Search results for: trajectory mining

Authors: Diego Mauricio Murillo Gomez, Enney Leon Gonzalez Ramirez, Hugo Piedrahita, Jairo Yate

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This paper proposes a methodology for the management of noise in open mining industries based on an integral concept, which takes into consideration occupational and environmental noise as a whole. The approach relies on the characterization of sources, the combination of several measurements’ techniques and the use of acoustic prediction software. A discussion about the difference between frequently used acoustic indicators such as Leq and LAV is carried out, aiming to establish common ground for homologation. The results show that the correct integration of this data not only allows for a more robust technical analysis but also for a more strategic route of intervention as several departments of the company are working together. Noise control measurements can be designed to provide a healthy acoustic surrounding in which the exposure workers but also the outdoor community is benefited.

Keywords: environmental noise, noise control, occupational noise, open mining

Procedia PDF Downloads 238

Authors: Jiameng Wang, Yunfei Yin, Xiyu Deng

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Parallel optimization is one of the important research topics of data mining at this stage. Taking Classification and Regression Tree (CART) parallelization as an example, this paper proposes a parallel data mining algorithm based on SSP-OGini-PCCP. Aiming at the problem of choosing the best CART segmentation point, this paper designs an S-SP model without data association; and in order to calculate the Gini index efficiently, a parallel OGini calculation method is designed. In addition, in order to improve the efficiency of the pruning algorithm, a synchronous PCCP pruning strategy is proposed in this paper. In this paper, the optimal segmentation calculation, Gini index calculation, and pruning algorithm are studied in depth. These are important components of parallel data mining. By constructing a distributed cluster simulation system based on SPARK, data mining methods based on SSP-OGini-PCCP are tested. Experimental results show that this method can increase the search efficiency of the best segmentation point by an average of 89%, increase the search efficiency of the Gini segmentation index by 3853%, and increase the pruning efficiency by 146% on average; and as the size of the data set increases, the performance of the algorithm remains stable, which meets the requirements of contemporary massive data processing.

Keywords: classification, Gini index, parallel data mining, pruning ahead

Procedia PDF Downloads 103

Authors: Rosendo Mendoza, María Carmen Hidalgo, María José Campos-Suñol, Julián Martínez, Javier Rey

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The impact of mining environmental liabilities and mine drainage on surface water quality has been investigated in the hydrographic basin of the La Carolina mining district (southern Spain). This abandoned mining district is characterized by the existence of important mineralizations of sulfoantimonides of Pb - Ag, and sulfides of Cu - Fe. All surface waters reach the main river of this mining area, the Grande River, which ends its course in the Rumblar reservoir. This waterbody is intended to supply 89,000 inhabitants, as well as irrigation and livestock. Therefore, the analysis and control of the metal(loid) concentration that exists in these surface waters is an important issue because of the potential pollution derived from metallic mining. A hydrogeochemical campaign consisting of 20 water sampling points was carried out in the hydrographic network of the Grande River, as well as two sampling points in the Rumbler reservoir and at the main tailings impoundment draining to the river. Although acid mine drainage (pH below 4) is discharged into the Grande river from some mine adits, the pH values in the river water are always neutral or slightly alkaline. This is mainly the result of a dilution process of the small volumes of mine waters by net alkaline waters of the river. However, during the dry season, the surface waters present high mineralization due to a constant discharge from the abandoned flooded mines and a decrease in the contribution of surface runoff. The concentrations of dissolved Cd and Pb in the water reach values of 2 and 81 µg/l, respectively, exceeding the limit established by the Environmental Quality Standard for surface water. In addition, the concentrations of dissolved As, Cu, and Pb in the waters of the Rumblar reservoir reached values of 10, 20, and 11 µg/l, respectively. These values are higher than the maximum allowable concentration for human consumption, a circumstance that is especially alarming.

Keywords: environmental quality, hydrogeochemistry, metal mining, surface water

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Ketan Mahawer, Abeer Mutto, S. K. Gupta

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The mining wastewater contains inorganic metal salts, which makes it saline and additionally contributes to contaminating the surface and underground freshwater reserves that exist nearby mineral processing industries. Therefore, treatment of wastewater and water recovery is obligatory by any available technology before disposing it into the environment. Currently, reverse osmosis (RO) is the commercially acceptable conventional membrane process for saline wastewater treatment, but consumes an enormous amount of energy and makes the process expensive. To solve this industrial problem with minimum energy consumption, we tested the feasibility of forward osmosis-nanofiltration (FO-NF) hybrid process for the mining wastewater treatment. The FO-NF process experimental results for 0.029M concentration of saline wastewater treated by 0.42 M sodium-sulfate based draw solution shows that specific energy consumption of the FO-NF process compared with standalone NF was slightly above (between 0.5-1 kWh/m3) from conventional process. However, average freshwater recovery was 30% more from standalone NF with same feed and operating conditions. Hence, FO-NF process in place of RO/NF offers a huge possibility for treating mining industry wastewater and concentrates the metals as the by-products without consuming an excessive/large amount of energy and in addition, mitigates the fouling in long periods of treatment, which also decreases the maintenance and replacement cost of the separation process.

Keywords: forward osmosis, nanofiltration, mining, draw solution, divalent solute

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Authors: Jing Yu, Hongyang Liu, Dong Hao

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On-Orbit Refueling is of great significance in extending space crafts' lifetime. The problem of minimum-fuel, time-fixed, Peer-to-Peer On-Orbit Refueling mission planning is addressed here with the particular aim of assigning fuel-insufficient satellites to the fuel-sufficient satellites and optimizing each rendezvous trajectory. Constraints including perturbation, communication link, sun illumination, hold points for different rendezvous phases, and sensor switching are considered. A planning model has established as well as a two-level solution method. The upper level deals with target assignment based on fuel equilibrium criterion, while the lower level solves constrained trajectory optimization using special maneuver strategies. Simulations show that the developed method could effectively resolve the Peer-to-Peer On-Orbit Refueling mission planning problem and deal with complex constraints.

Keywords: mission planning, orbital rendezvous, on-orbit refueling, space mission

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Authors: G. A. Gevorgyan, K. A. Ghazaryan, T. H. Derdzyan

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The heavy metal pollution of the soils around the mining area near Shamlugh town and related risks to human health were assessed. The investigations showed that the soils were polluted with heavy metals that can be ranked by anthropogenic pollution degree as follows: Cu>Pb>As>Co>Ni>Zn. The main sources of the anthropogenic metal pollution of the soils were the copper mining area near Shamlugh town, the Chochkan tailings storage facility and the trucks transferring are from the mining area. Copper pollution degree in some observation sites was unallowable for agricultural production. The total non-carcinogenic chronic hazard index (THI) values in some places, including observation sites in Shamlugh town, were above the safe level (THI<1) for children living in this territory. Although the highest heavy metal enrichment degree in the soils was registered in case of copper, the highest health risks to humans especially children were posed by cobalt which is explained by the fact that heavy metals have different toxicity levels and penetration characteristics.

Keywords: Armenia, copper mine, heavy metal pollution of soil, health risks

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Erika Ruíz, Luis Amaya, Diego Carreño

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The main objective of this study is to design a mathematical model for the logistics of mining collection centers in the northern region of the department of Boyacá (Colombia), determining the structure that facilitates the flow of products along the supply chain. In order to achieve this, it is necessary to define a suitable design of the distribution network, taking into account the products, customer’s characteristics and the availability of information. Likewise, some other aspects must be defined, such as number and capacity of collection centers to establish, routes that must be taken to deliver products to the customers, among others. This research will use one of the operation research problems, which is used in the design of distribution networks known as Location Routing Problem (LRP).

Keywords: location routing problem, logistic, mining collection, model

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Nidhin Dani Abraham, T. K. Sri Shilpa

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Asset liability management is an important aspect in banking business. Moreover, the today’s banking is based on BASEL III which strictly regulates on the counterparty default. This paper focuses on prediction and analysis of counter party default risk, which is a type of risk occurs when the customers fail to repay the amount back to the lender (bank or any financial institutions). This paper proposes an approach to reduce the counterparty risk occurring in the financial institutions using an appropriate data mining technique and thus predicts the occurrence of NPA. It also helps in asset building and restructuring quality. Liability management is very important to carry out banking business. To know and analyze the depth of liability of bank, a suitable technique is required. For that a data mining technique is being used to predict the dormant behaviour of various deposit bank customers. Various models are implemented and the results are analyzed of saving bank deposit customers. All these data are cleaned using data cleansing approach from the bank data warehouse.

Keywords: data mining, asset liability management, BASEL III, banking

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Authors: Felermino D. M. A. Ali, S. C. Ng

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E-learning is an evolutionary form of distance learning and has become better over time as new technologies emerged. Today, efforts are still being made to embrace E-learning systems with emerging technologies in order to make them better. Among these advancements, Educational Data Mining (EDM) is one that is gaining a huge and increasing popularity due to its wide application for improving the teaching-learning process in online practices. However, even though EDM promises to bring many benefits to educational industry in general and E-learning environments in particular, its principal drawback is the lack of easy to use tools. The current EDM tools usually require users to have some additional technical expertise to effectively perform EDM tasks. Thus, in response to these limitations, this study intends to design and implement an EDM application framework which aims at automating and simplify the development of EDM in E-learning environment. The application framework introduces a Service-Oriented Architecture (SOA) that hides the complexity of technical details and enables users to perform EDM in an automated fashion. The framework was designed based on abstraction, extensibility, and interoperability principles. The framework implementation was made up of three major modules. The first module provides an abstraction for data gathering, which was done by extending Moodle LMS (Learning Management System) source code. The second module provides data mining methods and techniques as services; it was done by converting Weka API into a set of Web services. The third module acts as an intermediary between the first two modules, it contains a user-friendly interface that allows dynamically locating data provider services, and running knowledge discovery tasks on data mining services. An experiment was conducted to evaluate the overhead of the proposed framework through a combination of simulation and implementation. The experiments have shown that the overhead introduced by the SOA mechanism is relatively small, therefore, it has been concluded that a service-oriented architecture can be effectively used to facilitate educational data mining in E-learning environments.

Keywords: educational data mining, e-learning, distributed data mining, moodle, service-oriented architecture, Weka

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining research discipline bridges the gap between data mining and business process modeling and analysis, it offers the process-centric and end-to-end methods/techniques for analyzing information of real-world process detailed in operational event-logs. In this paper, we have proposed a notation called trace logo for graphically representing control-flow perspective (order of execution of activities) of process. A trace logo consists of a stack of activity names at each position, sizes of the activity name indicates their frequency in the traces and the total height of the activity depicts the information content of the position. A trace logo created from a set of aligned traces generated using Multiple Trace Alignment technique.

Keywords: consensus trace, process mining, multiple trace alignment, trace logo

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Rizka Aisha Rahmi Hariadi, Chao Ou-Yang, Han-Cheng Wang, Rajesri Govindaraju

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As the increasing of medical check-up popularity, there are a huge number of medical check-up data stored in database and have not been useful. These data actually can be very useful for future strategic planning if we mine it correctly. In other side, a lot of patients come with unpredictable coming and also limited available facilities make medical check-up service offered by hospital not maximal. To solve that problem, this study used those medical check-up data to predict patient re-coming. Sequential pattern mining (SPM) and association rules method were chosen because these methods are suitable for predicting patient re-coming using sequential data. First, based on patient personal information the data was grouped into … groups then discriminant analysis was done to check significant of the grouping. Second, for each group some frequent patterns were generated using SPM method. Third, based on frequent patterns of each group, pairs of variable can be extracted using association rules to get general pattern of re-coming patient. Last, discussion and conclusion was done to give some implications of the results.

Keywords: patient re-coming, medical check-up, health examination, data mining, sequential pattern mining, association rules, discriminant analysis

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Authors: Yusra Abdulsalam Alqamati, Ahmed Alkilany

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The teaching staff is a distinct group whose impact is on the educational process and which plays an important role in enhancing the quality of the academic education process. To improve the management effectiveness of the academy, the Teaching Staff Management System (TSMS) proposes that all teacher processes be digitized. Since the BPMN approach can accurately describe the processes, it lacks a clear picture of the process flow map, something that the process mining approach has, which is extracting information from event logs for discovery, monitoring, and model enhancement. Therefore, these two methodologies were combined to create the most accurate representation of system operations, the ability to extract data records and mining processes, recreate them in the form of a Petri net, and then generate them in a BPMN model for a more in-depth view of process flow. Additionally, the TSMS processes will be orchestrated to handle all requests in a guaranteed small-time manner thanks to the integration of the Google Cloud Platform (GCP), the BPM engine, and allowing business owners to take part throughout the entire TSMS project development lifecycle.

Keywords: process mining, BPM, business process model and notation, Petri net, teaching staff, Google Cloud Platform

Procedia PDF Downloads 117

Authors: Zunaira Asif, Zhi Chen

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The sustainable measures on air quality management are recognized as one of the most serious environmental concerns in the mining region. The mining operations emit various types of pollutants which have significant impacts on the environment. This study presents a stochastic control strategy by developing the air pollution control model to achieve a cost-effective solution. The optimization method is formulated to predict the cost of treatment using linear programming with an objective function and multi-constraints. The constraints mainly focus on two factors which are: production of metal should not exceed the available resources, and air quality should meet the standard criteria of the pollutant. The applicability of this model is explored through a case study of an open pit metal mine, Utah, USA. This method simultaneously uses meteorological data as a dispersion transfer function to support the practical local conditions. The probabilistic analysis and the uncertainties in the meteorological conditions are accomplished by Monte Carlo simulation. Reasonable results have been obtained to select the optimized treatment technology for PM_2.5, PM₁₀, NO_x, and SO₂. Additional comparison analysis shows that baghouse is the least cost option as compared to electrostatic precipitator and wet scrubbers for particulate matter, whereas non-selective catalytical reduction and dry-flue gas desulfurization are suitable for NO_x and SO₂ reduction respectively. Thus, this model can aid planners to reduce these pollutants at a marginal cost by suggesting control pollution devices, while accounting for dynamic meteorological conditions and mining activities.

Keywords: air pollution, linear programming, mining, optimization, treatment technologies

Procedia PDF Downloads 179

Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Miroslav Čulík, Petr Gurný

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This paper is aimed at the financial assessment of the hard coal mining in a given region by real option methodology application. Hard coal mining in this mine makes net loss for the owner during the last years due to the long-term unfavourable mining conditions and significant drop in the coal prices during the last years. Management is going to shut down the operation and abandon the project to reduce the loss of the company. The goal is to assess whether the shutting down the operation is the only and correct solution of the problem. Due to the uncertainty in the future hard coal price evolution, the production might be again restarted if the price raises enough to cover the cost of the production. For the assessment, real option methodology is applied, which captures two important aspect of the financial decision-making: risk and flexibility. The paper is structured as follows: first, current state is described and problem is analysed. Next, methodology of real options is described. At last, project is evaluated by applying real option methodology. The results are commented and recommendations are provided.

Keywords: real option, investment, option to abandon, option to shut down and restart, risk, flexibility

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Authors: Carla Palencia-Aguilar

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In order to attain the 2050’s zero emissions goal, it is necessary to know the carbon dioxide changes over time either from pollution to attenuations in the mining industry versus at green zones to establish real goals and redirect efforts to reduce greenhouse effects. Two methods were used to compute the amount of CO2 tons in specific mining zones in Colombia. The former by means of NPP with MODIS MOD17A3HGF from years 2000 to 2021. The latter by using MODIS MYD021KM bands 33 to 36 with maximum values of 644 data points distributed in 7 sites corresponding to surface mineral mining of: coal, nickel, iron and limestone. The green zones selected were located at the proximities of the studied sites, but further than 1 km to avoid information overlapping. Year 2012 was selected for method 2 to compare the results with data provided by the Colombian government to determine range of values. Some data was compared with 2022 MODIS energy values and converted to kton of CO2 by using the Greenhouse Gas Equivalencies Calculator by EPA. The results showed that Nickel mining was the least pollutant with 81 kton of CO2 e.q on average and maximum of 102 kton of CO2 e.q. per year, with green zones attenuating carbon dioxide in 103 kton of CO2 on average and 125 kton maximum per year in the last 22 years. Following Nickel, there was Coal with average kton of CO2 per year of 152 and maximum of 188, values very similar to the subjacent green zones with average and maximum kton of CO2 of 157 and 190 respectively. Iron had similar results with respect to 3 Limestone sites with average values of 287 kton of CO2 for mining and 310 kton for green zones, and maximum values of 310 kton for iron mining and 356 kton for green zones. One of the limestone sites exceeded the other sites with an average value of 441 kton per year and maximum of 490 kton per year, eventhough it had higher attenuation by green zones than a close Limestore site (3.5 Km apart): 371 kton versus 281 kton on average and maximum 416 kton versus 323 kton, such vegetation contribution is not enough, meaning that manufacturing process should be improved for the most pollutant site. By comparing bands 33 to 36 for years 2012 and 2022 from January to August, it can be seen that on average the kton of CO2 were similar for mining sites and green zones; showing an average yearly balance of carbon dioxide emissions and attenuation. However, efforts on improving manufacturing process are needed to overcome the carbon dioxide effects specially during emissions’ peaks because surrounding vegetation cannot fully attenuate it.

Keywords: carbon dioxide, MODIS, surface mining, vegetation

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Authors: Mei-Yi Wu, Shang-Ming Huang

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The online shoppers nowadays often search the product information on the Internet using some keywords of products. To use this kind of information searching model, shoppers should have a preliminary understanding about their interesting products and choose the correct keywords. However, if the products are first contact (for example, the worn clothes or backpack of passengers which you do not have any idea about the brands), these products cannot be retrieved due to insufficient information. In this paper, we discuss and study the applications in E-commerce using image retrieval and text mining techniques. We design a reasonable E-commerce application system containing three layers in the architecture to provide users product information. The system can automatically search and retrieval similar images and corresponding web pages on Internet according to the target pictures which taken by users. Then text mining techniques are applied to extract important keywords from these retrieval web pages and search the prices on different online shopping stores with these keywords using a web crawler. Finally, the users can obtain the product information including photos and prices of their favorite products. The experiments shows the efficiency of proposed system.

Keywords: mobile image retrieval, text mining, product information service system, online marketing

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Authors: Şakir Bingöl, Bayram İslamoğlu, Ebubekir Furkan Tepeli, Fatih Mehmet Karakule, Fatih Küçük, Merve Sena Arpacık, Mustafa Taha Kabar, Muhammet Metin Molak, Osman Emre Turan, Ömer Faruk Yesir, Sıla İnanır

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This study introduces an IoT-based real-time safety system for mining, addressing global safety challenges. The wearable device, seamlessly integrated into miners' jackets, employs LoRa technology for communication and offers real-time monitoring of vital health and environmental data. Unique features include an LCD panel for immediate information display and sound-based location tracking for emergency response. The methodology involves sensor integration, data transmission, and ethical testing. Validation confirms the system's effectiveness in diverse mining scenarios. The study calls for ongoing research to adapt the system to different mining contexts, emphasizing its potential to significantly enhance safety standards in the industry.

Keywords: mining safety, internet of things, wearable technology, LoRa, RFID tracking, real-time safety system, safety alerts, safety measures

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Authors: Mohamed Abdel-Monem, Gamal Sowilam, Omar Hegazy

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This paper presents a technical assessment of an electric vehicle with two independent rear-wheel motor and an improved traction control system. The electric differential and the control strategy have been implemented to assure that in a straight trajectory, the two rear-wheels run exactly at the same speed, considering the same/different road conditions under the left and right side of the wheels. In case of turning to right/left, the difference between the two rear-wheels speeds assures a vehicle trajectory without sliding, thanks to a harmony between the electric differential and the control strategy. The present article demonstrates a complete model and analysis of a traction control system, considering four different traction scenarios, for two independent rear-wheels motors for electric vehicles. Furthermore, the vehicle model, including wheel dynamics, load forces, electric differential, and control strategy, is designed and verified by using MATLAB/Simulink environment.

Keywords: electric vehicle, energy saving, multi-motor, electric differential, simulation and control

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Authors: Dogaru-Ulieru Valentin, Sălișteanu Ioan Corneliu, Ardeleanu Mihăiță Nicolae, Broscăreanu Ștefan, Sălișteanu Bogdan, Mihai Mihail

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The SiaMOTO system is a communications and data processing platform for vehicle traffic. The human factor is the most important factor in the generation of this data, as the driver is the one who dictates the trajectory of the vehicle. Like any trajectory, specific parameters refer to position, speed and acceleration. Constant knowledge of these parameters allows complex analyses. Roadways allow many vehicles to travel through their confined space, and the overlapping trajectories of several vehicles increase the likelihood of collision events, known as road accidents. Any such event has causes that lead to its occurrence, so the conditions for its occurrence are known. The human factor is predominant in deciding the trajectory parameters of the vehicle on the road, so monitoring it by knowing the events reported by the DiaMOTO device over time, will generate a guide to target any potentially high-risk driving behavior and reward those who control the driving phenomenon well. In this paper, we have focused on detailing the communication infrastructure of the DiaMOTO device with the traffic data collection server, the infrastructure through which the database that will be used for complex AI/DLM analysis is built. The central element of this description is the data string in CODEC-8 format sent by the DiaMOTO device to the SiaMOTO collection server database. The data presented are specific to a functional infrastructure implemented in an experimental model stage, by installing on a number of 50 vehicles DiaMOTO unique code devices, integrating ADAS and GPS functions, through which vehicle trajectories can be monitored 24 hours a day.

Keywords: DiaMOTO, Codec-8, ADAS, GPS, driver monitoring

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Authors: Rezky Rifaini, Raden Bagus Fajriya Hakim

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This research was conducted at the Bolo primary health Care in Bima Regency. The purpose of the research is to find out the association pattern that is formed of medical record database from Bolo Primary health care’s patient. The data used is secondary data from medical records database PHC. Sequential pattern mining technique is the method that used to analysis. Transaction data generated from Patient_ID, Check_Date and diagnosis. Sequential Pattern Discovery Algorithms Using Equivalent Classes (SPADE) is one of the algorithm in sequential pattern mining, this algorithm find frequent sequences of data transaction, using vertical database and sequence join process. Results of the SPADE algorithm is frequent sequences that then used to form a rule. It technique is used to find the association pattern between items combination. Based on association rules sequential analysis with SPADE algorithm for minimum support 0,03 and minimum confidence 0,75 is gotten 3 association sequential pattern based on the sequence of patient_ID, check_Date and diagnosis data in the Bolo PHC.

Keywords: diagnosis, primary health care, medical record, data mining, sequential pattern mining, SPADE algorithm

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Authors: Jason West

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Low profitability in agriculture production along with increasing scrutiny over environmental effects is limiting food production at scale. In contrast, the mining sector offers access to resources including energy, water, transport and chemicals for food production at low marginal cost. Scalable agricultural production can benefit from the nexus of resources (water, energy, transport) offered by mining activity in remote locations. A decision support bioeconomic model for controlled environment vertical farms was used. Four submodels were used: crop structure, nutrient requirements, resource-crop integration, and economic. They escalate to a macro mathematical model. A demonstrable dynamic systems framework is needed to prove productive outcomes are feasible. We demonstrate a generalized bioeconomic macro model for controlled environment production systems in minesites using systems dynamics modeling methodology. Despite the complexity of bioeconomic modelling of resource-agricultural dynamic processes and interactions, the economic potential greater than general economic models would assume. Scalability of production as an input becomes a key success feature.

Keywords: crop production systems, mathematical model, mining, agriculture, dynamic systems

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Authors: A. De Serres-Lafontaine, S. Porlier, K. Poitras

Abstract:

Introduction: Placement is, without doubt, a challenging experience for both foster children and biological parents who witness their child being removed from their care. Yet, few studies have examined parenting in such a context through critical parental skills such as parental sensitivity and commitment. Sensitivity is described as the capacity of parents to respond accurately to their child’s needs in a warm, predictable and consistent way, whereas commitment is the ability of the parent to get involved physically and emotionally in an enduring relationship with his child. The research confirms the important role of parental sensitivity and parental commitment on child development following placement in foster care. Nevertheless, these studies were mainly conducted with foster parents, and few studies have examined these components of parenthood with biological parents. Method: This study evolves in two times. At first, 17 parents participated throughout a 90-minutes interview. It allowed to collect information regarding the sociodemographic situation, contacts, placement trajectory. Parental sensitivity is observed during a supervised parent-child contact. The second time occurred one to two years later and implied an at-home 90-minutes interview where we updated the information from the first interview and were able to assess the level of parental commitment. In this ongoing part of the study, five parents have already participated in implying the rest of them remain to be interviewed in the coming months - from October through December 2018. Results: Descriptive analysis from the first part of the study suggests the examination of two groups: 11 children have been reunified whereas six are still in foster care. Qualitative analysis allows to compare themes of sensitivity and commitment regarding if the reunification project occurs or not. Preliminary analysis about thematic content shows key components of parental commitment through parent’s reveal of the way they nurture a relationship with their child. Furthermore, preliminary analysis suggests that parental sensitivity is not associated with family reunification (r = 0,11, p = 0,74). Further analysis will be assessed with the date from the second part of the study to examine the potential association between commitment and reunification. Discussion: Parental sensitivity and commitment are fundamental to the well-being of the child in a placement trajectory. They need to be understood better as two different complex concepts and as two parenting skills that might have a way of echoing to one another when engaged in a specific context. Above all, a more accurate comprehension of parenting in a placement trajectory allows to sustain adequate intervention practices for birth parents and could change the way parental adequacy is assessed when reaching for reunification.

Keywords: child welfare, foster care, intervention practices, parenthood

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Authors: Wael M. Elawady, Mohamed F. Asar, Amany M. Sarhan

Abstract:

This paper adopts a continuous sliding mode control scheme for trajectory tracking control of robot manipulators with structured and unstructured uncertain dynamics and external disturbances. In this algorithm, the equivalent control in the conventional sliding mode control is replaced by a PID control action. Moreover, the discontinuous switching control signal is replaced by a continuous proportional-integral (PI) control term such that the implementation of the proposed control algorithm does not require the prior knowledge of the bounds of unknown uncertainties and external disturbances and completely eliminates the chattering phenomenon of the conventional sliding mode control approach. The closed-loop system with the adopted control algorithm has been proved to be globally stable by using Lyapunov stability theory. Numerical simulations using the dynamical model of robot manipulators with modeling uncertainties demonstrate the superiority and effectiveness of the proposed approach in high speed trajectory tracking problems.

Keywords: PID, robot, sliding mode control, uncertainties

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

Abstract:

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: Chahrazed Zouaoui

Abstract:

In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

Procedia PDF Downloads 359