Search results for: Permanent magnet synchronous machine (PMSM)

Authors: Nicolaas Unland, John Webb

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The acidification and mobilisation of metals during the oxidation of acid sulfate soils exposed during lake bed drying is an increasingly common phenomenon under climate scenarios with reduced rainfall. In order to assess the risk of generating high concentrations of acidity and dissolved metals, chromium suite analysis are fundamental, but sometimes limited in characterising the potential risks they pose. This study combines such fundamental test work, along with incubation tests and 1D modelling to investigate the risks associated with the drying of Third Reedy Lake in South East Australia. Core samples were collected from a variable depth of 0.5 m below the lake bed, at 19 locations across the lake’s footprint, using a boat platform. Samples were subjected to a chromium suite of analysis, including titratable actual acidity, chromium reducible sulfur and acid neutralising capacity. Concentrations of reduced sulfur up to 0.08 %S and net acidities up to 0.15 %S indicate that acid sulfate soils have formed on the lake bed during permanent inundation over the last century. A further sub-set of samples were prepared in 7 columns and subject to accelerated heating, drying and wetting over a period of 64 days in laboratory. Results from the incubation trial indicate that while pyrite oxidation proceeded, minimal change to soil pH or the acidity of leachate occurred, suggesting that the internal buffering capacity of lake bed sediments was sufficient to neutralise a large proportion of the acidity produced. A 1D mass balance model was developed to assess potential changes in lake water quality during drying based on the results of chromium suite and incubation tests. Results from the above test work and modelling suggest that acid sulfate soils pose a moderate to low risk to the Third Reedy Lake system. Further, the risks can be effectively managed during the initial stages of lake drying via flushing with available mildly alkaline water. The study finds that while test work such as chromium suite analysis are fundamental in characterizing acid sulfate soil environments, they can the overestimate risks associated with the soils. Subsequent incubation test work may more accurately characterise such soils and lead to better-informed management strategies.

Keywords: acid sulfate soil, incubation, management, model, risk

Procedia PDF Downloads 354

Authors: Junjie Peng, Jinbao Chen, Shuai Kong, Danxu Liu

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On the basis of traditional resource allocation strategies, the usage of resources on physical servers in cloud data center is great uncertain. It will cause waste of resources if the assignment of tasks is not enough. On the contrary, it will cause overload if the assignment of tasks is too much. This is especially obvious when the applications are the same type because of its resource preferences. Considering CPU intensive application is one of the most common types of application in the cloud, we studied the optimization strategy for CPU intensive applications on the same server. We used resource preferences to analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can predict the execution time for CPU intensive applications which run simultaneously. Based on the prediction model, we proposed the method to select the appropriate number of applications for a machine. Experiments show that the model can predict the execution time accurately for CPU intensive applications. To improve the execution efficiency of applications, we propose a scheduling model based on priority for CPU intensive applications. Extensive experiments verify the validity of the scheduling model.

Keywords: cloud computing, CPU intensive applications, resource optimization, strategy

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Authors: Sandra K. Enriquez, Claudia Camargo, Jesús E. Olguín, Juan A. López, German Galindo

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Currently companies have increased the number of disorders of cumulative trauma (CTDs), these are increasing significantly due to the Highly Repetitive Movements (HRM) performed in workstations, which causes economic losses to businesses, due to temporary and permanent disabilities of workers. This analysis focuses on the prevention of disorders caused by: repeatability, duration and effort; And focuses on reducing cumulative trauma disorders such as occupational diseases using sensory thermography as a noninvasive method, the above is to evaluate the injuries could have workers to perform repetitive motions. Objectives: The aim is to define rest periods or job rotation before they generate a CTD, this sensory thermography by analyzing changes in temperature patterns on wrists and elbows when the worker is performing HRM over a period of time 2 hours and 30 minutes. Information on non-work variables such as wrist and elbow injuries, weight, gender, age, among others, and work variables such as temperature workspace, repetitiveness and duration also met. Methodology: The analysis to 4 industrial designers, 2 men and 2 women to be specific was conducted in a business in normal health for a period of 12 days, using the following time ranges: the first day for every 90 minutes continuous work were asked to rest 5 minutes, the second day for every 90 minutes of continuous work were asked to rest 10 minutes, the same to work 60 and 30 minutes straight. Each worker was tested with 6 different ranges at least twice. This analysis was performed in a controlled room temperature between 20 and 25 ° C, and a time to stabilize the temperature of the wrists and elbows than 20 minutes at the beginning and end of the analysis. Results: The range time of 90 minutes working continuous and a rest of 5 minutes of activity is where the maximum temperature (Tmax) was registered in the wrists and elbows in the office, we found the Tmax was 35.79 ° C with a difference of 2.79 ° C between the initial and final temperature of the left elbow presented at the individual 4 during the 86 minutes, in of range in 90 minutes continuously working and rested for 5 minutes of your activity. Conclusions: It is possible with this alternative technology is sensory thermography predict ranges of rotation or rest for the prevention of CTD to perform HRM work activities, obtaining with this reduce occupational disease, quotas by health agencies and increasing the quality of life of workers, taking this technology a cost-benefit acceptable in the future.

Keywords: sensory thermography, temperature, cumulative trauma disorder (CTD), highly repetitive movement (HRM)

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Authors: Vivian Guo, Yan Qu

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Online marketplaces are not only digital places where consumers buy and sell merchandise, and they are also destinations for brands to connect with real consumers at the moment when customers are in the shopping mindset. For many marketplaces, brands have been important partners through advertising. There can be, however, a risk of advertising impacting a consumer’s shopping journey if it hurts the use experience or takes the user away from the site. Both could lead to the loss of transaction revenue for the marketplace. In this paper, we present user-based methods for cannibalization control by selectively turning off ads to users who are likely to be cannibalized by ads subject to business objectives. We present ways of measuring cannibalization of advertising in the context of an online marketplace and propose novel ways of measuring cannibalization through purchase propensity and uplift modeling. A/B testing has shown that our methods can significantly improve user purchase and engagement metrics while operating within business objectives. To our knowledge, this is the first paper that addresses cannibalization mitigation at the user-level in the context of advertising.

Keywords: cannibalization, machine learning, online marketplace, revenue optimization, yield optimization

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Authors: Ali Raza, Shafiq Ur Rehman Khan, Raja Sher Afgun Usmani, Asif Raza, Basit Umair

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Detection of Fake news and missing information is gaining popularity, especially after the advancement in social media and online news platforms. Social media platforms are the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to identify whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use two models BERT and Bi-LSTM to investigate, and it is concluded from BERT we get better results and 70% of true news are recurring and rest of 30% are non-recurring.

Keywords: natural language processing, fake news detection, machine learning, Bi-LSTM

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Authors: Khalid Ahmed Eldwaib

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In this study, Al-Mg alloy 5052 was used as the testing material. Corrosion fatigue life was studied for the alloy in 3.5% NaCl (pH=1, 3, 5, 7, 9, and 11), and 3.5% NaCl (pH=1) with inhibitors. The compound inhibitors were composed mainly of phosphate (PO4³-), adding a certain proportion of other nontoxic inhibitors so as to select alternatives to environmentally hazardous chromate (Cr2O7²-). The inhibitors were sodium dichromate Na2Cr2O7, sodium phosphate Na3PO4, sodium molybdate Na2MoO4, and sodium citrate Na3C6H5O7. The total amount of inhibiting pigments was at different concentrations (250,500,750, and 1000 ppm) in the solutions. Corrosion fatigue behavior was studied by using plane-bending corrosion fatigue machine with stress ratio R=0.5 and under the constant frequency of 13.3 Hz. Results show that in 3.5% NaCl the highest fatigue life (number of cycles to failure Nf) is obtained at pH=5 where the oxide film on aluminum has very low solubility, and the lowest number of cycles is obtained at pH=1, where the media is too aggressive (extremely acidic). When the concentration of inhibitor increases the cycles to failure increase. The surface morphology and fracture section of the specimens had been characterized through scanning electron microscope (SEM).

Keywords: Al-Mg alloy 5052, corrosion, fatigue, inhibitors

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Authors: Armana Huq, Wahidur Rahman, Sanwar Kader

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Inland waterways in Bangladesh play an important role in providing comfortable and low-cost transportation. However, a maritime accident takes away many lives and creates unwanted hazards every year. This article deals with a comprehensive review of inland waterway accidents in Bangladesh. Additionally, it includes a comparative study between international and local inland research studies based on maritime accidents. Articles from inland waterway areas are analyzed in-depth to make a comprehensive overview of the nature of the academic work, accident and risk management process and different statistical analyses. It is found that empirical analysis based on the available statistical data dominates the research domain. For this study, major maritime accident-related works in the last four decades in Bangladesh (1981-2020) are being analyzed for preparing a bibliometric analysis. A study of maritime accidents of passenger's vessels during (1995-2005) indicates that the predominant causes of accidents in the inland waterways of Bangladesh are collision and adverse weather (77%), out of which collision due to human error alone stands (56%) of all accidents. Another study refers that the major causes of waterway accidents are the collision (60.3%) during 2005-2015. About 92% of this collision occurs due to direct contact with another vessel during this period. Rest 8% of the collision occurs by contact with permanent obstruction on waterway roots. The overall analysis of another study from the last 25 years (1995-2019) shows that one of the main types of accidents is collisions, with about 50.3% of accidents being caused by collisions. The other accident types are cyclone or storm (17%), overload (11.3%), physical failure (10.3%), excessive waves (5.1%), and others (6%). Very few notable works are available in testing or comparing the methods, proposing new methods for risk management, modeling, uncertainty treatment. The purpose of this paper is to provide an overview of the evolution of marine accident-related research domain regarding inland waterway of Bangladesh and attempts to introduce new ideas and methods to abridge the gap between international and national inland maritime-related work domain which can be a catalyst for a safer and sustainable water transportation system in Bangladesh. Another fundamental objective of this paper is to navigate various national maritime authorities and international organizations to implement risk management processes for shipping accident prevention in waterway areas.

Keywords: inland waterways, safety, bibliometric analysis, risk management, accidents

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Authors: Hong Lu, Wei Fan, Yongquan Zhang, Junbo Zhang

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A cross coupling sliding synchronization control strategy is proposed for the dual-driving feed system. This technology will minimize the position error oscillation and achieve the precise synchronization performance in the high speed and high precision drive system, especially some high speed and high precision machine. Moreover, a cross coupling compensation matrix is provided to offset the mismatched disturbance and the disturbance observer is established to eliminate the chattering phenomenon. Performance comparisons of proposed dual-driving cross coupling sliding mode control (CCSMC), normal cross coupling control （CCC） strategy with PID control, and electronic virtual main shaft control (EVMSC) strategy with SMC control are investigated by simulation and a dual-driving control system; the results show the effectiveness of the proposed control scheme.

Keywords: cross coupling matrix, dual motors, synchronization control, sliding mode control

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Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Dalila Souilem, Salma Boumiza, Abdelkarim Abdelkader

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The Clarifier Dialogue Interface (CDI) is a part of an online teaching system based on human-machine communication in learning situation. This interface used in the system during the learning action specifically in the evaluation step, to clarify ambiguities in the learner's response. The CDI can generate patterns allowing access to an information system, using the selectors associated with lexical units. To instantiate these patterns, the user request (especially learner’s response), must be analyzed and interpreted to deduce the canonical form, the semantic form and the subject of the sentence. For the efficiency of this interface at the interpretation level, a set of substitution operators is carried out in order to extend the possibilities of manipulation with a natural language. A second approach that will be presented in this paper focuses on the object languages with new prospects such as combination of natural language with techniques of handling information system in the area of online education. So all operators, the CDI and other interfaces associated to the domain expertise and teaching strategies will be unified using FRAME representation form.

Keywords: dialogue, e-learning, FRAME, information system, natural language

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Amal Alshahrani, Allan Ramsay

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Paraphrasing is one of the important tasks in natural language processing; i.e. alternative ways to express the same concept by using different words or phrases. Paraphrases can be used in many natural language applications, such as Information Retrieval, Machine Translation, Question Answering, Text Summarization, or Information Extraction. To obtain pairs of sentences that are paraphrases we create a system that automatically extracts paraphrases from a corpus, which is built from different sources of news article since these are likely to contain paraphrases when they report the same event on the same day. There are existing simple standard approaches (e.g. TF-IDF vector space, cosine similarity) and alignment technique (e.g. Dynamic Time Warping (DTW)) for extracting paraphrase which have been applied to the English. However, the performance of these approaches could be affected when they are applied to another language, for instance Arabic language, due to the presence of phenomena which are not present in English, such as Free Word Order, Zero copula, and Pro-dropping. These phenomena will affect the performance of these algorithms. Thus, if we can analysis how the existing algorithms for English fail for Arabic then we can find a solution for Arabic. The results are promising.

Keywords: natural language processing, TF-IDF, cosine similarity, dynamic time warping (DTW)

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Authors: Tathiana Yesenia Coy Mondragón, Jossitt William Vargas Cruz, Cristian Leonardo Gutiérrez Gómez

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The study of cosmic rays is based on two fundamental pillars: the detection of secondary cosmic rays on the Earth's surface and the detection of the source and origin of the cascade. In addition, the constant flow of RC generates a lot of interest for study due to the incidence of various natural phenomena, which makes it relevant to characterize their incidence parameters to determine their effect not only at subsoil or terrestrial surface levels but also throughout the atmosphere. To determine the physical parameters of the primary cosmic ray, the implementation of robust algorithms capable of reconstructing the cascade from the measured values is required, with a high level of reliability. Therefore, it is proposed to build a machine learning system that will be fed from the cosmic ray simulations in CORSIKA at different energies that lie in a range [10⁹-10¹²] eV. in order to generate a trained particle and pattern recognition system to obtain greater efficiency when inferring the nature of the origin of the cascade for EAS in the atmosphere considering atmospheric models.

Keywords: CORSIKA, cosmic rays, eas, Colombia

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Authors: Mohieddine Benghersallah, Mohamed Zakaria Zahaf, Ali Medjber, Idriss Tibakh

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The objective of this study is to analyse the effects of cutting parameters on surface roughness in dry face milling using statistical techniques. We studied the effect of the microstructure of AISI 52100 steel on machinability before and after hardening. The machining tests were carried out on a high rigidity vertical milling machine with a 25 mm diameter face milling cutter equipped with micro-grain bicarbide inserts with PVD (Ti, AlN) coating in GC1030 grade. A Taguchi L9 experiment plan is adopted. Analysis of variance (ANOVA) was used to determine the effects of cutting parameters (Vc, fz, ap) on the roughness (Ra) of the machined surface. Regression analysis to assess the machinability of steel presented mathematical models of roughness and the combination of parameters to minimize it. The recorded results show that feed per tooth has the most significant effect on the surface condition for both steel treatment conditions. The best roughnesses were obtained for the hardened AISI 52100 steel.

Keywords: machinability, heat treatment, microstructure, surface roughness, Taguchi method

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Josselyn Mata Calidonio, Arianna I. Maddox, Kimberly Hamad-Schifferli

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Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown viruses has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross-reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

Keywords: adaptive immunoassay, detecting unknown viruses, gold nanoparticles, paper immunoassay, repurposing antibodies

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Hermann Kuehnle

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Developments in ICT totally reshape manufacturing as machines, objects and equipment on the shop floors will be smart and online. Interactions with virtualizations and models of a manufacturing unit will appear exactly as interactions with the unit itself. These virtualizations may be driven by providers with novel ICT services on demand that might jeopardize even well established business models. Context aware equipment, autonomous orders, scalable machine capacity or networkable manufacturing unit will be the terminology to get familiar with in manufacturing and manufacturing management. Such newly appearing smart abilities with impact on network behavior, collaboration procedures and human resource development will make distributed manufacturing a preferred model to produce. Computing miniaturization and smart devices revolutionize manufacturing set ups, as virtualizations and atomization of resources unwrap novel manufacturing principles. Processes and resources obey novel specific laws and have strategic impact on manufacturing and major operational implications. Mechanisms from distributed manufacturing engaging interacting smart manufacturing units and decentralized planning and decision procedures already demonstrate important effects from this shift of focus towards collaboration and interoperability.

Keywords: autonomous unit, networkability, smart manufacturing unit, virtualization

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Authors: Damian Czubak

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The Białowieża Forest is a very valuable natural area, included in the World Natural Heritage at UNESCO, where, due to infestation by the bark beetle (Ips typographus), norway spruce (Picea abies) have deteriorated. This catastrophic scenario led to an increase in fire danger. This was due to the occurrence of large amounts of dead wood and grass cover, as light penetrated to the bottom of the stands. These factors in a dry state are materials that favour the possibility of fire and the rapid spread of fire. One of the objectives of the study was to monitor the quantitative and qualitative changes of combustible material on the permanent decay plots of spruce stands from 2012-2022. In addition, the size of the area with highly flammable vegetation was monitored and a classification of the stands of the Białowieża Forest by flammability classes was made. The key factor that determines the potential fire hazard of a forest is combustible material. Primarily its type, quantity, moisture content, size and spatial structure. Based on the inventory data on the areas of forest districts in the Białowieża Forest, the average fire load and its changes over the years were calculated. The analysis was carried out taking into account the changes in the health status of the stands and sanitary operations. The quantitative and qualitative assessment of fallen timber and fire load of ground cover used the results of the 2019 and 2021 inventories. Approximately 9,000 circular plots were used for the study. An assessment was made of the amount of potential fuel, understood as ground cover vegetation and dead wood debris. In addition, monitoring of areas with vegetation that poses a high fire risk was conducted using data from 2019 and 2021. All sub-areas were inventoried where vegetation posing a specific fire hazard represented at least 10% of the area with species characteristic of that cover. In addition to the size of the area with fire-prone vegetation, a very important element is the size of the fire load on the indicated plots. On representative plots, the biomass of the land cover was measured on an area of 10 m2 and then the amount of biomass of each component was determined. The resulting element of variability of ground covers in stands was their flammability classification. The classification developed made it possible to track changes in the flammability classes of stands over the period covered by the measurements.

Keywords: classification, combustible material, flammable vegetation, Norway spruce

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Authors: Damola O. Lawal, David W. Gresty, Diane E. Gan, Louise Hewitt

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This paper investigates the feasibility of using a programmable USB such as the O.MG Cable to perform a file tampering attack. Here, the O.MG Cable, an apparently harmless mobile device charger, is used in an unauthorized way to alter the content of a file (accounts record-January_Contributions.xlsx). The aim is to determine if a forensics analyst can reliably determine who has altered the target file; the O.MG Cable or the user of the machine. This work highlights some of the traces of the O.MG Cable left behind on the target computer itself, such as the Product ID (PID) and Vendor ID (ID). Also discussed is the O.MG Cable’s behavior during the experiments. We determine if a forensics analyst could identify if any evidence has been left behind by the programmable device on the target file once it has been removed from the computer to establish if the analyst would be able to link the traces left by the O.MG Cable to the file tampering. It was discovered that the forensic analyst might mistake the actions of the O.MG Cable for the computer users. Experiments carried out in this work could further the discussion as to whether an innocent user could be punished for the unauthorized changes made by a programmable device.

Keywords: O.MG cable, programmable USB, file tampering attack, digital evidence credibility, miscarriage of justice, cyber fraud

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Authors: Gergely G. Karacsony

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Fundamental rights are the result of thousand years’ progress of legislation, adjudication and legal practice. They serve as the framework of peaceful cohabitation of people, protecting the individual from any abuse by the government or violation by other people. Artificial intelligence, however, is the development of the very recent past, being one of the most important prospects to the future. Artificial intelligence is now capable of communicating and performing actions the same way as humans; such acts are sometimes impossible to tell from actions performed by flesh-and-blood people. In a world, where human-robot interactions are more and more common, a new framework of peaceful cohabitation is to be found. Artificial intelligence, being able to take part in almost any kind of interaction where personal presence is not necessary without being recognized as a non-human actor, is now able to break the law, violate people’s rights, and disturb social peace in many other ways. Therefore, a code of peaceful coexistence is to be found or created. We should consider the issue, whether human rights can serve as the code of ethical and rightful conduct in the new era of artificial intelligence and human coexistence. In this paper, we will examine the applicability of fundamental rights to human-robot interactions as well as to the actions of artificial intelligence performed without human interaction whatsoever. Robot ethics has been a topic of discussion and debate of philosophy, ethics, computing, legal sciences and science fiction writing long before the first functional artificial intelligence has been introduced. Legal science and legislation have approached artificial intelligence from different angles, regulating different areas (e.g. data protection, telecommunications, copyright issues), but they are only chipping away at the mountain of legal issues concerning robotics. For a widely acceptable and permanent solution, a more general set of rules would be preferred to the detailed regulation of specific issues. We argue that human rights as recognized worldwide are able to be adapted to serve as a guideline and a common basis of coexistence of robots and humans. This solution has many virtues: people don’t need to adjust to a completely unknown set of standards, the system has proved itself to withstand the trials of time, legislation is easier, and the actions of non-human entities are more easily adjudicated within their own framework. In this paper we will examine the system of fundamental rights (as defined in the most widely accepted source, the 1966 UN Convention on Human Rights), and try to adapt each individual right to the actions of artificial intelligence actors; in each case we will examine the possible effects on the legal system and the society of such an approach, finally we also examine its effect on the IT industry.

Keywords: human rights, robot ethics, artificial intelligence and law, human-robot interaction

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Authors: Gadisa Alemayehu, Muralidhar Avvari, Atkilt Mulu G.

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Since 1962 EC, the Faffa Food Share Company has been producing and supplying flour (famix) and value-added flour (baby food) in Ethiopia. It meets nearly all of the country's total flour demand, both for relief and commercial markets. However, it is incompetent in the international market due to a poor maintenance management system. The results of recorded documents and stopwatches revealed that frequent failure machines, as well as a poor maintenance management system, cause increased production downtimes, resulting in a 29.19 percent decrease in production from the planned production. As a result, the current study's goal is to recommend newly developed software for use in and as a Computerized Maintenance Management System (CMMS). As a result, the system increases machine reliability and decreases the frequency of equipment failure, reducing breakdown time and maintenance costs. The company's overall manufacturing performance improved by 4.45 percent, particularly after the implementation of the CMMS.

Keywords: CMMS, manufacturing performance, delivery, availability, flexibility, Faffa Food Share Company

Procedia PDF Downloads 131

Authors: Toufic Sarieddine

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The current debate on the hegemonic impact of China’s Belt and Road Initiative (BRI) is of two opposing strands: Resilient and absolute US hegemony on the one hand and various models of multipolar hegemony such as bifurcation on the other. Bifurcation theories illustrate an unprecedented division of hegemonic functions between China and the US, whereby Beijing becomes the world’s economic hegemon, leaving Washington the world’s military hegemon and security guarantor. While consensus points to China being the main driver of unipolarity’s rupturing, the debate among bifurcationists is on the location of the first rupture. In this regard, the Middle East and North Africa (MENA) region has seen increasing Chinese foreign direct investment in recent years while that to other regions has declined, ranking it second in 2018 as part of the financing for the Maritime Silk Road Initiative (MSRI). China has also become the top trade partner of 11 states in the MENA region, as well as its top source of machine imports, surpassing the US and achieving an overall trade surplus almost double that of Washington’s. These are among other features outlined in world-systems analysis (WSA) literature which correspond with the emergence of a new hegemon. WSA is further utilized to gauge other facets of China’s increasing involvement in MENA and assess whether bifurcation is unfolding therein. These features of hegemony include the adoption of China’s modi operandi, economic dominance in production, trade, and finance, military capacity, cultural hegemony in ideology, education, and language, and the promotion of a general interest around which to rally potential peripheries (MENA states in this case). China’s modi operandi has seen some adoption with regards to support against the United Nations Convention on the Law of the Sea, oil bonds denominated in the yuan, and financial institutions such as the Shanghai Gold Exchange enjoying increasing Arab patronage. However, recent elections in Qatar, as well as liberal reforms in Saudi Arabia, demonstrate Washington’s stronger normative influence. Meanwhile, Washington’s economic dominance is challenged by China’s sizable machine exports, increasing overall imports, and widening trade surplus, but retains some clout via dominant arms and transport exports, as well as free-trade deals across the region. Militarily, Washington bests Beijing’s arms exports, has a dominant and well-established presence in the region, and successfully blocked Beijing’s attempt to penetrate through the UAE. Culturally, Beijing enjoys higher favorability in Arab public opinion, and its broadcast networks have found some resonance with Arab audiences. In education, the West remains MENA students’ preferred destination. Further, while Mandarin has become increasingly available in schools across MENA, its usage and availability still lag far behind English. Finally, Beijing’s general interest in infrastructure provision and prioritizing economic development over social justice and democracy provides an avenue for increased incorporation between Beijing and the MENA region. The overall analysis shows solid progress towards bifurcation in MENA.

Keywords: belt and road initiative, hegemony, Middle East and North Africa, world-systems analysis

Procedia PDF Downloads 105

Authors: Ashraf Shebl, Bassam Garah, Radah Youssef

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Sexual assault is usually a hidden crime where the only witnesses are the victim and the assailant. For a variety of reasons, even the victim may be unable to provide a detailed account of the assault or the identity of the perpetrator. Often the case history deteriorates into one person’s word against another. With such limited initial information, the physical and biological evidence collected from the victim, from the crime scene, and from the suspect will play a pivotal role in the objective and scientific reconstruction of the events in question. The aim of work is to examine whether DNA profiles could be recovered from repeated washed clothes after contaminated by semen stains. Fresh semen about 1ml. ( <1 h old) taken from donor was deposited on four types of clothes (cotton, silk, polyester, and jeans). Then leave to dry in room temperature and washed by washing machine at temperature (30°C-60°C) and by hand washing. Some items of clothing were washed once, some twice and others three times. DNA could be extracted from some of these samples even after multiple washing. This study demonstrates that complete DNA profiles can be obtained from washed semen stains on different types of clothes, even after many repeated washing. These results indicated that clothes of the victims must be examined even if they were washed many times.

Keywords: sexual assault, DNA, persistence, clothes

Procedia PDF Downloads 191

Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

Procedia PDF Downloads 328

Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

Abstract:

Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

Procedia PDF Downloads 112

Authors: Radhey Sham Rajoria

Abstract:

Pressure to increase the competitiveness in the manufacturing sector and for the survival in the market has led to the development of machining centres, which enhance productivity, improve quality, shorten the lead time, and reduce the manufacturing cost. With the innovation of machining centres in the manufacturing sector the production lines have been replaced by these machining centers, having the ability to machine various processes and multiple tooling with automatic tool changer (ATC) for the same part. Also the process plans can be easily generated for complex components. Some means are required to utilize the machining center at its best. The present work is concentrated on the automated part program generation, and in turn automated process plan generation for the turned components on Denford “MIRAC” 8 stations ATC lathe machining centre. A package in C++ on DOS platform is developed which generates the complete CNC part program, process plan and process sequence for the turned components. The input to this system is in the form of a blueprint in graphical format with machining parameters and variables, and the output is the CNC part program which is stored in a .mir file, ready for execution on the machining centre.

Keywords: CNC, MIRAC, ATC, process planning

Procedia PDF Downloads 264

Authors: Davender Kumar

Abstract:

Aim: It is important for the orthodontist to be familiar with the sliding resistance (SR) generated by the ligation method used during the space closure phase with sliding mechanics. To determine new, experimental non-conventional (slide) ligature demonstrates less friction in vitro when compared other ligatures on the market. Methods: Experimental in vitro were carried out to test the performance of the low-friction system with regard to assess the forces released by different bracket–ligature systems with bonded in iron plate mounted on an Instron machine. Results: The outcomes of experimental testing showed that the combination of the low-friction ligatures with the super elastic nickel-titanium and SS wires produced a significantly smaller amount of binding at the bracket/arch wire/ligature unit when compared to conventional elastomeric ligatures. Conclusion: The biomechanical consequences of the use of low-friction ligatures were shorter duration of orthodontic treatment during the levelling and aligning phase, concurrent dentoalveolar expansion of the dental arch, and the possibility of using biologically adequate orthodontic forces.

Keywords: archwire, bracket, friction, ligation

Procedia PDF Downloads 321

Authors: Ziad. Sh. Al Sarraf

Abstract:

Ultrasonic metal welding has been the subject of ongoing research and development, most recently concentrating on metal joining in miniature devices, for example to allow solder-free wire bonding. As well as at the small scale, there are also opportunities to research the joining of thicker sheet metals and to widen the range of similar and dissimilar materials that can be successfully joined using this technology. This study presents the design, characterisation and test of a lateral-drive ultrasonic metal spot welding device. The ultrasonic metal spot welding horn is modelled using finite element analysis (FEA) and its vibration behaviour is characterised experimentally to ensure ultrasonic energy is delivered effectively to the weld coupon. The welding stack and fixtures are then designed and mounted on a test machine to allow a series of experiments to be conducted for various welding and ultrasonic parameters. Weld strength is subsequently analysed using tensile-shear tests. The results show how the weld strength is particularly sensitive to the combination of clamping force and ultrasonic vibration amplitude of the welding tip, but there are optimal combinations of these and also limits that must be clearly identified.

Keywords: ultrasonic welding, vibration amplitude, welding force, weld strength

Procedia PDF Downloads 362