCNN standard model for processing images with different properties, and select the important parameters that give a significant result, also, the approaches towards find a way for the adaptation of the PCNN parameters to perform a specific task.

Keywords: cognitive system, image processing, segmentation, PCNN kernels

Procedia PDF Downloads 257

Authors: Van Chung Nguyen

Abstract:

The purpose of this study is to study the current status of the value chain for fish distribution at Quang Binh Fishing Port with 360 research samples in which the research subjects are fishermen, traders, retailers, and businesses. The research uses the approach of applying the value chain theoretical framework of Kaplinsky and Morris to quantify and describe market channels and actors participating in the value chain and analyze the value-added process of these companies according to market channels. The analysis results show that fishermen directly catch fish with high economic efficiency, but processing enterprises and, especially retailers, are the agents to obtain higher added value. Processing enterprises play a role that is not really clear due to outdated processing technology; in contrast, retailers have the highest added value. This shows that the added value of the fish supply chain at Quang Binh fishing port is still limited, leading to low output quality. Therefore, the selling price of fish to the market is still high compared to the abundant fish resources, leading to low consumption and limiting exports due to the quality of processing enterprises. This reduces demand and fishing capacity, and productivity is lower than potential. To improve the fish value chain at fishing ports, it is necessary to focus on improving product quality, strengthening linkages between actors, building brands and product consumption markets at the same time, improving the capacity of export processing enterprises.

Keywords: Quang Binh fishing port, value chain, market, distributions channel

Procedia PDF Downloads 48

Authors: Mamdouh Omran, Timo Fabritius

Abstract:

A growing interest in microwave heating has emerged recently. Many researchers have begun to pay attention to microwave energy as an alternative technique for processing various primary and secondary raw materials. Compared to conventional methods, microwave processing offers several advantages, such as selective heating, rapid heating, and volumetric heating. The present study gives a summary on our recent works related to the use of microwave energy for the recovery of valuable metals from primary and secondary raw materials. The research is mainly focusing on: Application of microwave for the recovery and recycling of metals from different metallurgical industries wastes (i.e. electric arc furnace (EAF) dust, blast furnace (BF), basic oxygen furnace (BOF) sludge). Application of microwave for upgrading and recovery of valuable metals from primary raw materials (i.e. iron ore). The results indicated that microwave heating is a promising and effective technique for processing primary and secondary steelmaking wastes. After microwave treatment of iron ore for 60 s and 900 W, about a 28.30% increase in grindability.Wet high intensity magnetic separation (WHIMS) indicated that the magnetic separation increased from 34% to 98% after microwave treatment for 90 s and 900 W. In the case of EAF dust, after microwave processing at 1100 W for 20 min, Zinc removal from 64 % to ~ 97 %, depending on mixture ratio and treatment time.

Keywords: dielectric properties, microwave heating, raw materials, secondary raw materials

Procedia PDF Downloads 62

Authors: Julia Ive, Lucia Specia

Abstract:

Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

Procedia PDF Downloads 143

Authors: Homa Ghave, Parmis Shahmaleki

Abstract:

This paper presents a new multi-criteria stochastic mathematical model for a single machine scheduling with outsourcing allowed. There are multiple jobs processing in batch. For each batch, all of job or a quantity of it can be outsourced. The jobs have stochastic processing time and lead time and deterministic due dates arrive randomly. Because of the stochastic inherent of processing time and lead time, we use the chance constrained programming for modeling the problem. First, the problem is formulated in form of stochastic programming and then prepared in a form of deterministic mixed integer linear programming. The objectives are considered in the model to minimize the maximum tardiness and outsourcing cost simultaneously. Several procedures have been developed to deal with the multi-criteria problem. In this paper, we utilize the concept of satisfaction functions to increases the manager’s preference. The proposed approach is tested on instances where the random variables are normally distributed.

Keywords: single machine scheduling, multi-criteria mathematical model, outsourcing strategy, uncertain lead times and processing times, chance constrained programming, satisfaction function

Procedia PDF Downloads 241

Authors: Zahid Ullah, Atlas Khan

Abstract:

The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

Procedia PDF Downloads 63

Authors: Michael Okeke, Andrew Blyth

Abstract:

Supervisory Control And Data Acquisition (SCADA) as the state of the art Industrial Control Systems (ICS) are used in many different critical infrastructures, from smart home to energy systems and from locomotives train system to planes. Security of SCADA systems is vital since many lives depend on it for daily activities and deviation from normal operation could be disastrous to the environment as well as lives. This paper describes how No-Trust-Zone (NTZ) architecture could be incorporated into SCADA Systems in order to reduce the chances of malicious intent. The architecture is made up of two distinctive parts which are; the field devices such as; sensors, PLCs pumps, and actuators. The second part of the architecture is designed following lambda architecture, which is made up of a detection algorithm based on Particle Swarm Optimization (PSO) and Hadoop framework for data processing and storage. Apache Spark will be a part of the lambda architecture for real-time analysis of packets for anomalies detection.

Keywords: industrial control system (ics, no-trust-zone (ntz), particle swarm optimisation (pso), supervisory control and data acquisition (scada), swarm intelligence (SI)

Procedia PDF Downloads 319