Search results for: Neural Processing Element (NPE)

Authors: Devadrita Dey Sarkar

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Regardless of the many technologic advances in the past decade, increased training and experience, and the obvious benefits of uniform standards, the false-negative rate in screening mammography remains unacceptably high .A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this abstract which employs features extracted by a new technique based on independent component analysis. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral breast images has the potential to improve the overall performance in the detection of breast lumps. Because breast lumps can be detected reliably by computer on lateral breast mammographs, radiologists’ accuracy in the detection of breast lumps would be improved by the use of CAD, and thus early diagnosis of breast cancer would become possible. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for breast CAD may include the computerized detection of breast nodules, as well as the computerized classification of benign and malignant nodules. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with these CAD systems, which would be reliable and useful method for quantifying the similarity of a pair of images for visual comparison by radiologists.

Keywords: CAD(computer-aided design), lesions, neural network, ROS(region of suspicion)

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Authors: M. Ezati Kooshki, A. Abbaszadeh Shahri

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A number of devices used to control seismic structures have been developed during the past decades. One of the effective ways to reduce seismic forces transmitted to the buildings is through the base isolation systems, but the use of these devices is currently limited to large and expensive buildings. This study was an attempt to introduce an effective and low cost way to protect of structures against grand motions by a semi base isolation system. In this new way, structures were not completely decoupled of bases and the natural frequency of structures was changed due to earthquake by changing the horizontal stiffness; therefore, ground excitation energy was dissipated before entering the structures. For analyzing the dynamic behavior, the new method used finite element software (ABAQUS 6-10-1). This investigation introduced a new package of semi base isolation devices with a new material constitutive, but common in automobile industries, seeking to evaluate the effects of additional new devices on the seismic response when compared with structures without additional devises for different ground motions. The proposed semi base isolation devices were applied to a one story frame and the time history analysis was conducted on the record of Kobe earthquake (1995). The results showed that the efficiency reduced the floor acceleration and displacement, as well as velocity.

Keywords: semi base isolation system, finite element, natural frequency, horizontal stiffness

Procedia PDF Downloads 386

Authors: Min-Pei Ling

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Aflatoxin is a fungal secondary metabolite with high toxicity capable of contaminating various types of food crops. It has been identified as a Group 1 human carcinogen by the International Agency for Research on Cancer. Chronic aflatoxin exposure has caused a worldwide public food safety concern. Peanuts and peanut products are the major sources of aflatoxin exposure. Therefore, some reduction interventions have been developed to minimize contamination through the peanut production chain. The purpose of this study is to estimate the efficacy of interventions in reducing the health impact of hepatocellular carcinoma caused by aflatoxin contamination in peanuts. The estimated total disability-adjusted life-years (DALYs) was calculated using FDA-iRISK online software. Six aflatoxin reduction strategies were evaluated, including good agricultural practice (GAP), biocontrol, Purdue Improved Crop Storage packaging, basic processing, ozonolysis, and ultraviolet irradiation. The results indicated that basic processing could prevent huge public health loss of 4,079.7–21,833 total DALYs per year, which accounted for 39.6% of all decreased total DALYs. GAP and biocontrol were both effective strategies in the farm field, while the other three interventions were limited in reducing total DALYs. In conclusion, this study could help farmers, processing plants, and government policymakers to alleviate aflatoxin contamination issues in the peanut production chain.

Keywords: aflatoxin, health burden, disability-adjusted life-years, peanuts

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Authors: Xianwei Zheng, Yuan Yan Tang

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Classical window Fourier transform has been widely used in signal processing, image processing, machine learning and pattern recognition. The related Gabor transform is powerful enough to capture the texture information of any given dataset. Recently, in the emerging field of graph signal processing, researchers devoting themselves to develop a graph signal processing theory to handle the so-called graph signals. Among the new developing theory, windowed graph Fourier transform has been constructed to establish a time-frequency analysis framework of graph signals. The windowed graph Fourier transform is defined by using the translation and modulation operators of graph signals, following the similar calculations in classical windowed Fourier transform. Specifically, the translation and modulation operators of graph signals are defined by using the Laplacian eigenvectors as follows. For a given graph signal, its translation is defined by a similar manner as its definition in classical signal processing. Specifically, the translation operator can be defined by using the Fourier atoms; the graph signal translation is defined similarly by using the Laplacian eigenvectors. The modulation of the graph can also be established by using the Laplacian eigenvectors. The windowed graph Fourier transform based on these two operators has been applied to obtain time-frequency representations of graph signals. Fundamentally, the modulation operator is defined similarly to the classical modulation by multiplying a graph signal with the entries in each Fourier atom. However, a single Laplacian eigenvector entry cannot play a similar role as the Fourier atom. This definition ignored the relationship between the translation and modulation operators. In this paper, a new definition of the modulation operator is proposed and thus another time-frequency framework for graph signal is constructed. Specifically, the relationship between the translation and modulation operations can be established by the Fourier transform. Specifically, for any signal, the Fourier transform of its translation is the modulation of its Fourier transform. Thus, the modulation of any signal can be defined as the inverse Fourier transform of the translation of its Fourier transform. Therefore, similarly, the graph modulation of any graph signal can be defined as the inverse graph Fourier transform of the translation of its graph Fourier. The novel definition of the graph modulation operator established a relationship of the translation and modulation operations. The new modulation operation and the original translation operation are applied to construct a new framework of graph signal time-frequency analysis. Furthermore, a windowed graph Fourier frame theory is developed. Necessary and sufficient conditions for constructing windowed graph Fourier frames, tight frames and dual frames are presented in this paper. The novel graph signal time-frequency analysis framework is applied to signals defined on well-known graphs, e.g. Minnesota road graph and random graphs. Experimental results show that the novel framework captures new features of graph signals.

Keywords: graph signals, windowed graph Fourier transform, windowed graph Fourier frames, vertex frequency analysis

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Authors: Chien-Hung Liu, Yu-Fen Chen

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This study used triangular laser probe and three-axial direction mobile platform for surface measurement, programmed it and applied it to real-time analytic statistics of different measured data. This structure was used to design a system integration program: using triangular laser probe for scattering or reflection non-contact measurement, transferring the captured signals to the computer through RS-232, and using RS-485 to control the three-axis platform for a wide range of measurement. The data captured by the laser probe are formed into a 3D surface. This study constructed an optical measurement application program in the concept of visual programming language. First, the signals are transmitted to the computer through RS-232/RS-485, and then the signals are stored and recorded in graphic interface timely. This programming concept analyzes various messages, and makes proper presentation graphs and data processing to provide the users with friendly graphic interfaces and data processing state monitoring, and identifies whether the present data are normal in graphic concept. The major functions of the measurement system developed by this study are thickness measurement, SPC, surface smoothness analysis, and analytical calculation of trend line. A result report can be made and printed promptly. This study measured different heights and surfaces successfully, performed on-line data analysis and processing effectively, and developed a man-machine interface for users to operate.

Keywords: laser probe, non-contact measurement, triangulation measurement principle, statistical process control, labVIEW

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Authors: Maria Pavlova

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In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems.

Keywords: camera, object recognition, OpenCV, Raspberry

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Authors: Ashwini Shripatwar, Mayur Biyani, Nikhil Rao, Rajendra Bodake, Sachin Sane

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This paper documents the correlation of valve cover sealing, cracking, and warpage Finite Element Modelling with observations on engine test development. The valve cover is a component mounted on engine head with a gasket which provides sealing against oil which flows around camshaft, valves, rockers, and other overhead components. Material nonlinearity and contact nonlinearity characteristics are taken into consideration because the valve cover is made of a composite material having temperature dependent elastic-plastic properties and because the gasket load-deformation curve is also nonlinear. The leakage is observed between the valve cover and the engine head due to the insufficient contact pressure. The crack is observed on the valve cover due to force application at a region with insufficient stiffness and with elevated temperature. The valve cover shrinkage is observed during the disassembly process on hot exhaust side bolt holes after the engine has been running. In this paper, an analytical approach is developed to correlate a Finite Element Model with the observed failures and to address the design issues associated with the failure modes in question by making design changes in the model.

Keywords: cracking issue, gasket sealing analysis, nonlinearity of contact and material, valve cover

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Authors: Louis Nahimana

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Most maps are in a raster or pdf format and it is not easy to get vector layers of published maps. Faced to the production of geological simplified map of the northern Lake Tanganyika countries without geological information in vector format, I tried a method of obtaining vector layers from raster layers created from geological maps of Rwanda and DR Congo in pdf and jpg format. The procedure was as follows: The original raster maps were georeferenced using ArcGIS10.2. Under Adobe Photoshop, map areas with the same color corresponding to a lithostratigraphic unit were selected all over the map and saved in a specific raster layer. Using the same image processing software Adobe Photoshop, each RGB raster layer was converted in grayscale type and improved before importation in ArcGIS10. After georeferencing, each lithostratigraphic raster layer was transformed into a multitude of polygons with the tool "Raster to Polygon (Conversion)". Thereafter, tool "Aggregate Polygons (Cartography)" allowed obtaining a single polygon layer. Repeating the same steps for each color corresponding to a homogeneous rock unit, it was possible to reconstruct the simplified geological constitution of Rwanda and the Democratic Republic of Congo in vector format. By using the tool «Append (Management)», vector layers obtained were combined with those from Burundi to achieve vector layers of the geology of the « Northern Lake Tanganyika countries ».

Keywords: creating raster layer under image processing software, raster to polygon, aggregate polygons, adobe photoshop

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Authors: Dhruv J. Limaye, Hanga Galfalvy, Cheick A. Sissoko, Yung-yu Huang, Chunanning Tang, Ying Liu, Shu-Chi Hsiung, Andrew J. Dwork, Gorazd B. Rosoklija, Victoria Arango, Lewis Brown, J. John Mann, Maura Boldrini

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Memory and emotion require hippocampal cell viability and connectivity and are disrupted in major depressive disorder (MDD). Applying shotgun proteomics and stereological quantification of neural progenitor cells (NPCs), intermediate neural progenitors (INPs), and mature granule neurons (GNs), to postmortem human hippocampus, identified differentially expressed proteins (DEPs), and fewer NPCs, INPs and GNs, in untreated MDD (uMDD) compared with non-psychiatric controls (CTRL) and antidepressant-treated MDD (MDDT). DEPs lower in uMDD vs. CTRL promote mitosis, differentiation, and prevent apoptosis. DEPs higher in uMDD vs. CTRL inhibit the cell cycle, and regulate cell adhesion, neurite outgrowth, and DNA repair. DEPs lower in MDDT vs. uMDD block cell proliferation. We observe group-specific correlations between numbers of NPCs, INPs, and GNs and an abundance of proteins regulating mitosis, differentiation, and apoptosis. Altered protein expression underlies hippocampus cellular and volume loss in uMDD, supports a trophic effect of antidepressants, and offers new treatment targets.

Keywords: proteomics, hippocampus, depression, mitosis, migration, differentiation, mitochondria, apoptosis, antidepressants, human brain

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Authors: Shuo Mu, Guangzhi Jiang, Jinsa Chen

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The analysis of Indel for RNA sequencing of clinical samples is easily affected by sequencing experiment errors and software selection. In order to improve the efficiency and accuracy of analysis, we developed an automatic reporting system for Indel recognition and annotation based on image snapshot of transcriptome reads alignment. This system includes sequence local-assembly and realignment, target point snapshot, and image-based recognition processes. We integrated high-confidence Indel dataset from several known databases as a training set to improve the accuracy of image processing and added a bioinformatical processing module to annotate and filter Indel artifacts. Subsequently, the system will automatically generate data, including data quality levels and images results report. Sanger sequencing verification of the reference Indel mutation of cell line NA12878 showed that the process can achieve 83% sensitivity and 96% specificity. Analysis of the collected clinical samples showed that the interpretation accuracy of the process was equivalent to that of manual inspection, and the processing efficiency showed a significant improvement. This work shows the feasibility of accurate Indel analysis of clinical next-generation sequencing (NGS) transcriptome. This result may be useful for RNA study for clinical samples with microsatellite instability in immunotherapy in the future.

Keywords: automatic reporting, indel, next-generation sequencing, NGS, transcriptome

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Dovile Petkeviciute-Barysiene

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Legal technologies not only create new ways for accessing and providing legal services but also transform the role of legal practitioners. Both lawyers and users of legal services expect automated solutions to outperform people with objectivity and impartiality. Although fairness of the automated decisions is crucial, research on assessing various characteristics of automated processes related to the perceived fairness has only begun. One of the major obstacles to this research is the lack of comprehensive understanding of what legal actions are automated and could be meaningfully automated, and to what extent. Neither public nor legal practitioners oftentimes cannot envision technological input due to the lack of general without illustrative examples. The aim of this study is to map decision making stages and automation levels which are and/or could be achieved in legal actions related to pre-trial and trial processes. Major legal decisions and judgments are identified during the consultations with legal practitioners. The dual-process model of information processing is used to describe cognitive processes taking place while making legal decisions and judgments during pre-trial and trial action. Some of the existing legal technologies are incorporated into the analysis as well. Several published automation level taxonomies are considered because none of them fit well into the legal context, as they were all created for avionics, teleoperation, unmanned aerial vehicles, etc. From the information processing perspective, analysis of the legal decisions and judgments expose situations that are most sensitive to cognitive bias, among others, also help to identify areas that would benefit from the automation the most. Automation level analysis, in turn, provides a systematic approach to interaction and cooperation between humans and algorithms. Moreover, an integrated map of legal decisions and judgments, information processing characteristics, and automation levels all together provide some groundwork for the research of legal technology perceived fairness and acceptance. Acknowledgment: This project has received funding from European Social Fund (project No 09.3.3-LMT-K-712-19-0116) under grant agreement with the Research Council of Lithuania (LMTLT).

Keywords: automation levels, information processing, legal judgment and decision making, legal technology

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Authors: E. Ghasemi Ardi, K. J. Dong, A. B. Yu, R. Y. Yang

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In current work, a numerical model based on the discrete element method (DEM) was developed which provided information about particle dynamic and impact event condition inside a laboratory scale impact mill (Fritsch). It showed that each particle mostly experiences three impacts inside the mill. While the first impact frequently happens at front surface of the rotor’s rib, the frequent location of the second impact is side surfaces of the rotor’s rib. It was also showed that while the first impact happens at small impact angle mostly varying around 35º, the second impact happens at around 70º which is close to normal impact condition. Also analyzing impact energy revealed that varying mill speed from 6000 to 14000 rpm, the ratio of first impact’s average impact energy and minimum required energy to break particle (Wₘᵢₙ) increased from 0.30 to 0.85. Moreover, it was seen that second impact poses intense impact energy on particle which can be considered as the main cause of particle splitting. Finally, obtained information from DEM simulation along with obtained data from conducted experiments was implemented in semi-empirical equations in order to find selection and breakage functions. Then, using a back-calculation approach, those parameters were used to predict the PSDs of ground particles under different impact energies. Results were compared with experiment results and showed reasonable accuracy and prediction ability.

Keywords: single particle breakage, particle dynamic, population balance model, particle size distribution, discrete element method

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Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

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Authors: Yogesh Karunakar, Praveen Kandaswamy

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Mother’s milk provides the most superior source of nutrition to a child. There is no other substitute to the mother’s milk. Postpartum secretions like breast milk can be analyzed on the go for testing the presence of any harmful pathogen before a mother can feed the child or donate the milk for the milk bank. Since breast feeding is one of the main causes for transmission of diseases to the newborn, it is mandatory to test the secretions. In this paper, we describe the detection of pathogens like E-coli, Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), Hepatitis C (HCV), Cytomegalovirus (CMV), Zika and Ebola virus through an innovative method, in which we are developing a unique chip for testing the mother’s milk sample. The chip will contain an antibody specific to the target pathogen that will show a color change if there are enough pathogens present in the fluid that will be considered dangerous. A smart-phone camera will then be acquiring the image of the strip and using various image processing techniques we will detect the color development due to antigen antibody interaction within 5 minutes, thereby not adding to any delay, before the newborn is fed or prior to the collection of the milk for the milk bank. If the target pathogen comes positive through this method, then the health care provider can provide adequate treatment to bring down the number of pathogens. This will reduce the postpartum related mortality and morbidity which arises due to feeding infectious breast milk to own child.

Keywords: postpartum, fluids, camera, HIV, HCV, CMV, Zika, Ebola, smart-phones, breast milk, pathogens, image processing techniques

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Authors: Sungho Kim, Dae Shik Kim, Jong Min Lee

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Fluid catalytic cracking (FCC) process is one of the most important process in modern refinery indusrty. This paper focuses on the fluid catalytic cracking (FCC) process. As the FCC process is difficult to model well, due to its nonlinearities and various interactions between its process variables, rigorous process modeling of whole FCC plant is demanded for control and plant-wide optimization of the plant. In this study, a process design for the FCC plant includes riser reactor, main fractionator, and gas processing unit was developed. A reactor model was described based on four-lumped kinetic scheme. Main fractionator, gas processing unit and other process units are designed to simulate real plant data, using a process flowsheet simulator, Aspen PLUS. The custom reactor model was integrated with the process flowsheet simulator to develop an integrated process model.

Keywords: fluid catalytic cracking, simulation, plant data, process design

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Authors: Sathapath Kilaso

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Nowadays, Today, wireless sensor networks are an important technology that works with Internet of Things. It is receiving various data from many sensor. Then sent to processing or storing. By wireless network or through the Internet. The devices around us are intelligent, can receiving/transmitting and processing data and communicating through the system. There are many applications of wireless sensor networks, such as smart city, smart farm, environmental management, weather. This article will explore the use of wireless sensor networks in Thailand and collect data from Thai Thesis database in 2012-2017. How to Implementing Wireless Sensor Network Technology. Advantage from this study To know the usage wireless technology in many fields. This will be beneficial for future research. In this study was found the most widely used wireless sensor network in agriculture field. Especially for smart farms. And the second is the adoption of the environment. Such as weather stations and water inspection.

Keywords: wireless sensor network, smart city, survey, Adhoc Network

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Authors: Mohammed A. Sakr, Tarek M. Khalifa, Walid N. Mansour

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External bonding of fiber reinforced polymer (FRP) plates to reinforced concrete (RC) beams is an effective technique for flexural strengthening. This paper presents an analytical parametric study on the behavior of RC continuous beams flexurally strengthened with externally bonded FRP plates on the upper and lower fibers, conducted using simple uniaxial nonlinear finite element model (UNFEM). UNFEM is able to estimate the load-carrying capacity, different failure modes and the interfacial stresses of RC continuous beams flexurally strengthened with externally bonded FRP plates on the upper and lower fibers. The study investigated the effect of five key parameters on the behavior and moment redistribution of FRP-reinforced continuous beams. The investigated parameters were the length of the FRP plate, the width and the thickness of the FRP plate, the ratio between the area of the FRP plate to the concrete area, the cohesive shear strength of the adhesive layer, and the concrete compressive strength. The investigation resulted in a number of important conclusions reflecting the effects of the studied parameters on the behavior of RC continuous beams flexurally strengthened with externally bonded FRP plates.

Keywords: continuous beams, parametric study, finite element, fiber reinforced polymer

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Authors: Yongmin You

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Electric machines for office automation device such as printer and scanner have been required the low noise and vibration performance. Many researches about the low noise and vibration of polygon mirror scanner motor have been also progressed. The noise and vibration of polygon mirror scanner motor can be classified by aerodynamic, structural and electromagnetic. Electromagnetic noise and vibration can be occurred by high cogging torque and nonsinusoidal back EMF. To improve the cogging torque and back EMF characteristic, we apply unequal air-gap. To analyze characteristic of a polygon mirror scanner motor, two dimensional finite element method is used. To minimize the cogging torque of a polygon mirror motor, Kriging based on latin hypercube sampling (LHS) is utilized. Compared to the initial model, the torque ripple of the optimized unequal air-gap model was reduced by 23.4 % while maintaining the back EMF and average torque. To verify the optimal design results, the experiment was performed. We measured the vibration in motors at 23,600 rpm which is the rated velocity. The radial and axial gravitational acceleration of the optimal model were declined more than seven times and three times, respectively. From these results, a shape optimized unequal polygon mirror scanner motor has shown the usefulness of an improvement in the torque ripple and electromagnetic vibration characteristic.

Keywords: polygon mirror scanner motor, optimal design, finite element method, vibration

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Authors: Esmat Momeni, Shabnam Basari, Mohammad Beheshtinia

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The purpose of this study is to provide a symbolic explanation of the four elements in Zoroastrianism and sustainable ecosystems with an ecological approach. The research method is fundamental and deductive content analysis. Data collection has been done through library and documentary methods and through reading books and related articles. The population and sample of the present study are Yazd city and Iran country after discovering symbolic concepts derived from the theoretical foundations of Zoroastrianism in four elements of water, air, soil, fire and conformity with Iranian architecture with the ecological approach in Yazd city, the sustainable ecosystem it is explained by the system of nature. The validity and reliability of the results are based on the trust and confidence of the research literature. Research findings show that Yazd was one of the bases of Zoroastrianism in Iran. Many believe that the first person to discuss the elements of nature and respect Zoroastrians is the Prophet of this religion. Keeping the environment clean and pure by paying attention to and respecting these four elements. The water element is a symbol of existence in Zoroastrianism, so the people of Yazd used the aqueduct and designed a pool in front of the building. The soil element is a symbol of the raw material of human creation in the Zoroastrian religion, the most readily available material in the desert areas of Yazd, used as bricks and adobes, creating one of the most magnificent roof coverings is the dome. The wind element represents the invisible force of the soul in Creation in Zoroastrianism, the most important application of wind in the windy, which is a highly efficient cooling system. The element of fire, which is always a symbol of purity in Zoroastrianism, is located in a special place in Yazd's Ataskadeh (altar/ temple), where the most important religious prayers are held in and against the fire. Consequently, indigenous knowledge and attention to indigenous architecture is a part of the national capital of each nation that encompasses their beliefs, values, methods, and knowledge. According to studies on the four elements of Zoroastrianism, the link between these four elements are that due to the hot and dry fire at the beginning, it is the fire that begins to follow the nature of the movement in the stillness of the earth, and arises from the heat of the fire and because of vigor and its decreases, cold (wind) emerges, and from cold, humidity and wetness. And by examining books and resources on Yazd's architectural design with an ecological approach to the values of the four elements Zoroastrianism has been inspired, it can be concluded that in order to have environmentally friendly architecture, it is essential to use sustainable architectural principles, to link religious and sacrament culture and ecology through architecture.

Keywords: ecology, architecture, quadruple elements of air, soil, water, fire, Zoroastrian religion, sustainable ecosystem, Iran, Yazd city

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Authors: Stephen J Futter, Michael I Okereke

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This paper presents an investigation of the premature in-service failure of the engine mounting towers that form part of the bedframe commonly used for industrial power generation applications. The client during a routine in-service assessment of the generator set observed that the engine mounting towers had cracked. Thus, this study has investigated in detail the origin of the crack and proffered solutions to prevent a re-occurrence. Seven step problem solving methodology was followed during this paper. The study used both experimental and numerical approaches to understand, monitor and evaluate the cause and evolution of the premature failure. Findings from this study indicated that the failure resulted from a combination of varied processes from procurement of material parts, material selection, welding processes and inaptly designed load-bearing mechanics of the generating set and its mounting arrangement. These in-field observations and experimental simulations provided insights to design and validate a numerical finite element sub-model of the cracked bedframe considering thermal cycling: designed as part of these investigations. Resulting findings led to a recommendation of several procedural changes that should be adopted by the manufacturer, in order to prevent the re-occurrence of such pre-mature failure in future industrial applications.

Keywords: Engine, Premature Failure, Failure Analysis, Finite Element Model

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Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: Jana Ismail, Layla Fallatah, Maha Alshmaisi

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ChatGPT, a cutting-edge language model built on the GPT-3.5 architecture, has garnered attention for its profound natural language processing capabilities, holding promise for transformative applications in customer service and content creation. This study delves into ChatGPT's architecture, aiming to comprehensively understand its strengths and potential limitations. Through systematic experiments across diverse domains, such as general knowledge and creative writing, we evaluated the model's coherence, context retention, and task-specific accuracy. While ChatGPT excels in generating human-like responses and demonstrates adaptability, occasional inaccuracies and sensitivity to input phrasing were observed. The study emphasizes the impact of prompt design on output quality, providing valuable insights for the nuanced deployment of ChatGPT in conversational AI and contributing to the ongoing discourse on the evolving landscape of natural language processing in artificial intelligence.

Keywords: artificial Inelegance, chatGPT, open AI, NLP

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Authors: Nisha Nandlal Sharma, Julaluk Carmai, Saiprasit Koetniyom, Bernd Markert

Abstract:

Whiplash Injuries are usually associated with car accidents. The sudden forward or backward jerk to head causes neck strain, which is the result of damage to the muscle or tendons. Neck pain and headaches are the two most common symptoms of whiplash. Symptoms of whiplash are commonly reported in studies but the Injury mechanism is poorly understood. Neck muscles are the most important factor to study the neck Injury. This study focuses on the development of finite element (FE) model of human neck muscle to study the whiplash injury mechanism and effect of active muscle contraction on occupant kinematics. A detailed study of Injury mechanism will promote development and evaluation of new safety systems in cars, hence reducing the occurrence of severe injuries to the occupant. In present study, an active human finite element (FE) model with 3D neck muscle model is developed. Neck muscle was modeled with a combination of solid tetrahedral elements and 1D beam elements. Muscle active properties were represented by beam elements whereas, passive properties by solid tetrahedral elements. To generate muscular force according to inputted activation levels, Hill-type muscle model was applied to beam elements. To simulate non-linear passive properties of muscle, solid elements were modeled with rubber/foam material model. Material properties were assigned from published experimental tests. Some important muscles were then inserted into THUMS (Total Human Model for Safety) 50th percentile male pedestrian model. To reduce the simulation time required, THUMS lower body parts were not included. Posterior to muscle insertion, THUMS was given a boundary conditions similar to experimental tests. The model was exposed to 4g and 7g rear impacts as these load impacts are close to low speed impacts causing whiplash. The effect of muscle activation level on occupant kinematics during whiplash was analyzed.

Keywords: finite element model, muscle activation, neck muscle, whiplash injury prevention

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Authors: Elida Gastelum-Martinez, Neith A. Pacheco-Lopez, Juan L. Morales-Landa

Abstract:

Cashew agroindustry obtained from cashew apple crop (Anacardium occidentale L.) generates large amounts of unused waste in Campeche, Mexico. Despite having a high content of nutritional compounds such as ascorbic acid, carotenoids, fiber, carbohydrates, and minerals, it is not consumed due to its astringent sensation. The aim of this work was to develop a processing method for cashew apple waste in order to obtain a powder with reduced astringency able to be used as an additive in the food industry. The processing method consisted first in reducing astringency by inducing tannins from cashew apple peel to react and form precipitating complexes with a colloid rich in proline and histidine. Then cashew apples were processed to obtain a dry powder. Astringency reduction was determined by total phenolic content and evaluated by sensorial analysis in cashew-apple-powder based cookies. Total phenolic content in processed powders showed up to 72% lower concentration compared to control samples. The sensorial evaluation indicated that cookies baked using cashew apple powder with reduced astringency were 96.8% preferred. Sensorial characteristics like texture, color and taste were also well-accepted attributes. In conclusion, the method applied for astringency reduction is a viable tool to produce cashew apple powder with desirable sensorial properties to be used in the development of food products.

Keywords: astringency reduction, cashew apple waste, food industry, sensorial evaluation

Procedia PDF Downloads 336

Authors: Usama Shahid, Muhammad Zunnurain Hussain

Abstract:

This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

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Authors: Palash Dey, Sudip Talukdar

Abstract:

In this work, a cross-breed approach is presented for obtaining both the amount of the damage intensity and location of damage existing in thin-walled members. This cross-breed approach is developed based on response surface methodology (RSM) and genetic algorithm (GA). Theoretical finite element (FE) model of cracked angle section thin walled curved beam has been linked to the developed approach to carry out trial experiments to generate response surface functions (RSFs) of free, forced and heterogeneous dynamic response data. Subsequently, the error between the computed response surface functions and measured dynamic response data has been minimized using GA to find out the optimum damage parameters (amount of the damage intensity and location). A single crack of varying location and depth has been considered in this study. The presented approach has been found to reveal good accuracy in prediction of crack parameters and possess great potential in crack detection as it requires only the current response of a cracked beam.

Keywords: damage parameters, finite element, genetic algorithm, response surface methodology, thin walled curved beam

Procedia PDF Downloads 234

Authors: Noriyoshi Yamauchi, Etsuo Horikawa, Takunori Tsuji

Abstract:

Training by exoskeleton robot is drawing attention as a rehabilitation method for body paralysis seen in many cases, and there are many forms that assist with the myoelectric signal generated by exercise commands from the brain. Rehabilitation requires more frequent training, but it is one of the reasons that the technology is required for the identification of the myoelectric potential derivation site and attachment of the device is preventing the spread of paralysis. In this research, we focus on improving the efficiency of gait training by exoskeleton type robots, improvement of myoelectric acquisition and analysis method using active matrix sensing method, and improvement of walking rehabilitation and walking by optimization of robot control.

Keywords: active matrix sensing, brain machine interface (BMI), the central pattern generator (CPG), myoelectric signal processing, robot rehabilitation

Procedia PDF Downloads 374

Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

Procedia PDF Downloads 116

Authors: Atabak Rashidian

Abstract:

In duplex backhaul wireless communication systems, two separate transmit and receive high-gain antennas are required if an antenna switch is not implemented. Although the switch loss, which is considerable and in the order of 1.5 dB at 60 GHz, is avoided, the large separate antenna systems make the design bulky and not cost-effective. To avoid two large reflectors for such a system, transmit and receive antenna feeds with a common phase center are required. The phase center should coincide with the focal point of the reflector to maximize the efficiency and gain. In this work, we present an ultra-compact design in which stacked patch antennas are used as the feeds for a 12-inch reflector. The transmit antenna is a 1 × 2 array and the receive antenna is a single element located in the middle of the transmit antenna elements. Antenna elements are designed as stacked patches to provide the required impedance bandwidth for four standard channels of WiGigTM applications. The design includes three metallic layers and three dielectric layers, in which the top dielectric layer is a 100 µm-thick protective layer. The top two metallic layers are specified to the main and parasitic patches. The bottom layer is basically ground plane with two circular openings (0.7 mm in diameter) having a center through via which connects the antennas to a single input/output Si-Ge Bi-CMOS transceiver chip. The reflection coefficient of the stacked patch antenna is fully investigated. The -10 dB impedance bandwidth is about 11%. Although the gap between transmit and receive antenna is very small (g = 0.525 mm), the mutual coupling is less than -12 dB over the desired frequency band. The three dimensional radiation patterns of the transmit and receive reflector antennas at 60 GHz is investigated over the impedance bandwidth. About 39 dBi realized gain is achieved. Considering over 15 dBm of output power of the silicon chip in the transmit side, the EIRP should be over 54 dBm, which is good enough for over one kilometer multi Gbps data communications. The performance of the reflector antenna over the bandwidth shows the peak gain is 39 dBi and 40 dBi for the reflector antenna with 2-element and single element feed, respectively. This type of the system design is cost-effective and efficient.

Keywords: Antenna, integrated circuit, millimeter-wave, phase center

Procedia PDF Downloads 107