Search results for: Deep mixed column

Authors: Bhavani Veasuvalingam

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Case-based learning (CBL) is a popular instructional method aimed to bridge theory to clinical practice. This study aims to explore CBL mixed modality curriculum in influencing students’ learning styles and strategies that support learning. An explanatory sequential mixed method study was employed with initial phase, 44-itemed Felderman’s Index of Learning Style (ILS) questionnaire employed across year one medical students (n=142) using convenience sampling to describe the preferred learning styles. The qualitative phase utilised three focus group discussions (FGD) to explore in depth on the multimodal learning style exhibited by the students. Most students preferred combination of learning stylesthat is reflective, sensing, visual and sequential i.e.: RSVISeq style (24.64%) from the ILS analysis. The frequency of learning preference from processing to understanding were well balanced, with sequential-global domain (66.2%); sensing-intuitive (59.86%), active- reflective (57%), and visual-verbal (51.41%). The qualitative data reported three major themes, namely Theme 1: CBL mixed modalities navigates learners’ learning style; Theme 2: Multimodal learners active learning strategies supports learning. Theme 3: CBL modalities facilitating theory into clinical knowledge. Both quantitative and qualitative study strongly reports the multimodal learning style of the year one medical students. Medical students utilise multimodal learning styles to attain the clinical knowledge when learning with CBL mixed modalities. Educators’ awareness of the multimodal learning style is crucial in delivering the CBL mixed modalities effectively, considering strategic pedagogical support students to engage and learn CBL in bridging the theoretical knowledge into clinical practice.

Keywords: case-based learning, learnign style, medical students, learning

Procedia PDF Downloads 94

Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

Procedia PDF Downloads 124

Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

Procedia PDF Downloads 350

Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Dounya Barrit, Ming-Chun Tang, Hoang Dang, Kai Wang, Detlef-M. Smilgies, Aram Amassian

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Several deposition methods have been developed for perovskite film preparation. The one-step spin-coating process has emerged as a more popular option thanks to its ability to produce films of different compositions, including mixed cation and mixed halide perovskites, which can stabilize the perovskite phase and produce phases with desired band gap. The two-step method, however, is not understood in great detail. There is a significant need and opportunity to adopt the two-step process toward mixed cation and mixed halide perovskites, but this requires deeper understanding of the two-step conversion process, for instance when using different cations and mixtures thereof, to produce high-quality perovskite films with uniform composition. In this work, we demonstrate using in situ investigations that the conversion of PbI₂ to perovskite is largely dictated by the state of the PbI₂ precursor film in terms of its solvated state. Using time-resolved grazing incidence wide-angle X-Ray scattering (GIWAXS) measurements during spin coating of PbI₂ from a DMF (Dimethylformamide) solution we show the film formation to be a sol-gel process involving three PbI₂-DMF solvate complexes: disordered precursor (P₀), ordered precursor (P₁, P₂) prior to PbI₂ formation at room temperature after 5 minutes. The ordered solvates are highly metastable and eventually disappear, but we show that performing conversion from P₀, P₁, P₂ or PbI₂ can lead to very different conversion behaviors and outcomes. We compare conversion behaviors by using MAI (Methylammonium iodide), FAI (Formamidinium Iodide) and mixtures of these cations, and show that conversion can occur spontaneously and quite rapidly at room temperature without requiring further thermal annealing. We confirm this by demonstrating improvements in the morphology and microstructure of the resulting perovskite films, using techniques such as in situ quartz crystal microbalance with dissipation monitoring, SEM and XRD.

Keywords: in situ GIWAXS, lead iodide, mixed cation, perovskite solar cell, sol-gel process, solvate phase

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Authors: Ting Gao, Mingyue He

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Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments.

Keywords: channel estimation, reconfigurable intelligent surface, wireless communication, deep learning

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Authors: Moses Mwangi, Geert Verbeke, Geert Molenberghs

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Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment with respect to a reference treatment (placebo or standard). The main advantage of using cross-over design over conventional parallel design is its flexibility, where every subject become its own control, thereby reducing confounding effect. Jones & Kenward, discuss in detail more recent developments in the analysis of cross-over trials. We revisit the simple piecewise linear mixed-effects model, proposed by Mwangi et. al, (in press) for its first application in the analysis of cross-over trials. We compared performance of the proposed piecewise linear mixed-effects model with two commonly cited statistical models namely, (1) Grizzle model; and (2) Jones & Kenward model, used in estimation of the treatment effect, in the analysis of randomized cross-over trial. We estimate two performance measurements (mean square error (MSE) and coverage probability) for the three methods, using data simulated from the proposed piecewise linear mixed-effects model. Piecewise linear mixed-effects model yielded lowest MSE estimates compared to Grizzle and Jones & Kenward models for both small (Nobs=20) and large (Nobs=600) sample sizes. It’s coverage probability were highest compared to Grizzle and Jones & Kenward models for both small and large sample sizes. A piecewise linear mixed-effects model is a better estimator of treatment effect than its two competing estimators (Grizzle and Jones & Kenward models) in the analysis of cross-over trials. The data generating mechanism used in this paper captures two time periods for a simple 2-Treatments x 2-Periods cross-over design. Its application is extendible to more complex cross-over designs with multiple treatments and periods. In addition, it is important to note that, even for single response models, adding more random effects increases the complexity of the model and thus may be difficult or impossible to fit in some cases.

Keywords: Evaluation, Grizzle model, Jones & Kenward model, Performance measures, Simulation

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Authors: Abdulmohsen Alruwaili

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A partial differential system featuring momentum and energy balance is often used to describe simulations of flow initiation and thermal shifting in boundary layers. The buoyancy force in terms of temperature is factored in the momentum balance equation. Buoyancy force causes the flow quantity to fluctuate along the streamwise direction 𝑋; therefore, the problem can be, to our best knowledge, analyzed through nonsimilar modeling. In this analysis, a nonsimilar model is evolved for radiative mixed convection of a magnetized power-law nanoliquid flow on top of a vertical plate installed in a stationary fluid. The upward linear stretching initiated the flow in the vertical direction. Assuming nanofluids are composite of copper (Cu) and alumina (Al₂O₃) nanoparticles, the viscous dissipation in this case is negligible. The nonsimilar system is dealt with analytically by local nonsimilarity (LNS) via numerical algorithm bvp4c. Surface temperature and flow field are shown visually in relation to factors like mixed convection, magnetic field strength, nanoparticle volume fraction, radiation parameters, and Prandtl number. The repercussions of magnetic and mixed convection parameters on the rate of energy transfer and friction coefficient are represented in tabular forms. The results obtained are compared to the published literature. It is found that the existence of nanoparticles significantly improves the temperature profile of considered nanoliquid. It is also observed that when the estimates of the magnetic parameter increase, the velocity profile decreases. Enhancement in nanoparticle concentration and mixed convection parameter improves the velocity profile.

Keywords: nanofluid, power law model, mixed convection, thermal radiation

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Authors: Francisco Alvarado, Luz Ramírez

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Corneal diseases are one of the main causes of visual impairment and affect almost 4 million, and this study assesses the effects of deep anterior lamellar keratoplasty (DALK) with porcine corneal stroma and postoperative topical treatment with tacrolimus in patients with infectious keratitis. No patient was observed with clinical graft rejection. Among the cases: 2 were positive to fungal culture, 2 with Aspergillus and the other 8 cases were confirmed by bacteriological culture. Corneal diseases are one of the main causes of visual impairment and affect almost 4 million. This study assesses the effects of deep anterior lamellar keratoplasty (DALK) with porcine corneal stroma and postoperative topical treatment with tacrolimus in patients with infectious keratitis. Receiver bed diameters ranged from 7.00 to 9.00 mm. No incidents of Descemet's membrane perforation were observed during surgery. During the follow-up period, no corneal graft splitting, IOP increase, or intolerance to tacrolimus were observed. Deep anterior lamellar keratoplasty seems to be the best option to avoid xenograft rejection, and it could help new surgical techniques in humans.

Keywords: ophthalmology, cornea, corneal transplant, xenografts, surgical innovations

Procedia PDF Downloads 82

Authors: M. L. Ibrahim, A. Abdulhamid

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A surface active molecule synthesized by a rhizobacterial strain Bacillus lentus isolated from Cajanus cajan was investigated. The bioemulsifier was extracted, purified and partially characterized using standard methods. Surface properties of the bioemulsifier were determined by studying the emulsification index, solubility test and stability studies. Partial purification of the bioemulsifier was carried out using FT-IR analysis, Silica-gel column chromatography and thin layer chromatography. GC-MS analysis was carried out to detect the composition and mass of the lipids and esters. The isolate showed an emulsifying activity of 57% and surface activity of 36mm. The stability studies revealed that the bioemulsifier had better stability at temperature of 70oC, 8% pH and 8% NaCl concentration. FT-IR indicated the bioemulsifier to contain peptide and aliphatic chain, TLC revealed the compound to be ninhydrin positive and Column chromatography showed the presence of three amino acids namely; glutamine, valine and cysteine. GC-MS indicated the lipid moiety to contain aliphatic chain ranging from C9-C16 and two major peaks of 1,2-benzenedicarboxylic acid diethyl octyl ester. Therefore, surface active agent from Bacillus lentus can be used effectively in a wide range of applications such as in MEOR and in the biosynthesis of plasticizers for industrial uses.

Keywords: Bacillus lentus, bioemulsifiers, phthalic acid ester, Rhizosphere

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Authors: Nassibeh Hosseini-Vasoukolaei, Amir Ahmad Akhavan, Mahmood Jeddi-Tehrani, Ali Khamesipour, Mohammad Reza Yaghoobi Ershadi, Kamhawi Shaden, Valenzuela Jesus, Hossein Mirhendi, Mohammad Hossein Arandian

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Zoonotic cutaneous leishmaniasis (ZCL) is an endemic disease in many rural areas of Iran. Sand flies were collected from rural areas of Esfahan province and were identified using valid identification keys. DNA was extracted from sand flies and Nested PCRs were done using specific primers. In this study, 44 out of 152 (28.9 %) sand flies were infected with L. majoralone. Eight sand flies showed mixed infection: four sand flies (2.6 %) were infected with L. major, L. turanicaand L. gerbili, one sand fly (0.7 %) was infected with L. major and L. turanica and three sand flies (2 %) were infected with L. turanicaand L. gerbili. Our results demonstrate the natural infection of P. papatasi sand fly with three species of L. major, L. turanica and L. gerbili which are circulating among R. opimusreservoir host and P. papatasi sand fly vector in central Iran.

Keywords: Phlebotomus papatasi, Leishmania major, Leishmania turanica, Leishmania gerbili, mixed infection, Iran

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Authors: Nusrat T. Chowdhury, M. F. Baki, A. Azab

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The production planning problem is concerned with specifying the optimal quantities to produce in order to meet the demand for a prespecified planning horizon with the least possible expenditure. Making the right decisions in production planning will affect directly the performance and productivity of a manufacturing firm, which is important for its ability to compete in the market. Therefore, developing and improving solution procedures for production planning problems is very significant. In this paper, we develop a Dantzig-Wolfe decomposition of a multi-item hierarchical production planning problem with capacity constraint and present a column generation approach to solve the problem. The original Mixed Integer Linear Programming model of the problem is decomposed item by item into a master problem and a number of subproblems. The capacity constraint is considered as the linking constraint between the master problem and the subproblems. The subproblems are solved using the dynamic programming approach. We also propose a multi-step iterative capacity allocation heuristic procedure to handle any kind of infeasibility that arises while solving the problem. We compare the computational performance of the developed solution approach against the state-of-the-art heuristic procedure available in the literature. The results show that the proposed heuristic-based decomposition approach improves the solution quality by 20% as compared to the literature.

Keywords: inventory, multi-level capacitated lot-sizing, emission control, setup carryover

Procedia PDF Downloads 137

Authors: Fezzani Boubaker

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In this work, a comprehensive study of respirometric method was performed to assess the biodegradable COD fractions of domestic sewage mixed with baker’s yeast factory effluent treated by wastewater treatment plant (WWTP) of Beja. Three respirometric runs were performed in a closed tank reactor to characterize this mixed raw effluent. Respirometric result indicated that the readily biodegradable fraction (SS) was in range of 6-22%, the slowly biodegradable fraction (Xs) was in range of 33-42%, heterotrophic biomass (XH) was in range of 9-40% and the inert fractions: XI and SI were in range of 2-40% and 6-12% respectively which were high due to the presence of baker’s yeast factory effluent compared to domestic effluent alone. The fractions of the total nitrogen showed that SNO fraction is between 6 and 9% of TKN, the fraction of nitrogen ammonia SNH was ranging from 5 to 68%. The organic fraction divided into two compartments SND (11-85%) and XND (5-20%) the inert particulate nitrogen fraction XNI was between 0.4 and 1% and the inert soluble fraction of nitrogen SNI was ranged from 0.4 to 3%.

Keywords: wastewater characterization, COD fractions, respirometry, domestic sewage

Procedia PDF Downloads 483

Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Arefeh Arabaninezhad, Ali Fakher

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Since the deterministic analysis methods fail to take system uncertainties into account, probabilistic and non-probabilistic methods are suggested. Geotechnical analyses are used to determine the stress and deformation caused by construction; accordingly, many input variables which depend on ground behavior are required for geotechnical analyses. The Random Set approach is an applicable reliability analysis method when comprehensive sources of information are not available. Using Random Set method, with relatively small number of simulations compared to fully probabilistic methods, smooth extremes on system responses are obtained. Therefore random set approach has been proposed for reliability analysis in geotechnical problems. In the present study, the application of random set method in reliability analysis of deep excavations is investigated through three deep excavation projects which were monitored during the excavating process. A finite element code is utilized for numerical modeling. Two expected ranges, from different sources of information, are established for each input variable, and a specific probability assignment is defined for each range. To determine the most influential input variables and subsequently reducing the number of required finite element calculations, sensitivity analysis is carried out. Input data for finite element model are obtained by combining the upper and lower bounds of the input variables. The relevant probability share of each finite element calculation is determined considering the probability assigned to input variables present in these combinations. Horizontal displacement of the top point of excavation is considered as the main response of the system. The result of reliability analysis for each intended deep excavation is presented by constructing the Belief and Plausibility distribution function (i.e. lower and upper bounds) of system response obtained from deterministic finite element calculations. To evaluate the quality of input variables as well as applied reliability analysis method, the range of displacements extracted from models has been compared to the in situ measurements and good agreement is observed. The comparison also showed that Random Set Finite Element Method applies to estimate the horizontal displacement of the top point of deep excavation. Finally, the probability of failure or unsatisfactory performance of the system is evaluated by comparing the threshold displacement with reliability analysis results.

Keywords: deep excavation, random set finite element method, reliability analysis, uncertainty

Procedia PDF Downloads 267

Authors: Selim M. Khan, Dustin D. Pearson, Tryggve Rönnqvist, Markus E. Nielsen, Joshua M. Taron, Aaron A. Goodarzi

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Accumulation of radioactive radon gas in indoor air poses a serious risk to human health by increasing the lifetime risk of lung cancer and is classified by IARC as a category one carcinogen. Radon exposure risks are a function of geologic, geographic, design, and human behavioural variables and can change over time. Using time series and deep machine learning modelling, we analyzed long-term radon test outcomes as a function of building metrics from 25,489 Canadian and 38,596 Swedish residential properties constructed between 1945 to 2020. While Canadian and Swedish properties built between 1970 and 1980 are comparable (96–103 Bq/m³), innate radon risks subsequently diverge, rising in Canada and falling in Sweden such that 21st Century Canadian houses show 467% greater average radon (131 Bq/m³) relative to Swedish equivalents (28 Bq/m³). These trends are consistent across housing types and regions within each country. The introduction of energy efficiency measures within Canadian and Swedish building codes coincided with opposing radon level trajectories in each nation. Deep machine learning modelling predicts that, without intervention, average Canadian residential radon levels will increase to 176 Bq/m³ by 2050, emphasizing the importance and urgency of future building code intervention to achieve systemic radon reduction in Canada.

Keywords: radon health risk, time-series, deep machine learning, lung cancer, Canada, Sweden

Procedia PDF Downloads 83

Authors: Farhad Hekmatipour, M. A. Akhavan-Behabadi, Farzad Hekmatipour

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In this paper, experiments were conducted to investigate the heat transfer of Copper Oxide-Heat Transfer Oil (CuO-HTO) nanofluid laminar flow in vertical smooth and microfin tubes as the surface temperature is constant. The effect of adding the nanoparticle to base fluid and Richardson number on the heat transfer enhancement is investigated as Richardson number increases from 0.1 to 0.7. The experimental results demonstrate that the combined forced-natural convection heat transfer rate may be improved significantly with an increment of mass nanoparticle concentration from 0% to 1.5%. In this experiment, a correlation is also proposed to predict the mixed convection heat transfer rate of CuO-HTO nanofluid flow. The maximum deviation of both correlations is less than 14%. Moreover, a correlation is presented to estimate the Nusselt number inside vertical smooth and microfin tubes as Rayleigh number is between 2´105 and 6.8´106 with the maximum deviation of 12%.

Keywords: mixed convection, heat transfer, nanofluid, vertical tube, microfin tube

Procedia PDF Downloads 378

Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Han-Bin Park, Taesam Kang, SunKi Hong, Jeong Hoi Gu

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The piezoelectric material is being used widely for actuators due to its large power density with simple structure. It can generate a larger force than the conventional actuators with the same size. Furthermore, the response time of piezoelectric actuators is very short, and thus, it can be used for very fast system applications with compact size. To control the piezoelectric actuator, we need analog signal conditioning circuits as well as digital microcontrollers. Conventional microcontrollers are not equipped with analog parts and thus the control system becomes bulky compared with the small size of the piezoelectric devices. To overcome these weaknesses, we are developing one-chip micro controller that can handle analog and digital signals simultaneously using mixed signal FPGA technology. We used the SmartFusion™ FPGA device that integrates ARM®Cortex-M3, analog interface and FPGA fabric in a single chip and offering full customization. It gives more flexibility than traditional fixed-function microcontrollers with the excessive cost of soft processor cores on traditional FPGAs. In this paper we introduce the design of single chip controller using mixed signal FPGA, SmartFusion™[1] device. To demonstrate its performance, we implemented a PI controller for power driving circuit and a 5th order H-infinity controller for the system with piezoelectric actuator in the FPGA fabric. We also demonstrated the regulation of a power output and the operation speed of a 5th order H-infinity controller.

Keywords: mixed signal FPGA, PI control, piezoelectric actuator, SmartFusion™

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Authors: Boonyarat Chaleephay

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The purpose of this research paper was to describe the relationship of acupressure points on the anterior surface of the upper limb in accordance with Applied Thai Traditional Massage (ATTM) and the deep structures located at those acupressure points. There were 2 population groups; normal subjects and cadaver specimens. Eighteen males with age ranging from 20-40 years old and seventeen females with ages ranging from 30-97 years old were studies. This study was able to obtain a fundamental knowledge concerning acupressure point and the deep structures that related to those acupressure points. It might be used as the basic knowledge for clinically applying and planning treatment as well as teaching in ATTM.

Keywords: acupressure point (AP), applie Thai traditional medicine (ATTM), paresthesia, numbness

Procedia PDF Downloads 239

Authors: Bing Lu, Shuang Li, Hongyuan Zhou

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The steel–concrete hybrid structure has been extensively employed in high–rise buildings and super high–rise buildings. The light gauge steel–concrete hybrid structure, including light gauge steel structure and concrete hybrid structure, is a new–type steel–concrete hybrid structure, which possesses some advantages of light gauge steel structure and concrete hybrid structure. The seismic behavior and loss assessment of three high–rise buildings with three different concrete hybrid structures were investigated through finite element software, respectively. The three concrete hybrid structures are reinforced concrete column–steel beam (RC‒S) hybrid structure, concrete–filled steel tube column–steel beam (CFST‒S) hybrid structure, and tubed concrete column–steel beam (TC‒S) hybrid structure. The nonlinear time-history analysis of three high–rise buildings under 80 earthquakes was carried out. After simulation, it indicated that the seismic performances of three high–rise buildings were superior. Under extremely rare earthquakes, the maximum inter–storey drifts of three high–rise buildings are significantly lower than 1/50. The inter–storey drift and floor acceleration of high–rise building with CFST‒S hybrid structure were bigger than those of high–rise buildings with RC‒S hybrid structure, and smaller than those of high–rise building with TC‒S hybrid structure. Then, based on the time–history analysis results, the post-earthquake repair cost ratio and repair time of three high–rise buildings were predicted through an economic performance analysis method proposed in FEMA‒P58 report. Under frequent earthquakes, basic earthquakes and rare earthquakes, the repair cost ratio and repair time of three high-rise buildings were less than 5% and 15 days, respectively. Under extremely rare earthquakes, the repair cost ratio and repair time of high-rise buildings with TC‒S hybrid structure were the most among three high rise buildings. Due to the advantages of CFST-S hybrid structure, it could be extensively employed in high-rise buildings subjected to earthquake excitations.

Keywords: seismic behavior, loss assessment, light gauge steel–concrete hybrid structure, high–rise building, time–history analysis

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Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

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Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

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Authors: Birute Bugelyte, Ingrida Jurkute, Vida Vickackaite

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The most complicated step of the determination of volatile compounds in complex matrices is the separation of analytes from the matrix. Traditional analyte separation methods (liquid extraction, Soxhlet extraction) require a lot of time and labour; moreover, there is a risk to lose the volatile analytes. In recent years, headspace gas chromatography has been used to determine volatile compounds. To date, traditional extraction solvents have been used in headspace gas chromatography. As a rule, such solvents are rather volatile; therefore, a large amount of solvent vapour enters into the headspace together with the analyte. Because of that, the determination sensitivity of the analyte is reduced, a huge solvent peak in the chromatogram can overlap with the peaks of the analyts. The sensitivity is also limited by the fact that the sample can’t be heated at a higher temperature than the solvent boiling point. In 2018 it was suggested to replace traditional headspace gas chromatographic solvents with non-volatile, eco-friendly, biodegradable, inexpensive, and easy to prepare deep eutectic solvents (DESs). Generally, deep eutectic solvents have low vapour pressure, a relatively wide liquid range, much lower melting point than that of any of their individual components. Those features make DESs very attractive as matrix media for application in headspace gas chromatography. Also, DESs are polar compounds, so they can be applied for microwave assisted extraction. The aim of this work was to investigate the possibility of applying deep eutectic solvents for microwave assisted extraction and headspace gas chromatographic determination of hexanal in fat-rich food. Hexanal is considered one of the most suitable indicators of lipid oxidation degree as it is the main secondary oxidation product of linoleic acid, which is one of the principal fatty acids of many edible oils. Eight hydrophilic and hydrophobic deep eutectic solvents have been synthesized, and the influence of the temperature and microwaves on their headspace gas chromatographic behaviour has been investigated. Using the most suitable DES, microwave assisted extraction conditions and headspace gas chromatographic conditions have been optimized for the determination of hexanal in potato chips. Under optimized conditions, the quality parameters of the prepared technique have been determined. The suggested technique was applied for the determination of hexanal in potato chips and other fat-rich food.

Keywords: deep eutectic solvents, headspace gas chromatography, hexanal, microwave assisted extraction

Procedia PDF Downloads 192

Authors: Teja Koduru

Abstract:

Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

Procedia PDF Downloads 239

Authors: TranThi Ly, Ligang Yang, Hechun Liu, Dengfeng Xu, Haiteng Zhou, Shaokang Wang, Shiqing Chen, Guiju Sun

Abstract:

This study aims to evaluate the oxidative stability of Chinese vegetable oils during repeated deep-frying. For frying media, palm oil (PO), sunflower oil (SFO), soybean oil (SBO), and canola oil (CO) were used. French fries were fried in oils heated to 180 ± 50℃. The temperature was kept constant during the eight h of the frying process. The oil quality was measured according to the fatty acid (FA) content, trans fatty acid (TFA) compounds, and chemical properties such as peroxide value (PV), acid value (AV), anisidine value (AnV), and malondialdehyde (MDA). Additionally, the sensory characteristics such as color, flavor, greasiness, crispiness, and overall acceptability of the French fries were assessed. Results showed that the PV, AV, AnV, MDA, and TFA content of SFO, CO, and SBO significantly increased in conjunction with prolonged frying time. During the deep-frying process, the SBO showed the lowest oxidative stability at all indices, while PO retained oxidative stability and generated the lowest level of TFA. The French fries fried in PO also offered better sensory properties than the other oils. Therefore, results regarding oxidative stability and sensory attributes suggested that among the examined vegetable oils, PO appeared to be the best oil for frying food products.

Keywords: vegetable oils, French fries, oxidative stability, sensory properties, frying oil

Procedia PDF Downloads 115

Authors: Constanza A. Barriga, Rafael Bernardi, Amokrane Berdja, Christian D. Guzman

Abstract:

Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariable in the image plane. However, this latter condition is not always satisfied with real optical systems. PSF angular variations cannot be evaluated directly from the observations, neither be corrected at a pixel resolution. We have developed a method for the restoration of images affected by static and anisotropic aberrations using deep neural networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T-80 telescope optical system, an 80 cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system. Once trained, the network can be used directly on sky images, outputting a corrected version of the image, which has a constant and known PSF across its field-of-view. The method was tested with the T-80 telescope, achieving better results than with PSF deconvolution techniques. We present the method and results on this telescope.

Keywords: aberrations, deep neural networks, image restoration, variable point spread function, wide field images

Procedia PDF Downloads 134

Authors: Nashrawan Naser Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: mixed methods, social network analysis, multi-cultural learning, social network dynamics

Procedia PDF Downloads 508

Authors: Sahira Nsayef Muslim, Israa M. S. Al-Kadmy, Nadheema Hammood Hussein, Alaa Naseer Mohammed Ali, Buthainah Mohammed Taha, Rayim Sabah Abbood, Sarah Naji Aziz

Abstract:

A novel strain of Bacillus licheniformis isolated from spoilt cucumber and pepper samples have the ability to produce the chitosanase enzyme when grown on chitosan substrate. Chitosanase was purified to homogeneity with a recovery yield of 35.71% and 5.5 fold of purification by using ammonium sulfate at 45% saturation followed by ion exchange chromatography on DEAE-cellulose column and gel filtration chromatography on Sephadex G-100 column. The purified chitosanase inhibited the biofilm formation ability for all Gram-negative and Gram-positive biofilm-forming bacteria (biofilm producers) after using Congo Red agar and Microtiter plates methods. Highly antibiofilm of chitosanase recorded against Pseudomonas aeruginosa followed by Klebsiella pneumoniae with reduction of biofilm formation ratio to 22 and 29%, respectively compared with (100)% of control. Thus, chitosanase has promising benefit as antibiofilm agent against biofilm forming pathogenic bacteria and has promising application as alternative antibiofilm agents to combat the growing number of multidrug-resistant pathogen-associated infections, especially in situation where biofilms are involved.

Keywords: chitosanase, Bacillus licheniformis, vegetables, biofilm

Procedia PDF Downloads 381

Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

Abstract:

This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

Procedia PDF Downloads 152

Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

Procedia PDF Downloads 444