Search results for: image subset selection

Authors: B. Atika, S. Lehmann, E. Wimmer

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The defense strategy is very common in the insect world. Defensive substances play a wide variety of functions for beetles, such as repellents, toxicants, insecticides, and antimicrobics. Beetles react to predators, invaders, and parasitic microbes with the release of toxic and repellent substances. Defensive substances are directed against a large array of potential target organisms or may function for boiling bombardment or as surfactants. Usually, Coleoptera biosynthesize and store their defensive compounds in a complex secretory organ, known as odoriferous defensive stink glands. The red flour beetle, Tribolium castaneum (Coleoptera: Tenebrionidae), uses these glands to produce antimicrobial p-benzoquinones and 1-alkenes. In the past, the morphology of stink gland has been studied in detail in tenebrionid beetles; however, very little is known about the genes that are involved in the production of gland secretion. In this study, we studied a subset of genes that are essential for the benzoquinone production in red flour beetle. In the first phase, we selected 74 potential candidate genes from a genome-wide RNA interference (RNAi) knockdown screen named 'iBeetle.' All these 74 candidate genes were functionally characterized by RNAi-mediated gene knockdown. Therefore, they were selected for a subsequent gas chromatography-mass spectrometry (GC-MS) analysis of secretion volatiles in respective RNAi knockdown glands. 33 of them were observed to alter the phenotype of stink gland. In the GC-MS analysis, 7 candidate genes were noted to display a strongly altered gland, in terms of secretion color and chemical composition, upon knockdown, showing their key role in the biosynthesis of gland secretion. Morphologically altered stink glands were found for odorant receptor and protein kinase superfamily. Subsequent GC-MS analysis of secretion volatiles revealed reduced benzoquinone levels in LIM domain, PDZ domain, PBP/GOBP family knockdowns and a complete lack of benzoquinones in the knockdown of sulfatase-modifying factor enzyme 1, sulfate transporter family. Based on stink gland transcriptome data, we analyzed the function of sulfatase-modifying factor enzyme 1 and sulfate transporter family via RNAi-mediated gene knockdowns, GC-MS, in situ hybridization, and enzymatic activity assays. Morphologically altered stink glands were noted in knockdown of both these genes. Furthermore, GC-MS analysis of secretion volatiles showed a complete lack of benzoquinones in the knockdown of these two genes. In situ hybridization showed that these two genes are expressed around the vesicle of certain subgroup of secretory stink gland cells. Enzymatic activity assays on stink gland tissue showed that these genes are involved in p-benzoquinone biosynthesis. These results suggest that sulfatase-modifying factor enzyme 1 and sulfate transporter family play a role specifically in benzoquinone biosynthesis in red flour beetles.

Keywords: Red Flour Beetle, defensive stink gland, benzoquinones, sulfate transporter, sulfatase-modifying factor enzyme 1

Procedia PDF Downloads 141

Authors: Sibawu Witness Siyepu

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This study explores the effects of instructional design using blended learning in the learning of radian measures among Engineering students. Blended learning is an education programme that combines online digital media with traditional classroom methods. It requires the physical presence of both lecturer and student in a mathematics computer laboratory. Blended learning provides element of class control over time, place, path or pace. The focus was on the use of Khan Academy to supplement traditional classroom interactions. Khan Academy is a non-profit educational organisation created by educator Salman Khan with a goal of creating an accessible place for students to learn through watching videos in a computer assisted computer. The researcher who is an also lecturer in mathematics support programme collected data through instructing students to watch Khan Academy videos on radian measures, and by supplying students with traditional classroom activities. Classroom activities entails radian measure activities extracted from the Internet. Students were given an opportunity to engage in class discussions, social interactions and collaborations. These activities necessitated students to write formative assessments tests. The purpose of formative assessments tests was to find out about the students’ understanding of radian measures, including errors and misconceptions they displayed in their calculations. Identification of errors and misconceptions serve as pointers of students’ weaknesses and strengths in their learning of radian measures. At the end of data collection, semi-structure interviews were administered to a purposefully sampled group to explore their perceptions and feedback regarding the use of blended learning approach in teaching and learning of radian measures. The study employed Algebraic Insight Framework to analyse data collected. Algebraic Insight Framework is a subset of symbol sense which allows a student to correctly enter expressions into a computer assisted systems efficiently. This study offers students opportunities to enter topics and subtopics on radian measures into a computer through the lens of Khan Academy. Khan academy demonstrates procedures followed to reach solutions of mathematical problems. The researcher performed the task of explaining mathematical concepts and facilitated the process of reinvention of rules and formulae in the learning of radian measures. Lastly, activities that reinforce students’ understanding of radian were distributed. Results showed that this study enthused the students in their learning of radian measures. Learning through videos prompted the students to ask questions which brought about clarity and sense making to the classroom discussions. Data revealed that sense making through reinvention of rules and formulae assisted the students in enhancing their learning of radian measures. This study recommends the use of Khan Academy in blended learning to be introduced as a socialisation programme to all first year students. This will prepare students that are computer illiterate to become conversant with the use of Khan Academy as a powerful tool in the learning of mathematics. Khan Academy is a key technological tool that is pivotal for the development of students’ autonomy in the learning of mathematics and that promotes collaboration with lecturers and peers.

Keywords: algebraic insight framework, blended learning, Khan Academy, radian measures

Procedia PDF Downloads 295

Authors: Changchun Liang, Xiaodong Zhang, Xin Liu, Pengfei Sun

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Space intelligent robotic systems are expected to play an increasingly important role in the future. The robotic on-orbital service, whose key is the tracking and capturing technology, becomes research hot in recent years. In this paper, the authors propose a vision servo control system for target capturing. Robotic manipulator will be an intelligent robotic system with large-scale movement, functional agility, and autonomous ability, and it can be operated by astronauts in the space station or be controlled by the ground operator in the remote operation mode. To realize the autonomous movement and capture mission of SRM, a kind of autonomous programming strategy based on multi-camera vision fusion is designed and the selection principle of object visual position and orientation measurement information is defined for the better precision. Distributed control system hierarchy is designed and reliability is considering to guarantee the abilities of control system. At last, a ground experiment system is set up based on the concept of robotic control system. With that, the autonomous target capturing experiments are conducted. The experiment results validate the proposed algorithm, and demonstrates that the control system can fulfill the needs of function, real-time and reliability.

Keywords: control system, on-orbital service, space robot, vision servo

Procedia PDF Downloads 409

Authors: Samira Mahmoudkelayeh, Katayoun Taghizade, Mitra Pourvaziri, Elnaz Asadian

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Over the recent decades, depletion of resources and environmental concerns made researchers and practitioners present sustainable approaches. Since construction process consumes a great deal of both renewable and non-renewable resources, it is of great significance regarding environmental impacts. Choosing sustainable construction materials is a remarkable strategy presented in many researches and has a significant effect on building’s environmental footprint. This paper represents an assessment framework for selecting best sustainable materials for exterior enclosure in the city of Tehran based on sustainability principles (eco-friendly, cost effective and socio-cultural viable solutions). To perform a comprehensive analysis of environmental impacts, life cycle assessment, a cradle to grave approach is used. A questionnaire survey of construction experts has been conducted to determine the relative importance of criteria. Analytic Network Process (ANP) is applied as a multi-criteria decision-making method to choose sustainable material which consider interdependencies of criteria and sub-criteria. Finally, it prioritizes and aggregates relevant criteria into ultimate assessed score.

Keywords: sustainable materials, building, analytic network process, life cycle assessment

Procedia PDF Downloads 226

Authors: Siyuan Cai

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This paper takes community segregation during major infectious diseases as the background, based on the physiological needs and safety needs of citizens during home segregation, and based on the selection of convenient facilities and medical facilities as the main research objects. Based on the POI data of public facilities in Chongqing, the spatial distribution characteristics of the convenience and medical facilities in the 15-minute living circle centered on three neighborhoods in Shapingba, namely Shengwei Community, Anju Commmunity and Fengtian Garden Community, were explored by means of GIS spatial analysis. The results show that the spatial distribution of convenience and medical facilities in this area has significant clustering characteristics, with a point-like distribution pattern of "dense in the west and sparse in the east", and a grouped and multi-polar spatial structure. The spatial structure is multi-polar and has an obvious tendency to the intersections and residential areas with dense pedestrian flow. This study provides a preliminary exploration of the distribution of medical and convenience facilities within the 15-minute living circle of a segregated community, which makes up for the lack of spatial research in this area.

Keywords: ArcGIS, community segregation, convenient facilities; distribution pattern, medical facilities, POI, 15-minute community life circle

Procedia PDF Downloads 101

Authors: Manik Sharma, Ganapathy Krishnamurthi

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The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

Procedia PDF Downloads 96

Authors: Vineetha Bettaiah, Heggere S. Ranganath

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This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity.

Keywords: data mining, dimensionality reduction, piecewise linear representation, time series representation

Procedia PDF Downloads 264

Authors: Akrem Sellami, Imed Riadh Farah, Basel Solaiman

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With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the semantic interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the semantic interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the semantic interpretation of HSI.

Keywords: band selection, dimensionality reduction, feature extraction, hyperspectral imagery, semantic interpretation

Procedia PDF Downloads 338

Authors: Blessing Olamide Taiwo, Andrew Palangio, Chirag Savaliya, Jenil Patel

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Oversized material resulting from blasting presents a notable drawback in the transportation of run-off-mine material due to increased expenses associated with handling, decreased efficiency in loading, and greater wear on digging equipment. Its irregular size and weight demand additional resources and time for secondary breakage, impacting overall productivity and profitability. This paper addresses the limitations of interpreting image analysis software results and applying them to the assessment of blast-generated oversized materials. This comprehensive guide utilizes both ortho mosaic and digital photos to provide critical approaches for optimizing fragmentation analysis and improving decision-making in mining operations. It briefly covers post-blast assessment, blast block heat map interpretation, and material loading decision-making recommendations.

Keywords: blast result assessment, WipFrag, oversize identification, orthomosaic images, production optimization

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

Procedia PDF Downloads 446

Authors: S. M. Rifat, André L. Marchildon, Mark F. Tachie

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The effect of upstream surface roughness over a smooth forward facing step in an open channel was investigated using a particle image velocimetry technique. Three different upstream surface topographies consisting of hydraulically smooth wall, sandpaper 36 grit and sand grains were examined. Besides the wall roughness conditions, all other upstream flow characteristics were kept constant. It was also observed that upstream roughness decreased the approach velocity by 2% and 10% but increased the turbulence intensity by 14% and 35% at the wall-normal distance corresponding to the top plane of the step compared to smooth upstream. The results showed that roughness decreased the reattachment lengths by 14% and 30% compared to smooth upstream. Although the magnitudes of maximum positive and negative Reynolds shear stress in separated and reattached region were 0.02Ue for all the cases, the physical size of both the maximum and minimum contour levels were decreased by increasing upstream roughness.

Keywords: forward facing step, open channel, separated and reattached turbulent flows, wall roughness

Procedia PDF Downloads 341

Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

Procedia PDF Downloads 138

Authors: Nizar Souiden, Riadh Ladhari

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Like tobacco and alcohol, recreational marijuana falls under the umbrella of ‘sin’ industries’. Notwithstanding this general negative image surrounding marijuana use, some scholars argue that most of the widely believed claims made about recreational marijuana users are irrelevant and that marijuana use can even improve individuals’ decision-making. This study intends to shed light on this particular product category (i.e., marijuana) often overlooked or portrayed as taboo from a business view. More specifically, it investigates whether legalizing the consumption of recreational marijuana would be perceived as ethical and whether companies/organizations involved in the commercialization of this particular product would be held socially responsible. Based on primary data collected in Canada, this study aims to answer the following questions: 1) What moral thoughts do individuals hold with regard to the consumption of recreational marijuana? 2) How do these moral thoughts determine consumers’ attitude toward the consumption of recreational marijuana? Regardless of the legalization of recreational marijuana in some countries such as Canada, probing people’s opinions, and investigating their attitudes toward the consumption of recreational marijuana is of important interest to different stakeholders such as consumers, public organizations, private businesses, and trade associations.

Keywords: recreational marijuana, moral thoughts, ethics, attitude

Procedia PDF Downloads 126

Authors: Nursel Koyuncu, Nihal Ata Tutkun

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Frailty model is a survival model that takes into account the unobserved heterogeneity for exploring the relationship between the survival of an individual and several covariates. In the recent years, proposed survival models become more complex and this feature causes convergence problems especially in large data sets. Therefore selection of sample from these big data sets is very important for estimation of parameters. In sampling literature, some authors have defined new sampling schemes to predict the parameters correctly. For this aim, we try to see the effect of sampling design in semi-parametric frailty model. We conducted a simulation study in R programme to estimate the parameters of semi-parametric frailty model for different sample sizes, censoring rates under classical simple random sampling and ranked set sampling schemes. In the simulation study, we used data set recording 17260 male Civil Servants aged 40–64 years with complete 10-year follow-up as population. Time to death from coronary heart disease is treated as a survival-time and age, systolic blood pressure are used as covariates. We select the 1000 samples from population using different sampling schemes and estimate the parameters. From the simulation study, we concluded that ranked set sampling design performs better than simple random sampling for each scenario.

Keywords: frailty model, ranked set sampling, efficiency, simple random sampling

Procedia PDF Downloads 196

Authors: S. M. Kulkarni, D. S. Bormane, S. L. Nalbalwar

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The rapid development in the technology have dramatic impact on the medical health care field. Medical data base obtained with latest machines like CT Machine, MRI scanner requires large amount of memory storage and also it requires large bandwidth for transmission of data in telemedicine applications. Thus, there is need for video compression. As the database of medical images contain number of frames (slices), hence while coding of these images there is need of motion estimation. Motion estimation finds out movement of objects in an image sequence and gets motion vectors which represents estimated motion of object in the frame. In order to reduce temporal redundancy between successive frames of video sequence, motion compensation is preformed. In this paper three step search (TSS) block matching algorithm is implemented on different types of video sequences. It is shown that three step search algorithm produces better quality performance and less computational time compared with exhaustive full search algorithm.

Keywords: block matching, exhaustive search motion estimation, three step search, video compression

Procedia PDF Downloads 466

Authors: Andrii Biloshchytskyi, Serik Omirbayev, Alexandr Neftissov, Sapar Toxanov, Svitlana Biloshchytska, Adil Faizullin

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Kazakhstan has adopted a carbon neutrality strategy until 2060. In accordance with this strategy, it is necessary to introduce various tools to maintain the environmental safety of the environment. The use of IoT, in combination with the characteristics and requirements of Kazakhstan's environmental legislation, makes it possible to develop a modern environmental monitoring system. The article proposes a solution for developing an example of an automated system for the continuous collection of data on the concentration of pollutants in the atmosphere based on an open architecture. The Audino-based device acts as a microcontroller. It should be noted that the transmission of measured values is carried out via an open wireless communication protocol. The architecture of the system, which was used to build a prototype based on sensors, an Arduino microcontroller, and a wireless data transmission module, is presented. The selection of elementary components may change depending on the requirements of the system; the introduction of new units is limited by the number of ports. The openness of solutions allows you to change the configuration depending on the conditions. The advantages of the solutions are openness, low cost, versatility and mobility. However, there is no comparison of the working processes of the proposed solution with traditional ones.

Keywords: environmental monitoring, greenhouse gases emissions, environmental pollution, Industry 4.0, IoT, microcontroller, automated monitoring system.

Procedia PDF Downloads 20

Authors: Mohammad Muhebbullah Ibne Hoque, Zheng Jun, Wang Guoying

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Salinity stress is one of the most important abiotic factors contributing to crop growth and yield loss. Exploring the genetic basis is necessary to develop maize varieties with salinity tolerance. In order to discover the inherent basis for salinity tolerance traits in maize, 121 polymorphic SSR markers were used to analyze 163 F2 individuals derived from a single cross of inbred line B73 (a salt susceptible inbred line) and CZ-7 (a salt tolerant inbred line). A linkage map was constructed and the map covered 1195.2 cM of maize genome with an average distance of 9.88 cM between marker loci. Ten salt tolerance traits at seedling stage were evaluated for QTL analysis in maize seedlings. A total of 41 QTLs associated with seedling shoot and root traits were detected, with 16 and 25 QTLs under non-salinity and salinity condition, respectively. And only 4 major stable QTLs were detected in two environments. The detected QTLs were distributed on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, and chromosome 10. Phenotypic variability for the identified QTLs for all the traits was in the range from 6.27 to 21.97%. Fourteen QTLs with more than 10% contributions were observed. Our results and the markers associated with the major QTL detected in this study have the potential application for genetic improvement of salt tolerance in maize through marker-assisted selection.

Keywords: salt tolerance, seedling stage, root shoot traits, quantitative trait loci, simple sequence repeat, maize

Procedia PDF Downloads 293

Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

Procedia PDF Downloads 95

Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

Procedia PDF Downloads 421

Authors: Kelemewerk Destalem, Joongjae Cho, Jaeseong Lee, Ju H. Park, Joonhyuk Yoo

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Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method.

Keywords: background subtraction, background updating, real time, light weight algorithm, temporal difference

Procedia PDF Downloads 323

Authors: Peter Dzakah Fanam, Hong O. Nguyen, Stephen Cahoon

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The objective of this study is to explore the buying behavior of the freight forwarders and to evaluate how their buying decision affects the ocean container carriers’ market share. This study analysed the buying decisions of the freight forwarders and validated the process of stages that the freight forwarders’ pass through before choosing an ocean container carrier. Factor analysis was applied to data collected from 105 freight forwarding companies to unveil the influential factors the freight forwarders’ consider important when selecting an ocean container carrier. This study did not only analysed the buying behaviour of the freight forwarders but also unveiled the influential factors affecting the competitiveness of the ocean container carriers in their market share maximisation. Furthermore, the study have made a methodological contribution that helps in better understanding of the critical factors influencing the selection of the ocean container carriers from the freight forwarders’ perspective. The implications of the freight forwarders’ buying behaviour is important to the ocean container carriers because it have severe effect on the market share of the ocean container carriers and the percentage of customers they control within the liner shipping sector. The findings of this study will help the ocean container carriers to formulate relevant marketing strategies in attracting the freight forwarders in purchasing the liner shipping service.

Keywords: ocean carrier, freight forwarder, buying behaviour, influential factors

Procedia PDF Downloads 234

Authors: Mohamed Amine El Hameur, Lyes Tarabet, Mahfoudh Cerdoun, Boubkr Zebiri, Giovanni Ferrara

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Due to the drastic environmental and energy regulations regarding the reduction of exhaust emissions and fuel consumption, added to the increasing demand for powerful performance, several automotive manufacturers are constantly obliged to redesign their existing products and/or develop novel powertrain techniques to respond to the aforementioned restrictions. In this aspect, an implemented approach is proposed in the present work to boost a 1.5 L, three-cylinder Diesel engine with a new turbocharger, based on 1D preliminary design codes, 3D design, and numerical assessment of a suitable radial turbine followed by an accurate selection procedure of an adequate centrifugal compressor. Furthermore, to investigate the effect of the turbine’s rotor position on the simulation convergence, stability, and calculation time; two combinations (rotor blade- volute) have been assessed. Consequently, significant results are obtained when comparing the original turbocharged engine and the new one at the engine’s full load and rated speed (@4500rpm) conditions. A maximum improvement in terms of brake-specific fuel consumption, thermal efficiency, total-to-static turbine efficiency, and total-to-total compressor efficiency equal 6.5% (corresponding to a decrease of 2.3 litre/hr in fuel consumption), 7%, 10.9%, and 19.9%, respectively.

Keywords: CFD investigation, engine boosting, turbine design, turbocharger, rotor blade positioning

Procedia PDF Downloads 101

Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

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In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

Procedia PDF Downloads 232

Authors: Shilpesh R. Rajpurohit, Harshit K. Dave

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Fused deposition modeling (FDM) gains popularity in recent times, due to its capability to create prototype as well as functional end use product directly from CAD file. Parts fabricated using FDM process have mechanical properties comparable with those of injection-molded parts. However, performance of the FDM part is severally affected by the poor mechanical properties of the part due to nature of layered structure of printed part. Mechanical properties of the part can be improved by proper selection of process variables. In the present study, a comparative study between unidirectional and bidirectional raster angle has been carried out at a combination of different layer height and raster width. Unidirectional raster angle varied at five different levels, and bidirectional raster angle has been varied at three different levels. Fabrication of tensile specimen and tensile testing of specimen has been conducted according to ASTM D638 standard. From the results, it can be observed that higher tensile strength has been obtained at 0° raster angle followed by 45°/45° raster angle, while lower tensile strength has been obtained at 90° raster angle. Analysis of fractured surface revealed that failure takes place along with raster deposition direction for unidirectional and zigzag failure can be observed for bidirectional raster angle.

Keywords: additive manufacturing, fused deposition modeling, unidirectional, bidirectional, raster angle, tensile strength

Procedia PDF Downloads 166

Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

Procedia PDF Downloads 73

Authors: Hossein Alimohammadi, Mohsen Amirmojahedi, Mehrdad Rowhani

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Soil properties are used by geotechnical engineers to evaluate and analyze site conditions for designing purposes. Although basic soil classification tests are easy to perform and provide useful information to determine the properties of soils, it may take time to get the result and add some costs to the projects. Standard Penetration Test (SPT) provides an opportunity to evaluate soil parameters without performing laboratory tests. In addition to its simplicity and cheapness, the results become available immediately. This research provides a guideline on the application of the SPT test method, reliability of adapting the SPT test results in evaluating soil physical and mechanical properties such as Atterberg limits, shear strength, and compressive strength compressibility parameters. A total of 70 boreholes were investigated in this study by taking soil samples between depths of 1.2 to 15.25 meters. The project site was located in Morrow County, Ohio. A regression-based formula was proposed based on Tobit regression with a stepwise variable selection analysis conducted between SPT and other typical soil properties obtained from soil tests. The results of the research illustrated that the shear strength and physical properties of the soil affect the SPT number. The proposed correlation can help engineers to use SPT test results in their design with higher accuracy.

Keywords: standard penetration test, soil properties, soil classification, regression method

Procedia PDF Downloads 166

Authors: Olaniyi Awe, Adelana R. Adetunji, Abraham Adeleke

Abstract:

Mining of alluvial gold ore by artisanal miners has been going on for decades at Itagunmodi, Southwestern Nigeria. In order to optimize the traditional panning gravity separation method commonly used in the area, a mineral particle size analysis study is critical. This study analyzed alluvial gold ore samples collected at identified five different locations in the area with a view to determine the ore particle size distributions. 500g measured of as-received alluvial gold ore sample was introduced into the uppermost sieve of an electrical sieve shaker consisting of sieves arranged in the order of decreasing nominal apertures of 5600μm, 3350μm, 2800μm, 355μm, 250μm, 125μm and 90μm, and operated for 20 minutes. The amount of material retained on each sieve was measured and tabulated for analysis. A screen analysis graph using the Gaudin Schuman method was drawn for each of the screen tests on the alluvial samples. The study showed that the percentages of fine particle size -125+90 μm fraction were 45.00%, 36.00%, 39.60%, 43.00% and 36.80% for the selected samples. These primary ore characteristic results provide reference data for the alluvial gold ore processing method selection, process performance measurement and optimization.

Keywords: alluvial gold ore, sieve shaker, particle size, Gaudin Schumann

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Authors: Zahra Abdolkarimi, Naser Zouri

Abstract:

Nowadays, there is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Additionally, robotics system recommended RoboCup factor as a provider of some standardization and testing method in case of computer discussion widely. The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. In addition, decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidently. Consequences, shows method of our discussion is the best way for Particle Swarm Optimization and Fuzzy system compare to other decision of robotics algorithmic.

Keywords: PSO algorithm, inference fuzzy system, chaos theory, soccer robot league

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Authors: Jui-Pui Hung, Yong-Run Chen, Wei-Cheng Shih, Shen-He Tsui, Kung-Da Wu

Abstract:

This paper presents the effect on the tooling conditions on the machining stabilities of a milling machine tool. The machining stability was evaluated in different feeding direction in the X-Y plane, which was referred as the orientation-dependent machining stability. According to the machining mechanics, the machining stability was determined by the frequency response function of the cutter. Thus, we first conducted the vibration tests on the spindle tool of the milling machine to assess the tool tip frequency response functions along the principal direction of the machine tool. Then, basing on the orientation dependent stability analysis model proposed in this study, we evaluated the variation of the dynamic characteristics of the spindle tool and the corresponding machining stabilities at a specific feeding direction. Current results demonstrate that the stability boundaries and limited axial cutting depth of a specific cutter were affected to vary when it was fixed in the tool holder with different overhang length. The flute of the cutter also affects the stability boundary. When a two flute cutter was used, the critical cutting depth can be increased by 47 % as compared with the four flute cutter. The results presented in study provide valuable references for the selection of the tooling conditions for achieving high milling performance.

Keywords: tooling condition, machining stability, milling machine, chatter

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Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

Abstract:

With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

Procedia PDF Downloads 106