Search results for: machine and plant engineering

Authors: Sneha Leslie

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The current situation in the cyber world is that crimes performed by Botnets are increasing and the masterminds (botmaster) are not detectable easily. The botmaster in the botnet compromises the legitimate host machines in the network and make them bots or zombies to initiate the cyber-attacks. This paper will focus on the live detection of the botmaster in the network by using the strong framework 'metasploit', when distributed denial of service (DDOS) attack is performed by the botnet. The affected victim machine will be continuously monitoring its incoming packets. Once the victim machine gets to know about the excessive count of packets from any IP, that particular IP is noted and details of the noted systems are gathered. Using the vulnerabilities present in the zombie machines (already compromised by botmaster), the victim machine will compromise them. By gaining access to the compromised systems, applications are run remotely. By analyzing the incoming packets of the zombies, the victim comes to know the address of the botmaster. This is an effective and a simple system where no specific features of communication protocol are considered.

Keywords: bonet, DDoS attack, network security, detection system, metasploit framework

Procedia PDF Downloads 254

Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

Procedia PDF Downloads 109

Authors: T. A. Faria, C. C. Toniolo, L. F. Ribeiro

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In order to satisfy the costumer’s needs, many sectors of industry and services has been spending major effort to make its processes more efficient. Facing a situation, when its production cannot cover the demand, the traditional way to achieve the production required involves, mostly, adding shifts, workforce, or even more machines. This paper narrates how lean manufacturing supported a dramatic increase of productivity at a rail welding plant in Brazil in order to meet the demand for the next years.

Keywords: productivity, lean manufacturing, rail welding, value stream mapping

Procedia PDF Downloads 364

Authors: Oveis Abedinia, Noradin Ghadimi, Nasser Mikaeilvand, Roza Poursoleiman, Asghar Poorfaraj

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In this paper a robust Fuzzy Proportional Integral Differential (PID) controller is applied to multi-machine power system based on Modified Shuffled Frog Leaping (MSFL) algorithm. This newly proposed controller is more efficient because it copes with oscillations and different operating points. In this strategy the gains of the PID controller is optimized using the proposed technique. The nonlinear problem is formulated as an optimization problem for wide ranges of operating conditions using the MSFL algorithm. The simulation results demonstrate the effectiveness, good robustness and validity of the proposed method through some performance indices such as ITAE and FD under wide ranges operating conditions in comparison with TS and GSA techniques. The single-machine infinite bus system and New England 10-unit 39-bus standard power system are employed to illustrate the performance of the proposed method.

Keywords: fuzzy PID, MSFL, multi-machine, low frequency oscillation

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Authors: Hala Alioua, Alberic Tellier

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This paper is designed to spotlight the research on corporate strategic planning, by emphasizing the role of middle manager of SBU’s and related issues such as the context of vision change. Previous research on strategic vision has been focused principally at the SME, with relatively limited consideration given to the role of middle managers SBU’s in the context of change. This project of research has been done by using a single case study. We formulated through our immersion for 2.5 years on the ground and by a qualitative method and abduction approach. This entity that we analyze is a subsidiary of multinational companies headquartered in Germany, specialized in manufacturing automotive equipment. The "Delta Company" is a French manufacturing plant that has undergone numerous changes over the past three years. The two major strategic changes that have a significant impact on the Delta plant are the strengths of its core business through « lead plant strategy» in 2011 and the implementation of a new strategic vision in 2014. These consecutive changes impact the purpose of the mission of the middle managers. The plant managers ask the following questions: How the middle managers make sense of the corporate strategic planning imposed by the parent company? How they appropriate the new vision and decline it into actions on the ground? We chose the individual interview technique through open-ended questions as the source of data collection. We first of all carried out an exploratory approach by interviewing 8 members of the Management committee’s decision and 19 heads of services. The first findings and results show that exist a divergence of opinion and interpretations of the corporate strategic planning among organization members and there are difficulties to make sense and interpretations of the signals of the environment. The lead plant strategy enables new projects which insure the workload of Delta Company. Nevertheless, it creates a tension and stress among the middle managers because its provoke lack of resources to the detriment of their main jobs as manufacturer plant. The middle managers does not have a clear vision and they are wondering if the new strategic vision means more autonomy and less support from the group.

Keywords: change, middle managers, vision, sensemaking

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

Procedia PDF Downloads 128

Authors: Amna Khan, Zareena Kausar, Saad Malik

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Bio Mechatronics has extended applications in the field of rehabilitation. It has been contributing since World War II in improving the applicability of prosthesis and assistive devices in real life scenarios. In this paper, classification accuracies have been compared for two classifiers against five positions of elbow. Electromyography (EMG) signals analysis have been acquired directly from skeletal muscles of human forearm for each of the three defined positions and at modified extreme positions of elbow flexion and extension using 8 electrode Myo armband sensor. Features were extracted from filtered EMG signals for each position. Performance of two classifiers, support vector machine (SVM) and linear discriminant analysis (LDA) has been compared by analyzing the classification accuracies. SVM illustrated classification accuracies between 90-96%, in contrast to 84-87% depicted by LDA for five defined positions of elbow keeping the number of samples and selected feature the same for both SVM and LDA.

Keywords: classification accuracies, electromyography, linear discriminant analysis (LDA), Myo armband sensor, support vector machine (SVM)

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Authors: Abida Kausar, Shagufta Parveen

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The priming of seeds was carried out by two amino acids (phenylalanine and glycine) to improve the drought tolerance potential of two wheat varieties. As wheat is a staple food of more than half of the population of the world, including Pakistan. However, its productivity is mainly adversely affected by abiotic stresses. The current research plan was to investigate the effect of hydropriming and priming by amino acids on wheat varieties under drought stress (50% field capacity). Therefore morphological, biochemical, physiological, and yield attributes were recorded. It was revealed that drought stress significantly decreased the biochemical, morpho-physiological, and growth attributes of the wheat crop. However, the priming treatments have shown a positive correlation with all the studied attributes. It was concluded that priming might involve plant signaling to produce the drought tolerance metabolites under stress conditions which, as a consequence, enhanced the drought tolerance potential of crops.

Keywords: plant biomass, biochemical parameters, chlorophyll contents, yield

Procedia PDF Downloads 89

Authors: Sharon Li, Zhonghang Xia

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Post-database search is an essential phase in peptide identification using tandem mass spectrometry (MS/MS) to refine peptide-spectrum matches (PSMs) produced by database search engines. These engines frequently face difficulty differentiating between correct and incorrect peptide assignments. Despite advances in statistical and machine learning methods aimed at improving the accuracy of peptide identification, challenges remain in selecting critical features for these models. In this study, two machine learning models—a random forest tree and a support vector machine—were applied to three datasets to enhance PSMs. SHAP values were utilized to determine the significance of each feature within the models. The experimental results indicate that the random forest model consistently outperformed the SVM across all datasets. Further analysis of SHAP values revealed that the importance of features varies depending on the dataset, indicating that a feature's role in model predictions can differ significantly. This variability in feature selection can lead to substantial differences in model performance, with false discovery rate (FDR) differences exceeding 50% between different feature combinations. Through SHAP value analysis, the most effective feature combinations were identified, significantly enhancing model performance.

Keywords: peptide identification, SHAP value, feature selection, random forest tree, support vector machine

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Authors: Kamal Khan Abro, Attaullah Ansari, Mahpara Pirzada

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Hoverflies are commonly known as flower flies, sun flies or garden flies. Hoverflies are very important group of insects because their ecosystem services are diverse. They are an attractive group of insects with their striped abdomens. They are day-flying insects from small to large size, have worldwide distribution, but mostly prefer to live in relatively cold weather areas. In the world, about 6,000 species of 200 genera of two sub-families have been described. Their larvae exhibit a variety of feeding modes i.e. aphidophagous, saprophagous, zoophagous and Phytophagus, where adults are floral visitors of hundreds of different plants species. These floral resources enhance the longevity and fecundity of adult dipterous flies. Many syrphid species also have been documented as efficient crop pollinators. Aphids are commonly called plant louse, greenflies and blackflies. They are major pest of crops; about 4000 species of aphids have been described, feeding on 250 species of plants.

Keywords: host plant range, aphidophagous hoverflies, their prey aphids, Thatta Pakistan

Procedia PDF Downloads 212

Authors: Fezzani Boubaker

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The modified activated sludge model (ASM1 or Mantis) is a generic structured model and a common platform for dynamic simulation of varieties of aerobic processes for optimization and upgrading of existing plants and for new facilities design. In this study, the modified ASM1 included in the GPS-X software was used to simulate the wastewater treatment plant (WWTP) of Beja treating domestic sewage mixed with baker‘s yeast factory effluent. The results of daily measurements and operating records were used to calibrate the model. A sensitivity and an automatic optimization analysis were conducted to determine the most sensitive and optimal parameters. The results indicated that the ASM1 model could simulate with good accuracy: the COD concentration of effluents from the WWTP of Beja for all months of the year 2012. In addition, it prevents the disruption observed at the output of the plant by injecting the baker‘s yeast factory effluent at high concentrations varied between 20 and 80 g/l.

Keywords: ASM1, activated sludge, baker’s yeast effluent, modelling, simulation, GPS-X 5.1 software

Procedia PDF Downloads 343

Authors: Zzibu Mark Brian

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As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

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Authors: H. Hruschka

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This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories.

Keywords: binary factor analysis, deep belief net, market basket analysis, restricted Boltzmann machine, topic models

Procedia PDF Downloads 199

Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

Procedia PDF Downloads 106

Authors: Anjuri Srivastava, Akhouri Nishant Bhanu

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Soil is a natural material with a distinctive form. It is regarded to be a natural source of nutrients and minerals for plants. It meets many of our needs through the crops, trees, and inhabited places that have grown on or underneath it. Productive and rich soil plays a crucial role in both its wealth and well-being. If any external substance changes the soil's composition, it directly impacts the plant that was grown in that soil. If the soil is deficient in one or more essential components, fly ash can be utilized as fertilizer by incorporating it into the soil. This can also increase the porosity of the soil. Fly ash has a sufficient concentration of essential components to promote the growth of plants. The high concentration of elements in fly ash, including C, Na, K, Fe, and Zn, increases crop yields. Hazardous compounds harm plant life as soon as they get into the soil. The US Environmental Protection Agency and other regulatory agencies have found it as non-hazardous. By employing fly ash as a potential fertilizer supplement for degraded soils, the problem of disposing of solid waste can be partially handled. Fly ash's rapid growth can slow down mineralization because it contains a higher proportion of harmful heavy metals. The chemical characteristics, inclusion ratio, and composting process of fly ash have a significant impact on the fly ash compost’s potential to improve soil nutrition. Research institutions and regulatory agencies have been thoroughly investigating fly ash for a long time. Guard cells on plant leaves that accumulate fly ash trigger the regulatory system. Fly ash increases both chemical and physical damage at certain humidity levels. The lengthy sowing period is caused by the high levels of fly ash in the soil, which also slows down seedling germination and growth. For the sake of human health, it is crucial to consider the bioaccumulation of dangerous heavy metals and their necessary concentrations in plant tissues and soil.

Keywords: soil, fly ash, plant, fertilizer, composts

Procedia PDF Downloads 97

Authors: F. Viprey, H. Nouira, S. Lavernhe, C. Tournier

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Manufacturing of freeform parts may be achieved on 5-axis machine tools currently considered as a common means of production. In particular, the geometrical quality of the freeform parts depends on the accuracy of the multi-axis structural loop, which is composed of several component assemblies maintaining the relative positioning between the tool and the workpiece. Therefore, to reach high quality of the geometries of the freeform parts the geometric errors of the 5 axis machine should be evaluated and compensated, which leads one to master the deviations between the tool and the workpiece (volumetric accuracy). In this study, a novel hole-bar design was developed and used for the characterization of the geometric errors of a RRTTT 5-axis machine tool. The hole-bar standard design is made of Invar material, selected since it is less sensitive to thermal drift. The proposed design allows once to extract 3 intrinsic parameters: one linear positioning and two straightnesses. These parameters can be obtained by measuring the cylindricity of 12 holes (bores) and 11 cylinders located on a perpendicular plane. By mathematical analysis, twelve 3D points coordinates can be identified and correspond to the intersection of each hole axis with the least square plane passing through two perpendicular neighbour cylinders axes. The hole-bar was calibrated using a precision CMM at LNE traceable the SI meter definition. The reversal technique was applied in order to separate the error forms of the hole bar from the motion errors of the mechanical guiding systems. An inter-comparison was additionally conducted between four NMIs (National Metrology Institutes) within the EMRP IND62: JRP-TIM project. Afterwards, the hole-bar was integrated in RRTTT 5-axis machine tool to identify its volumetric errors. Measurements were carried out in real time and combine raw data acquired by the Renishaw RMP600 touch probe and the linear and rotary encoders. The geometric errors of the 5 axis machine were also evaluated by an accurate laser tracer interferometer system. The results were compared to those obtained with the hole bar.

Keywords: volumetric errors, CMM, 3D hole-bar, inter-comparison

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Authors: Kashif Jaeel, Bingru Huang

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Stress-induced accumulation of ethylene exacerbates drought damages in plants, and suppressing stress induction of ethylene may promote plant tolerance to heat stress. The objective of this study was to investigate the effects of endophytic bacteria (Paraburkholderia aspalathi) with 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase enzymes in suppressing ethylene production on plant tolerance to heat stress and underlying physiological mechanisms of P. aspalathi-regulation in creeping bentgrass (Agrostis stolonifera). A novel strain of P. aspalathi, ‘WSF23’, with ACC deaminase activity was used to inoculate the roots of plants (cv. ‘Penncross’) subjected to heat stress in controlled-environment chambers. Inoculation with WSF23 bacteria resulted in improved shoot and root growth during heat stress. The differential changes in metabolite regulation due to the bacterial inoculation could contribute to ACC deamination bacteria-improved heat tolerance in cool-season grass species.

Keywords: rhizobacteria, grass, heat, plant metabolism, soil bacteria

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Authors: S. Brain, P. J. Storer, H. Strydom, Z. M. Solaiman

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Increasing farmer awareness of soil health, the impact of agricultural management practices, and the requirement for high-quality agricultural produce are major factors driving the rapid adoption of biological seed treatments - currently valued globally at USD 1.5 billion. Biological seed coatings with multistrain plant beneficial microbial technology have the capability to affect plant establishment, growth, and development positively. These beneficial plant microbes can potentially increase soil health, plant yield, and nutrition – acting as bio fertilisers, rhizoremediators, phytostimulators, and stress modulators, and can ultimately reduce the overall use of agrichemicals. A field trial was conducted on MACE wheat in the central wheat belt of Western Australia to evaluate a proprietary seed coating technology (Langleys Bio-EnergeticTM Microbe blend (BMB)) on a conventional program (+/- BMB microbes) and a Regenerative Biomineral fertiliser program (+/- BMB microbes). The Conventional (+BMB) and Biomineral (+BMB) treated plants had no fungicide treatments and had no disease issues. Control (No fertiliser, No microbes), Conventional (No Microbes), and Biomineral (No Microbes) were treated with fungicides (seed dressing and foliar). From the research findings, compared to control and no microbe treatments, both the Conventional (+ BMB) and Biomineral (+ BMB) showed significant increases in Soil Carbon (SOC), Seed germination, nutrient use efficiency (NUE) of nitrogen, phosphate and mineral nutrients, grain mineral nutrient uptake, protein %, hectolitre weight, and fewer screenings, yield, and gross margins.

Keywords: biological seed coating, biomineral fertiliser, plant nutrition, regenerative and conventional agriculture

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Authors: Youn-Sung Kim, Mi-Sung Kim, Jae-Kun Lee

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This paper deals with the accelerated life cycle test method of the motor for home appliances that demand high reliability. Life Cycle of parts in home appliances also should be 10 years because life cycle of the home appliances such as washing machine, refrigerator, TV is at least 10 years. In case of washing machine, the life cycle test method of motor is advanced for 3000 cycle test (1cycle = 2hours). However, 3000 cycle test incurs loss for the time and cost. Objectives of this study are to reduce the life cycle test time and the number of test samples, which could be realized by using acceleration factor for the test time and reduction factor for the number of sample.

Keywords: accelerated life cycle test, motor reliability test, motor for washing machine, BLDC motor

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Authors: Haile Araya Nigusse, Hiram M. Ndiritu, Robert Kiplimo

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Energy consumption is an indispensable component for the continued development of the human population. The global energy demand increases with development and population rise. The increase in energy demand, high cost of fossil fuels and the link between energy utilization and environmental impacts have resulted in the need for a sustainable approach to the utilization of the low grade energy resources. The Organic Rankine Cycle (ORC) power plant is an advantageous technology that can be applied in generation of power from low temperature brine of geothermal reservoirs. The power plant utilizes a low boiling organic working fluid such as a refrigerant or a hydrocarbon. Researches indicated that the performance of ORC power plant is highly dependent upon factors such as proper organic working fluid selection, types of heat exchangers (condenser and evaporator) and turbine used. Despite a high pressure drop, shell-tube heat exchangers have satisfactory performance for ORC power plants. This study involved the design, fabrication and performance assessment of the components of a model Organic Rankine Cycle power plant to utilize the low grade geothermal brine. Two shell and tube heat exchangers (evaporator and condenser) and a single stage impulse turbine have been designed, fabricated and the performance assessment of each component has been conducted. Pentane was used as a working fluid and hot water simulating the geothermal brine. The results of the experiment indicated that the increase in mass flow rate of hot water by 0.08 kg/s caused a rise in overall heat transfer coefficient of the evaporator by 17.33% and the heat transferred was increased by 6.74%. In the condenser, the increase of cooling water flow rate from 0.15 kg/s to 0.35 kg/s increased the overall heat transfer coefficient by 1.21% and heat transferred was increased by 4.26%. The shaft speed varied from 1585 to 4590 rpm as inlet pressure was varied from 0.5 to 5.0 bar and power generated was varying from 4.34 to 14.46W. The results of the experiments indicated that the performance of each component of the model Organic Rankine Cycle power plant operating at low temperature heat resources was satisfactory.

Keywords: brine, heat exchanger, ORC, turbine

Procedia PDF Downloads 649

Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Thabiso Michael Mokotjomela, Jasper Knight

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Intensification of agricultural food production in sub-Saharan Africa is of paramount importance as a means of increasing the food security of communities that are already experiencing a range of environmental and socio-economic stresses. However, achieving this aim faces several challenges including ongoing climate change, increased resistance of diseases and pests, extreme environmental degradation partly due to biological invasions, land tenure and management practices, socio-economic developments of rural populations, and national population growth. In particular, non-native plant species tend to display greater adaptation capacity to environmental stress than native species that form important food resource base for human beings, thus suggesting a potential for usage to shift accordingly. Based on review of the historical benefits of non-native plant species in food production in sub-Saharan Africa, we propose that use of non-invasive, non-native plant species and/or the genetic modification of native species might be viable options for future agricultural sustainability in this region. Coupled with strategic foresight planning (e.g. use of biological control agents that suppress plant species’ invasions), the consumptive use of already-introduced non-native species might help in containment and control of possible negative environmental impacts of non-native species on native species, ecosystems and biodiversity, and soil fertility and hydrology. Use of non-native species in food production should be accompanied by low cost agroecology practices (e.g. conservation agriculture and agrobiodiversity) that may promote the gradual recovery of natural capital, ecosystem services, and promote conservation of the natural environment as well as enhance food security.

Keywords: food security, invasive species, agroecology, agrobiodiversity, socio-economic stresses

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: R. Knizek, D. Karhankova

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The production of nanofibers and the machinery for their production is a current issue. The pioneer, in the industrial production of nanofibers, is the machinery with the sales descriptions Nanospider^TM from the company Elmarco, which came into being in 2008. Most of the production facilities, like Nanospider^TM, use electrospinning. There are also other methods of industrial production of nanofibers, such as the centrifugal spinning process, which is used by FibeRio Technology Corporation. However, each method and machine has its advantages, but also disadvantages and that is the reason why a new machine called as Nanomachine, which eliminates the disadvantages of other production facilities producing nanofibers, has been developed.

Keywords: nanomachine, nanospider, spinning slat, electrospinning

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Authors: Jingwei Du

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The need of optimizing air conditioning systems for existing buildings calls for control methods designed with energy-efficiency as a primary goal. The majority of current control methods boil down to two categories: empirical and model-based. To be effective, the former heavily relies on engineering expertise and the latter requires extensive historical data. Reinforcement Learning (RL), on the other hand, is a model-free approach that explores the environment to obtain an optimal control strategy often referred to as “policy”. This research adopts Proximal Policy Optimization (PPO) to improve chiller plant control, and enable the RL agent to collaborate with experienced engineers. It exploits the fact that while the industry lacks historical data, abundant operational data is available and allows the agent to learn and evolve safely under human supervision. Thanks to the development of language models, renewed interest in RL has led to modern, online, policy-based RL algorithms such as the PPO. This research took inspiration from “alignment”, a process that utilizes human feedback to finetune the pretrained model in case of unsafe content. The methodology can be summarized into three steps. First, an initial policy model is generated based on minimal prior knowledge. Next, the prepared PPO agent is deployed so feedback from both critic model and human experts can be collected for future finetuning. Finally, the agent learns and adapts itself to the specific chiller plant, updates the policy model and is ready for the next iteration. Besides the proposed approach, this study also used traditional RL methods to optimize the same simulated chiller plants for comparison, and it turns out that the proposed method is safe and effective at the same time and needs less to no historical data to start up.

Keywords: chiller plant, control methods, energy efficiency, proximal policy optimization, reinforcement learning

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Authors: Moon Byung Woo, Seok Chang-Sung, Koo Jae-Mean, Kim Sang-Young, Choi Jae Gu, Huh Nam-Su

Abstract:

Recently, the occurrence of the earthquake has increased sharply and many of the casualties have occurred worldwide, due to the influence of earthquakes. Especially, the Fukushima nuclear power plant accident which was caused by the earthquake in 2011 has significantly increased the fear of people and the demand for the safety of the nuclear power plant. Thus, in order to prevent the earthquake accident at nuclear power plant, it is important to evaluate the fracture toughness considering the seismic loading rate. To obtain fracture toughness for the safety evaluation of nuclear power plant, it is desirable to perform experiments with a real scale pipe which is expensive and hard to perform. Therefore, many researchers have proposed various test specimens to replicate the fracture toughness of a real scale pipe. Since such specimens have several problems, the equivalent stress gradient (ESG) specimen has been recently suggested. In this study, in order to consider the effects of the dynamic loading speed on fracture toughness, the experiment was conducted by applying five different kinds of test speeds using an ESG specimen. In addition, after we performed the fracture toughness test under dynamic loading with different speeds using an ESG specimen and a standard specimen, we compared them with the test results under static loading.

Keywords: dynamic loading speed, fracture toughness, load-ratio-method, equivalent stress gradient (ESG) specimen

Procedia PDF Downloads 309

Authors: Mina Dabirinezhad, Mohsen Bayat Pour, Amin Dabirinejad

Abstract:

This paper is intended to introduce the technology to solve some of the deficiencies of the direct digital radiology. Nowadays, digital radiology is the latest progression in dental imaging, which has become an essential part of dentistry. There are two main parts of the direct digital radiology comprised of an intraoral X-ray machine and a sensor (digital image receptor). The dentists and the dental nurses experience afflictions during the taking image process by the direct digital X-ray machine. For instance, sometimes they need to readjust the sensor in the mouth of the patient to take the X-ray image again due to the low quality of that. Another problem is, the position of the sensor may move in the mouth of the patient and it triggers off an inappropriate image for the dentists. It means that it is a time-consuming process for dentists or dental nurses. On the other hand, taking several the X-ray images brings some problems for the patient such as being harmful to their health and feeling pain in their mouth due to the pressure of the sensor to the jaw. The author provides a technology to solve the above-mentioned issues that is called “Self-Recognition Direct Digital Radiology” (SDDR). This technology is based on the principle that the intraoral X-ray machine is capable to diagnose the location of the sensor in the mouth of the patient automatically. In addition, to solve the aforementioned problems, SDDR technology brings out fewer environmental impacts in comparison to the previous version.

Keywords: Dental direct digital imaging, digital image receptor, digital x-ray machine, and environmental impacts

Procedia PDF Downloads 138

Authors: W. S. Hsu, Y. Chiang, Y. S. Tseng, J. R. Wang, C. Shih, S. W. Chen

Abstract:

Kuosheng nuclear power plant (NPP) is a BWR/6 plant in Taiwan. There is more concern for the safety of NPPs in Taiwan after Japan Fukushima NPP disaster occurred. Hence, in order to estimate the safety of Kuosheng NPP spent fuel pool (SFP), by using TRACE, MELCOR, and SNAP codes, the safety analysis of Kuosheng NPP SFP was performed. There were two main steps in this research. First, the Kuosheng NPP SFP models were established. Second, the transient analysis of Kuosheng SFP was done by TRACE and MELCOR under the cooling system failure condition (Fukushima-like condition). The results showed that the calculations of MELCOR and TRACE were very similar in this case, and the fuel uncover happened roughly at 4^th day after the failure of cooling system. The above results indicated that Kuosheng NPP SFP may be unsafe in the case of long-term SBO situation. In addition, future calculations were needed to be done by the other codes like FRAPTRAN for the cladding calculations.

Keywords: TRACE, MELCOR, SNAP, spent fuel pool

Procedia PDF Downloads 331

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

Abstract:

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

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

Procedia PDF Downloads 162

Authors: Pouyan Jahanbin, Mahmoud Houshmand, Omid Fatahi Valilai

Abstract:

A flexible manufacturing system (FMS) is a manufacturing system having the capability of handling the variations of products features that is the result of ever-changing customer demands. The flexibility of the manufacturing systems help to utilize the resources in a more effective manner. However, the control of such systems would be complicated and challenging. FMS needs CNC machines and robots and other resources for establishing the flexibility and enhancing the efficiency of the whole system. Also it needs to integrate the resources to reach required efficiency and flexibility. In order to reach this goal, an integrator framework is proposed in which the machining data of CNC machine tools is received through a STEP-NC file. The interoperability of the system is achieved by the information system. This paper proposes an information system that its data model is designed based on object oriented approach and is implemented through a knowledge-based system. The framework is connected to a database which is filled with robot’s control commands. The framework programs the robots by rules embedded in its knowledge based system. It also controls the interactions of CNC machine tools for loading and unloading actions by robot. As a result, the proposed framework improves the integration of manufacturing resources in Flexible Manufacturing Systems.

Keywords: CNC machine tools, industrial robots, knowledge-based systems, manufacturing recourses integration, flexible manufacturing system (FMS), object-oriented data model

Procedia PDF Downloads 455