Search results for: fault tree

Authors: Fitri Rahmi Fadhilah, Edhyana Sahiratmadja, Ani Melani Maskoen, Ratu Safitri, Supartini Syarif, Herman Susanto

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Cervical cancer is the second most common cancer in women after breast cancer. In Indonesia, the incidence of cervical cancer cases is estimated at 25-40 per 100,000 women per year. Human papillomavirus (HPV) infection is a major cause of cervical cancer, and HPV-16 is the most common genotype that infects the cervical tissue. The major late protein L1 may be associated with infectivity and pathogenicity and its variation can be used to classify HPV isolates. The aim of this study was to determine the phylogenetic tree of HPV 16 L1 gene from cervical cancer patient isolates in Bandung. After confirming HPV-16 by Linear Array Genotyping Test, L1 gene was amplified using specific primers and subject for sequencing. Phylogenetic analysis revealed that HPV 16 from Bandung was in the subgroup of Asia and East Asia, showing the close host-agent relationship among the Asian type.

Keywords: L1 HPV 16, cervical cancer, bandung, phylogenetic

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Authors: Muneeb Ahmad, Ali Raza

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A computational method inspired by the immune system (IS) is presented, leveraging its shared characteristics of robustness, fault tolerance, scalability, and adaptability with swarm intelligence. This method aims to showcase flocking behaviors in a swarm of robots (SR). The innate part of the IS offers a variety of reactive and probabilistic cell functions alongside its self-regulation mechanism which have been translated to enable swarming behaviors. Although, the research is specially focused on flocking behaviors in a variety of simulated environments using e-puck robots in a physics-based simulator (CoppeliaSim); the artificial innate immune system (AIIS) can exhibit other swarm behaviors as well. The effectiveness of the immuno-inspired approach has been established with extensive experimentations, for scalability and adaptability, using standard swarm benchmarks as well as the immunological regulatory functions (i.e., Dendritic Cells’ Maturity and Inflammation). The AIIS-based approach has proved to be a scalable and adaptive solution for emulating the flocking behavior of SR.

Keywords: artificial innate immune system, flocking swarm, immune system, swarm intelligence

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Armin Rahimi

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In this study, we reveal co-seismic signals of two combined earthquakes, the 2006 Mw8.3 thrust and 2007 Mw8.1 normal fault earthquakes of the central Kuril Islands from GRACE observations. We compute monthly full gravitational gradient tensor in the local north-east-down frame for Kuril Islands earthquakes without spatial averaging and de-striping filters. Some of the gravitational gradient components (e.g. ΔVxx, ΔVxz) enhance high frequency components of the earth gravity field and reveal more details in spatial and temporal domain. Therefore that preseismic activity can be better illustrated. We show that the positive-negative-positive co-seismic ΔVxx due to the Kuril Islands earthquakes ranges from − 0.13 to + 0.11 milli Eötvös, and ΔVxz shows a positive-negative-positive pattern ranges from − 0.16 to + 0.13 milli Eötvös, agree well with seismic model predictions.

Keywords: GRACE observation, gravitational gradient changes, Kuril island earthquakes, PSGRN/PSCMP

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Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

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Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

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Authors: Triano Nurhikmat

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Along with the progress of science and technology, the development of the industrialized world in Indonesia took place very rapidly. This leads to a process of industrialization of society Indonesia faster with the establishment of the company and the workplace are diverse. Development of the industry relates to the activity of the worker. Where in these work activities do not cover the possibility of an impending crash on either the workers or on a construction project. The cause of the occurrence of industrial accidents was the fault of electrical damage, work procedures, and error technique. The method of an association rule is one of the main techniques in data mining and is the most common form used in finding the patterns of data collection. In this research would like to know how relations of the association between the incidence of any industrial accidents. Therefore, by using methods of analysis association rule patterns associated with combination obtained two iterations item set (2 large item set) when every factor of industrial accidents with a West Jakarta so industrial accidents caused by the occurrence of an electrical value damage = 0.2 support and confidence value = 1, and the reverse pattern with value = 0.2 support and confidence = 0.75.

Keywords: association rule, data mining, industrial accidents, rules

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Hamisu Idi

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The present study focuses on studying the impact of influencer recommendation in the quality of cement production. Exploratory research was done on monthly basis, where data were obtained from secondary source i.e. the record kept by an automated recompilation machine. The machine keeps all the records of the mills downtime which the process manager checks for validation and refer the fault (if any) to the department responsible for maintenance or measurement taking so as to prevent future occurrence. The findings indicated that the product of the Ashaka Cement Plc. were considered as qualitative, since all the production processes were found to be in control (preset specifications) with the exception of the natural cause of variation which is normal in the production process as it will not affect the outcome of the product. It is reduced to the bearest minimum since it cannot be totally eliminated. It is also hopeful that the findings of this study would be of great assistance to the management of Ashaka cement factory and the process manager in particular at various levels in the monitoring and implementation of statistical process control. This study is therefore of great contribution to the knowledge in this regard and it is hopeful that it would open more research in that direction.

Keywords: cement, quality, variation, assignable cause, common cause

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Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

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Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: central machine learning, embedded machine learning, energy consumption, local machine learning, wireless sensor networks, WSN

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Mehmet Salih Bayraktutan, Emre Tokmak

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Great Ararat Volcano is the highest peak in South Caucasus Volcanic Plateau. Uplifted by Quaternary basaltic pyroclastic and lava flows. Numerous volcanic cones formed along with the tensional fractures under N-S compressional geodynamic framework. Basaltic flows have fresh surface morphology give ages of 650-680 K years. Hyperstene andesites constitute a major mass of Greater Ararat gives ages of 450-490 K years. During the early eruption period, predominately pyroclastics, cinder, lapilly-ash volcanic bombs were extruded. Third-period eruptions dominantly basaltic lava flows. Andesitic domes aligned along with the NW-SE striking fractures. Hyalo basalt and hornblende basaltic lavas are the latest lava eruptions. Hyalo-basaltic eruptions occurred via parasitic cones distributed far from the center. Parasitic cones are most common at the foot of Mount covered by recent NW flowing basaltic lava. Some of the cones are distributed on a circular pattern. One of the most hazardous disasters recorded in Eastern Turkey was July 1840 Cehennem Canyon Flood. Volcanic activities seismically triggered resulted in melting of glacier cap, mixed with ash and pyroclastics, flowed down along the Valley. Mud rich Slush urged catastrophically northwards, crossed Ars River and damned Surmeli Basin, forming reservoir behind. Ararat volcanoes are located on NW-SE striking Agri Fault Zone. Right lateral extensional faults, along which a series of andesitic domes formed. Great Ararat, in general strato-type volcano. This huge structure, developed in two main parts with different topographic and morphological features. The large lower base covers a widespread area composed of predominantly pyroclastics, ignimbrites, aglomerates, thick pumice, perlite deposits. Approximately 1/3 of the Crest by height formed of this basement. And 2/3 of the upper part with a conic- shape composed of basaltic lava flows. The active tectonic structure consists of three different patterns. The first network is radially distributed fractures formed during the last stage of lava eruptions. The second group of active faults striking in NW direction, and continue in N30W strike, formes Igdir Fault Zone. The third set of faults, dipping in the northwest with 75-80 degrees, strikes NE- SW across the whole Mount, slicing Great Ararat into four segments. In the upper stage of Cehennem Canyon, this set cutting volcanic layers caused numerous Waterfalls, Rock Avalanches, Mud Flows along the canyon, threatens the Village of Yanidogan, at the apex of flood deposits. Great Ararat Region has high seismo-tectonic risk and by occurrence frequency and magnitude, which caused in history caused heavy disasters, at villages surrounding the Ararat Basement.

Keywords: Eastern Turkey, geohazard, great ararat volcano, seismo-tectonic features

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Authors: Yasser M. Saad, Heba El-Sebaie Abd El-Sadek

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Some Metapenaeus monoceros cox1 gene fragments were isolated, purified, sequenced, and comparatively analyzed with some other Crustacean Cox1 gene sequences (obtained from National Center for Biotechnology Information). This work was designed for testing the efficiency of this system in reconstruction of phylogenetic relations among some Crustacean species belonging to four genera (Metapenaeus, Artemia, Daphnia and Calanus). The single nucleotide polymorphism and haplotype diversity were calculated for all estimated mt-DNA fragments. The genetic distance values were 0.292, 0.015, 0.151, and 0.09 within Metapenaeus species, Calanus species, Artemia species, and Daphnia species, respectively. The reconstructed phylogenetic tree is clustered into some unique clades. Cytochrome oxidase subunit 1 gene (cox1) was a powerful system in reconstruction of phylogenetic relations among evaluated crustacean species.

Keywords: crustaceans, genetics, Cox1, phylogeny

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Authors: Mostafa Feiz, Hossein Hadizadeh, Mohammad Safari

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The Chalpo copper area is located in northeastern Iran, which is part of the structural zone of central Iran and the back-arc basin of Sabzevar. This sedimentary basin accumulated in destructive-Oligomiocene sediments is named the Nasr-Chalpo-Sangerd (NCS) basin. The sedimentary layers in this basin originated mainly from Upper Cretaceous ophiolitic rocks and intermediate to mafic-post ophiolitic volcanic rocks, deposited as a nonconformity. The mineralized sandstone layers in the Chalpo area include leached zones (with a thickness of 5 to 8 meters) and mineralized lenses with a thickness of 0.5 to 0.7 meters. Ore minerals include primary sulfide minerals, such as chalcocite, chalcopyrite, and pyrite, as well as secondary minerals, such as covellite, digenite, malachite, and azurite, formed in three stages that comprise primary, simultaneously, and supergene stage. The best agents that control the mineralization in this area include the permeability of host rocks, the presence of fault zones as the conduits for copper oxide solutions, and significant amounts of plant fossils, which create a reducing environment for the deposition of mineralized layers. Statistical studies on copper layers indicate that Ag, Cd, Mo, and S have the maximum positive correlation with Cu, whereas TiO₂, Fe₂O₃, Al₂O₃, Sc, Tm, Sn, and the REEs have a negative correlation. The calculations of mass changes on copper-bearing layers and primary sandstone layers indicate that Pb, As, Cd, Te, and Mo are enriched in the mineralized zones, whereas SiO₂, TiO₂, Fe₂O₃, V, Sr, and Ba are depleted. The combination of geological, stratigraphic, and geochemical studies suggests that the origin of copper may have been the underlying red strata that contained hornblende, plagioclase, biotite, alkaline feldspar, and labile minerals. Dehydration and hydrolysis of these minerals during the diagenetic process caused the leaching of copper and associated elements by circling fluids, which formed an oxidant-hydrothermal solution. Copper and silver in this oxidant solution might have moved upwards through the basin-fault zones and deposited in the reducing environments in the sandstone layers that have had abundant organic matters. Copper in these solutions probably was carried by chloride complexes. The collision of oxidant and reduced solutions caused the deposition of Cu and Ag, whereas some stable elements in oxidant environments (e.g., Fe₂O₃, TiO₂, SiO₂, REEs) become unstable in the reduced condition. Therefore, the copper-bearing sandstones in the study area are depleted from these elements resulting from the leaching process. The results indicate that during the mineralization stage, LREEs and MREEs were depleted, but Cu, Ag, and S were enriched. Based on field evidence, it seems that the circulation of connate fluids in the reb-bed strata, produced by diagenetic processes, encountered to reduced facies, which formed earlier by abundant fossil-plant debris in the sandstones, is the best model for precipitating sulfide-copper minerals.

Keywords: Chalpo, oligo-miocene red beds, sandstone-hosted copper mineralization, mass change, LREEs, MREEs

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Authors: Ummeh Saika, Toshio Kikuchi

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Population growth and urbanization is a global phenomenon. As the rapid progress of technology, many cities in the international community are facing serious problems of urbanization. There is no doubt that the urbanization will proceed to have significant impact on the ecology, economy and society at local, regional, and global levels. The inhabitants of Dhaka city suffer from lack of proper urban facilities. The green spaces are needed for different functional and leisure activities of the urban dwellers. Again growing densification, a number of green space are transferred into open space in the Dhaka city. As a result greenery of the city's decreases gradually. Moreover, the existing green space is frequently threatened by encroachment. The role of green space, both at community and city level, is important to improve the natural environment and social ties for future generations. Therefore, it seems that the green space needs to be more effective for public interaction. The main objective of this study is to address the effectiveness of urban green space (Urban Park) of Dhaka City. Two approaches are selected to fulfill the study. Firstly, analyze the long-term spatial changes of urban green space using GIS and secondly, investigate the relationship of urban park network with physical and social environment. The case study site covers eight urban parks of Dhaka metropolitan area of Bangladesh. Two aspects (Physical and Social) are applied for this study. For physical aspect, satellite images and aerial photos of different years are used to find out the changes of urban parks. And for social aspect, methods are used as questionnaire survey, interview, observation, photographs, sketch and previous information of parks to analyze about the social environment of parks. After calculation of all data by descriptive statistics, result is shown by maps using GIS. According to physical size, parks of Dhaka city are classified into four types: Small, Medium, Large and Extra Large parks. The observed result showed that the physical and social environment of urban parks varies with their size. In small size parks physical environment is moderate by newly tree plantation and area expansion. However, in medium size parks physical environment are poor, example- tree decrease, exposed soil increase. On the other hand, physical environment of large size and extra large size parks are in good condition, because of plenty of vegetation and well management. Again based on social environment, in small size parks people mainly come from surroundings area and mainly used as waiting place. In medium-size parks, people come to attend various occasion from different places. In large size and extra large size parks, people come from every part of the city area for tourism purpose. Urban parks are important source of green space. Its influence both physical and social environment of urban area. Nowadays green space area gradually decreases and transfer into open space. The consequence of this research reveals that changes of urban parks influence both physical and social environment and also impact on urban life.

Keywords: physical environment, social environment, urban life, urban parks

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Authors: Ludivine Saint-Mard, Masato Nakajima, Gloria Senfaute

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In areas with low to moderate seismicity, the probabilistic seismic hazard analysis frequently uses a Poisson approach, which assumes independence in time and space of events to determine the annual probability of earthquake occurrence. Nevertheless, in countries with high seismic rate, such as Japan, it is frequently use non-poissonian model which assumes that next earthquake occurrence depends on the date of previous one. The objective of this paper is to apply a non-poissonian models in a region of low to moderate seismicity to get a feedback on the following questions: can we overcome the lack of data to determine some key parameters?, and can we deal with uncertainties to apply largely this methodology on an industrial context?. The Brownian-Passage-Time model was applied to a fault located in France and conclude that even if the lack of data can be overcome with some calculations, the amount of uncertainties and number of scenarios leads to a numerous branches in PSHA, making this method difficult to apply on a large scale of low to moderate seismicity areas and in an industrial context.

Keywords: probabilistic seismic hazard, non-poissonian model, earthquake occurrence, low seismicity

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Authors: Nada Slimane, Foued Theljani, Faouzi Bouani

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The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark.

Keywords: clustering, diagnosis, Kalman Filtering, k-means, regularized regression

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Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: M. Lghoul, N. El Goumi, M. Guernouche

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In order to predict the structural and tectonic framework of the Bahira basin and to have a 3D geological modeling of the basin, an integrated multidisciplinary work has been conducted using gravity, magnetic and geological data. The objective of the current study is delineating the subsurfacefeatures, faults, and geological limits, using airborne magnetic and gravity data analysis of the Bahira basin. To achieve our goal, we have applied different enhanced techniques on magnetic and gravity data: power spectral analysis techniques, reduction to pole (RTP), upward continuation, analytical signal, tilt derivative, total horizontal derivative, 3D Euler deconvolutionand source parameter imagining. The major lineaments/faults trend are: NE–SW, NW-SE, ENE–WSW, and WNW–ESE. The 3D Euler deconvolution analysis highlighted a number of fault trend, mainly in the ENE-WSW, WNW-ESE directions. The depth tothe top of the basement sources in the study area ranges between 200 m, in the southern and northern part of the Bahira basin, to 5000 m located in the Eastern part of the basin.

Keywords: magnetic, gravity, structural trend, depth to basement

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Authors: Watheq Mansour, Waleed Bin Owais, Mohammad Basheer Kotit, Khaled Khan

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Efficient query processing is one of the utmost requisites in any business environment to satisfy consumer needs. This paper investigates the various types of indexing models, viz. primary, secondary, and multi-level. The investigation is done under the ambit of various types of queries to which each indexing model performs with efficacy. This study also discusses the inherent advantages and disadvantages of each indexing model and how indexing models can be chosen based on a particular environment. This paper also draws parallels between various indexing models and provides recommendations that would help a Database administrator to zero-in on a particular indexing model attributed to the needs and requirements of the production environment. In addition, to satisfy industry and consumer needs attributed to the colossal data generation nowadays, this study has proposed two novel indexing techniques that can be used to index highly unstructured and structured Big Data with efficacy. The study also briefly discusses some best practices that the industry should follow in order to choose an indexing model that is apposite to their prerequisites and requirements.

Keywords: indexing, hashing, latent semantic indexing, B-tree

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Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: Shailaja Grover, Purvi Tiwari, Vigneshwaran S. R., U. Dinesh Kumar

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In India, grapes are one of the most important horticulture crops. Grapes are most vulnerable to downy mildew, which is one of the most devasting diseases. In the absence of a precise weather-based advisory system, farmers spray pesticides on their crops extensively. There are two main challenges associated with using these pesticides. Firstly, most of these sprays were panic sprays, which could have been avoided. Second, farmers use more expensive "Preventive and Eradicate" chemicals than "Systemic, Curative and Anti-sporulate" chemicals. When these chemicals are used indiscriminately, they can enter the fruit and cause health problems such as cancer. This paper utilizes decision trees and predictive modeling techniques to provide grape farmers with customized advice on grape disease management. This model is expected to reduce the overall use of chemicals by approximately 50% and the cost by around 70%. Most of the grapes produced will have relatively low residue levels of pesticides, i.e., below the permissible level.

Keywords: analytics in agriculture, downy mildew, weather based advisory, decision tree, predictive modelling

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Authors: Vishwambhar Prasad Sati

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This article examines cloudburst-triggered natural hazards mainly flashfloods and landslides in the Uttarakhand Himalaya. It further describes mechanism and implications of natural hazards and illustrates the preventive and mitigation measures. We conducted this study through collection of archival data, case study of cloudburst hit areas, and rapid field visit of the affected regions. In the second week of August 2017, about 50 people died and huge losses to property were noticed due to cloudburst-triggered flashfloods. Our study shows that although cloudburst triggered hazards in the Uttarakhand Himalaya are natural phenomena and unavoidable yet, disasters can be minimized if preventive measures are taken up appropriately. We suggested that construction of human settlements, institutions and infrastructural facilities along the seasonal streams and the perennial rivers should be avoided to prevent disasters. Further, large-scale tree plantation on the degraded land will reduce the magnitude of hazards.

Keywords: cloudburst, flash floods, landslides, fragile landscape

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Authors: Admassu Addi, Sebesebe Demissew, Teshome Soromessa, Zemede Asfaw

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This study measures the carbon stock of the Moist Afromontane Gesha-Sayilem forest found in Gesha and Sayilem District in southwest Ethiopia. A stratified sampling method was used to identify the number of sampling point through the Global Positioning System. A total of 90 plots having nested plots to collect tree species and soil data were demarcated. The results revealed that the total carbon stock of the forest was 362.4 t/ha whereas the above ground carbon stock was 174.95t/ha, below ground litter, herbs, soil, and dead woods were 34.3,1.27, 0.68, 128 and 23.2 t/ha (up to 30 cm depth) respectively. The Gesha- Sayilem Forest is a reservoir of high carbon and thus acts as a great sink of the atmospheric carbon. Thus conservation of the forest through introduction REDD+ activities is considered an appropriate action for mitigating climate change.

Keywords: carbon sequestration, carbon stock, climate change, allometric, Ethiopia

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Authors: Eui-Jin Kim, Hasik Lee, Su-Jin Park, Dong-Kyu Kim

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Smart-card data are expected to provide information on activity pattern as an alternative to conventional person trip surveys. The focus of this study is to propose a method for training the person trip surveys to supplement the smart-card data that does not contain the purpose of each trip. We selected only available features from smart card data such as spatiotemporal information on the trip and geographic information system (GIS) data near the stations to train the survey data. XGboost, which is state-of-the-art tree-based ensemble classifier, was used to train data from multiple sources. This classifier uses a more regularized model formalization to control the over-fitting and show very fast execution time with well-performance. The validation results showed that proposed method efficiently estimated the trip purpose. GIS data of station and duration of stay at the destination were significant features in modeling trip purpose.

Keywords: activity pattern, data fusion, smart-card, XGboost

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Authors: Kiavash Heidarzadeh

Abstract:

The seismic behavior of portal frames is mainly based on the shape of their joints. In these structures, vertical and inclined connections are the two general forms of connections. The shapes of connections can make differences in seismic responses of portal frames. Hence, in this paper, for the first step, the non-linear performance of portal frames with vertical and inclined connections has been investigated by monotonic analysis. Also, the effect of section sizes is considered in this analysis. For comparison, hysteresis curves have been evaluated for two model frames with different forms of connections. Each model has three various sizes of the column and beam. Other geometrical parameters have been considered constant. In the second step, for every model, an appropriate size of sections has been selected from the previous step. Next, the seismic behavior of each model has been analyzed by the time history method under three near-fault earthquake records. Finite element ABAQUS software is used for simulation and analysis of samples. Outputs show that connections form can impact on reaction forces of portal frames under earthquake loads. Also, it is understood that the load capacity in frames with vertical connections is more than the frames with inclined connections.

Keywords: inclined connections, monotonic, portal frames, seismic behavior, time history, vertical connections

Procedia PDF Downloads 210

Authors: Kharisma Utomo Mulyodinoto, Suwarno, A. Abu-Siada

Abstract:

Dissolved Gas Analysis is one of impressive technique to detect and predict internal fault of transformers by using gas generated by transformer oil sample. A number of methods are used to interpret the dissolved gas from transformer oil sample: Doernenberg Ratio Method, IEC (International Electrotechnical Commission) Ratio Method, and Duval Triangle Method. While the assessment of dissolved gas within transformer oil samples has been standardized over the past two decades, analysis of the results is not always straight forward as it depends on personnel expertise more than mathematical formulas. To get over this limitation, this paper is aimed at improving the interpretation of Doernenberg Ratio Method, IEC Ratio Method, and Duval Triangle Method using Two Stages Adaptive Neuro-Fuzzy Inference System (ANFIS). Dissolved gas analysis data from 520 faulty transformers was analyzed to establish the proposed ANFIS model. Results show that the developed ANFIS model is accurate and can standardize the dissolved gas interpretation process with accuracy higher than 90%.

Keywords: ANFIS, dissolved gas analysis, Doernenberg ratio method, Duval triangular method, IEC ratio method, transformer

Procedia PDF Downloads 132

Authors: İmren Öztürk Yılmaz, Abdullah Yasin Bilici, Yasin Atalay Candemir

Abstract:

The system consists of 4 main elements: motion system, image acquisition system, image processing software, and control interface. The parts coming out of the production line to enter the image processing system with the conveyor belt at the end of the line. The 3D scanning of the produced part is performed with the laser scanning system integrated into the system entry side. With the 3D scanning method, it is determined at what position and angle the parts enter the system, and according to the data obtained, parameters such as part origin and conveyor speed are calculated with the designed software, and the robot is informed about the position where it will take part. The robot, which receives the information, takes the produced part on the belt conveyor and shows it to high-resolution cameras for quality control. Measurement processes are carried out with a maximum error of 20 microns determined by the experiments.

Keywords: quality control, industry 4.0, image processing, automated fault detection, digital visual inspection

Procedia PDF Downloads 90

Authors: Mohammad Amjad Sabir, Ishtiaq Khan, Shahid Ali, Umar Shabbir, Aneel Ahmad

Abstract:

Creep, being the main silt contributor to the rivers, is a slow, downhill flow of soils. The creep velocity is measured in millimeters to a couple of centimeters per year and is determined with the help of tilt caused by creep in the vertical objects and needs at least ten years to get a reliable creep velocity. This project was devised to calculate creep velocity using dendrochronology and looking for the difference of creep velocity registered by different trees on the same slope. It was concluded that dendrochronology provides a very reliable procedure of creep velocity estimation if ‘J’ shaped trees are studied for their horizontal movement and age. The age of these trees was measured using tree coring, and the horizontal movement was measured with a conventional tape. Using this procedure it does not require decades and additionally the data reveals the creep velocity for up to 150 years and even more instead of just a decade. It was also concluded that the creep velocity does not only depend on bulk density of soil hence no pronounced effect of bulk density was detected.

Keywords: creep velocity, Galiyat, Pakistan, dendrochronology, Nagri Bala

Procedia PDF Downloads 293

Authors: R. M. López-Gutiérrez, J. A. Michel-Macarty, H. Cervantes-De Avila, J. I. Nieto-Hipólito, C. Cruz-Hernández, L. Cardoza-Avendaño, S. Cortiant-Velez

Abstract:

It is well know that mankind benefits from the application of robot control by virtual handlers in industrial environments. In recent years, great interest has emerged in the control of multiple robots in order to carry out collective tasks. One main trend is to copy the natural organization that some organisms have, such as, ants, bees, school of fish, birds’ migration, etc. Surely, this collaborative work, results in better outcomes than those obtain in an isolated or individual effort. This topic has a great drive because collaboration between several robots has the potential capability of carrying out more complicated tasks, doing so, with better efficiency, resiliency and fault tolerance, in cases such as: coordinate navigation towards a target, terrain exploration, and search-rescue operations. In this work, synchronization of multiple autonomous robots is shown over a variety of coupling topologies: star, ring, chain, and global. In all cases, collective synchronous behavior is achieved, in the complex networks formed with mobile robots. Nodes of these networks are modeled by a mass using Matlab to simulate them.

Keywords: robots, synchronization, bidirectional, coordinate navigation

Procedia PDF Downloads 334