Search results for: deep beams

Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Arun Prasad, P. B. Ramudu, Deep Shikha, Deep Jyoti Singh

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A number of studies have been conducted recently to investigate the influence of randomly oriented fibers on some engineering properties of cohesive soils.Freezing and thawing of soil affects the strength, durability and permeability of soil adversely. Experiments were carried out in order to investigate the effect of inclusion of randomly distributed polypropylene fibers on the strength, hydraulic conductivity and durability of local soil (CL) subjected to freeze–thaw cycles. For evaluating the change in strength of soil, a series of unconfined compression tests as well as tri-axial tests were carried out on reinforced and unreinforced soil samples. All the samples were subjected to seven cycles of freezing and thawing. Freezing was carried out at a temperature of - 15 to -18 °C; and thawing was carried out by keeping the samples at room temperature. The reinforcement of soil samples was done by mixing with polypropylene fibers, 12 mm long and with an aspect ratio of 240. The content of fibers was varied from 0.25 to 1% by dry weight of soil. The maximum strength of soil was found in samples having a fiber content of 0.75% for all the samples that were prepared at optimum moisture content (OMC), and if the OMC was increased (+2% OMC) or decreased (-2% OMC), the maximum strength observed at 0.5% fiber inclusion. The effect of fiber inclusion and freeze–thaw on the hydraulic conductivity was studied increased from around 25 times to 300 times that of the unreinforced soil, without subjected to any freeze-thaw cycles. For studying the increased durability of soil, mass loss after each freeze-thaw cycle was calculated and it was found that samples reinforced with polypropylene fibers show 50-60% less loss in weight than that of the unreinforced soil.

Keywords: fiber reinforcement, freezingand thawing, hydraulic conductivity, unconfined compressive strength

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

Procedia PDF Downloads 189

Authors: Ali Raheli, Rahim Habibifar, Behzad Mohammadi-Alasti, Mahdi Abbasgholipour

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This paper presents vibration behavior of a FGM micro-beam and its pull-in instability under a nonlinear electrostatic pressure. An exponential function has been applied to show the continuous gradation of the properties along thickness. Nonlinear integro-differential-electro-mechanical equation based on Euler–Bernoulli beam theory has been derived. The governing equation in the static analysis has been solved using Step-by-Step Linearization Method and Finite Difference Method. Fixed points or equilibrium positions and singular points have been shown in the state control space. In order to find the response to a step DC voltage, the nonlinear equation of motion has been solved using Galerkin-based reduced-order model and time histories and phase portrait for different applied voltages have been shown. The effects of electrostatic pressure on stability of FGM micro-beams having various amounts of the ceramic constituent have been investigated.

Keywords: FGM, MEMS, nonlinear vibration, electrical, dynamic pull-in voltage

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

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We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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Authors: Mizam Doğan

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Adapazarı railway car factory, the only railway car factory of Turkey, was constructed in 1950. It was a steel design and it had filled beam sections and truss beam systems. Columns were steel profiles and box sections. The factory was damaged heavily on Izmit Earthquake and closed. In this earthquake 90% of damaged structures are reinforced concrete, the others are %7 prefabricated and 3% steel construction. As can be seen in statistical data, damaged industrial buildings in this earthquake were generally reinforced concrete and prefabricated structures. Adapazari railway car factory is the greatest steel structure damaged in the earthquake. This factory has 95% of the total damaged steel structure area. In this paper; earthquake damages on beams and columns of the factory are studied by considering TS648 'Turkish Standard Building Code for Steel Structures' and also damaged connection elements as welds, rivets and bolts are examined. A model similar to the damaged system is made and high-stress zones are searched. These examinations, conclusions, suggestions are explained by damage photos and details.

Keywords: column-beam connection, seismic analysis, seismic load, steel structure

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Authors: Nasrud Din, Fawad Saeed, Sajid Hussain, Rai Muhammad Dawood Sultan, Premkumar Sellan, Qasim Khan, Wei Lei

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The continuously increasing external quantum efficiencies of Perovskite light-emitting diodes (LEDs) have received significant interest in the scientific community. The need for monitoring and medical diagnostics has experienced a steady growth in recent years, primarily caused by older people and an increasing number of heart attacks, tumors, and cancer disorders among patients. The application of Perovskite near-infrared light-emitting diode (PeNIRLEDs) has exhibited considerable efficacy in bioimaging, particularly in the visualization and examination of blood arteries, blood clots, and tumors. PeNIRLEDs exhibit exciting potential in the field of blood vessel imaging because of their advantageous attributes, including improved depth penetration and less scattering in comparison to visible light. In this study, we synthesized FAPbI₃ Perovskite doped with different concentrations of 5-Aminovaleric acid (5-AVA) 1-6 mg. The incorporation of 5-AVA as a dopant during the FAPbI₃ Perovskite formation influences the FAPbI3 Perovskite’s structural and optical properties, improving its stability, photoluminescence efficiency, and charge transport characteristics. We found a resulting PL emission peak wavelength of 850 nm and bandwidth of 44 nm, along with a calculated quantum yield of 75%. The incorporation of 5-AVA-modified FAPbI₃ Perovskite into LEDs will show promising results, enhancing device efficiency, color purity, and stability. Making it suitable for various medical applications, including subcutaneous deep vein imaging, blood flow visualization, and tumor illumination.

Keywords: perovskite light-emitting diodes, deep vein imaging, blood flow visualization, tumor illumination

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Authors: K. Nirmalkumar

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An acute water scarcity is prevailing in the dry season in and around Perundurai (Erode district, Tamil Nadu, India) where there are more number of tannery units. Hence an attempt was made to use the effluent from the tannery industry for construction purpose. The mechanical properties such as compressive strength, tensile strength, flexural strength and the special properties such as chloride attack, sulphate attack and chemical attack were studied by casting various concrete specimens in form of cube, cylinders and beams, etc. It was observed that the concrete had some reduction in strength while subjected to chloride attack, sulphate attack and chemical attack. So admixtures were selected and optimized in suitable proportion to counter act the adverse effects and the results were found to be satisfactory. In this research study the corrosion results of specimens prepared by using treated and untreated tannery effluent were compared with the concrete specimens prepared by using potable water. It was observed that by the addition of admixtures, the adverse effects due to the usage of the treated and untreated tannery effluent are counteracted.

Keywords: corrosion, calcium nitrite, concrete, fly ash

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Authors: Hau-Wei Lee, Yu-Chi Liu, Chien-Hung Liu

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A mathematical model is presented for a system that measures rotational errors in a shaft using a ball lens. The geometric optical characteristics of the ball lens mounted on the shaft allows the measurement of rotation axis errors in both the radial and axial directions. The equipment used includes two quadrant detectors (QD), two laser diodes and a ball lens that is mounted on the rotating shaft to be evaluated. Rotational errors in the shaft cause changes in the optical geometry of the ball lens. The resulting deflection of the laser beams is detected by the QDs and their output signals are used to determine rotational errors. The radial and the axial rotational errors can be calculated as explained by the mathematical model. Results from system calibration show that the measurement error is within ±1 m and resolution is about 20 nm. Using a direct drive motor (DD motor) as an example, experimental results show a rotational error of less than 20 m. The most important features of this system are that it does not require the use of expensive optical components, it is small, very easy to set up, and measurements are highly accurate.

Keywords: ball lens, quadrant detector, axial error, radial error

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Authors: Mahmoud S. Zaghloul, Mohammed M. Khalaf, Wael N. Thabet, Haidy N. Asham

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Background. Hypertrophic scarring is a difficult problem for burn patients, and scar management is an essential aspect of outpatient burn therapy. Post-burn pathologic scars involve functional and aesthetic limitations that have a dramatic influence on the patient’s quality of life. The aim was to investigate the use of extracorporeal shock wave therapy (ESWT), which targets the fibroblasts in scar tissue, as an effective modality for scar treatment in burn patients. Subjects and methods: forty patients with post-burn scars were assigned randomly into two equal groups; their ages ranged from 20-45 years. The study group received ESWT and traditional physical therapy program (deep friction massage, stretching exercises). The control group received traditional physical therapy program (deep friction massage, stretching exercises). All groups received two sessions per week for six successful weeks. The data were collected before and after the same period of treatment for both groups. Evaluation procedures were carried out to measure scar thickness using ultrasonography and Vancouver Scar Scale (VSS) was completed before and after treatment. Results: Post-treatment results showed that there was a significant improvement difference in scar thickness in both groups in favor of the study group. Percentage of improvement in scar thickness in the study group was 42.55%, while it was 12.15% in the control group. There was also a significant improvement difference between results obtained using VSS in both groups in favor of the study group. Conclusion: ESWT is effective in management of pathologic post burn scars.

Keywords: extracorporeal shock wave therapy, post-burn scars, ultrasonography, Vancouver scar scale

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Authors: M. Najafi

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In the present paper, a large turbo-generator shaft train including a heavy-duty gas turbine engine, a coupling, and a generator is established. The method of analysis is based on finite element simplified model for lateral and torsional vibration calculation. The basic elements of rotor are the shafts and the disks which are represented as circular cross section flexible beams and rigid body elements, respectively. For more accurate results, the gyroscopic effect and bearing dynamics coefficients and function of rotation are taken into account, and for the influence of shear effect, rotor has been modeled in the form of Timoshenko beam. Lateral critical speeds, critical speed map, damped mode shapes, Campbell diagram, zones of instability, amplitudes, phase angles response due to synchronous forces of excitation and amplification factor are calculated. Also, in the present paper, the effect of imbalanced rotor and effects of changing in internal force and temperature are studied.

Keywords: rotor dynamic analysis, finite element method, shaft train, Campbell diagram

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Arfanara Najnin, Michael W. Roach, Jr., Dr. Jianhong Cecilia Xia

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The SkyCar Rapid Transit System (SRT) is an innovative intelligent transport system for the sustainable urban transport system. This system will increase the urban area network connectivity and decrease urban area traffic congestion. The SRT system is designed as a suspended Personal Rapid Transit (PRT) system that travels under a guideway 5m above the ground. A driver-less passenger is via pod-cars that hang from slender beams supported by columns that replace existing lamp posts. The beams are setup in a series of interconnecting loops providing non-stop travel from beginning to end to assure journey time. The SRT forward movement is effected by magnetic motors built into the guideway. Passenger stops are at either at line level 5m above the ground or ground level via a spur guideway that curves off the main thoroughfare. The main objective of this paper is to propose an integrated Automated Transit Network (ATN) technology for the future intelligent transport system in the urban built environment. To fulfil the objective a 4D simulated model in the urban built environment has been proposed by using the concept of SRT-ATN system. The methodology for the design, construction and testing parameters of a Technology Demonstrator (TD) for proof of concept and a Simulator (S) has been demonstrated. The completed TD and S will provide an excellent proving ground for the next development stage, the SRT Prototype (PT) and Pilot System (PS). This paper covered by a 4D simulated model in the virtual built environment is to effectively show how the SRT-ATN system works. OpenSim software has been used to develop the model in a virtual environment, and the scenario has been simulated to understand and visualize the proposed SkyCar Rapid Transit Network model. The SkyCar system will be fabricated in a modular form which is easily transported. The system would be installed in increasingly congested city centers throughout the world, as well as in airports, tourist resorts, race tracks and other special purpose for the urban community. This paper shares the lessons learnt from the proposed innovation and provides recommendations on how to improve the future transport system in urban built environment. Safety and security of passengers are prime factors to be considered for this transit system. Design requirements to meet the safety needs to be part of the research and development phase of the project. Operational safety aspects would also be developed during this period. The vehicles, the track and beam systems and stations are the main components that need to be examined in detail for safety and security of patrons. Measures will also be required to protect columns adjoining intersections from errant vehicles in vehicular traffic collisions. The SkyCar Rapid Transit takes advantage of all current disruptive technologies; batteries, sensors and 4G/5G communication and solar energy technologies which will continue to reduce the costs and make the systems more profitable. SkyCar's energy consumption is extremely low compared to other transport systems.

Keywords: SkyCar, rapid transit, Intelligent Transport System (ITS), Automated Transit Network (ATN), urban built environment, 4D Visualization, smart city

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Authors: Hermalina Sinay, Estri L. Arumingtyas

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The research objective was to determine the expression level of dehydration responsive element binding/DREB gene of local corn cultivars from Kisar Island Maluku. The study design was a randomized block design with single factor consist of six local corn cultivars obtained from farmers in Kisar Island and one reference varieties wich has been released by the government as a drought-tolerant varieties and obtained from Cereal Crops Research Institute (ICERI) Maros South Sulawesi. Leaf samples were taken is the second leaf after the flag leaf at the 65 days after planting. Isolation of total RNA from leaf samples was carried out according to the protocols of the R & A-BlueTM Total RNA Extraction Kit and was used as a template for cDNA synthesis. The making of cDNA from total RNA was carried out according to the protocol of One-Step Reverse Transcriptase PCR Premix Kit. Real Time-PCR was performed on cDNA from reverse transcription followed the procedures of Real MODTM Green Real-Time PCR Master Mix Kit. Data obtained from the real time-PCR results were analyzed using relative quantification method based on the critical point / Cycle Threshold (CP / CT). The results of gene expression analysis of DREB gene showed that the expression level of the gene was highest obtained at Deep Yellow local corn cultivar, and the lowest one was obtained at the Rubby Brown Cob cultivar. It can be concluded that the expression level of DREB gene of Deep Yellow local corn cultivar was highest than other local corn cultivars and Srikandi variety as a reference variety.

Keywords: expression, level, DREB gene, local corn cultivars, Kisar Island, Maluku

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Authors: Hussein Bizimana, Osman Sönmez

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Constructed wetlands are current the most widely recognized waste water treatment option, especially in developing countries where they have the potential for improving water quality and creating valuable wildlife habitat in ecosystem with treatment requirement relatively simple for operation and maintenance cost. Lack of grey waste water treatment facilities in Kigali İnstitute of Science and Technology in Rwanda, causes pollution in the surrounding localities of Rugunga sector, where already a problem of poor sanitation is found. In order to treat grey water produced at Kigali İnstitute of Science and Technology, with high BOD concentration, high nutrients concentration and high alkalinity; a Horizontal Sub-surface Flow pilot-scale constructed wetland was designed and can operate in Kigali İnstitute of Science and Technology. The study was carried out in a sedimentation tank of 5.5 m x 1.42 m x 1.2 m deep and a Horizontal Sub-surface constructed wetland of 4.5 m x 2.5 m x 1.42 m deep. The grey waste water flow rate of 2.5 m3/d flew through vegetated wetland and sandy pilot plant. The filter media consisted of 0.6 to 2 mm of coarse sand, 0.00003472 m/s of hydraulic conductivity and cattails (Typha latifolia spp) were used as plants species. The effluent flow rate of the plant is designed to be 1.5 m3/ day and the retention time will be 24 hrs. 72% to 79% of BOD, COD, and TSS removals are estimated to be achieved, while the nutrients (Nitrogen and Phosphate) removal is estimated to be in the range of 34% to 53%. Every effluent characteristic will meet exactly the Rwanda Utility Regulatory Agency guidelines primarily because the retention time allowed is enough to make the reduction of contaminants within effluent raw waste water. Treated water reuse system was developed where water will be used in the campus irrigation system again.

Keywords: constructed wetlands, hydraulic conductivity, grey waste water, cattails

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Authors: Mahmood Heshmati, Farhang Daneshmand

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Material scientists and engineers have introduced novel materials with complex geometries due to the recent technological advances and promotion of manufacturing methods. Among them, lattice structures with graded architectures denoted by functionally graded porous materials (FGPMs) have been developed to optimize the structural response. FGPMs are achieved by tailoring the size and density of the internal pores in one or more directions that lead to the desired mechanical properties and structural responses. Also, FGPMs provide more flexible transition and the possibility of designing and fabricating structural elements with complex and variable properties. In this paper, wave propagation in lattice structures with functionally graded (FG) porosity is investigated in order to examine the ability of shock absorbing effect. The behavior of FG porous beams with different porosity distributions under impact load and the effects of porosity distribution and porosity content on the wave speed are studied. Important conclusions are made, along with a discussion of the future scope of studies on FGPMs structures.

Keywords: functionally graded, porous materials, wave propagation, impact load, finite element

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Isao Tomita

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We develop a periodically-poled LiNbO3 (PPLN) device for highly-efficient third-harmonic generation (THG), where the THG efficiency is enhanced with a cavity. THG can usually be produced via χ(3)-nonlinear materials by optical pumping with very high pump-power. Instead, we here propose THG by moderate-power pumping through a specially-designed PPLN device containing only χ(2)-nonlinearity, where sum-frequency generation in the χ(2) process is employed for the mixing of a pump beam and a second-harmonic-generation (SHG) beam produced from the pump beam. The cavity is designed to increase the SHG power with dichroic mirrors attached to both ends of the device that perfectly reflect the SHG beam back to the device and yet let the pump and THG beams pass through the mirrors. This brings about a THG-power enhancement because of THG power proportional to the enhanced SHG power. We examine the THG-efficiency dependence on the mirror reflectance and show that very high THG-efficiency is obtained at moderate pump-power when compared with that of a cavity-free PPLN device.

Keywords: cavity, periodically-poled LiNbO₃, sum-frequency generation, third-harmonic generation

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Authors: Clotilde Guyon, Nada Jmari, Yen-Chin Li, Jean Denoyel, Noriyuki Fujikado, Christophe Blanchet, David Root, Matthieu Giraud

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Aire induces ectopic expression of a large repertoire of tissue-specific antigen (TSA) genes in thymic medullary epithelial cells (MECs), driving immunological self-tolerance in maturing T cells. Although important mechanisms of Aire-induced transcription have recently been disclosed through the identification and the study of Aire’s partners, the fine transcriptional functions underlied by a number of them and conferred to Aire are still unknown. Alternative cleavage and polyadenylation (APA) is an essential mRNA processing step regulated by the termination complex consisting of 85 proteins, 10 of them have been related to Aire. We evaluated APA in MECs in vivo by microarray analysis with mRNA-spanning probes and RNA deep sequencing. We uncovered the preference of Aire-dependent transcripts for short-3’UTR isoforms and for proximal poly(A) site selection marked by the increased binding of the cleavage factor Cstf-64. RNA interference of the 10 Aire-related proteins revealed that Clp1, a member of the core termination complex, exerts a profound effect on short 3’UTR isoform preference. Clp1 is also significantly upregulated in the MECs compared to 25 mouse tissues in which we found that TSA expression is associated with longer 3’UTR isoforms. Aire-dependent transcripts escape a global 3’UTR lengthening associated with MEC differentiation, thereby potentiating the repressive effect of microRNAs that are globally upregulated in mature MECs. Consistent with these findings, RNA deep sequencing of actinomycinD-treated MECs revealed the increased stability of short 3’UTR Aire-induced transcripts, resulting in TSA transcripts accumulation and contributing for their enrichment in the MECs.

Keywords: Aire, central tolerance, miRNAs, transcription termination

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Authors: Hardisiswo Soedjana, Inne Caroline

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Deep dermal or full thickness burns are inevitably lead to post-burn contractures. These contractures remain to be one of the most concerning late complications of burn injuries. Surgical management includes releasing the contracture followed by resurfacing the defect accompanied by post-operative rehabilitation. Optimal treatment of post-burn contractures depends on the characteristics of the contractures. This study is aimed to describe clinical characteristics, problems, and management of post-burn contractures in adults and children. A retrospective analysis was conducted from medical records of patients suffered from contractures after burn injuries admitted to Hasan Sadikin general hospital between January 2016 and January 2018. A total of 50 patients with post burn contractures were included in the study. There were 17 adults and 33 children. Most patients were male, whose age range within 15-59 years old and 5-9 years old. Educational background was mostly senior high school among adults, while there was only one third of children who have entered school. Etiology of burns was predominantly flame in adults (82.3%); whereas flame and scald were the leading cause of burn injury in children (11%). Based on anatomical regions, hands were the most common affected both in adults (35.2%) and children (48.5%). Contractures were identified in 6-12 months since the initial burns. Most post-burn hand contractures were resurfaced with full-thickness skin graft (FTSG) both in adults and children. There were 11 patients who presented with recurrent contracture after previous history of contracture release. Post-operative rehabilitation was conducted for all patients; however, it is important to highlight that it is still challenging to control splinting and exercise when patients are discharged and especially the compliance in children. In order to improve quality of life in patients with history of deep burn injuries, prevention of contractures should begin right after acute care has been established. Education for the importance of splinting and exercise should be administered as comprehensible as possible for adult patients and parents of pediatric patients.

Keywords: burn, contracture, education, exercise, splinting

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Authors: Balakrishna Shetty

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Introduction: Stem Cell Imaging is a challenging field since the advent of Stem Cell treatment in humans. Series of research on tagging and tracking the stem cells has not been very effective. The present study is an effort by the authors to track the stem cells injected into calf muscles by Magnetic Resonance Diffusion Weighted Imaging. Materials and methods: Stem Cell injection deep into the calf muscles of patients with peripheral vascular disease is one of the recent treatment modalities followed in our institution. 5 patients who underwent deep intramuscular injection of stem cells as treatment were included for this study. Pre and two hours Post injection MRI of bilateral calf regions was done using 1.5 T Philips Achieva, 16 channel system using 16 channel torso coils. Axial STIR, Axial Diffusion weighted images with b=0 and b=1000 values with back ground suppression (DWIBS sequence of Philips MR Imaging Systems) were obtained at 5 mm interval covering the entire calf. The invert images were obtained for better visualization. 120ml of autologous bone marrow derived stem cells were processed and enriched under c-GMP conditions and reduced to 40ml solution containing mixture of above stem cells. Approximately 40 to 50 injections, each containing 0.75ml of processed stem cells, was injected with marked grids over the calf region. Around 40 injections, each of 1ml normal saline, is injected into contralateral leg as control. Results: Significant Diffusion hyper intensity is noted at the site of injected stem cells. No hyper intensity noted before the injection and also in the control side where saline was injected conclusion: This is one of the earliest studies in literature showing diffusion hyper intensity in intramuscularly injected stem cells. The advantages and deficiencies in this study will be discussed during the presentation.

Keywords: stem cells, imaging, DWI, peripheral vascular disease

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Authors: Tahani Hallek, Dhaou Akrout, Riadh Ahmadi, Mabrouk Montacer

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The present work is an organic geochemical study of the Fahdene Formation outcrops at the Mahjouba region belonging to the Eastern part of the Kalaat Senan structure in northwestern Tunisia (the Kef-Tedjerouine area). The analytical study of the organic content of the samples collected, allowed us to point out that the Formation in question is characterized by an average to good oil potential. This fossilized organic matter has a mixed origin (type II and III), as indicated by the relatively high values of hydrogen index. This origin is confirmed by the C29 Steranes abundance and also by tricyclic terpanes C19/(C19+C23) and tetracyclic terpanes C24/(C24+C23) ratios, that suggest a marine environment of deposit with high plants contribution. We have demonstrated that the heterogeneity of organic matter between the marine aspect, confirmed by the presence of foraminifera, and the continental contribution, is the result of an episodic anomaly in relation to the sequential stratigraphy. Given that the study area is defined as an outer platform forming a transition zone between a stable continental domain to the south and a deep basin to the north, we have explained the continental contribution by successive forced regressions, having blocked the albian transgression, allowing the installation of the lowstand system tracts. This aspect is represented by the incised valleys filling, in direct contact with the pelagic and deep sea facies. Consequently, the Fahdene Formation, in the Kef-Tedjerouine area, consists of transgressive system tracts (TST) brutally truncated by extras of continental progradation; resulting in a mixed influence deposition having retained a heterogeneous organic material.

Keywords: molecular geochemistry, biomarkers, forced regression, deposit environment, mixed origin, Northern Tunisia

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Authors: Yuan Li, Yitao Han, Zhao Zhu

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In this paper, the effect of blasting loads on steel-concrete composite bridges has been investigated considering the slope reflection effect. Reasonable values of girder size, plate thickness, stiffening rib, and other design parameters were selected according to design specifications. Modified RHT (Riedel-Hiermaier-Thoma) was used as constitutive relation in analyses. In order to simulate the slope reflection effect, the slope of the bridge was precisely built in the model. Different blasting conditions, including top, middle, and bottom explosions, were simulated. The multi-Euler domain method based on fully coupled Lagrange and Euler models was adopted for the structural analysis of the explosion process using commercial software AUTODYN. The obtained results showed that explosion overpressure was increased by 3006, 879, and 449kPa, corresponding to explosions occurring at the top, middle, and bottom of the slope, respectively. At the same time, due to energy accumulation and transmission dissipation caused by slope reflection, the corresponding yield lengths of steel beams were increased by 8, 0, and 5m, respectively.

Keywords: steel-concrete composite bridge, explosion damage, slope reflection, blasting loads, RHT

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Authors: Kimia Khoshdel Vajari, Saber Saffar

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Reducing the residual stresses caused by welding is desirable for the industry. The effect of welding sequence, as well as the effect of yield stress on the number of residual stresses generated in Inconel X750 superalloy sheets and beams, have been investigated. The finite element model used in this research is a three-dimensional thermal and mechanical model, and the type of analysis is indirect coupling. This analysis is done in two stages. First, thermal analysis is performed, and then the thermal changes of the first analysis are used as the applied load in the second analysis. ABAQUS has been used for modeling, and the Dflux subroutine has been used in the Fortran programming environment to move the arc and the molten pool. The results of this study show that the amount of tensile residual stress in symmetric, discontinuous, and symmetric-discontinuous welds is reduced to a maximum of 27%, 54%, and 37% compared to direct welding, respectively. The results also show that the amount of residual stresses created by welding increases linearly with increasing yield stress with a slope of 40%.

Keywords: residual stress, X750 superalloy, finite element, welding, thermal analysis

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Authors: Rui Wu

Abstract:

In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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