Search results for: machine harvested grapes

Authors: Donghee Noh, Seonhyeong Kim, Junhwan Choi, Heegon Kim, Sooho Jung, Keunho Park

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In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest.

Keywords: aerial image, image process, machine vision, open field smart farm, segmentation

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Authors: Arezou Javadi

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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

Procedia PDF Downloads 72

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: Rajkumar Ghosh

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Rapid urbanization, rising population, changing lifestyles and in-migration, Lucknow is groundwater over-exploited area, with an abstract rate of 1968 m3/day/km2 in Gomti Nagar. The groundwater situation in Gomti Nagar is deteriorating day-by-day. According to the work, the calculated annual water deficiency in Gomti Nagar area will be 28061 Million Litre (ML) in 2022. Within 30 yrs., the water deficiency will be 735570 ML (till 2051). The calculated groundwater recharge in Gomti Nagar was 10813 ML/y (in 2022). The annual groundwater abstraction from Gomti Nagar area was 35332 ML/yr. (in 2022). Bye-laws (≥ 300 sq.m) existing RTRWHs can recharge 17.71 ML/yr. in Gomti Nagar area. The existing RTRWHs are contributing 0.07% for recharging groundwater table. In Gomti Nagar, the water level is dropping at a rate of 1.0 metre per year, and the depth of the water table is less than 30 metre below ground level (mbgl). Natural groundwater recharge is affected by the geomorphological conditions of the surrounding area. Gomti Nagar is located on the erosional terrace (Te) and depositional terrace (d) of the Gomti River. The flood plain in Lucknow city is less active due to the embankments on the both sides of the Gomti River. The alluvium is composed of clay sandy up to a depth of 30m, and the alignment of the Gomti River reveals the presence of sandy soil at shallow depths. Aquifer depth 120 metre. Recharge as in Gomti Nagar (it may vary) 0 – 150 metre. Infiltration rates in alluvial floodplains range from 0.8 to 74 cm/hr. Geologically and Geomorphologically support rapid percolation of rainwater through alluvium in Gomti Nagar, Lucknow city, Uttar Pradesh. Over-exploitation of groundwater causes natural hazards viz. land subsidence, development of cracks on roads and buildings, development of vacuum and compactness of soil/clay which leads towards land subsidence, devastating effects on natural stream flow. Gomti River already transitioning phase from ‘effluent’ to ‘influent’, and saline intrusion in Aquifer –II (among Five aquifers in Lucknow city). A 250 m long crack developed in 2007 due to groundwater depletion in Dullu Khera and Vader Khera village of Kakori, Uttar Pradesh. The groundwater table of Lucknow is declining and water table imbalance occurs due to 17 times less recharge than groundwater exploitation. Uttar Pradesh along with four states have extracted 49% of groundwater in the entire country. In Gomti Nagar area, 27305 no of houses are present and available build up area 3.8 sq. km (60% of plot area) based on Lucknow Development Authority (LDA) Master plan 2031. If RTRWHs would install in all the houses, then 12% harvested rainwater contribute to the water table in Gomti Nagar area. Till 2051, Gomti Nagar area will harvest 91110 ML of rainwater. There are minimalistic chances that any incidence of flood can occur due to RTRWH. Thus, it can conclud that RTRWH is not related to flood happening in urban areas viz. Gomti Nagar.

Keywords: RTRWH, aquifer, groundwater table, rainwater, infiltration

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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: Hollie Ashworth, Sarah Heath, Nick Bryan, Liam Abrahamsen, Simon Kellet

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Sellafield has a number of fuel storage ponds, some of which have been open to the air for a number of decades. This has caused corrosion of the fuel resulting in a release of some activity into solution, reduced water clarity, and accumulation of sludge at the bottom of the pond consisting of brucite (Mg(OH)2) and other uranium corrosion products. Both of these phases are also present as colloidal material. 90Sr and 137Cs are known to constitute a small volume of the radionuclides present in the pond, but a large fraction of the activity, thus they are most at risk of challenging effluent discharge limits. Organic molecules are known to be present also, due to the ponds being open to the air, with occasional algal blooms restricting visibility further. The contents of the pond need to be retrieved and safely stored, but dealing with such a complex, undefined inventory poses a unique challenge. This work aims to determine and understand the sorption-desorption interactions of 90Sr and 137Cs to brucite and uranium phases, with and without the presence of organic molecules from chemical degradation and bio-organisms. The influence of organics on these interactions has not been widely studied. Partitioning of these radionuclides and organic molecules has been determined through LSC, ICP-AES/MS, and UV-vis spectrophotometry coupled with ultrafiltration in both binary and ternary systems. Further detailed analysis into the surface and bonding environment of these components is being investigated through XAS techniques and PHREEQC modelling. Experiments were conducted in CO2-free or N2 atmosphere across a high pH range in order to best simulate conditions in the pond. Humic acid used in brucite systems demonstrated strong competition against 90Sr for the brucite surface regardless of the order of addition of components. Variance of pH did have a small effect, however this range (10.5-11.5) is close to the pHpzc of brucite, causing the surface to buffer the solution pH towards that value over the course of the experiment. Sorption of 90Sr to UO2 obeyed Ho’s rate equation and demonstrated a slow second-order reaction with respect to the sharing of valence electrons from the strontium atom, with the initial rate clearly dependent on pH, with the equilibrium concentration calculated at close to 100% sorption. There was no influence of humic acid seen when introduced to these systems. Sorption of 137Cs to UO3 was significant, with more than 95% sorbed in just over 24 hours. Again, humic acid showed no influence when introduced into this system. Both brucite and uranium based systems will be studied with the incorporation of cyanobacterial cultures harvested at different stages of growth. Investigation of these systems provides insight into, and understanding of, the effect of organics on radionuclide partitioning to brucite and uranium phases at high pH. The majority of sorption-desorption work for radionuclides has been conducted at neutral to acidic pH values, and mostly without organics. These studies are particularly important for the characterisation of legacy wastes at Sellafield, with a view to their safe retrieval and storage.

Keywords: caesium, legacy wastes, organics, sorption-desorption, strontium, uranium

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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: 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: 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: Zanele D. Ngwenya, Mustapha Mohammed, Felix D. Dakora

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Legumes are a major source of biological nitrogen, and therefore play a crucial role in maintaining soil productivity in smallholder agriculture in southern Africa. Through their ability to fix atmospheric nitrogen in root nodules, legumes are a better option for sustainable nitrogen supply in cropping systems than chemical fertilisers. For decades, farmers have been highly receptive to the use of rhizobial inoculants as a source of nitrogen due mainly to the availability of elite rhizobial strains at a much lower compared to chemical fertilisers. To improve the efficiency of the legume-rhizobia symbiosis in African soils would require the use of highly effective rhizobia capable of nodulating a wide range of host plants. This study assessed the morphogenetic diversity, photosynthetic functioning and relative symbiotic effectiveness (RSE) of groundnut, jack bean and soybean microsymbionts in Eswatini soils as a first step to identifying superior isolates for inoculant production. According to the manufacturer's instructions, rhizobial isolates were cultured in yeast-mannitol (YM) broth until the late log phase and the bacterial genomic DNA was extracted using GenElute bacterial genomic DNA kit. The extracted DNA was subjected to enterobacterial repetitive intergenic consensus-PCR (ERIC-PCR) and a dendrogram constructed from the band patterns to assess rhizobial diversity. To assess the N2-fixing efficiency of the authenticated rhizobia, photosynthetic rates (A), stomatal conductance (gs), and transpiration rates (E) were measured at flowering for plants inoculated with the test isolates. The plants were then harvested for nodulation assessment and measurement of plant growth as shoot biomass. The results of ERIC-PCR fingerprinting revealed the presence of high genetic diversity among the microsymbionts nodulating each of the three test legumes, with many of them showing less than 70% ERIC-PCR relatedness. The dendrogram generated from ERIC-PCR profiles grouped the groundnut isolates into 5 major clusters, while the jack bean and soybean isolates were grouped into 6 and 7 major clusters, respectively. Furthermore, the isolates also elicited variable nodule number per plant, nodule dry matter, shoot biomass and photosynthetic rates in their respective host plants under glasshouse conditions. Of the groundnut isolates tested, 38% recorded high relative symbiotic effectiveness (RSE >80), while 55% of the jack bean isolates and 93% of the soybean isolates recorded high RSE (>80) compared to the commercial Bradyrhizobium strains. About 13%, 27% and 83% of the top N₂-fixing groundnut, jack bean and soybean isolates, respectively, elicited much higher relative symbiotic efficiency (RSE) than the commercial strain, suggesting their potential for use in inoculant production after field testing. There was a tendency for both low and high N₂-fixing isolates to group together in the dendrogram from ERIC-PCR profiles, which suggests that RSE can differ significantly among closely related microsymbionts.

Keywords: genetic diversity, relative symbiotic effectiveness, inoculant, N₂-fixing

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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: 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: Belkacem Laimouche

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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

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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

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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

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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

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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

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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

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Authors: Mustahsan Mir, Ahmed Hassanin, Mohammed A. Al-Saleh

Abstract:

The layout optimization of building blocks of unequal areas has applications in many disciplines including VLSI floorplanning, macrocell placement, unequal-area facilities layout optimization, and plant or machine layout design. A number of heuristics and some analytical and hybrid techniques have been published to solve this problem. This paper presents an efficient high-quality building-block layout design technique especially suited for solving large-size problems. The higher efficiency and improved quality of optimized solutions are made possible by introducing the concept of Promising Infant Clusters in a constructive placement procedure. The results presented in the paper demonstrate the improved performance of the presented technique for benchmark problems in comparison with published heuristic, analytic, and hybrid techniques.

Keywords: block layout problem, building-block layout design, CAD, optimization, search techniques

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

Abstract:

Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

Abstract:

Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Mohammad A. Khasawneh

Abstract:

The design of high pressure water jet based polishing equipment and its fabrication conducted in this study is reported herein, together with some preliminary test results for assessing its applicability for HMA surface polishing. This study also provides preliminary findings concerning the test variables, such as the rotational speed, the water jet pressure, the abrasive agent used, and the impact angel that were experimentally investigated in this study. The preliminary findings based on four trial tests (two on large slab specimens and two on small size gyratory compacted specimens), however, indicate that both friction and texture values tend to increase with the polishing durations for two combinations of pressure and rotation speed of the rotary deck. It seems that the more polishing action the specimen is subjected to; the aggregate edges are created such that the surface texture values are increased with the accompanied increase in friction values. It may be of interest (but which is outside the scope of this study) to investigate if the similar trend exist for HMA prepared with aggregate source that is sand and gravel.

Keywords: high-pressure, water jet, friction, texture, polishing, statistical analysis

Procedia PDF Downloads 470