Search results for: time transformation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 19462

Search results for: time transformation

17872 Expanding Trading Strategies By Studying Sentiment Correlation With Data Mining Techniques

Authors: Ved Kulkarni, Karthik Kini

Abstract:

This experiment aims to understand how the media affects the power markets in the mainland United States and study the duration of reaction time between news updates and actual price movements. it have taken into account electric utility companies trading in the NYSE and excluded companies that are more politically involved and move with higher sensitivity to Politics. The scrapper checks for any news related to keywords, which are predefined and stored for each specific company. Based on this, the classifier will allocate the effect into five categories: positive, negative, highly optimistic, highly negative, or neutral. The effect on the respective price movement will be studied to understand the response time. Based on the response time observed, neural networks would be trained to understand and react to changing market conditions, achieving the best strategy in every market. The stock trader would be day trading in the first phase and making option strategy predictions based on the black holes model. The expected result is to create an AI-based system that adjusts trading strategies within the market response time to each price movement.

Keywords: data mining, language processing, artificial neural networks, sentiment analysis

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17871 Real-Time Adaptive Obstacle Avoidance with DS Method and the Influence of Dynamic Environments Change on Different DS

Authors: Saeed Mahjoub Moghadas, Farhad Asadi, Shahed Torkamandi, Hassan Moradi, Mahmood Purgamshidian

Abstract:

In this paper, we present real-time obstacle avoidance approach for both autonomous and non-autonomous DS-based controllers and also based on dynamical systems (DS) method. In this approach, we can modulate the original dynamics of the controller and it allows us to determine safety margin and different types of DS to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle and especially when robot moves very fast in changeable complex environments. The method is validated in simulation and influence of different autonomous and non-autonomous DS such as limit cycles, and unstable DS on this algorithm and also the position of different obstacles in complex environment is explained. Finally, we describe how the avoidance trajectories can be verified through different parameters such as safety factor.

Keywords: limit cycles, nonlinear dynamical system, real time obstacle avoidance, DS-based controllers

Procedia PDF Downloads 389
17870 RGB Color Based Real Time Traffic Sign Detection and Feature Extraction System

Authors: Kay Thinzar Phu, Lwin Lwin Oo

Abstract:

In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset.

Keywords: adaptive thresholding based on RGB color, blue color detection, feature extraction, yellow color detection

Procedia PDF Downloads 313
17869 Effect of Pre-Aging and Aging Parameters on Mechanical Behavior of Be-Treated 7075 Aluminum Alloys: Experimental Correlation using Minitab Software

Authors: M. Tash, S. Alkahtani

Abstract:

The present study was undertaken to investigate the effect of pre-aging and aging parameters (time and temperature) on the mechanical properties of Al-Mg-Zn (7075) alloys. Ultimate tensile strength, 0.5% offset yield strength and % elongation measurements were carried out on specimens prepared from cast and heat treated 7075 alloys. Duplex aging treatments were carried out for the as solution treated (SHT) specimens (pre-aged at different time and temperature followed by high temperature aging). A statistical design of experiments (DOE) approach using fractional factorial design was applied to determine the influence of controlling variables of pre-aging and aging treatment parameters and any interactions between them on the mechanical properties of 7075 alloys. A mathematical models are developed to relate the alloy ultimate tensile strength, yield strength and % elongation with the different pre-aging and aging parameters i.e. Pre-aging Temperature (PA T0C), Pre-aging time (PA t h), Aging temperature (AT0C), Aging time (At h), to acquire an understanding of the effects of these variables and their interactions on the mechanical properties of be-treated 7075 alloys.

Keywords: aging heat Treatment, tensile properties, be-treated cast Al-Mg-Zn (7075) alloys, experimental correlation

Procedia PDF Downloads 275
17868 Index t-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings

Authors: Gaelle Candel, David Naccache

Abstract:

t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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17867 Survey of the Elimination of Red Acid Dye by Wood Dust

Authors: N. Ouslimani, T. Abadlia, M. Fadel

Abstract:

This work focused on the elimination of acid textile dye (red bermacide acid dye BN-CL-200), widely used for dyeing wool and polyamide fibers, by adsorption on a natural material, wood sawdust, in the static mode by keeping under continuous stirring, a specific mass of the adsorbent, with a dye solution of known concentration. The influence of various parameters is studied like the influence of particle size, mass, pH and time. The best results were obtained with 0.4 mm grain size, mass of 3g, Temperature of 20 °C, pH 2 and Time contact of 120 min.

Keywords: acid dye, environment, wood sawdust, wastewater

Procedia PDF Downloads 442
17866 Driver Readiness in Autonomous Vehicle Take-Overs

Authors: Abdurrahman Arslanyilmaz, Salman Al Matouq, Durmus V. Doner

Abstract:

Level 3 autonomous vehicles are able to take full responsibility over the control of the vehicle unless a system boundary is reached or a system failure occurs, in which case, the driver is expected to take-over the control of the vehicle. While this happens, the driver is often not aware of the traffic situation or is engaged in a secondary task. Factors affecting the duration and quality of take-overs in these situations have included secondary task type and nature, traffic density, take-over request (TOR) time, and TOR warning type and modality. However, to the best of the authors’ knowledge, no prior study examined time buffer for TORs when a system failure occurs immediately before intersections. The first objective of this study is to investigate the effect of time buffer (3 and 7 seconds) on the duration and quality of take-overs when a system failure occurs just prior to intersections. In addition, eye-tracking has become one of the most popular methods to report what individuals view, in what order, for how long, and how often, and it has been utilized in driving simulations with various objectives. However, to the extent of authors’ knowledge, none has compared drivers’ eye gaze behavior in the two different time buffers in order to examine drivers’ attention and comprehension of salient information. The second objective is to understand the driver’s attentional focus on comprehension of salient traffic-related information presented on different parts of the dashboard and on the roads.

Keywords: autonomous vehicles, driving simulation, eye gaze, attention, comprehension, take-over duration, take-over quality, time buffer

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17865 Seismic Performance Evaluation of Existing Building Using Structural Information Modeling

Authors: Byungmin Cho, Dongchul Lee, Taejin Kim, Minhee Lee

Abstract:

The procedure for the seismic retrofit of existing buildings includes the seismic evaluation. In the evaluation step, it is assessed whether the buildings have satisfactory performance against seismic load. Based on the results of that, the buildings are upgraded. To evaluate seismic performance of the buildings, it usually goes through the model transformation from elastic analysis to inelastic analysis. However, when the data is not delivered through the interwork, engineers should manually input the data. In this process, since it leads to inaccuracy and loss of information, the results of the analysis become less accurate. Therefore, in this study, the process for the seismic evaluation of existing buildings using structural information modeling is suggested. This structural information modeling makes the work economic and accurate. To this end, it is determined which part of the process could be computerized through the investigation of the process for the seismic evaluation based on ASCE 41. The structural information modeling process is developed to apply to the seismic evaluation using Perform 3D program usually used for the nonlinear response history analysis. To validate this process, the seismic performance of an existing building is investigated.

Keywords: existing building, nonlinear analysis, seismic performance, structural information modeling

Procedia PDF Downloads 386
17864 Robust Model Predictive Controller for Uncertain Nonlinear Wheeled Inverted Pendulum Systems: A Tube-Based Approach

Authors: Tran Gia Khanh, Dao Phuong Nam, Do Trong Tan, Nguyen Van Huong, Mai Xuan Sinh

Abstract:

This work presents the problem of tube-based robust model predictive controller for a class of continuous-time systems in the presence of input disturbances. The main objective is to point out the state trajectory of closed system being maintained inside a sequence of tubes. An estimation of attraction region of the closed system is pointed out based on input state stability (ISS) theory and linearized model in each time interval. The theoretical analysis and simulation results demonstrate the performance of the proposed algorithm for a wheeled inverted pendulum system.

Keywords: input state stability (ISS), tube-based robust MPC, continuous-time nonlinear systems, wheeled inverted pendulum

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17863 Change of Flavor Characteristics of Flavor Oil Made Using Sarcodon aspratus (Sarcodon aspratus Berk. S. Ito) According to Extraction Temperature and Extraction Time

Authors: Gyeong-Suk Jo, Soo-Hyun Ji, You-Seok Lee, Jeong-Hwa Kang

Abstract:

To develop an flavor oil using Sarcodon aspratus (Sarcodon aspratus Berk. S. Ito), infiltration extraction method was used to add dried mushroom flavor of Sarcodon aspratus to base olive oil. Edible base oil used during infiltration extraction was pressed olive oil, and infiltration extraction was done while varying extraction temperature to 20, 30, 40 and 50(℃) extraction time to 24 hours, 48 hours and 72 hours. Amount of Sarcodon aspratus added to base oil was 20% compared to 100% of base oil. Production yield of Sarcodon aspratus flavor oil decreased with increasing extraction frequency. Aroma intensity was 2195~2447 (A.U./1㎖), and it increased with increasing extraction temperature and extraction time. Chromaticity of Sarcodon aspratus flavor oil was bright pale yellow with pH of 4.5, sugar content of 71~72 (°Brix), and highest average turbidity of 16.74 (Haze %) shown by the 40℃ group. In the aromatic evaluation, increasing extraction temperature and extraction time resulted in increase of cheese aroma, savory sweet aroma and beef jerky aroma, as well as spicy taste comprised of slight bitter taste, savory taste and slight acrid taste, to make aromatic oil with unique flavor.

Keywords: Flavor Characteristics, Flavor Oil, Infiltration extraction method, mushroom, Sarcodon aspratus (Sarcodon aspratus Berk. S. Ito)

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17862 Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method

Authors: Cheng Zhang, James Marco, Walid Allafi, Truong Q. Dinh, W. D. Widanage

Abstract:

Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The presented method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the presented CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the presented method for online SOC estimation is also verified on test data.

Keywords: electric circuit model, continuous time domain estimation, linear integral filter method, parameter and SOC estimation, recursive least square

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17861 Characterization of Pectinase from Local Microorganisms to Support Industry Based Green Chemistry

Authors: Sasangka Prasetyawan, Anna Roosdiana, Diah Mardiana, Suratmo

Abstract:

Pectinase are enzymes that hydrolyze pectin compounds. The use of this enzyme is primarily to reduce the viscosity of the beverage thus simplifying the purification process. Pectinase activity influenced by microbial sources . Exploration of two types of microbes that Aspergillus spp. and Bacillus spp. pectinase give different performance, but the use of local strain is still not widely studied. The aim of this research is exploration of pectinase from A. niger and B. firmus include production conditions and characterization. Bacillus firmus incubated and shaken at a speed of 200 rpm at pH variation (5, 6, 7, 8, 9, 10), temperature (30, 35, 40, 45, 50) °C and incubation time (6, 12, 18, 24, 30, 36 ) hours. Media was centrifuged at 3000 rpm, pectinase enzyme activity determined. Enzyme production by A. niger determined to variations in temperature and pH were similar to B. firmus, but the variation of the incubation time was 24, 48, 72, 96, 120 hours. Pectinase crude extract was further purified by precipitation using ammonium sulfate saturation in fraction 0-20 %, 20-40 %, 40-60 %, 60-80 %, then dialyzed. Determination of optimum conditions pectinase activity performed by measuring the variation of enzyme activity on pH (4, 6, 7, 8, 10), temperature (30, 35, 40, 45, 50) °C, and the incubation time (10, 20, 30, 40, 50) minutes . Determination of kinetic parameters of pectinase enzyme reaction carried out by measuring the rate of enzyme reactions at the optimum conditions, but the variation of the concentration of substrate (pectin 0.1 % , 0.2 % , 0.3 % , 0.4 % , 0.5 % ). The results showed that the optimum conditions of production of pectinase from B. firmus achieved at pH 7-8.0, 40-50 ⁰C temperature and fermentation time 18 hours. Purification of pectinase showed the highest purity in the 40-80 % ammonium sulfate fraction. Character pectinase obtained : the optimum working conditions of A. niger pectinase at pH 5 , while pectinase from B. firmus at pH 7, temperature and optimum incubation time showed the same value, namely the temperature of 50 ⁰C and incubation time of 30 minutes. The presence of metal ions can affect the activity of pectinase , the concentration of Zn 2 + , Pb 2 + , Ca 2 + and K + and 2 mM Mg 2 + above 6 mM inhibit the activity of pectinase .

Keywords: pectinase, Bacillus firmus, Aspergillus niger, green chemistry

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17860 Load Balancing and Resource Utilization in Cloud Computing

Authors: Gagandeep Kaur

Abstract:

Cloud computing uses various computing resources such as CPU, memory, processor etc. which is used to deliver service over the network and is one of the emerging fields for large scale distributed computing. In cloud computing, execution of large number of tasks with available resources to achieve high performance, minimal total time for completion, minimum response time, effective utilization of resources etc. are the major research areas. In the proposed research, an algorithm has been proposed to achieve high performance in load balancing and resource utilization. The proposed algorithm is used to reduce the makespan, increase the resource utilization and performance cost for independent tasks. Further scheduling metrics based on algorithm in cloud computing has been proposed.

Keywords: resource utilization, response time, load balancing, performance cost

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17859 Study of Motion of Impurity Ions in Poly(Vinylidene Fluoride) from View Point of Microstructure of Polymer Solid

Authors: Yuichi Anada

Abstract:

Electrical properties of polymer solid is characterized by dielectric relaxation phenomenon. Complex permittivity shows a high dependence on frequency of external stimulation in the broad frequency range from 0.1mHz to 10GHz. The complex-permittivity dispersion gives us a lot of useful information about the molecular motion of polymers and the structure of polymer aggregates. However, the large dispersion of permittivity at low frequencies due to DC conduction of impurity ions often covers the dielectric relaxation in polymer solid. In experimental investigation, many researchers have tried to remove the DC conduction experimentally or analytically for a long time. On the other hand, our laboratory chose another way of research for this problem from the point of view of a reversal in thinking. The way of our research is to use the impurity ions in the DC conduction as a probe to detect the motion of polymer molecules and to investigate the structure of polymer aggregates. In addition to the complex permittivity, the electric modulus and the conductivity relaxation time are strong tools for investigating the ionic motion in DC conduction. In a non-crystalline part of melt-crystallized polymers, free spaces with inhomogeneous size exist between crystallites. As the impurity ions exist in the non-crystalline part and move through these inhomogeneous free spaces, the motion of ions reflects the microstructure of non-crystalline part. The ionic motion of impurity ions in poly(vinylidene fluoride) (PVDF) is investigated in this study. Frequency dependence of the loss permittivity of PVDF shows a characteristic of the direct current (DC) conduction below 1 kHz of frequency at 435 K. The electric modulus-frequency curve shows a characteristic of the dispersion with the single conductivity relaxation time. Namely, it is the Debye-type dispersion. The conductivity relaxation time analyzed from this curve is 0.00003 s at 435 K. From the plot of conductivity relaxation time of PVDF together with the other polymers against permittivity, it was found that there are two group of polymers; one of the group is characterized by small conductivity relaxation time and large permittivity, and another is characterized by large conductivity relaxation time and small permittivity.

Keywords: conductivity relaxation time, electric modulus, ionic motion, permittivity, poly(vinylidene fluoride), DC conduction

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17858 Colour and Curcuminoids Removal from Turmeric Wastewater Using Activated Carbon Adsorption

Authors: Nattawat Thongpraphai, Anusorn Boonpoke

Abstract:

This study aimed to determine the removal of colour and curcuminoids from turmeric wastewater using granular activated carbon (GAC) adsorption. The adsorption isotherm and kinetic behavior of colour and curcuminoids was invested using batch and fixed bed columns tests. The results indicated that the removal efficiency of colour and curcuminoids were 80.13 and 78.64%, respectively at 8 hr of equilibrium time. The adsorption isotherm of colour and curcuminoids were well fitted with the Freundlich adsorption model. The maximum adsorption capacity of colour and curcuminoids were 130 Pt-Co/g and 17 mg/g, respectively. The continuous experiment data showed that the exhaustion concentration of colour and curcuminoids occurred at 39 hr of operation time. The adsorption characteristic of colour and curcuminoids from turmeric wastewater by GAC can be described by the Thomas model. The maximum adsorption capacity obtained from kinetic approach were 39954 Pt-Co/g and 0.0516 mg/kg for colour and curcuminoids, respectively. Moreover, the decrease of colour and curcuminoids concentration during the service time showed a similar trend.

Keywords: adsorption, turmeric, colour, curcuminoids, activated carbon

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17857 Fire Characteristic of Commercial Retardant Flame Polycarbonate under Different Oxygen Concentration: Ignition Time and Heat Blockage

Authors: Xuelin Zhang, Shouxiang Lu, Changhai Li

Abstract:

The commercial retardant flame polycarbonate samples as the main high speed train interior carriage material with different thicknesses were investigated in Fire Propagation Apparatus with different external heat fluxes under different oxygen concentration from 12% to 40% to study the fire characteristics and quantitatively analyze the ignition time, mass loss rate and heat blockage. The additives of commercial retardant flame polycarbonate were intumescent and maintained a steady height before ignition when heated. The results showed the transformed ignition time (1/t_ig)ⁿ increased linearly with external flux under different oxygen concentration after deducting the heat blockage due to pyrolysis products, the mass loss rate was taken on linearly with external heat fluxes and the slop of the fitting line for mass loss rate and external heat fluxes decreased with the enhanced oxygen concentration and the heat blockage independent on external heat fluxes rose with oxygen concentration increasing. The inquired data as the input of the fire simulation model was the most important to be used to evaluate the fire risk of commercial retardant flame polycarbonate.

Keywords: ignition time, mass loss rate, heat blockage, fire characteristic

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17856 A Neurofeedback Learning Model Using Time-Frequency Analysis for Volleyball Performance Enhancement

Authors: Hamed Yousefi, Farnaz Mohammadi, Niloufar Mirian, Navid Amini

Abstract:

Investigating possible capacities of visual functions where adapted mechanisms can enhance the capability of sports trainees is a promising area of research, not only from the cognitive viewpoint but also in terms of unlimited applications in sports training. In this paper, the visual evoked potential (VEP) and event-related potential (ERP) signals of amateur and trained volleyball players in a pilot study were processed. Two groups of amateur and trained subjects are asked to imagine themselves in the state of receiving a ball while they are shown a simulated volleyball field. The proposed method is based on a set of time-frequency features using algorithms such as Gabor filter, continuous wavelet transform, and a multi-stage wavelet decomposition that are extracted from VEP signals that can be indicative of being amateur or trained. The linear discriminant classifier achieves the accuracy, sensitivity, and specificity of 100% when the average of the repetitions of the signal corresponding to the task is used. The main purpose of this study is to investigate the feasibility of a fast, robust, and reliable feature/model determination as a neurofeedback parameter to be utilized for improving the volleyball players’ performance. The proposed measure has potential applications in brain-computer interface technology where a real-time biomarker is needed.

Keywords: visual evoked potential, time-frequency feature extraction, short-time Fourier transform, event-related spectrum potential classification, linear discriminant analysis

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17855 Comparative Study of Deep Reinforcement Learning Algorithm Against Evolutionary Algorithms for Finding the Optimal Values in a Simulated Environment Space

Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

Abstract:

Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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17854 From Comfort to Safety: Assessing the Influence of Car Seat Design on Driver Reaction and Performance

Authors: Sabariah Mohd Yusoff, Qamaruddin Adzeem Muhamad Murad

Abstract:

This study investigates the impact of car seat design on driver response time, addressing a critical gap in understanding how ergonomic features influence both performance and safety. Controlled driving experiments were conducted with fourteen participants (11 male, 3 female) across three locations chosen for their varying traffic conditions to account for differences in driver alertness. Participants interacted with various seat designs while performing driving tasks, and objective metrics such as braking and steering response times were meticulously recorded. Advanced statistical methods, including regression analysis and t-tests, were employed to identify design factors that significantly affect driver response times. Subjective feedback was gathered through detailed questionnaires—focused on driving experience and knowledge of response time—and in-depth interviews. This qualitative data was analyzed thematically to provide insights into driver comfort and usability preferences. The study aims to identify key seat design features that impact driver response time and to gain a deeper understanding of driver preferences for comfort and usability. The findings are expected to inform evidence-based guidelines for optimizing car seat design, ultimately enhancing driver performance and safety. The research offers valuable implications for automotive manufacturers and designers, contributing to the development of seats that improve driver response time and overall driving safety.

Keywords: car seat design, driver response time, cognitive driving, ergonomics optimization

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17853 Simulation and Experimental Research on Pocketing Operation for Toolpath Optimization in CNC Milling

Authors: Rakesh Prajapati, Purvik Patel, Avadhoot Rajurkar

Abstract:

Nowadays, manufacturing industries augment their production lines with modern machining centers backed by CAM software. Several attempts are being made to cut down the programming time for machining complex geometries. Special programs/software have been developed to generate the digital numerical data and to prepare NC programs by using suitable post-processors for different machines. By selecting the tools and manufacturing process then applying tool paths and NC program are generated. More and more complex mechanical parts that earlier were being cast and assembled/manufactured by other processes are now being machined. Majority of these parts require lots of pocketing operations and find their applications in die and mold, turbo machinery, aircraft, nuclear, defense etc. Pocketing operations involve removal of large quantity of material from the metal surface. The modeling of warm cast and clamping a piece of food processing parts which the used of Pro-E and MasterCAM® software. Pocketing operation has been specifically chosen for toolpath optimization. Then after apply Pocketing toolpath, Multi Tool Selection and Reduce Air Time give the results of software simulation time and experimental machining time.

Keywords: toolpath, part program, optimization, pocket

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17852 Scaling Siamese Neural Network for Cross-Domain Few Shot Learning in Medical Imaging

Authors: Jinan Fiaidhi, Sabah Mohammed

Abstract:

Cross-domain learning in the medical field is a research challenge as many conditions, like in oncology imaging, use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of a Siamese neural network was able to be trained on a small sample with remarkable accuracy, FSL fails to be effective for use in multiple domains as their convolution weights are set for task-specific applications. In this paper, we are addressing this problem by enabling FSL to possess the ability to shift across domains by designing a two-layer FSL network that can learn individually from each domain and produce a shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical datasets like the Medical-MNIST reveal promising results. We aim to continue this research to perform full-scale analytics for testing our cross-domain FSL learning.

Keywords: Siamese neural network, few-shot learning, meta-learning, metric-based learning, thick data transformation and analytics

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17851 Non-Signaling Chemokine Receptor CCRL1 and Its Active Counterpart CCR7 in Prostate Cancer

Authors: Yiding Qu, Svetlana V. Komarova

Abstract:

Chemokines acting through their cognate chemokine receptors guide the directional migration of the cell along the chemokine gradient. Several chemokine receptors were recently identified as non-signaling (decoy), based on their ability to bind the chemokine but produce no measurable signal in the cell. The function of these decoy receptors is not well understood. We examined the expression of a decoy receptor CCRL1 and a signaling receptor that binds to the same ligands, CCR7, in prostate cancer using publically available microarray data (www.oncomine.org). The expression of both CCRL1 and CCR7 increased in an approximately half of prostate carcinoma samples and the majority of metastatic cancer samples compared to normal prostate. Moreover, the expression of CCRL1 positively correlated with the expression of CCR7. These data suggest that CCR7 and CCRL1 can be used as clinical markers for the early detection of transformation from carcinoma to metastatic cancer. In addition, these data support our hypothesis that the non-signaling chemokine receptors actively stimulate cell migration.

Keywords: bioinformatics, cell migration, decoy receptor, meta-analysis, prostate cancer

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17850 Analysis of Brain Signals Using Neural Networks Optimized by Co-Evolution Algorithms

Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

Abstract:

Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

Procedia PDF Downloads 282
17849 Rice Husk Silica as an Alternative Material for Renewable Energy

Authors: Benedict O. Ayomanor, Cookey Iyen, Ifeoma S. Iyen

Abstract:

Rice hull (RH) biomass product gives feasible silica for exact temperature and period. The minimal fabrication price turns its best feasible produce to metallurgical grade silicon (MG-Si). In this work, to avoid ecological worries extending from CO₂ release to oil leakage on water and land, or nuclear left-over pollution, all finally add to the immense topics of ecological squalor; high purity silicon > 98.5% emerge set from rice hull ash (RHA) by solid-liquid removal. The RHA derived was purified by nitric and hydrochloric acid solutions. Leached RHA sieved, washed in distilled water, and desiccated at 1010ºC for 4h. Extra cleansing was achieved by carefully mixing the SiO₂ ash through Mg dust at a proportion of 0.9g SiO₂ to 0.9g Mg, galvanised at 1010ºC to formula magnesium silicide. The solid produced was categorised by X-ray fluorescence (XRF), X-ray diffractometer (XRD), and Fourier transformation infrared (FTIR) spectroscopy. Elemental analysis using XRF found the percentage of silicon in the material is approximately 98.6%, main impurities are Mg (0.95%), Ca (0.09%), Fe (0.3%), K (0.25%), and Al (0.40%).

Keywords: siliceous, leached, biomass, solid-liquid extraction

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17848 The Relationship between Market Orientation, Human Resource Management, Adoption of Information Communication Technology, Performance of Small and Medium Enterprises and Mediating Cash Management

Authors: Azizah Hashim, Rohana Ngah

Abstract:

Transformation of Economic Development is aimed to transform Malaysia to become a high-income developed nation with a knowledge-based economy by 2020. To achieve this national agenda, the country needs to further strengthen its economic development, growth and well-being. Therefore, this study aspires to examine the relationship between market orientation, human resource management and adoption of information communication technology and SMEs performance and cash management as a mediator. This study will employ quantitative approaches. Questionnaires will be distributed to managers and owners in service sectors. The data collected will be analyzed using SPSS and Structural Equation Modelling. Resource Based Theory (RBT) adopts as an integral part of management literature that explains the performance of organizations through building resources and implement of their strategies.

Keywords: small medium enterprises (SMEs), market orientation, human resource management, adoption of information communication technology

Procedia PDF Downloads 277
17847 Dynamic Analysis of Submerged Floating Tunnel Subjected to Hydrodynamic and Seismic Loadings

Authors: Naik Muhammad, Zahid Ullah, Dong-Ho Choi

Abstract:

Submerged floating tunnel (SFT) is a new solution for the transportation infrastructure through sea straits, fjords, and inland waters, and can be a good alternative to long span suspension bridges. SFT is a massive cylindrical structure that floats at a certain depth below the water surface and subjected to extreme environmental conditions. The identification of dominant structural response of SFT becomes more important due to intended environmental conditions for the design of SFT. The time domain dynamic problem of SFT moored by vertical and inclined mooring cables/anchors is formulated. The dynamic time history analysis of SFT subjected to hydrodynamic and seismic excitations is performed. The SFT is modeled by finite element 3D beam, and the mooring cables are modeled by truss elements. Based on the dynamic time history analysis the displacements and internal forces of SFT were calculated. The response of SFT is presented for hydrodynamic and seismic excitations. The transverse internal forces of SFT were the maximum compared to vertical direction, for both hydrodynamic and seismic cases; this indicates that the cable system provides very small stiffness in transverse direction as compared to vertical direction of SFT.

Keywords: submerged floating tunnel, hydrodynamic analysis, time history analysis, seismic response

Procedia PDF Downloads 329
17846 Kalman Filter Gain Elimination in Linear Estimation

Authors: Nicholas D. Assimakis

Abstract:

In linear estimation, the traditional Kalman filter uses the Kalman filter gain in order to produce estimation and prediction of the n-dimensional state vector using the m-dimensional measurement vector. The computation of the Kalman filter gain requires the inversion of an m x m matrix in every iteration. In this paper, a variation of the Kalman filter eliminating the Kalman filter gain is proposed. In the time varying case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix and the inversion of an m x m matrix in every iteration. In the time invariant case, the elimination of the Kalman filter gain requires the inversion of an n x n matrix in every iteration. The proposed Kalman filter gain elimination algorithm may be faster than the conventional Kalman filter, depending on the model dimensions.

Keywords: discrete time, estimation, Kalman filter, Kalman filter gain

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17845 Evaluation of Combined System of Constructed Wetland/Expended Clay Aggregate in Greywater Treatment

Authors: Eya Hentati, Mona Lamine, Jalel Bouzid

Abstract:

In this study, a laboratory-scale was designed and fabricated to treat single house greywater in the north of Tunisia with a combination of physical and natural treatments systems. The combined system includes a bio-filter composed of LECA® (lightweight expanded clay aggregate) followed by a vertical up-flow constructed wetland planted with Iris pseudacorus and Typha Latifolia. Applied two hydraulic retention times (HRTs) with two different plants types showed that a bio-filter planted with Typha Latifolia has an optimum removal efficiency for degradation of organic matter and transformation of nitrogen and phosphate at HRT of 30 h. The optimum removal efficiency of biochemical oxygen demand (BOD), chemical oxygen demand (COD), and suspended solids (SS) ranged between 48-65%, between while the nutrients removal was in the range of 70% to 90%. Fecal coliforms dropped by three to four orders of magnitude from their initial concentration, but this steel does not meet current regulations for unlimited irrigation. Hence further improvement procedures are suggested.

Keywords: constructed wetland, greywater treatment, nutriments, organics

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17844 Experimental Study on the Heat Transfer Characteristics of the 200W Class Woofer Speaker

Authors: Hyung-Jin Kim, Dae-Wan Kim, Moo-Yeon Lee

Abstract:

The objective of this study is to experimentally investigate the heat transfer characteristics of 200 W class woofer speaker units with the input voice signals. The temperature and heat transfer characteristics of the 200 W class woofer speaker unit were experimentally tested with the several input voice signals such as 1500 Hz, 2500 Hz, and 5000 Hz respectively. From the experiments, it can be observed that the temperature of the woofer speaker unit including the voice-coil part increases with a decrease in input voice signals. Also, the temperature difference in measured points of the voice coil is increased with decrease of the input voice signals. In addition, the heat transfer characteristics of the woofer speaker in case of the input voice signal of 1500 Hz is 40% higher than that of the woofer speaker in case of the input voice signal of 5000 Hz at the measuring time of 200 seconds. It can be concluded from the experiments that initially the temperature of the voice signal increases rapidly with time, after a certain period of time it increases exponentially. Also during this time dependent temperature change, it can be observed that high voice signal is stable than low voice signal.

Keywords: heat transfer, temperature, voice coil, woofer speaker

Procedia PDF Downloads 360
17843 Infrastructure Change Monitoring Using Multitemporal Multispectral Satellite Images

Authors: U. Datta

Abstract:

The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection

Procedia PDF Downloads 135