Search results for: multi-agent double deep Q-network (MADDQN)

Authors: Abd Elrahman Elgamala, N. Darwish, I. Bondouk, Sh. Hamada

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Available experimental angular distributions for ⁶Li elastically scattered from ¹⁶O nucleus in the energy range 13.0–50.0 MeV are investigated and reanalyzed using optical model of the conventional phenomenological potential and also using double folding optical model of different interaction models: DDM3Y1, CDM3Y1, CDM3Y2, and CDM3Y3. All the involved models of interaction are of M3Y Paris except DDM3Y1 which is of M3Y Reid and the main difference between them lies in the different values for the parameters of the incorporated density distribution function F(ρ). We have extracted the renormalization factor N_R for ⁶Li+¹⁶O nuclear system in the energy range 13.0–50.0 MeV using the aforementioned interaction models.

Keywords: elastic scattering, optical model, folding potential, density distribution

Procedia PDF Downloads 131

Authors: Chiau Yuan Lim, Hayyiratul Fatimah Mohd Zaid, Fai Kait Chong

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Deep Eutectic Solvent (DES) is used in various applications due to its simplicity in synthesis procedure, biodegradable, inexpensive and easily available chemical ingredients. Graphene Oxide is a popular catalyst that being used in various processes due to its stacking carbon sheets in layer which theoretically rapid up the catalytic processes. In this study, choline chloride based DESs were synthesized and ChCl-PEG(1:4) was found to be the most effective DES in performing desulfurization, which it is able to remove up to 47.4% of the sulfur content in the model oil in just 10 minutes, and up to 95% of sulfur content after repeat the process for six times. ChCl-PEG(1:4) able to perform up to 32.7% desulfurization on real diesel after 6 multiple stages. Thus, future research works should focus on removing the impurities on real diesel before utilising DESs in petroleum field.

Keywords: choline chloride, deep eutectic solvent, fuel desulfurization, graphene oxide

Procedia PDF Downloads 136

Authors: Hitendra Raj Joshi, Bipin Lamichhane

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Groundwater is the main source of water for the industries of Balaju Industrial District (BID) and the denizens of Balaju-Boratar area. The quantity of groundwater is in a fatal condition in the area than earlier days. Water levels in shallow wells have highly lowered and deep wells are not providing an adequate amount of water as before because of higher extraction rate than the recharge rate. The main recharge zone of the shallow aquifer lies at the foot of Nagarjuna mountain, where recent colluvial debris are accumulated. Urbanization in the area is the main reason for decreasing water table. Recharge source for the deep aquifer in the region is aquiclude leakage. Sand layer above the Kalimati clay is the shallow aquifer zone, which is limited only in Balaju and eastern part of the Boratar, while the layer below the Kalimati clay spreading around Gongabu, Machhapohari, and Balaju area is considered as a potential area of deep aquifer. Over extraction of groundwater without considering water balance in the aquifers may dry out the source and can initiate the land subsidence problem. Hence, all the responsible of the industries in BID area and the denizens of Balaju-Boratar area should be encouraged to practice artificial groundwater recharge.

Keywords: aquiclude leakage, Kalimati clay, groundwater recharge

Procedia PDF Downloads 476

Authors: Yukiko Sasaki Alam, Shahid Alam

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Syntactic parsing is vital for semantic treatment by many applications related to natural language processing (NLP), because form and content coincide in many cases. However, it has not yet reached the levels of reliable performance. By manually examining and analyzing individual machine translation output errors that involve syntax as well as semantics, this study attempts to discover what is required for improving syntactic and semantic parsing.

Keywords: syntactic parsing, error analysis, machine translation, deep parsing

Procedia PDF Downloads 533

Authors: James A. Adeyanju, John O. Olajide, Akinbode A. Adedeji

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A predictive mathematical model based on the fundamental principles of mass transfer was developed to simulate the moisture content and oil content during Deep-Fat Frying (DFF) process of dodo. The resulting governing equation, that is, partial differential equation that describes rate of moisture loss and oil uptake was solved numerically using explicit Finite Difference Technique (FDT). Computer codes were written in MATLAB environment for the implementation of FDT at different frying conditions and moisture loss as well as oil uptake simulation during DFF of dodo. Plantain samples were sliced into 5 mm thickness and fried at different frying oil temperatures (150, 160 and 170 ⁰C) for periods varying from 2 to 4 min. The comparison between the predicted results and experimental data for the validation of the model showed reasonable agreement. The correlation coefficients between the predicted and experimental values of moisture and oil transfer models ranging from 0.912 to 0.947 and 0.895 to 0.957, respectively. The predicted results could be further used for the design, control and optimization of deep-fat frying process.

Keywords: frying, moisture loss, modelling, oil uptake

Procedia PDF Downloads 426

Authors: Sue-Yi Chen, Wei-Chun Hsu, Jon-Yiew Gan

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Si-based double-junction tandem solar cells have become a popular research topic because of the advantages of low manufacturing cost and high energy conversion efficiency. However, there is no set of calculations to select the appropriate top cell materials. Therefore, this paper will propose a simple but practical selection method. First of all, we calculate the S-Q limit and explain the reasons for developing tandem solar cells. Secondly, we calculate the theoretical energy conversion efficiency of the double-junction tandem solar cells while combining the commercial monocrystalline Si and materials' practical efficiency to consider the actual situation. Finally, we conservatively conclude that if considering 75% performance of the theoretical energy conversion efficiency of the top cell, the suitable bandgap energy range will fall between 1.38eV to 2.5eV. Besides, we also briefly describe some improvements of several proper materials, CZTS, CdSe, Cu2O, ZnTe, and CdS, hoping that future research can select and manufacture high-efficiency Si-based tandem solar cells based on this paper successfully. Most importantly, our calculation method is not limited to silicon solely. If other materials’ performances match or surpass silicon's ability in the future, researchers can also apply this set of deduction processes.

Keywords: high-efficiency solar cells, material selection, Si-based double-junction solar cells, Tandem solar cells, photovoltaics.

Procedia PDF Downloads 97

Authors: Muhammad Sufyan, Muhammad J. Arif, Muhammad Arshad, Usman Shoukat

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In Pakistan, wheat (Triticum aestivum L.) is seriously attacked by the wheat ‎aphid. Naturally, bio control agents play an important role in managing wheat aphid. However, association ‎among pest, natural enemies and host plant is highly affected by food resource ‎concentration and predator/parasitoid factor of any ecosystem. The present ‎study was conducted to estimate the effect of different dose levels of macro ‎and micronutrients on the aphid population and its entomophagous insect ‎on wheat and their tri-trophic association. The experiment was laid out in ‎RCBD with six different combinations of macro and micronutrients and a control treatment. The data was initiated from the second week of ‎the February till the maturity of the crop. Data regarding aphid population and ‎coccinellids counts were collected on weekly basis and subjected to analysis of ‎variance and mean comparison. The data revealed that aphid ‎population was at peak in the last week of March. Coccinellids population ‎increased side by side with aphid population and declined after second week of ‎April. Aphid parasitism was maximum 25% on recommended dose of Double and ‎Flasher and minimum 8.67% on control treatment. Maximum aphid population was observed on first April with 687.2 specimens. However, this maximum population was shown against the application of Double + Flasher treatment. The minimum aphid population was recorded after the application of HiK Gold + Flasher recommended dose on 15th April. The coccinellids population was at peak level at on 8th April and against the treatment double recommended dose of HiK gold + Flasher. Amount of nitrogen, phosphorus and potassium percentage dry leaves ‎components was maximum (2.33, 0.18 and 2.62 % dry leaves. respectively) in ‎plots treated with recommended double dose mixture of Double + Flasher and ‎Hi-K Gold + Flasher while it was minimum (1.43, 0.12 and 1.77 dry leaves ‎respectively) in plots where no nutrients applied. The result revealed that maximum parasitism was at recommended level of micro and macro nutrients application.‎ Maximum micro nutrients zinc, copper, manganese, iron and boron found with values 46.67 ppm, 21.81 ppm, 62.35 ppm, 152.69 ppm and 36.78 respectively. The result also showed that Over application of macro and micro nutrients should be avoided because it do not help in pest control, conversely it may cause stress on plant. The treatment Double and Flasher recommended dose ratio is almost comparable with recommended dose and present studies confirm its usefulness on wheat.

Keywords: entomophagous insects, macro and micro nutrients, tri-trophic, wheat aphid

Procedia PDF Downloads 211

Authors: Abderrahmane El Kachani, El Mahjoub Chakir, Anass Ait Laachir, Abdelhamid Niaaniaa, Jamal Zerouaoui, Tarik Jarou

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This paper presents a wind turbine based on the doubly fed induction generator (DFIG) connected to the utility grid through a shunt active power filter (SAPF). The whole system is controlled by a double nonlinear predictive controller (DNPC). A Taylor series expansion is used to predict the outputs of the system. The control law is calculated by optimization of the cost function. The first nonlinear predictive controller (NPC) is designed to ensure the high performance tracking of the rotor speed and regulate the rotor current of the DFIG, while the second one is designed to control the SAPF in order to compensate the harmonic produces by the three-phase diode bridge supplied by a passive circuit (rd, Ld). As a result, we obtain sinusoidal waveforms of the stator voltage and stator current. The proposed nonlinear predictive controllers (NPCs) are validated via simulation on a 1.5 MW DFIG-based wind turbine connected to an SAPF. The results obtained appear to be satisfactory and promising.

Keywords: wind power, doubly fed induction generator, shunt active power filter, double nonlinear predictive controller

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Authors: J. V. Gonzalez Fernandez, T. Mozume, S. Gozu, A. Lastras Martinez, L. F. Lastras Martinez, J. Ortega Gallegos, R. E. Balderas Navarro

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We report on a theoretical-experimental study of photoreflectance anisotropy (PRA) spectroscopy of coupled double quantum wells. By probing the in-plane interfacial optical anisotropies, we demonstrate that PRA spectroscopy has the capacity to detect and distinguish layers with quantum dimensions. In order to account for the experimental PRA spectra, we have used a theoretical model at k=0 based on a linear electro-optic effect through a piezoelectric shear strain.

Keywords: coupled double quantum well (CDQW), linear electro-optic (LEO) effect, photoreflectance anisotropy (PRA), piezoelectric shear strain

Procedia PDF Downloads 677

Authors: Partha Sarathi Majee, S. Bhattacharyya

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Migration of a core-shell soft particle under the influence of an external electric field in an electrolyte solution is studied numerically. The soft particle is coated with a positively charged polyelectrolyte layer (PEL) and the rigid core is having a uniform surface charge density. The Darcy-Brinkman extended Navier-Stokes equations are solved for the motion of the ionized fluid, the non-linear Nernst-Planck equations for the ion transport and the Poisson equation for the electric potential. A pressure correction based iterative algorithm is adopted for numerical computations. The effects of convection on double layer polarization (DLP) and diffusion dominated counter ions penetration are investigated for a wide range of Debye layer thickness, PEL fixed surface charge density, and permeability of the PEL. Our results show that when the Debye layer is in order of the particle size, the DLP effect is significant and produces a reduction in electrophoretic mobility. However, the double layer polarization effect is negligible for a thin Debye layer or low permeable cases. The point of zero mobility and the existence of mobility reversal depending on the electrolyte concentration are also presented.

Keywords: debye length, double layer polarization, electrophoresis, mobility reversal, soft particle

Procedia PDF Downloads 333

Authors: A. O. Idris, J. Virgone, A. I. Ibrahim, D. David, E. Vergnault

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In hot countries, the major challenge is the air conditioning. The increase in energy consumption by air conditioning stems from the need to live in more comfortable buildings, which is understandable. But in Djibouti, one of the countries with the most expensive electricity in the world, this need is exacerbated by an architecture that is inappropriate and unsuitable for climatic conditions. This paper discusses the design of the roof which is the surface receiving the most solar radiation. The roof determines the general behavior of the building. The study presents Computational Fluid Dynamics (CFD) modeling and analysis of the energy performance of a double skin ventilated roof. The particularity of this study is that it considers the climate of Djibouti characterized by hot and humid conditions in winter and very hot and humid in summer. Roof simulations are carried out using the Ansys Fluent software to characterize the flow and the heat transfer induced in the ventilated roof in steady state. This modeling is carried out by comparing the influence of several parameters such as the internal emissivity of the upper surface, the thickness of the insulation of the roof and the thickness of the ventilated channel on heat gain through the roof. The energy saving potential compared to the current construction in Djibouti is also presented.

Keywords: building, double skin roof, CFD, thermo-fluid analysis, energy saving, forced convection, natural convection

Procedia PDF Downloads 246

Authors: Amel Souidi, S. Bentata, B. Bouadjemi, T. Lantri, Z. Aziz

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Perovskite materials which include magnetic elements have relevance due to the technological perspectives in the spintronics industry. In this work, we have investigated the structural, electronic and magnetic properties of double perovskites Ba2MnXO6 with X= Mo and W by using the full-potential linearized augmented plane wave (FP-LAPW) method based on Density Functional Theory (DFT) [1, 2] as implemented in the WIEN2K [3] code. The interchange-correlation potential was included through the generalized gradient approximation (GGA) [4] as well as taking into account the on-site coulomb repulsive interaction in (GGA+U) approach. We have analyzed the structural parameters, charge and spin densities, total and partial densities of states. The results show that the materials crystallize in the 225 space group (Fm-3m) and have a lattice parameter of about 7.97 Å and 7.95 Å for Ba2MnMoO6 and Ba2MnWO6, respectively. The band structures reveal a metallic ferromagnetic (FM) ground state in Ba2MnMoO6 and half-metallic (HM) ferromagnetic (FM) ground state in the Ba2MnWO6 compound, with total magnetic moment equal 2.9951μB (Ba2MnMoO6 ) and 4.0001μB (Ba2MnWO6 ). The GGA+U calculations predict an energy gap in the spin-up bands in Ba2MnWO6. So we estimate that this material with HM-FM nature implies a promising application in spin-electronics technology.

Keywords: double perovskites, electronic structure, first-principles, semiconductors

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Authors: Inseok Yeo, Tae-Ho Song

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To practically apply vacuum insulation panels (VIPs) to buildings or home appliances, VIPs have demanded long-term lifespan with outstanding insulation performance. Service lives of VIPs enveloped with Al-foil and three-layer Al-metallized envelope are calculated. For Al-foil envelope, the service life is longer but edge conduction is too large compared with the Al metallized envelope. To increase service life even more, the proposed double enveloping method and metal-barrier-added enveloping method are further analyzed. The service lives of the VIP to employ two enveloping methods are calculated. Also, pressure increase and thermal insulation performance characteristics are investigated. For the metal- barrier-added enveloping method, effective thermal conductivity increase with time is close to that of Al-foil envelope, especially, for getter-inserted VIPs. For the double enveloping method, if water vapor is perfectly adsorbed, the effect of service life enhancement becomes much greater. From these methods, the VIP can be guaranteed for the service life of more than 20 years.

Keywords: vacuum insulation panels, service life, double enveloping, metal-barrier-added enveloping, edge conduction

Procedia PDF Downloads 411

Authors: A. M. Gsiea, J. P. Goss, P. R. Briddon, Ramadan. M. Al-habashi, K. M. Etmimi, Khaled. A. S. Marghani

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Using first-principles methods based on density functional theory and pseudopotentials, we have performed a details study of native defects in ZnO. Native point defects are unlikely to be cause of the unintentional n-type conductivity. Oxygen vacancies, which considered most often been invoked as shallow donors, have high formation energies in n-type ZnO, in edition are a deep donors. Zinc interstitials are shallow donors, with high formation energies in n-type ZnO, and thus unlikely to be responsible on their own for unintentional n-type conductivity under equilibrium conditions, as well as Zn antisites which have higher formation energies than zinc interstitials. Zinc vacancies are deep acceptors with low formation energies for n-type and in which case they will not play role in p-type coductivity of ZnO. Oxygen interstitials are stable in the form of electrically inactive split interstitials as well as deep acceptors at the octahedral interstitial site under n-type conditions. Our results may provide a guide to experimental studies of point defects in ZnO.

Keywords: DFT, native, n-type, ZnO

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Authors: Kaan Yamanturk, Cihan Dogruoz

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Seismic isolators have been utilized around the world to protect the structures, nonstructural components and contents from the damaging effects of earthquakes. In Structural Engineering, seismic isolation is used for protecting buildings and its vibration-sensitive contents from earthquakes. Seismic isolation is a passive control system that lowers effective earthquake forces by utilizing flexible bearings. One of the most significant isolation systems is seismic isolators. In this paper, double pendulum type Teflon coated seismic isolators utilized in a city hospital project by Guris Construction and Engineering Co. Inc, located in Kutahya, Turkey, have been investigated. Totally, 498 seismic isolators were applied in the project. These isolators are double friction pendulum type seismic isolation devices. The review of current practices is also examined in this study. The focus of this study is related to the application of passive seismic isolation systems for buildings as practiced in Kutahya City Hospital Project. Based on the study, the acceleration at the top floor will be 0.18 g and it will decrease 0.01 g in every floor. Therefore, seismic isolators are very important for buildings located in earthquake zones.

Keywords: maximum considered earthquake, moment resisting frame, seismic isolator, seismic design

Procedia PDF Downloads 135

Authors: Hailyie Tekleselase

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Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 109

Authors: Soon-Gil Yoon, Min-Ju Choi, Sung-Ho Shin, Junghyo Nah, Jin-Seok Choi, Hyun-A Song, Goeun Choi, Jin-Ho Choy

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Layered double hydroxides (LDHs) with positively charged brucite-like layers and negatively charged interlayer anions are considered a critical nanoscale building block with potential for application in catalysts, biological sensors, and optical, electrical, and magnetic devices. LDHs also have a great potential as an energy conversion device, a key component in common modern electronics. Although LDHs are theoretically predicted to be centrosymmetric, we report here the first observations of the flexoelectric nature of LDHs and demonstrate their potential as an effective energy conversion material. We clearly show a linear energy conversion relationship between the output powers and curvature radius via bending with both the LDH nanosheets and thin films, revealing a direct evidence for flexoelectric effects. These findings potentially open up avenues to incorporate a flexoelectric coupling phenomenon into centrosymmetric materials such as LDHs and to harvest high-power energy using LDH nanosheets. In the present study, for enhancement of the output power, Zn-Al:LDH nanosheets were composited with piezoelectric CH3NH3PbI3 (MAPbI3) dye films and their enhanced energy harvesting was demonstrated in detail.

Keywords: layered double hydroxides, flexoelectric, piezoelectric, energy harvesting

Procedia PDF Downloads 472

Authors: Mihai I. Sturza, Laura T. Corredor, Kaustuv Manna, Gizem A. Cansever, Tushar Dey, Andrey Maljuk, Olga Kataeva, Sabine Wurmehl, Anja Wolter, Bernd Buchner

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Recently, the iridate double perovskite Sr₂YIrO₆ has attracted considerable attention due to the report of unexpected magnetism in this Ir⁵⁺ material, in which according to the Jeff model, a non-magnetic ground state is expected. Structural, magnetic and thermodynamic investigations of Sr₂YIrO₆ and Ba2YIrO6 single crystals, with emphasis on the temperature and magnetic field dependence of the specific heat will be presented. The single crystals were grown by using SrCl₂ and BaCl₂ as flux. Single-crystal X-ray diffraction measurements performed on several crystals from different preparation batches showed a high quality of the crystals, proven by the good internal consistency of the data collected using the full-sphere mode and an extremely low R factor. In agreement with the expected non-magnetic ground state of Ir⁵⁺ (5d4) in these iridates, no magnetic transition is observed down to 430 mK. Moreover, our results suggest that the low-temperature anomaly observed in the specific heat is not related to the onset of long-range magnetic order. Instead, it is identified as a Schottky anomaly caused by paramagnetic impurities present in the sample, of the order of

Keywords: double perovskites, iridates, self-flux grown synthesis, spin-orbit coupling

Procedia PDF Downloads 314

Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: baby care system, Internet of Things, deep learning, machine vision

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Authors: Dimple Juneja, Aarti Singh, Renu Hooda

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Knowledge Query Manipulation Language (KQML) and FIPA ACL are two prime communication languages existing in multi agent systems (MAS). Both languages are more or less similar in terms of semantics (based on speech act theory) and offer cutting edge competition while establishing agent communication across Internet. In contrast to the fact that software agents operating on the internet are required to be more safeguarded from their counter-peer, both protocols lack security performatives. The paper proposes a three tier security interface with few novel security related performatives enhancing the basic architecture of KQML. The three levels are attestation, certification and trust establishment which enforces a tight security and hence reduces the security breeches.

Keywords: multiagent systems, KQML, FIPA ACL, performatives

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Authors: A. Ja, J. Belabid, A. Cheddadi

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This paper reports the numerical simulation of double diffusive natural convection flows within a horizontal annular filled with a saturated porous medium. The analysis concerns the influence of the different parameters governing the problem, namely, the Rayleigh number Ra, the Lewis number Le and the buoyancy ratio N, on the heat and mass transfer and on the flow structure, in the case of a fixed radius ratio R = 2. The numerical model used for the discretization of the dimensionless equations governing the problem is based on the finite difference method, using the ADI scheme. The study is focused on steady-state solutions in the cooperation situation.

Keywords: natural convection, double-diffusion, porous medium, annular geometry, finite differences

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Authors: Ian Omung'a

Abstract:

Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

Procedia PDF Downloads 78

Authors: Hui Zhang, Sherif Beskhyroun

Abstract:

Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Prakash Karrun, M. Manoj Deepak, Mathivanan, K. Venkatachalam

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Our study aims to evaluate the rates of healing and the efficacy of the arthroscopic repair of the rotator cuff tears. 40 patients who had rotator cuff tears were taken up for the study and arthroscopic repair was done with double row technique.They were evaluated and followed up for a minimum of 2 years minimum.The functional status,range of motion and healing rates were compared post operatively. All the patients were followed up with serial questionnaires and MRI at the end of 2 years. There was significant improvement in the functional status of the patient. The MRI showed better rates of healing in these patients.Thus our study effectively proves the efficacy of our operating technique.

Keywords: rotator cuff tear, arthroscopic repair, double stich, healing

Procedia PDF Downloads 333

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

Procedia PDF Downloads 124

Authors: Teja Sai Puligadda

Abstract:

Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Yo-Sheng Lin, Run-Chi Liu, Chien-Chu Ji, Chih-Chung Chen, Chien-Chin Wang

Abstract:

A W-band double-balanced mixer for direct up-conversion using standard 90 nm CMOS technology is reported. The mixer comprises an enhanced double-balanced Gilbert cell with PMOS negative resistance compensation for conversion gain (CG) enhancement and current injection for power consumption reduction and linearity improvement, a Marchand balun for converting the single LO input signal to differential signal, another Marchand balun for converting the differential RF output signal to single signal, and an output buffer amplifier for loading effect suppression, power consumption reduction and CG enhancement. The mixer consumes low power of 6.9 mW and achieves LO-port input reflection coefficient of -17.8~ -38.7 dB and RF-port input reflection coefficient of -16.8~ -27.9 dB for frequencies of 90~100 GHz. The mixer achieves maximum CG of 3.6 dB at 95 GHz, and CG of 2.1±1.5 dB for frequencies of 91.9~99.4 GHz. That is, the corresponding 3 dB CG bandwidth is 7.5 GHz. In addition, the mixer achieves LO-RF isolation of 36.8 dB at 94 GHz. To the authors’ knowledge, the CG, LO-RF isolation and power dissipation results are the best data ever reported for a 94 GHz CMOS/BiCMOS up-conversion mixer.

Keywords: CMOS, W-band, up-conversion mixer, conversion gain, negative resistance compensation, output buffer amplifier

Procedia PDF Downloads 516

Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

Abstract:

Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

Procedia PDF Downloads 396

Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

Abstract:

Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

Procedia PDF Downloads 168