Search results for: deep wells

Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

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Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

Procedia PDF Downloads 58

Authors: Yifan Fan, Xudong Luo, Pingping Lin

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An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: deep learning, generative, knowledge, response generation, retrieval

Procedia PDF Downloads 123

Authors: Mohamed Ahmed Khalil

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The number of deep well anode ground beds (GBs) have been retrieved due to unoperated anode chains. New identical magnetite anode chains (MAC) have been installed at Raslanuf complex impressed current Cathodic protection (ICCP) system, distributed at different plants (Utility, ethylene and polyethylene). All problems associated with retrieving and installation of MACs have been discussed, rectified and presented. All GB-associated severely corroded wellhead casings were well maintained and/or replaced by new fabricated and modified ones. The main cause of the wellhead casing's severe internal corrosion was discussed and the conducted remedy action to overcome future corrosion problems is presented. All GB-connected anode junction boxes (AJBs) and shunts were closely inspected, maintained and necessary replacement and/or modifications were carried out on shunts. All damaged GB concrete foundations (CF) have been inspected and completely replaced. All GB-associated Transformer-Rectifiers Units (TRU) were subjected to thorough inspection and necessary maintenance was performed on each individual TRU. After completion of all MACs and TRU maintenance activities, each cathodic protection station (CPS) has been re-operated, alternative current (AC), direct current (DC), voltage and structure to soil potential (S/P) measurements have been conducted, recorded and all obtained test results are presented. DC current outputs have been adjusted and DC current outputs of each MAC have been recorded for each GB AJB.

Keywords: magnetite anodes, deep well, ground beds, cathodic protection, transformer rectifier, impressed current, junction boxes

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Authors: Mehmet Halkaci, Ekrem Öztürk, Mevlüt Türköz, H. Selçuk Halkacı

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Hydromechanical deep drawing (HMD) process is an advanced manufacturing process used to form deep parts with only one forming step. In this process, sheet metal blank can be drawn deeper by means of fluid pressure acting on sheet surface in the opposite direction of punch movement. High limiting drawing ratio, good surface quality, less springback characteristic and high dimensional accuracy are some of the advantages of this process. The performance of the HMD process is affected by various process parameters such as fluid pressure, blank holder force, punch-die radius, pre-bulging pressure and height, punch diameter, friction between sheet-die and sheet-punch. The fluid pressure and bank older force are the main loading parameters and affect the formability of HMD process significantly. The punch diameter also influences the limiting drawing ratio (the ratio of initial sheet diameter to punch diameter) of the sheet metal blank. In this research, optimal loading (fluid pressure and blank holder force) profiles were determined for AA 5754-O sheet material through fuzzy control algorithm developed in previous study using LS-DYNA finite element analysis (FEA) software. In the preceding study, the fuzzy control algorithm was developed utilizing geometrical criteria such as thinning and wrinkling. In order to obtain the final desired part with the developed algorithm in terms of the punch diameter requested, the effect of punch diameter, which is the one of the process parameters, on loading profiles was investigated separately using blank thickness of 1 mm. Thus, the practicality of the previously developed fuzzy control algorithm with different punch diameters was clarified. Also, thickness distributions of the sheet metal blank along a curvilinear distance were compared for the FEA in which different punch diameters were used. Consequently, it was found that the use of different punch diameters did not affect the optimal loading profiles too much.

Keywords: Finite Element Analysis (FEA), fuzzy control, hydromechanical deep drawing, optimal loading profiles, punch diameter

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Authors: Kruti Jaruriya

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Distribution of arsenic (As) and its compound and related toxicology are serious concerns. This is particularly so since millions worldwide are suffering from toxicity due to drinking of As-contaminated groundwater. The Bengal delta plain, formed by the Ganga– Padma–Meghna–Brahmaputra river basin, covering several districts of West Bengal, India and Bangladesh is considered as the worst As affected alluvial basin. However, some equally affected, if not more, areas are emerging in upper Brahmaputra plains. The present study was carried out to examine As contamination trends in the worst affected part of Assam, India. Arsenic (As) mobilization to the groundwater of Brahmaputra floodplains was investigated in Titabor, Jorhat District, located in the North Eastern part of India. The groundwater and the aquifer geochemistry were characterized. The groundwater is characterized by high dissolved Fe, Mn, and HCO-3 and low concentrations of NO-3 and SO2-4 indicating anoxic conditions prevailing in the groundwater. Fifty groundwater samples collected from shallow and deep tubewells of Titabor, Jorhat district (Assam) were examined. Along with total As, examination of concentration levels of other key parameters, viz., pH, EC, Fe, Mn , Mg2+, Ca2+, Na+, K+, PO43- , HCO-3 , NO3- ,Cl - and SO42- was also carried out. In respect to the permissible guideline of World Health Organization (WHO: As 0.01 ppm, Fe 1.0 ppm, and Mn 0.3 ppm for potable water), the range of As concentration in the groundwater varied from 0.014 to 0.604 mg/L with mean concentration 0.184 mg/L. The present study showed that out of the 50 groundwater samples,100%, 54%, and 42% were found contaminated with higher metal contents (for total As, Fe, and Mn, respectively). The results of hydrogeochemical study revealed that the reductive dissolution of MnOOH and FeOOH represents an important mechanism of arsenic release in the study area along with major cations playing an important role in leaching of As into the groundwater. Arsenic released by oxidation of pyrite, as water levels are drawn down and air enters the aquifer, contributes negligibly to the problem of As pollution. Identification of the mechanism of As release to groundwater helps to provide a framework to guide the placement of new water wells so that they will have acceptable concentrations of As.

Keywords: arsenic, assam, brahmaputra floodplain, groundwater, hydrogeochemistry

Procedia PDF Downloads 300

Authors: Sanjay Patil, Shakil A. Kagzi, Harit K. Raval

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Tailor Welded Blanks (TWB) are utilized in automotive industries widely because of their advantage of weight and cost reduction and maintaining required strength and structural integrity. TWB consist of two or more sheet having dissimilar or similar material and thickness; welded together to form a single sheet before forming it to desired shape. Forming of the tailor welded blank is affected by ratio of thickness of blanks, ratio of their strength, etc. mainly due to in-homogeneity of material. In the present work the relative effect of these parameters on weld line movement is studied during deep drawing of TWB using FE simulation using HYPERWORKS. The simulation is validated with results from the literature. Simulations were than performed based on Taguchi orthogonal array followed by the ANOVA analysis to determine the significance of these parameters on forming of TWB.

Keywords: ANOVA, deep drawing, Tailor Welded Blank (TWB), weld line movement

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Authors: Anil Koklu, Amin Mansoorifar, Ali Beskok

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Dielectric spectroscopy (DS) is a noninvasive, label free technique for a long term real-time measurements of the impedance spectra of biological cells. DS enables characterization of cellular dielectric properties such as membrane capacitance and cytoplasmic conductivity. We have developed a lab-on-a-chip device that uses an electro-activated microwells array for loading, DS measurements, and unloading of biological cells. We utilized from dielectrophoresis (DEP) to capture target cells inside the wells and release them after DS measurement. DEP is a label-free technique that exploits differences among dielectric properties of the particles. In detail, DEP is the motion of polarizable particles suspended in an ionic solution and subjected to a spatially non-uniform external electric field. To the best of our knowledge, this is the first microfluidic chip that combines DEP and DS to analyze biological cells using electro-activated wells. Device performance is tested using two different cell lines of prostate cancer cells (RV122, PC-3). Impedance measurements were conducted at 0.2 V in the 10 kHz to 40 MHz range with 6 s time resolution. An equivalent circuit model was developed to extract the cell membrane capacitance and cell cytoplasmic conductivity from the impedance spectra. We report the time course of the variations in dielectric properties of PC-3 and RV122 cells suspended in low conductivity medium (LCB), which enhances dielectrophoretic and impedance responses, and their response to sudden pH change from a pH of 7.3 to a pH of 5.8. It is shown that microfluidic chip allowed online measurements of dielectric properties of prostate cancer cells and the assessment of the cellular level variations under external stimuli such as different buffer conductivity and pH. Based on these data, we intend to deploy the current device for single cell measurements by fabricating separately addressable N × N electrode platforms. Such a device will allow time-dependent dielectric response measurements for individual cells with the ability of selectively releasing them using negative-DEP and pressure driven flow.

Keywords: microfluidic, microfabrication, lab on a chip, AC electrokinetics, dielectric spectroscopy

Procedia PDF Downloads 138

Authors: Ameer A. Jebur, William Atherton, Rafid M. Alkhaddar, Edward Loffill

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Understanding the behaviour of the piles under the action of the independent lateral loads and the precise prediction of the capacity of piles subjected to different lateral loads are vital topics in foundation design and analysis. Moreover, the laterally loaded behaviour of deep foundations penetrated in cohesive and non-cohesive soils is basically analysed by the Winkler Model (beam on elastic foundation), in which the interaction between the pile embedded depth and contacted soil is simulated by nonlinear p–y curves. The presence of many approaches to interpret the behaviour of soil-pile interaction has resulted in numerous outputs and indicates that no general approach has yet been adopted. The current study presents the result of numerical modelling of the behaviour of steel tubular piles (25.4mm) outside diameter with various embedment depth-to-diameter ratios (L/d) embedded in a sand calibrated chamber of known relative density. The study revealed that the shear strength parameters of the sand specimens and the (L/d) ratios are the most significant factor influencing the response of the pile and its capacity while taking into consideration the complex interaction between the pile and soil. Good agreement has been achieved when comparing the application of this modelling approach with experimental physical modelling carried out by another researcher.

Keywords: deep foundations, slenderness ratio, soil-pile interaction, winkler model (beam on elastic foundation), non-cohesive soil

Procedia PDF Downloads 286

Authors: S. Jagadeesh Kumar

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This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

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Authors: Akshat Mathur, Sunil Kumar Khatri

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This paper presents brief about how the use of Artificial intelligence in respect to GUI testing can reduce workload by using DL-fueled method. This paper also discusses about how graphical user interface and event driven software testing can derive benefits from the use of AI techniques. The use of AI techniques not only reduces the task and work load but also helps in getting better output than manual testing. Although results are same, but the use of Artifical intelligence techniques for GUI testing has proven to provide ideal results. DL-fueled framework helped us to find imperfections of the entire webpage and provides test failure result in a score format between 0 and 1which signifies that are test meets it quality criteria or not. This paper proposes DL-fueled method which helps us to find the genuine GUI bugs and defects and also helped us to scale the existing labour-intensive and skill-intensive methodologies.

Keywords: graphical user interface, GUI, artificial intelligence, deep learning, ML technology

Procedia PDF Downloads 158

Authors: Nejmeddine Ouhichi, Fethi Lachaal, Radhouane Hamdi, Olivier Grunberger

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Located in Cap Bon peninsula (Tunisia), the Lebna dam was built in 1987 to balance local water salt intrusion taking place in the coastal aquifer of Korba. The first intention was to reduce coastal groundwater over-pumping by supplying surface water to a large irrigation system. The unpredicted beneficial effect was recorded with the occurrence of a direct localized recharge to the coastal aquifer by leakage through the geological material of the southern bank of the lake. The hydrological balance of the reservoir dam gave an estimation of the annual leakage volume, but dynamic processes and sound quantification of recharge inputs are still required to understand the localized effect of the recharge in terms of piezometry and quality. Present work focused on simulating the recharge process to confirm the hypothesis, and established a sound quantification of the water supply to the coastal aquifer and extend it to multi-annual effects. A spatial frame of 30km² was used for modeling. Intensive outcrops and geophysical surveys based on 68 electrical resistivity soundings were used to characterize the aquifer 3D geometry and the limit of the Plio-quaternary geological material concerned by the underground flow paths. Permeabilities were determined using 17 pumping tests on wells and piezometers. Six seasonal piezometric surveys on 71 wells around southern reservoir dam banks were performed during the 2019-2021 period. Eight monitoring boreholes of high frequency (15min) piezometric data were used to examine dynamical aspects. Model boundary conditions were specified using the geophysics interpretations coupled with the piezometric maps. The dam-groundwater flow model was performed using Visual MODFLOW software. Firstly, permanent state calibration based on the first piezometric map of February 2019 was established to estimate the permanent flow related to the different reservoir levels. Secondly, piezometric data for the 2019-2021 period were used for transient state calibration and to confirm the robustness of the model. Preliminary results confirmed the temporal link between the reservoir level and the localized recharge flow with a strong threshold effect for levels below 16 m.a.s.l. The good agreement of computed flow through recharge cells on the southern banks and hydrological budget of the reservoir open the path to future simulation scenarios of the dilution plume imposed by the localized recharge. The dam reservoir-groundwater flow-model simulation results approve a potential for storage of up to 17mm/year in existing wells, under gravity-feed conditions during level increases on the reservoir into the three years of operation. The Lebna dam groundwater flow model characterized a spatiotemporal relation between groundwater and surface water.

Keywords: leakage, MODFLOW, saltwater intrusion, surface water-groundwater interaction

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Authors: Hatem K. Hamed, Mohamed S. Hrouda

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The Middle Paleocene Lower Beda Formation was deposited in a ramp system with local shoaling. The main constituent is limestone, with subordinate dolomites and Shales. Reservoir quality is largely influenced by depositional environments and diagenesis processes. Generally the reservoir quality of Lower Beda Formation is low risk on the Inferred Horst and in the Southern Shelf where the Lower Beda formation comprises mainly of calcarenties. In the vicinity of the well GG1 the Lower Beda comprise mainly of argillaceous calcilutites and shale. The reservoir quality gradually improves from high risk to moderate risk towards KK1, LL1 and NN1 wells. The average gross thickness of Lower Beda Formation is about 300 ft. The net thickness varies from about 270 ft. in the E1-71 well to about 30 ft. in the vicinity of GG1-71 well. The net thickest of Lower Beda form a NNW-SSW trend with an average of 250 ft. the change in facies is due to change in the depositional environment, from lagoonal to shoal barrier to open marine affected the reservoir quality. The Upper Cretaceous Sirte Shale is the main source rock. It is developed within the three troughs surrounding the study area. S-Marada Trough to the N- E, Gerad Trough to the N N-W, and Abu Tummym Sub-basin to the S-W of the Inferred Horst. Sirte shale reaches 1000ft, of organically rich section. It has good organic contents over large area 2% to 3%. Hydrocarbon shows were encountered in several wells in Beda Formation this is an indication of vertical and lateral migration of hydrocarbon. The overlying Upper Paleocene Khalifa Formation is a transgressive shale, it is an effective regional top seal. Lithofacies variations in Khalifa Shale, from shales to limestones in the southern shelf in R1-71 well approximately 50-75% of the secession is limestone. About 47 million barrel of hydrocarbon recoverable reserves is expected to be trapped in structural and stratigraphic traps in Beda Formation in the study area.

Keywords: Sirte basin, Beda formation, concession 71, petroleum play fairway analysis

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Authors: Mhenga Agneta, Li Zhaomin, Zhang Chao

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When velocity is high enough, gas can entrain fluid and carry to the surface, but as time passes by, velocity drops to a critical point where fluids will start to hold up in the tubing and cause liquid loading which prevents gas production and may lead to the death of the well. Foam injection is widely used as one of the methods to unload liquid. Since wells have different characteristics, it is not guaranteed that foam can be applied in all of them and bring successful results. This research presents a technology to optimize the efficiency of foam to unload liquid by air compression. Two methods are used to explain optimization; (i) mathematical formulas are used to solve and explain the myth of how density and critical velocity could be minimized when air is compressed into foaming agents, then the relationship between flow rates and pressure increase which would boost up the bottom hole pressure and increase the velocity to lift liquid to the surface. (ii) Experiments to test foam carryover capacity and stability as a function of time and surfactant concentration whereby three surfactants anionic sodium dodecyl sulfate (SDS), nonionic Triton 100 and cationic hexadecyltrimethylammonium bromide (HDTAB) were probed. The best foaming agents were injected to lift liquid loaded in a created vertical well model of 2.5 cm diameter and 390 cm high steel tubing covered by a transparent glass casing of 5 cm diameter and 450 cm high. The results show that, after injecting foaming agents, liquid unloading was successful by 75%; however, the efficiency of foaming agents to unload liquid increased by 10% with an addition of compressed air at a ratio of 1:1. Measured values and calculated values were compared and brought about ± 3% difference which is a good number. The successful application of the technology indicates that engineers and stakeholders could bring water flooded gas wells back to production with optimized results by firstly paying attention to the type of surfactants (foaming agents) used, concentration of surfactants, flow rates of the injected surfactants then compressing air to the foaming agents at a proper ratio.

Keywords: air compression, foaming agents, gas well, liquid loading

Procedia PDF Downloads 126

Authors: Khalil Bachiri, Ali Yahyaouy, Nicoleta Rogovschi

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Improved user experience is a goal of contemporary recommender systems. Recommender systems are starting to incorporate reinforcement learning since it easily satisfies this goal of increasing a user’s reward every session. In this paper, we examine the most effective Reinforcement Learning agent tactics on the Movielens (1M) dataset, balancing precision and a variety of recommendations. The absence of variability in final predictions makes simplistic techniques, although able to optimize ranking quality criteria, worthless for consumers of the recommendation system. Utilizing the stochasticity of Leonard-Ornstein processes, our suggested strategy encourages the agent to investigate its surroundings. Research demonstrates that raising the NDCG (Discounted Cumulative Gain) and HR (HitRate) criterion without lowering the Ornstein-Uhlenbeck process drift coefficient enhances the diversity of suggestions.

Keywords: recommender systems, reinforcement learning, deep learning, DDPG, Leonard-Ornstein process

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Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian

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In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.

Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning

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Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

Procedia PDF Downloads 55

Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

Procedia PDF Downloads 119

Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Amira Rjiba, Mohamed Ghanmi, Tahar Aifa, Achref Boulares

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This study, involving the interpretation of geological outcrops data (structures, and lithostratigraphiec colones) and subsurface structures (seismic and gravimetric data) help us to identify and precise (i) the lithology of the sedimentary formations between the Aptian and the recent formations, (ii) to differentiate the sedimentary formations it from the salt-bearing Triassic (iii) and to specify the major structures though the tectonics effects having affected the region during its geological evolution. By placing our study area placed in the context of Tunisia, located on the southern margin of the Tethys show us through tectonic traces and structural analysis conducted, that this area was submitted during the Triassic perio at an active rifting triggered extensional tectonic events and extensive respectively in the Cretaceous and Paleogene. Lithostratigraphic correlations between outcrops and seismic data sets on those of six oil wells conducted in the region have allowed us to better understand the structural complexity and the role of different tectonic faults having contributed to the current configuration, and marked by the current rifts. Indeed, three directions of NW-SE faults, NNW-SSE to NS and NE-SW to EW had a major role in the genesis of folds and open ditches collapse of NW-SE direction. These results were complemented by seismic reflection data to clarify the geometry of the southern and western areas of Kalaa Khasba ditch. The eight selected seismic lines for this study allowed to characterize the main structures, with isochronous maps, contour and isovitesse of Serdj horizon that presents the main reservoir in the region. The line L2, keyed by the well 6, helped highlight the NW-SE compression that has resulted in persistent discrepancies widely identifiable in its lithostratigraphic column. The gravity survey has confirmed the extension of most of the accidents deep subsurface whose activity seems to go far. Gravimetry also reinforced seismic interpretation confirming, at the L2 well, that both SW and NE flank of the moat are two opposite faults and trace the boundaries of NNW-SSE direction graben whose sedimentation of Mio-Pliocene age and Quaternary.

Keywords: graben, graben collapse, gravity, Kalat Es Senan, seismic, tectogenesis

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Mohammad Parvaresh, Mahdi Babaee, Bahareh Arghand, Davood Nourmohammadi

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Environmental degradation is caused by social inequalities and the inappropriate use of nature and a factor in the violation of human rights. Indeed, the right to a safe, healthy and ecologically-balanced environment is an independent human right. Therefore, the relationship between human rights and environmental protection is crucial for the study of social justice and sustainable development, and environmental problems are a result of the failure to realize social and economic justice. In this regard, 'article 50 of the constitution of the Islamic Republic of Iran as a general principle have many of the concepts of sustainable development, including: the growth and improvement of human life, the rights of present and future generations, and the integrity of the inner and outer generation, the prohibition of any environmental degradation'. Also, Charter on Citizen’s Rights, which was conveyed by the President of the Islamic Republic of Iran, Mr. Rouhani refers to the right to a healthy environment and sustainable development. In this regard in 2013, Tehran Province Water and Wastewater Co. defined a plan called 'Tehran’s Health Line' was includes Western and Eastern part by about 26 kilometers of water transferring pipelines varied 1000 to 2000 mm diameters. This project aims to: (1) Transfer water from the northwest water treatment plant to the southwest areas, which suffer from qualitative and quantitative water, in order to mix with the improper wells’ water; (2) Reducing the water consumption provided by harvesting from wells which results in improving the underground water resources, causing the large settlements and stopping the immigrating slums into the center or north side of the city. All of the financial resources accounted for 53,000,000 US$ which is mobilized by Tehran Province Water and Wastewater Co. to expedite the work. The present study examines the Tehran Health Line plan and the purpose of implementation of this plan to achieve environmental protection, environmental justice and citizenship rights for all people who live in Tehran.

Keywords: environmental justice, international environmental law, erga omnes, charter on citizen's rights, Tehran health line

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Rajnarayan Damor, Gayatri Swarnakar

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Fasciola gigantica are present in the biliary ducts of liver and gall bladder of domestic buffaloes. They are very harmful and causes significant lose to live stock owners, on account of poor growth and lower productivity of domestic buffaloes. Synthetic veterinary drugs have been used to eliminate parasites from cattle but these drugs are unaffordable and inaccessible for poor cattle farmers. The in vitro anthelmintic effect of Citrullus colocynthis fruit extract against Fasciola gigantica parasites were observed by light and scanning electron microscopy. Fruit extracts of C. colocynthis exhibit highest mortality 100% at 50 mg/ml in 15th hour of exposure. The oral and ventral sucker appeared to be slightly more swollen than control and synthetic drug albendazole. The tegument showed submerged spines by the swollen tegument around them. The tegument of the middle region showed deep furrows, folding and submerged spines which either lied very flat against the surface or had become submerged in the tegument by the swollen tegument around them leaving deep furrows. Posterior region showed with deep folding in the tegument, completely disappearance of spines and swelling of the tegument led to completely submerged spines leaving spine socket. The present study revealed that fruit extracts of Citrullus colocynthis found to be potential sources for novel anthelmintic and justify their ethno-veterinary use.

Keywords: anthelmintic, buffalo, Citrullus colocynthis, Fasciola gigantica, mortality, tegument

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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World-wide spread of coronavirus grows the concern about planning for the excess demand of hospital services in response to COVID-19 pandemic. The surge in the hospital services demand beyond the current capacity leads to shortage of ICU beds and ventilators in some parts of US. In this study, we forecast the required number of hospital beds and possible shortage of beds in US during COVID-19 pandemic to be used in the planning and hospitalization of new cases. In this paper, we used a data on COVID-19 deaths and patients’ hospitalization besides the data on hospital capacities and utilization in US from publicly available sources and national government websites. we used a novel ensemble modelling of deep learning networks, based on stacking different linear and non-linear layers to predict the shortage in hospital beds. The results showed that our proposed approach can predict the excess hospital beds demand very well and this can be helpful in developing strategies and plans to mitigate this gap.

Keywords: COVID-19, deep learning, ensembled models, hospital capacity planning

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Authors: Kyoungrean Kim

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Importance of deep-sea mineral resources is dramatically increasing due to the depletion of land mineral resources corresponding to increasing human’s economic activities. Korea has acquired exclusive exploration licenses at four areas which are the Clarion-Clipperton Fracture Zone in the Pacific Ocean (2002), Tonga (2008), Fiji (2011) and Indian Ocean (2014). The preparation for commercial mining of Nautilus minerals (Canada) and Lockheed martin minerals (USA) is expected by 2020. The London Protocol 1996 (LP) under International Maritime Organization (IMO) and International Seabed Authority (ISA) will set environmental guidelines for deep-sea mining until 2020, to protect marine environment. In this research, the applicability of washing/extraction treatment for the remediation of deep-sea mining tailings was mainly evaluated in order to present preliminary data to develop practical remediation technology in near future. Polymetallic nodule samples were collected at the Clarion-Clipperton Fracture Zone in the Pacific Ocean, then stored at room temperature. Samples were pulverized by using jaw crusher and ball mill then, classified into 3 particle sizes (> 63 µm, 63-20 µm, < 20 µm) by using vibratory sieve shakers (Analysette 3 Pro, Fritsch, Germany) with 63 µm and 20 µm sieve. Only the particle size 63-20 µm was used as the samples for investigation considering the lower limit of ore dressing process which is tens to 100 µm. Rhamnolipid and sodium alginate as biosurfactant and aluminum sulfate which are mainly used as flocculant were used as environmentally friendly additives. Samples were adjusted to 2% liquid with deionized water then mixed with various concentrations of additives. The mixture was stirred with a magnetic bar during specific reaction times and then the liquid phase was separated by a centrifugal separator (Thermo Fisher Scientific, USA) under 4,000 rpm for 1 h. The separated liquid was filtered with a syringe and acrylic-based filter (0.45 µm). The extracted heavy metals in the filtered liquid were then determined using a UV-Vis spectrometer (DR-5000, Hach, USA) and a heat block (DBR 200, Hach, USA) followed by US EPA methods (8506, 8009, 10217 and 10220). Polymetallic nodule was mainly composed of manganese (27%), iron (8%), nickel (1.4%), cupper (1.3 %), cobalt (1.3%) and molybdenum (0.04%). Based on remediation standards of various countries, Nickel (Ni), Copper (Cu), Cadmium (Cd) and Zinc (Zn) were selected as primary target materials. Throughout this research, the use of rhamnolipid was shown to be an effective approach for removing heavy metals in samples originated from manganese nodules. Sodium alginate might also be one of the effective additives for the remediation of deep-sea mining tailings such as polymetallic nodules. Compare to the use of rhamnolipid and sodium alginate, aluminum sulfate was more effective additive at short reaction time within 4 h. Based on these results, sequencing particle separation, selective extraction/washing, advanced filtration of liquid phase, water treatment without dewatering and solidification/stabilization may be considered as candidate technologies for the remediation of deep-sea mining tailings.

Keywords: deep-sea mining tailings, heavy metals, remediation, extraction, additives

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Authors: Yildiray Korkmaz, Mehmet Aksoy

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In today's operations, countries want to reach their aims in the shortest way due to economical, political and humanitarian aspects. The most effective way of achieving this goal is to be able to penetrate strategic targets. Strategic targets are generally located deep inside of the countries and are defended by modern and efficient surface to air missiles (SAM) platforms which are operated as integrated with Intelligence, Surveillance and Reconnaissance (ISR) systems. On the other hand, these high valued targets are buried deep underground and hardened with strong materials against attacks. Therefore, to penetrate these targets requires very detailed intelligence. This intelligence process should include a wide range that is from weaponry to threat assessment. Accordingly, the framework of the attack package will be determined. This mission package has to execute missions in a high threat environment. The way to minimize the risk which depends on loss of life is to use packages which are formed by UAVs. However, some limitations arising from the characteristics of UAVs restricts the performance of the mission package consisted of UAVs. So, the mission package should be formed with UAVs under the leadership of a fifth generation manned aircraft. Thus, we can minimize the limitations, easily penetrate in the deep inside of the enemy territory with minimum risk, make a decision according to ever-changing conditions and finally destroy the strategic targets. In this article, the strengthens and weakness aspects of UAVs are examined by SWOT analysis. And also, it revealed features of a mission package and presented as an example what kind of a mission package we should form in order to get marginal benefit and penetrate into strategic targets with the development of autonomous mission execution capability in the near future.

Keywords: UAV, autonomy, mission package, strategic attack, mission planning

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Authors: Maham Malik, Amjad Ali, Muhammad Asif

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Current study aims to examine the relationship of emotional labor strategies - deep acting and surface acting - with employees' job satisfaction, organizational commitment and intentions to quit. The study also examines the mediating role of job satisfaction and organizational commitment for relationship of emotional labor strategies with intentions to quit. Data were conveniently collected from 307 nurses by using self-administered questionnaire. Linear regression test was applied to find the relationship between the variables. Mediation was checked through Baron and Kenny Model and Sobel test. Results prove the existence of partial mediation of job satisfaction between the emotional labor strategies and quitting intentions. The study recommends that deep acting should be promoted because it is positively associated with quality of work life, work engagement and organizational citizenship behavior of employees.

Keywords: emotional labor strategies, intentions to quit, job satisfaction, organizational commitment, nursing

Procedia PDF Downloads 132