Search results for: covering machine

Authors: Arezou Javadi

Abstract:

The use of algorithmic models dependent on software calculations and simulation of new government management assays with the help of specialized software had increased the productivity and efficiency of the government management system recently. This has caused the management approach to change from the old bitch & fix model, which has low efficiency and less usefulness, to the capable management model with higher efficiency called the partnership with resident model. By using Govpilot TM software, the relationship between people in a system and the government was examined. The method of two tailed interaction was the outsourcing of a goal in a system, which is formed in the order of goals, qualified executive people, optimal executive model, and finally, summarizing additional activities at the different statistical levels. The results showed that the participation of people in a financial implementation system with a statistical potential of P≥5% caused a significant increase in investment and initial capital in the government system with maximum implement project in a smart government.

Keywords: machine learning, financial income, statistical potential, govpilot

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Authors: Arezou Javadi

Abstract:

The use of algorithmic models dependent on software calculations and simulation of new government management assays with the help of specialized software had increased the productivity and efficiency of the government management system recently. This has caused the management approach to change from the old bitch & fix model, which has low efficiency and less usefulness, to the capable management model with higher efficiency called the partnership with resident model. By using Govpilot TM software, the relationship between people in a system and the government was examined. The method of two tailed interaction was the outsourcing of a goal in a system, which is formed in the order of goals, qualified executive people, optimal executive model, and finally, summarizing additional activities at the different statistical levels. The results showed that the participation of people in a financial implementation system with a statistical potential of P≥5% caused a significant increase in investment and initial capital in the government system with maximum implement project in a smart government.

Keywords: machine learning, financial income, statistical potential, govpilot

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

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In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: dynamic system modeling, neural network, normal equation, second order gradient descent

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

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Authors: Ashish Kumar, Kaptan Singh, Amit Saxena

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Today, it is believed that crimes have the greatest impact on a person's ability to progress financially and personally. Identifying places where individuals shouldn't go is crucial for preventing crimes and is one of the key considerations. As society and technologies have advanced significantly, so have crimes and the harm they wreak. When there is a concentration of people in one place and changes happen quickly, it is even harder to prevent. Because of this, many crime prevention strategies have been embraced as a component of the development of smart cities in numerous cities. However, crimes can occur anywhere; all that is required is to identify the pattern of their occurrences, which will help to lower the crime rate. In this paper, an analysis related to crime has been done; information related to crimes is collected from all over India that can be accessed from anywhere. The purpose of this paper is to investigate the relationship between several factors and India's crime rate. The review has covered information related to every state of India and their associated regions of the period going in between 2001- 2014. However various classes of violations have a marginally unique scope over the years.

Keywords: K-nearest neighbor, random forest, decision tree, pre-processing

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Authors: Hugh Battye

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In recent years, Muslim identity in China has strengthened against the backdrop of a worldwide Islamic revival. One discussion arising from this has been focused around the Hui, an ethnicity created by the Communist government in the 1950s covering the Chinese speaking 'Sino-Muslims' as opposed to those with their own language. While the term Hui in Chinese has traditionally meant 'Muslim', the strengthening of Hui identity in recent decades has led to a debate among scholars as to whether this identity is primarily ethnically or religiously driven. This article looks at the case of a mixed ethnic community in rural Gansu Province, Central Northwest China, which not only contains the official Hui ethnicity but also members of the smaller Muslim Salar and Bonan minority groups. In analyzing the close interaction between these groups, the paper will argue that, despite government attempts to promote the Hui as an ethnicity within its modern ethnic paradigm, in rural Gansu and the general region, Hui is still essentially seen as a religious identity. Having provided an overview of the historical evolution of the Hui ethnonym in China and presented the views of some of the important scholars involved in the discussion, the paper will then offer its findings based on participant observation and survey work in Gansu. The results will show that, firstly, for the local Muslims, religious identity clearly dominates ethnic identity. On the ground, the term Hui continues to be used as a catch-all term for Muslims, whether they belong to the official 'Hui' nationality or not, and against this backdrop, the ethnic importance of being 'Hui', 'Bonan' or 'Salar' within the Muslim community itself is by contrast minimal. Secondly, however, this local Muslim solidarity is not at present pointing towards some kind of national pan-ethnic Islamic movement that could potentially set itself up in opposition to the Chinese government; rather it is better seen as part of an ongoing negotiation by local Muslims with the state in the context of its ascribed ethnic categories. The findings of this study in a region where many of the Muslims are more conservative in their beliefs is not necessarily replicated in other contexts, such as in urban areas and in eastern and southern China, and hence reification of the term Hui as one idea extending all across China should be avoided, whether in terms of a united religious 'ummah' or of a real or imagined 'ethnic group.' Rather, this localized case study seeks to demonstrate ways in which Muslims of rural Central Northwest China are 'being Hui,' as a contribution to the broader discussion on what it means to be Muslim and Chinese in the reform era.

Keywords: China, ethnicity, Hui, identity, Muslims

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Authors: Fuad M. Alkoot

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We aim at highlighting the benefits of classifier systems especially in supporting educational management decisions. The paper aims at using classifiers in an educational application where an outcome is predicted based on given input parameters that represent various conditions at the institute. We present a classifier system that is designed using a limited training set with data for only one semester. The achieved system is able to reach at previously known outcomes accurately. It is also tested on new input parameters representing variations of input conditions to see its prediction on the possible outcome value. Given the supervised expectation of the outcome for the new input we find the system is able to predict the correct outcome. Experiments were conducted on one semester data from two departments only, Switching and Mathematics. Future work on other departments with larger training sets and wider input variations will show additional benefits of classifier systems in supporting the management decisions at an educational institute.

Keywords: machine learning, pattern recognition, classifier design, educational management, outcome estimation

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Authors: Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath

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Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent.

Keywords: pronunciation variations, dynamic programming, machine learning, natural language processing

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Authors: Serge Provost, Yishan Zang

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Copulas are currently utilized in finance, reliability theory, machine learning, signal processing, geodesy, hydrology and biostatistics, among several other fields of scientific investigation. It follows from Sklar's theorem that the joint distribution function of a multidimensional random vector can be expressed in terms of its associated copula and marginals. Since marginal distributions can easily be determined by making use of a variety of techniques, we address the problem of securing the distribution of the copula. This will be done by using several approaches. For example, we will obtain bivariate least-squares approximations of the empirical copulas, modify the kernel density estimation technique and propose a criterion for selecting appropriate bandwidths, differentiate linearized empirical copulas, secure Bernstein polynomial approximations of suitable degrees, and apply a corollary to Sklar's result. Illustrative examples involving actual observations will be presented. The proposed methodologies will as well be applied to a sample generated from a known copula distribution in order to validate their effectiveness.

Keywords: copulas, Bernstein polynomial approximation, least-squares polynomial approximation, kernel density estimation, density approximation

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Authors: Thippharat Wongsilarat, Parichat tuntilanon, Chonlakan Prataksitorn

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Recurrent adverse drug reactions are harmful to patients with mild to fatal illnesses, and affect not only patients but also their relatives, and organizations. To compare safe use of medicine among patients before and after using the recurrent adverse drug reaction prevention program . Quasi-experimental research with the target population of 598 patients with drug allergy history. Data were collected through an observation form tested for its validity by three experts (IOC = 0.87), and analyzed with a descriptive statistic (percentage). The research was conducted jointly with a multidisciplinary team to analyze and determine the weak points and strong points in the recurrent adverse drug reaction prevention system during the past three years, and 546, 329, and 498 incidences, respectively, were found. Of these, 379, 279, and 302 incidences, or 69.4; 84.80; and 60.64 percent of the patients with drug allergy history, respectively, were found to have caused by incomplete warning system. In addition, differences in practice in caring for patients with drug allergy history were found that did not cover all the steps of the patient care process, especially a lack of repeated checking, and a lack of communication between the multidisciplinary team members. Therefore, the recurrent adverse drug reaction prevention program was developed with complete warning points in the information technology system, the repeated checking step, and communication among related multidisciplinary team members starting from the hospital identity card room, patient history recording officers, nurses, physicians who prescribe the drugs, and pharmacists. Including in the system were surveillance, nursing, recording, and linking the data to referring units. There were also training concerning adverse drug reactions by pharmacists, monthly meetings to explain the process to practice personnel, creating safety culture, random checking of practice, motivational encouragement, supervising, controlling, following up, and evaluating the practice. The rate of prescribing drugs to which patients were allergic per 1,000 prescriptions was 0.08, and the incidence rate of recurrent drug reaction per 1,000 prescriptions was 0. Surveillance of recurrent adverse drug reactions covering all service providing points can ensure safe use of medicine for patients.

Keywords: recurrent drug, adverse reaction, safety, use of medicine

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Authors: Khaled Ibrahim, Mathew Parackal, Damien Mather, Paul Hansen

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The integration between marketing and technology has given brands unprecedented opportunities to reach accurate customer data and competence to change customers' behaviour. Technology has generated a transformation within brands from traditional branding to algorithmic branding. However, brands have utilised customer data in non-cognitive programmatic targeting. This algorithmic persuasion may be effective in reaching the targeted audience. But it may encounter a moral conflict simultaneously, as it might not consider our social principles. Moral branding is a critical topic; particularly, with the increasing interest in commercial settings to teaching machines human morals, e.g., autonomous vehicles and chatbots; however, it is understudied in the marketing literature. Therefore, this paper aims to investigate the recent moral branding literature. Furthermore, applying human-like mind theory as initial framing to this paper explores a more comprehensive concept involving human morals, machine behaviour, and branding.

Keywords: brand machines, conceptual framework, moral branding, moral machines

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Authors: Usama Mohamed Ahamed

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This research deals with the bond behavior of carbon FRP composite wraps adhered/bonded to the surface of the concrete. Four concrete mixes were designed to achieve a concrete compressive strength of 18, 22.5,25 and 30 MP after 28 days of curing. The focus of the study is on bond degradation when the hybrid structure is exposed to hot weather conditions. Specimens were exposed to 50 0C temperature duration 6 months and other specimens were sustained in laboratory temperature ( 20-24) 0C. Upon removing the specimens from their conditioning environment, tension tests were performed in the machine using a specially manufactured concrete cube holder. A lightweight mortar layer is used to protect the bonded carbon FRP layer on the concrete surface. The results show that the higher the concrete's compressive, the higher the bond strength. The high temperature decreases the bond strength between concrete and carbon fiber-reinforced polymer. The use of a protection layer is essential for concrete exposed to hot weather.

Keywords: concrete, bond, hot weather and carbon fiber, carbon fiber reinforced polymers

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Authors: Lubaina Soni, Claire Farrell, Christopher Szota, Tim D. Fletcher

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Green roofs are a promising engineered ecosystem for reducing stormwater runoff and restoring vegetation cover in cities. Plants can contribute to rainfall retention by rapidly depleting water in the substrate; however, this increases the risk of plant drought stress. Green roof configurations, therefore, need to provide plants the opportunity to efficiently deplete the substrate but also avoid severe drought stress. This study used green roof modules placed in a rainout shelter during a six-month rainfall regime simulated in Melbourne, Australia. Rainfall was applied equally with an overhead irrigation system on each module. Aside from rainfall, modules were under natural climatic conditions, including temperature, wind, and radiation. A single species, Ficinia nodosa, was planted with five different treatments and three replicates of each treatment. In this experiment, we tested the impact of three plant cover treatments (0%, 50% and 100%) on rainfall retention and plant drought stress. We also installed two runoff zone treatments covering 50% of the substrate surface for additional modules with 0% and 50% plant cover to determine whether directing rainfall resources towards plant roots would reduce drought stress without impacting rainfall retention. The retention performance for the simulated rainfall events was measured, quantifying all components for hydrological performance and survival on green roofs. We found that evapotranspiration and rainfall retention were similar for modules with 50% and 100% plant cover. However, modules with 100% plant cover showed significantly higher plant drought stress. Therefore, planting at a lower cover/density reduced plant drought stress without jeopardizing rainfall retention performance. Installing runoff zones marginally reduced evapotranspiration and rainfall retention, but by approximately the same amount for modules with 0% and 50% plant cover. This indicates that reduced evaporation due to the installation of the runoff zones likely contributed to reduced evapotranspiration and rainfall retention. Further, runoff occurred from modules with runoff zones faster than those without, indicating that we created a faster pathway for water to enter and leave the substrate, which also likely contributed to lower overall evapotranspiration and retention. However, despite some loss in retention performance, modules with 50% plant cover installed with runoff zones showed significantly lower drought stress in plants compared to those without runoff zones. Overall, we suggest that reducing plant cover represents a simple means of optimizing green roof performance but creating runoff zones may reduce plant drought stress at the cost of reduced rainfall retention.

Keywords: green roof, plant cover, plant drought stress, rainfall retention

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

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This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: landslide, limit analysis, artificial neural network, soil properties

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Authors: Lior B. Navon

Abstract:

The objective of this study is to investigate how technology, such as social media, can influence the biopower hierarchy between the medical knowledge about the body and women’s bodily knowledge through the case study of the hashtag 'notpee'. In January 2015, the hashtag #notpee, relating to a feminine physiological phenomenon called female ejaculation (FE) or squirting (SQ) started circulating on twitter. This hashtag, born as a reaction to a medical study claiming that SQ is essentially involuntary emission of urine during sexual activity, sparked an unusual public discourse about FE, a phenomenon that is usually not discussed or referred to in socio-legitimate public spheres. This unusual backlash got the attention of women’s magazines and blogs, as well as more mainstream large and respected outlets such as The Guardian and CNN. Both the tweets on twitter, as well as the media coverage of them, were mainly aimed at rejecting the research’s findings. While not offering an alternative and choosing to define the phenomenon by negation, women argued that the fluid extracted was not pee based on their personal experiences. Based on a critical discourse analysis of 742 tweets with the hashtag 'notpee' between January 2015 and January 2016, and of 15 articles covering the backlash, this study suggests that the #notpee backlash challenged the power balance between the medical knowledge about the feminine body and the feminine bodily knowledge through two different, yet related, forms of resistance to biopower. The first resistance is to the authority over knowledge production — who has the power to produce 'true' statements when it comes to the body? Is it the women who experience the phenomenon, or is it the medical institution? The second resistance to biopower has to do with what we regard as facts or veracity. A critical discourse analysis reveals that while both the scientific field, as well as the women arguing against its findings, use empirical information, they, nevertheless, rely on two dichotomic databases- while the scientific research relies on samples from the 'dead like body', these woman are relying on their lived subjective senses as a source for fact making. Nevertheless, while #notpee is asking to change the power relations between the feminine subjective bodily knowledge and the seemingly objective masculine medical knowledge about the body, it by no means dismisses it. These women are essentially asking the medical institution to take into consideration the subjective body as well as the objective one while acknowledging and accepting the power of the latter over knowledge production.

Keywords: biopower, female ejaculation, new media, bodily knowledge

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Authors: Elissavet Feloni, Ioannis Kourtis, Konstantinos Kotsifakis, Evangelos Baltas

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Water inadequacy in small dry islands scattered in the Aegean Sea (Greece) is a major problem regarding Water Resources Management (WRM), especially during the summer period due to tourism. In the present work, various WRM schemes are designed and presented. The WRM schemes take into account current infrastructure and include Rainwater Harvesting tanks and Reverse Osmosis Desalination Units. The energy requirements are covered mainly by wind turbines and/or a seawater pumped storage system. Sizing is based on the available data for population and tourism per island, after taking into account a slight increase in the population (up to 1.5% per year), and it guarantees at least 80% reliability for the energy supply and 99.9% for potable water. Evaluation of scenarios is carried out from a financial perspective, after calculating the Life Cycle Cost (LCC) of each investment for a lifespan of 30 years. The wind-powered desalination plant was found to be the most cost-effective practice, from an economic point of view. Finally, in order to estimate the Climate Change (CC) impact, six different CC scenarios were investigated. The corresponding rate of on-grid versus off-grid energy required for ensuring the targeted reliability for the zero and each climatic scenario was investigated per island. The results revealed that under CC the grid-on energy required would increase and as a result, the reduction in wind turbines and seawater pumped storage systems’ reliability will be in the range of 4 to 44%. However, the range of this percentage change does not exceed 22% per island for all examined CC scenarios. Overall, CC is proposed to be incorporated into the design process for WRM-related projects. Acknowledgements: This research is co-financed by Greece and the European Union (European Social Fund - ESF) through the Operational Program «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Development of a combined rain harvesting and renewable energy-based system for covering domestic and agricultural water requirements in small dry Greek Islands” (MIS 5004775).

Keywords: small dry islands, water resources management, climate change, desalination, RES, seawater pumped storage system, rainwater harvesting

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Authors: M. V. Shivaani, P. K. Jain, Surendra S. Yadav

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Worldwide regulators, practitioners and researchers view risk-disclosure as one of the most important steps that will promote corporate accountability and transparency. Recognizing this growing significance of risk disclosures, the paper first develops a risk disclosure index. Covering 69 risk items/themes, this index is developed by employing thematic content analysis and encompasses three attributes of disclosure: namely, nature (qualitative or quantitative), time horizon (backward-looking or forward-looking) and tone (no impact, positive impact or negative impact). As the focus of study is on substantive rather than symbolic disclosure, content analysis has been carried out manually. The study is based on non-financial companies of Nifty500 index and covers a ten year period from April 1, 2005 to March 31, 2015, thus yielding 3,872 annual reports for analysis. The analysis reveals that (on an average) only about 14% of risk items (i.e. about 10 out 69 risk items studied) are being disclosed by Indian companies. Risk items that are frequently disclosed are mostly macroeconomic in nature and their disclosures tend to be qualitative, forward-looking and conveying both positive and negative aspects of the concerned risk. The second objective of the paper is to gauge the factors that affect the level of disclosures in annual reports. Given the panel nature of data, and possible endogeneity amongst variables, Diff-GMM regression has been applied. The results indicate that age and size of firms have a significant positive impact on disclosure quality, whereas growth rate does not have a significant impact. Further, post-recession period (2009-2015) has witnessed significant improvement in quality of disclosures. In terms of corporate governance variables, board size, board independence, CEO duality, presence of CRO and constitution of risk management committee appear to be significant factors in determining the quality of risk disclosures. It is noteworthy that the study contributes to literature by putting forth a variant to existing disclosure indices that not only captures the quantity but also the quality of disclosures (in terms of semantic attributes). Also, the study is a first of its kind attempt in a prominent emerging market i.e. India. Therefore, this study is expected to facilitate regulators in mandating and regulating risk disclosures and companies in their endeavor to reduce information asymmetry.

Keywords: risk disclosure, voluntary disclosures, corporate governance, Diff-GMM

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Authors: Ritesh Joshi, Kishan Fuse, Gopal Zinzala, Nishit Nirmal

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Ti-6Al-4V is a titanium alloy having high strength, low weight and corrosion resistant which is a required characteristic for a material to be used in aerospace industry. Titanium, being a hard alloy is difficult to the machine via conventional methods, so it is a call to use non-conventional processes. In present work, the effects on Ti-6Al-4V by drilling a hole of Ø 6 mm using copper (99%) electrode in Electric Discharge Machining (EDM) process is analyzed. Effect of various input parameters like peak current, pulse-on time and pulse-off time on output parameters viz material removal rate (MRR) and electrode wear rate (EWR) is studied. Multi-objective optimization technique Grey relational analysis is used for process optimization. Experiments are designed using an L9 orthogonal array. ANOVA is used for finding most contributing parameter followed by confirmation tests for validating the results. Improvement of 7.45% in gray relational grade is observed.

Keywords: ANOVA, electric discharge machining, grey relational analysis, Ti-6Al-4V

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Anthony Townley

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Contract negotiation is fundamental to commercial law practice. For this study, genre and discourse analytical methodology was used to examine the legal negotiation of a Merger & Acquisition (M&A) deal undertaken by legal and business professionals in English across different jurisdictions in Europe. While some of the most delicate negotiations involved in this process were carried on face-to-face or over the telephone, these were generally progressed more systematically – and on the record – in the form of emails, email attachments, and as comments and amendments recorded in successive ‘marked-up’ versions of the contracts under negotiation. This large corpus of textual data was originally obtained by the author, in 2012, for the purpose of doctoral research. For this study, the analysis is particularly concerned with the use of emails and covering letters to exchange legal advice about the negotiations. These two genres help to stabilize and progress the negotiation process and account for negotiation activities. Swalesian analysis of functional Moves and Steps was able to identify structural similarities and differences between these text types and to identify certain salient discursive features within them. The analytical findings also indicate how particular linguistic strategies are more appropriately and more effectively associated with one legal genre rather than another. The concept of intertextuality is an important dimension of contract negotiation discourse and this study also examined how the discursive relationships between the different texts influence the way that texts are constructed. In terms of materials development, the research findings can contribute to more authentic English for Legal & Business Purposes pedagogies for students and novice lawyers and business professionals. The findings can first be used to design discursive maps that provide learners with a coherent account of the intertextual nature of the contract negotiation process. These discursive maps can then function as a framework in which to present detailed findings about the textual and structural features of the text types by applying the Swalesian genre analysis. Based on this acquired knowledge of the textual nature of contract negotiation, the authentic discourse materials can then be used to provide learners with practical opportunities to role-play negotiation activities and experience professional ways of thinking and using language in preparation for the written discourse challenges they will face in this important area of legal and business practice.

Keywords: English for legal and business purposes, discourse analysis, genre analysis, intertextuality, pedagogical materials

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Authors: Onan Kilicaslan, Anil Kabaklarli, Levent Subasi, Erdem Bektas, Rifat Yilmaz

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The Aluminum-Copper casting alloys are well known for their high mechanical strength, especially when compared to more commonly used Aluminum-Silicon alloys. A201 is one of the best in terms of strength vs. weight ratio among other aluminum alloys, which makes it suitable for premium quality casting applications in aerospace and automotive industries. It is reported that A201 has low castability, but it is easy to machine. However, there is a need to specifically determine the process window for feasible machining. This research investigates the machinability of A201 alloy after T7 heat treatment in terms of chip/burr formation, surface roughness, hardness, and microstructure. The samples are cast with low-pressure sand casting method and milling experiments are performed with uncoated carbide tools using different cutting speeds and feeds. Statistical analysis is used to correlate the machining parameters to surface integrity. It is found that there is a strong dependence of the cutting conditions on machinability and a process window is determined.

Keywords: A201-T7, machinability, milling, surface integrity

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Belkacem Laimouche

Abstract:

With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Jinghui Peng, Shanyu Tang, Jia Li

Abstract:

Steganalysis seeks to detect the presence of secret data embedded in cover objects, and there is an imminent demand to detect hidden messages in streaming media. This paper shows how a steganalysis algorithm based on Fast Fourier Transform (FFT) can be used to detect the existence of secret data embedded in streaming media. The proposed algorithm uses machine parameter characteristics and a network sniffer to determine whether the Internet traffic contains streaming channels. The detected streaming data is then transferred from the time domain to the frequency domain through FFT. The distributions of power spectra in the frequency domain between original VoIP streams and stego VoIP streams are compared in turn using t-test, achieving the p-value of 7.5686E-176 which is below the threshold. The results indicate that the proposed FFT-based steganalysis algorithm is effective in detecting the secret data embedded in VoIP streaming media.

Keywords: steganalysis, security, Fast Fourier Transform, streaming media

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Authors: Efrain Rodriguez, Sergio Pertuz, Cristhian Riano

Abstract:

Tool-path generation is an essential step in the FFF (Fused Filament Fabrication)-based Additive Manufacturing (AM) process planning. In the manufacture of a mechanical part by using additive processes, high resource consumption and prolonged production times are inherent drawbacks of these processes mainly due to non-optimized tool-path generation. In this work, we propose a heuristic-search intelligent algorithm-based approach for optimized tool-path generation for FFF-based AM. The main benefit of this approach is a significant reduction of travels without material deposition when the AM machine performs moves without any extrusion. The optimization method used reduces the number of travels without extrusion in comparison with commercial software as Slic3r or Cura Engine, which means a reduction of production time.

Keywords: additive manufacturing, tool-path optimization, fused filament fabrication, process planning

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Authors: Tomasz Sapiński, Dorota Kamińska, Adam Pelikant, Egils Avots, Cagri Ozcinar, Gholamreza Anbarjafari

Abstract:

People express emotions through different modalities. Integration of verbal and non-verbal communication channels creates a system in which the message is easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human-machine interaction. In this article, the authors present a Polish emotional database composed of three modalities: facial expressions, body movement and gestures, and speech. The corpora contains recordings registered in studio conditions, acted out by 16 professional actors (8 male and 8 female). The data is labeled with six basic emotions categories, according to Ekman’s emotion categories. To check the quality of performance, all recordings are evaluated by experts and volunteers. The database is available to academic community and might be useful in the study on audio-visual emotion recognition.

Keywords: body movement, emotion recognition, emotional corpus, facial expressions, gestures, multimodal database, speech

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

Abstract:

Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Han Kexi, Lv Xuewei, Song Bing

Abstract:

This paper focuses on the preparation of pelletizing with the Panzhihua ilmenite concentrate to satisfy the requirement of smelting titania slag. The effects of the moisture content, mixing time of raw materials, pressure of pellet, roller rotating speed of roller, drying temperature and time on the pelletizing yield and compressive strength were investigated. The experimental results show that the moister content was controlled at 2.0%~2.5%, mixing time at 20 min, the pressure of the ball forming machine at 13~15 mpa, the pelletizing yield can reach up 85%. When the roller rotating speed is 6~8 r/min while the drying temperature and time respectively is 350 ℃ and 40~60 min, the compressive strength of pelletizing more than 1500 N. The preparation of pelletizing can meet the requirement of smelting titania slag.

Keywords: Panzhihua fine ilmenite concentrate, pelletizing, pelletizing yield, compressive strength, drying

Procedia PDF Downloads 218