Search results for: edge detection algorithm

Authors: Tsepiso Mofolo, Luna Bergh

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The Lesotho construction industry fails to embrace environmental practices, which has then lead to excessive consumption of resources, land degradation, air and water pollution, loss of habitats, and high energy usage. The industry is highly inefficient, and this undermines its capability to yield the optimum contribution to social, economic and environmental developments. Sustainable construction is, therefore, imperative to ensure the cultivation of benefits from all these intrinsic themes of sustainable development. The development of a sustainable construction industry requires a holistic approach that takes into consideration the interaction between Lean Construction principles, socio-economic and environmental policies, technological advancement and the principles of construction or project management. Sustainable construction is a cutting-edge phenomenon, forming a component of a subjectively defined concept called sustainable development. Sustainable development can be defined in terms of attitudes and judgments to assist in ensuring long-term environmental, social and economic growth in society. The key concept of sustainable construction is Lean Construction. Lean Construction emanates from the principles of the Toyota Production System (TPS), namely the application and adaptation of the fundamental concepts and principles that focus on waste reduction, the increase in value to the customer, and continuous improvement. The focus is on the reduction of socio-economic waste, and protestation of environmental degradation by reducing carbon dioxide emission footprint. Lean principles require a fundamental change in the behaviour and attitudes of the parties involved in order to overcome barriers to cooperation. Prevalent barriers to adoption of Lean Construction in Lesotho are mainly structural - such as unavailability of financing, corruption, operational inefficiency or wastage, lack of skills and training and inefficient construction legislation and political interferences. The consequential effects of these problems trigger down to quality, cost and time of the project - which then result in an escalation of operational costs due to the cost of rework or material wastage. Factor and correlation analysis of these barriers indicate that they are highly correlated, which then poses a detrimental potential to the country’s welfare, environment and construction safety. It is, therefore, critical for Lesotho’s construction industry to develop a robust governance through bureaucracy reforms and stringent law enforcement.

Keywords: construction industry, sustainable development, sustainable construction industry, lean construction, barriers to sustainable construction

Procedia PDF Downloads 274

Authors: Norm Josephy, Lucy Kimball, Victoria Steblovskaya

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Hedging of a portfolio of European options under proportional transaction costs is considered. Our discrete time financial market model extends the binomial market model with transaction costs to the case where the underlying stock price ratios are distributed over a bounded interval rather than over a two-point set. An optimal hedging strategy is chosen from a set of admissible non-self-financing hedging strategies. Our approach to optimal hedging of a portfolio of options is based on theoretical foundation that includes determination of a no-arbitrage option price interval as well as on properties of the non-self-financing strategies and their residuals. A computational algorithm for optimizing an investor relevant criterion over the set of admissible non-self-financing hedging strategies is developed. Applicability of our approach is demonstrated using both simulated data and real market data.

Keywords: extended binomial model, non-self-financing hedging, optimization, proportional transaction costs

Procedia PDF Downloads 248

Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

Procedia PDF Downloads 127

Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

Procedia PDF Downloads 314

Authors: Adriana Muñoz-Talavera, Ana Rosa Rincón-Sánchez, Abraham Escobedo-Moratilla, María Cristina Islas-Carbajal, Miguel Ángel Gómez-Lim

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The highest rates of diabetes incidence and prevalence worldwide will increase the number of diabetic patients requiring insulin or insulin analogues. Then, current production systems would not be sufficient to meet the future market demands. Therefore, developing efficient expression systems for insulin and insulin analogues are needed. In addition, insulin analogues with better pharmacokinetics and pharmacodynamics properties and without mitogenic potential will be required. SCI-57 (single chain insulin-57) is an insulin analogue having 10 times greater affinity to the insulin receptor, higher resistance to thermal degradation than insulin, native mitogenicity and biological effect. Plants as expression platforms have been used to produce recombinant proteins because of their advantages such as cost-effectiveness, posttranslational modifications, absence of human pathogens and high quality. Immunoglobulin production with a yield of 50% has been achieved by transient expression in Nicotiana benthamiana (Nb). The aim of this study is to produce SCI-57 by transient expression in Nb. Methodology: DNA sequence encoding SCI-57 was cloned in pICH31070. This construction was introduced into Agrobacterium tumefaciens by electroporation. The resulting strain was used to infiltrate leaves of Nb. In order to isolate SCI-57, leaves from transformed plants were incubated 3 hours with the extraction buffer therefore filtrated to remove solid material. The resultant protein solution was subjected to anion exchange chromatography on an FPLC system and ultrafiltration to purify SCI-57. Detection of SCI-57 was made by electrophoresis pattern (SDS-PAGE). Protein band was digested with trypsin and the peptides were analyzed by Liquid chromatography tandem-mass spectrometry (LC-MS/MS). A purified protein sample (20µM) was analyzed by ESI-Q-TOF-MS to obtain the ionization pattern and the exact molecular weight determination. Chromatography pattern and impurities detection were performed using RP-HPLC using recombinant insulin as standard. The identity of the SCI-57 was confirmed by anti-insulin ELISA. The total soluble protein concentration was quantified by Bradford assay. Results: The expression cassette was verified by restriction mapping (5393 bp fragment). The SDS-PAGE of crude leaf extract (CLE) of transformed plants, revealed a protein of about 6.4 kDa, non-present in CLE of untransformed plants. The LC-MS/MS results displayed one peptide with a high score that matches SCI-57 amino acid sequence in the sample, confirming the identity of SCI-57. From the purified SCI-57 sample (PSCI-57) the most intense charge state was 1069 m/z (+6) on the displayed ionization pattern corresponding to the molecular weight of SCI-57 (6412.6554 Da). The RP-HPLC of the PSCI-57 shows the presence of a peak with similar retention time (rt) and UV spectroscopic profile to the insulin standard (SCI-57 rt=12.96 and insulin rt=12.70 min). The collected SCI-57 peak had ELISA signal. The total protein amount in CLE from transformed plants was higher compared to untransformed plants. Conclusions: Our results suggest the feasibility to produce insulin analogue SCI-57 by transient expression in Nicotiana benthamiana. Further work is being undertaken to evaluate the biological activity by glucose uptake by insulin-sensitive and insulin-resistant murine and human cultured adipocytes.

Keywords: insulin analogue, mass spectrometry, Nicotiana benthamiana, transient expression

Procedia PDF Downloads 341

Authors: U. S. N. Raju, Kothuri Sai Kiran, Meena G. Kamal, Vinay Nikhil Pabba, Suresh Kanaparthi

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There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efficient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework.

Keywords: video lectures, big video data, video retrieval, hadoop

Procedia PDF Downloads 518

Authors: Marin Senila, Oana Cadar, Thorsten Janisch, Patrick Lacroix-Desmazes

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The study presents the analytical capability and validation of a method based on microwave-assisted acid digestion for quantitative determination of platinum and palladium in catalysts using inductively coupled plasma optical emission spectrometry (ICP-OES). In order to validate the method, the main figures of merit such as limit of detection and limit of quantification, precision and accuracy were considered and the measurement uncertainty was estimated based on the bottom-up approach according to the international guidelines of ISO/IEC 17025. Limit of detections, estimated from blank signal using 3 s criterion, were 3.0 mg/kg for Pt and respectively 3.6 mg/kg for Pd, while limits of quantification were 9.0 mg/kg for Pt and respectively 10.8 mg/kg for Pd. Precisions, evaluated as standard deviations of repeatability (n=5 parallel samples), were less than 10% for both precious metals. Accuracies of the method, verified by recovery estimation certified reference material NIST SRM 2557 - pulverized recycled monolith, were 99.4 % for Pt and 101% for Pd. The obtained limit of quantifications and accuracy were satisfactory for the intended purpose. The paper offers all the steps necessary to validate the determination method for Pt and Pd in catalysts using inductively coupled plasma optical emission spectrometry.

Keywords: catalyst analysis, ICP-OES, method validation, platinum, palladium

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Authors: Md. Aminul Islam, Adel Merabet, Rachid Beguenane, Hussein Ibrahim

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This paper presents a simulation and mathematical model of stand-alone solar-wind-diesel based hybrid energy system (HES). A power management system is designed for multiple energy resources in a stand-alone hybrid energy system. Both Solar photovoltaic and wind energy conversion system consists of maximum power point tracking (MPPT), voltage regulation, and basic power electronic interfaces. An additional diesel generator is included to support and improve the reliability of stand-alone system when renewable energy sources are not available. A power management strategy is introduced to distribute the generated power among resistive load banks. The frequency regulation is developed with conventional phase locked loop (PLL) system. The power management algorithm was applied in Matlab®/Simulink® to simulate the results.

Keywords: solar photovoltaic, wind energy, diesel engine, hybrid energy system, power management, frequency and voltage regulation

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Authors: Prudhvinath Reddy Ankireddy, Narasimha Mangadoddy

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Spiral concentrators are commonly used in various industries, including mineral and coal processing, to efficiently separate materials based on their density and size. In these concentrators, a mixture of solid particles and fluid (usually water) is introduced as feed at the top of a spiral channel. As the mixture flows down the spiral, centrifugal and gravitational forces act on the particles, causing them to stratify based on their density and size. Spiral flows exhibit complex fluid dynamics, and interactions involve multiple phases and components in the process. Understanding the behavior of these phases within the spiral concentrator is crucial for achieving efficient separation. An experimental bi-component particle interaction study is conducted in this work utilizing magnetite (heavier density) and silica (lighter density) with different proportions processed in the spiral concentrator. The observation separation reveals that denser particles accumulate towards the inner region of the spiral trough, while a significant concentration of lighter particles are found close to the outer edge. The 5th turn of the spiral trough is partitioned into five zones to achieve a comprehensive distribution analysis of bicomponent particle segregation. Samples are then gathered from these individual streams using an in-house sample collector, and subsequent analysis is conducted to assess component segregation. Along the trough, there was a decline in the concentration of coarser particles, accompanied by an increase in the concentration of lighter particles. The segregation pattern indicates that the heavier coarse component accumulates in the inner zone, whereas the lighter fine component collects in the outer zone. The middle zone primarily consists of heavier fine particles and lighter coarse particles. The zone-wise results reveal that there is a significant fraction of segregation occurs in inner and middle zones. Finer magnetite and silica particles predominantly accumulate in outer zones with the smallest fraction of segregation. Additionally, numerical simulations are also carried out using the computational fluid dynamics (CFD) model based on the volume of fluid (VOF) approach incorporating the RSM turbulence model. The discrete phase model (DPM) is employed for particle tracking, thereby understanding the particle segregation of magnetite and silica along the spiral trough.

Keywords: spiral concentrator, bi-component particle segregation, computational fluid dynamics, discrete phase model

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Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

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Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

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Authors: Andrii Natochii

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The UA9 collaboration in the last two years has installed two different types of detectors to investigate the channeling effect in the bent silicon crystals with high-energy particles beam on the CERN accelerator facilities: Cherenkov detector CpFM and silicon pixel detector Timepix. In the current work, we describe the main performances of the Timepix detector operation at the SPS and H8 extracted beamline at CERN. We are presenting some detector calibration results and tuning. Our research topics also cover a cluster analysis algorithm for the particle hits reconstruction. We describe the optimal acquisition setup for the Timepix device and the edges of its functionality for the high energy and flux beam monitoring. The measurements of the crystal parameters are very important for the future bent crystal applications and needs a track reconstruction apparatus. Thus, it was decided to construct a short range (1.2 m long) particle telescope based on the Timepix sensors and test it at H8 SPS extraction beamline. The obtained results will be shown as well.

Keywords: beam monitoring, channeling, particle tracking, Timepix detector

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Authors: Hsu-Hui Cheng, Peng-Chian Chen, Shu-Yuan Chang, Jie-Ying Zhang

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Activities in the music practice rooms range from playing, listening, rehearsing to music performing. The good room acoustics in a music practice room enables a music teacher to teach more effectively subtle concepts such as intonation, articulation, balance, dynamics and tone production. A poor acoustical environment would deeply affect the development of basic musical skills of music students. Practicing in the music practice room is an essential daily activity for music students; consequently, music practice rooms are very important facilities in a music school or department. The purpose of this survey is to measure and analyze the acoustic condition of piano practice rooms at the department of music in Zhaoqing University and accordingly apply a more effective teaching method to music students. The volume of the music practice room is approximately 25 m³, and it has existing curtains and some wood hole sound-absorbing panels. When all small music practice rooms are in constant use for teaching, it was found that the values of the background noise at 45, 46, 42, 46, 45 dB(A) in the small music practice room ( the doors and windows were close), respectively. The noise levels in the small music practice room to higher than standard levels (35dB(A)).

Keywords: acoustical performance, music practice room, noise level, piano room

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Authors: Nassira Ouslimani

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The electronic structure of transition metals gives them many catalytic possibilities in many types of reactions, particularly cobalt and molybdenum. It is in this context that this study is part of the synthesis and characterization of mass catalysts based on cobalt and molybdenum Co1₋xMoO4 (X=0 and X=0.5 and X=1). The two catalysts were prepared by Co-precipitation using ammonia as a precipitating agent and one by precipitation. The samples obtained were analyzed by numerous physic-chemical analysis techniques: ATG-ATD-DSC, DRX-HT, SEM-EDX, and the elemental composition of the catalysts was verified by SAA as well as the FTIR. The ATG-DSC shows a mass loss for all the catalysts of approximately 8%, corresponding to the loss of water and the decomposition of nitrates. The DRX-HT analysis allows the detection of the two CoMoO4 phases with diffraction peaks which increase with the increase in temperature. The results of the FTIR analysis made it possible to highlight the vibration modes of the bonds of the structure of the prepared catalysts. The SEM images of the solids show very different textures with almost homogeneous surfaces with a more regular particle size distribution and a more defined grain shape. The EDX analysis showed the presence of the elements Co, Mo, and O in proportions very close to the nominal proportions. Finally, the actual composition, evaluated by SAA, is close to the theoretical composition fixed during the preparation. This testifies to the good conditions for the preparation of the catalysts by the co-precipitation method.

Keywords: catalytic, molybdenum, coprecipitation, cobalt, ammonia

Procedia PDF Downloads 79

Authors: Ajay Mandal, Jitendar Kumar Tiwari, N. Sathish, A. K. Srivastava

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A fundamental investigation is performed on the development of graphene (Gr) reinforced stainless steel 316L (SS 316L) metal matrix composite via selective laser melting (SLM) in order to improve specific strength and wear resistance property of SS 316L. Firstly, SS 316L powder and graphene were mixed in a fixed ratio using low energy planetary ball milling. The milled powder is then subjected to the SLM process to fabricate composite samples at a laser power of 320 W and exposure time of 100 µs. The prepared composite was mechanically tested (hardness and tensile test) at ambient temperature, and obtained results indicate that the properties of the composite increased significantly with the addition of 0.2 wt. % Gr. Increment of about 25% (from 194 to 242 HV) and 70% (from 502 to 850 MPa) is obtained in hardness and yield strength of composite, respectively. Raman mapping and XRD were performed to see the distribution of Gr in the matrix and its effect on the formation of carbide, respectively. Results of Raman mapping show the uniform distribution of graphene inside the matrix. Electron back scatter diffraction (EBSD) map of the prepared composite was analyzed under FESEM in order to understand the microstructure and grain orientation. Due to thermal gradient, elongated grains were observed along the building direction, and grains get finer with the addition of Gr. Most of the mechanical components are subjected to several types of wear conditions. Therefore, it is very necessary to improve the wear property of the component, and hence apart from strength and hardness, a tribological property of composite was also measured under dry sliding condition. Solid lubrication property of Gr plays an important role during the sliding process due to which the wear rate of composite reduces up to 58%. Also, the surface roughness of worn surface reduces up to 70% as measured by 3D surface profilometry. Finally, it can be concluded that SLM is an efficient method of fabricating cutting edge metal matrix nano-composite having Gr like reinforcement, which was very difficult to fabricate through conventional manufacturing techniques. Prepared composite has superior mechanical and tribological properties and can be used for a wide variety of engineering applications. However, due to the unavailability of a considerable amount of literature in a similar domain, more experimental works need to perform, such as thermal property analysis, and is a part of ongoing study.

Keywords: selective laser melting, graphene, composite, mechanical property, tribological property

Procedia PDF Downloads 133

Authors: Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan

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Email marketing is one of the most important segments of online marketing. It has been proved to be the most effective way to acquire and retain customers. The email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of email has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy.

Keywords: email marketing, email content, reinforcement learning, machine learning, Q-learning

Procedia PDF Downloads 186

Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

Procedia PDF Downloads 236

Authors: Mohamad Khairi Ishak

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Conventional public key crypto systems such as RSA (Ron Rivest, Adi Shamir and Leonard Adleman), DSA (Digital Signature Algorithm), and Elgamal are no longer efficient to be implemented in the small, memory constrained devices. Elliptic Curve Cryptography (ECC), which allows smaller key length as compared to conventional public key crypto systems, has thus become a very attractive choice for many applications. This paper describes implementation of an elliptic curve cryptography (ECC) encryption engine on a FPGA. The system has been implemented in 2 different key sizes, which are 131 bits and 163 bits. Area and timing analysis are provided for both key sizes for comparison. The crypto system, which has been implemented on Altera’s EPF10K200SBC600-1, has a hardware size of 5945/9984 and 6913/9984 of logic cells for 131 bits implementation and 163 bits implementation respectively. The crypto system operates up to 43 MHz, and performs point multiplication operation in 11.3 ms for 131 bits implementation and 14.9 ms for 163 bits implementation. In terms of speed, our crypto system is about 8 times faster than the software implementation of the same system.

Keywords: elliptic curve cryptography, FPGA, key sizes, memory

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Authors: Yu T. Tsai, Jin H. Huang

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In this paper, the specific sound transmission loss (TL) of the laminated composite plate (LCP) with different material properties in each layer is investigated. The numerical method to obtain the TL of the LCP is proposed by using elastic plate theory. The transfer matrix approach is novelty presented for computational efficiency in solving the numerous layers of dynamic stiffness matrix (D-matrix) of the LCP. Besides the numerical simulations for calculating the TL of the LCP, the material properties inverse method is presented for the design of a laminated composite plate analogous to a metallic plate with a specified TL. As a result, it demonstrates that the proposed computational algorithm exhibits high efficiency with a small number of iterations for achieving the goal. This method can be effectively employed to design and develop tailor-made materials for various applications.

Keywords: sound transmission loss, laminated composite plate, transfer matrix approach, inverse problem, elastic plate theory, material properties

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Authors: Kaiying Lai, Suiping Wang, Luodi Yu, Yang Zhang, Pengmin Qin

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Abnormalities in auditory processing are one of the most commonly reported sensory processing impairments in children with Autism Spectrum Disorder (ASD). Tonal language speaker with autism has enhanced neural sensitivity to pitch changes in pure tone. However, not all children with ASD exhibit the same performance in pitch processing due to different auditory sensitivity. The current study aimed to examine auditory change detection in ASD with different auditory sensitivity. K-means clustering method was adopted to classify ASD participants into two groups according to the auditory processing scores of the Sensory Profile, 11 autism with hypersensitivity (mean age = 11.36 ; SD = 1.46) and 18 with hypo-sensitivity (mean age = 10.64; SD = 1.89) participated in a passive auditory oddball paradigm designed for eliciting mismatch negativity (MMN) under the pure tone condition. Results revealed that compared to hypersensitive autism, the children with hypo-sensitivity showed smaller MMN responses to pure tone stimuli. These results suggest that ASD with auditory hypersensitivity and hypo-sensitivity performed differently in processing pure tone, so neural responses to pure tone hold promise for predicting the auditory sensitivity of ASD and targeted treatment in children with ASD.

Keywords: ASD, sensory profile, pitch processing, mismatch negativity, MMN

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Authors: Gaurav Sancheti

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This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Ryan Messina, Mehedi Hasan

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This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

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Authors: Bola Adeleke, Kayode Ogunsusi

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Heritage sites have evolved to preserve culture and heritage and also to educate and entertain tourists. Tourist travel decisions and behavior are influenced by destination image and value of the experience of tourists. Perceived value is one of the important tools for securing a competitive edge in tourism destinations. The model of Ritchie and Crouch distinguished 36 attributes of competitiveness which are classified into five factors which are quality of experience, touristic attractiveness, environment and infrastructure, entertainment/outdoor activities and cultural traditions. The study extended this model with a different grouping of the determinants of destination competitiveness. The theoretical framework used for this study assumes that apart from attractions already situated in the grove, satisfaction with destination common service, and entertainment and events, can all be used in creating a positive image for/and in attracting customers (destination selection) to visit Osun Sacred Osogbo Grove during and after annual celebrations. All these will impact positively on travel experience of customers as well as their spiritual fulfillment. Destination image has a direct impact on tourists’ satisfaction which consequently impacts on tourists’ likely future behavior on whether to revisit a cultural destination or not. The study investigated the variables responsible for destination image competitiveness of the Heritage Site; assessed the factors enhancing the destination image; and evaluated the perceived value realized by tourists from their cultural experience at the grove. A complete enumeration of tourists above 18 years of age who visited the Heritage Site within the month of March and April 2017 was taken. 240 respondents, therefore, were used for the study. The structured questionnaire with 5 Likert scales was administered. Five factors comprising 63 variables were used to determine the destination image competitiveness through principal component analysis, while multiple regressions were used to evaluate perceived value of tourists at the grove. Results revealed that 11 out of the 12 variables determining the destination image competitiveness were significant in attracting tourists to the grove. From the R-value, all factors predicted tourists’ value of experience strongly (R= 0.936). The percentage variance of customer value was explained by 87.70% of the variance of destination common service, entertainment and event satisfaction, travel environment satisfaction and spiritual satisfaction, with F-value being significant at 0.00. Factors with high alpha value contributed greatly to adding value to enhancing destination and tourists’ experience. 11 variables positively predicted tourist value with significance. Managers of Osun World Heritage Site should improve on variables critical to adding values to tourists’ experience.

Keywords: competitiveness, destination image, Osun Osogbo world heritage site, tourists

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Authors: M. N. Boukhatem, M. A. Ferhat, K. Mansour

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Bovine raw milk represents a favorable environment for the growth of several food-spoilage strains and some pathogens. It must meet stringent standards to ensure the highest microbiological and toxicological qualities.In order to assess the microbiological risks associated with the consumption of this food, we conducted this study to determine the microbiological quality of bovine raw milk (54 samples) commercialized at the state of Blida (Algeria). The samples analyzed were unsatisfactory in terms of total flora where 61.11% of samples were considered as non acceptable in terms of quality standards, fecal coliforms (40.74%), fecal streptococci (55.55%) and staphylococci (74.07%). Salmonella and Clostridium strains were not detected in all the samples. Furthermore, antibiotic residues were found in 26% of analysed samples. These results reflect non-compliance with the rules of good hygiene practices at milking, storage, transportatio, and sale of milk. Bovine raw milk consumed presents a serious health risk to the population of the study areas.The livestock coaching actors and dissemination of good hygiene practices throughout the production chain are needed to improve the quality of local milk.

Keywords: bovine raw milk, microbiological quality, fecal coliforms, antibiotic residue, Blida state

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Authors: Nootchanat Mairuae, Walaiporn Tongjaroenbuangam, Chalisa Louicharoen Cheepsunthorn, Poonlarp Cheepsunthorn

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The aim of this study was to investigate the anti-oxidative effect, the anti-inflammatory effects, and the molecular mechanisms of okra (Abelmoschus esculentus Linn.) on lipopolysaccharide (LPS)-stimulated BV2 microglial cells. The BV2 cells were treated with LPS in the presence or absence of okra. Reactive oxygen species (ROS) and nitric oxide (NO) production were measured using the ROS detection reagent DCF-DA and the Griess reaction, respectively. The phosphorylation levels of nuclear factor-kappa B (NF-kB) p65 was detected by Western blot assay. Treatment of BV2 microglia cells with okra was found to significantly suppress the LPS-induced inflammatory mediator NO as well as ROS compared to untreated cells. The levels of LPS-induced NF-kB p65 phosphorylation were significantly decreased following okra treatment too. These results show that okra exerts anti-oxidative and anti-inflammatory effects in LPS-stimulated BV2 microglial cells by suppressing the NF-κB pathway. This suggests okra might be a valuable agent for treatment of anti-neuroinflammatory diseases mediated by microglial cells.

Keywords: Abelmoschus esculentus Linn, microglia, neuroinflammation, reactive oxygen spicy

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Authors: Shiuh-Jer Huang, Su-Shean Chen, Sheam-Chyun Lin

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Two-wheel inverted pendulum robot (TWIPR) is designed with two-hub DC motors for human riding and motion control evaluation. In order to measure the tilt angle and angular velocity of the inverted pendulum robot, accelerometer and gyroscope sensors are chosen. The mobile robot’s moving position and velocity were estimated based on DC motor built in hall sensors. The control kernel of this electric mobile robot is designed with embedded Arduino Nano microprocessor. A handle bar was designed to work as steering mechanism. The intelligent model-free fuzzy sliding mode control (FSMC) was employed as the main control algorithm for this mobile robot motion monitoring with different control purpose adjustment. The intelligent controllers were designed for balance control, and moving speed control purposes of this robot under different operation conditions and the control performance were evaluated based on experimental results.

Keywords: balance control, speed control, intelligent controller, two wheel inverted pendulum

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Authors: Gyoutae Park, Seungho Han, Byungduk Kim, Youngdo Jo, Yongsop Shim, Yeonjae Lee, Sangguk Ahn, Hiesik Kim, Jungil Park

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In this paper, we presented not only development technology of an explosion proof type and portable combustible gas leak detector but also algorithm to improve accuracy for measuring gas concentrations. The presented techniques are to apply the flame-proof enclosure and intrinsic safe explosion proof to an infrared gas leak detector at first in Korea and to improve accuracy using linearization recursion equation and Lagrange interpolation polynomial. Together, we tested sensor characteristics and calibrated suitable input gases and output voltages. Then, we advanced the performances of combustible gaseous detectors through reflecting demands of gas safety management fields. To check performances of two company's detectors, we achieved the measurement tests with eight standard gases made by Korea Gas Safety Corporation. We demonstrated our instruments better in detecting accuracy other than detectors through experimental results.

Keywords: accuracy improvement, IR gas sensor, gas leak, detector

Procedia PDF Downloads 385

Authors: Shahbaz G. Hassan

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Water quality models are very important to predict the changes in surface water quality for environmental management. The aim of this paper is to give an overview of the water qualities, and to provide directions for selecting models in specific situation. Water quality models include one kind of model based on a mechanistic approach, while other models simulate water quality without considering a mechanism. Mechanistic models can be widely applied and have capabilities for long-time simulation, with highly complexity. Therefore, more spaces are provided to explain the principle and application experience of mechanistic models. Mechanism models have certain assumptions on rivers, lakes and estuaries, which limits the application range of the model, this paper introduces the principles and applications of water quality model based on the above three scenarios. On the other hand, mechanistic models are more easily to compute, and with no limit to the geographical conditions, but they cannot be used with confidence to simulate long term changes. This paper divides the empirical models into two broad categories according to the difference of mathematical algorithm, models based on artificial intelligence and models based on statistical methods.

Keywords: empirical models, mathematical, statistical, water quality

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Authors: E. K. K. Agyeman, P. Mousseau, A. Sarda, D. Edelin

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During the cooling of hot metals by the circulation of water in canals formed by boring holes in the metal, the rapid phase change of the water due to the high initial temperature of the metal leads to a non homogenous distribution of the phases within the canals. The liquid phase dominates towards the entrance of the canal while the gaseous phase dominates towards the exit. As a result of the different thermal properties of both phases, the metal is not uniformly cooled. This poses a problem during the cooling of moulds, where a uniform temperature distribution is needed in order to ensure the integrity of the part being formed. In this study, the simultaneous use of multiple water jets and an airflow for the uniform and controlled cooling of a steel block is investigated. A circular hole is bored at the centre of the steel block along its length and a perforated steel pipe is inserted along the central axis of the hole. Water jets that impact the internal surface of the steel block are generated from the perforations in the steel pipe when the water within it is put under pressure. These jets are oriented in the opposite direction to that of gravity. An intermittent airflow is imposed in the annular space between the steel pipe and the surface of hole bored in the steel block. The evolution of the temperature with respect to time of the external surface of the block is measured with the help of thermocouples and an infrared camera. Due to the high initial temperature of the steel block (350 °C), the water changes phase when it impacts the internal surface of the block. This leads to high heat fluxes. The strategy used to control the cooling speed of the block is the intermittent impingement of its internal surface by the jets. The intervals of impingement and of non impingement are varied in order to achieve the desired result. An airflow is used during the non impingement periods as an additional regulator of the cooling speed and to improve the temperature homogeneity of the impinged surface. After testing different jet positions, jet speeds and impingement intervals, it’s observed that the external surface of the steel block has a uniform temperature distribution along its length. However, the temperature distribution along its width isn’t uniform with the maximum temperature difference being between the centre of the block and its edge. Changing the positions of the jets has no significant effect on the temperature distribution on the external surface of the steel block. It’s also observed that reducing the jet impingement interval and increasing the non impingement interval slows down the cooling of the block and improves upon the temperature homogeneity of its external surface while increasing the duration of jet impingement speeds up the cooling process.

Keywords: cooling speed, homogenous cooling, jet impingement, phase change

Procedia PDF Downloads 119

Authors: Sıdıka Kaya, Gülay Sain Güven, Seda Karsavuran, Onur Toka

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This study is part of an ongoing research project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 114K404, and participation to this conference was supported by Hacettepe University Scientific Research Coordination Unit under Project Number 10243. Evaluation of hospital readmissions is gaining importance in terms of quality and cost, and is becoming the target of national policies. In Turkey, the topic of hospital readmission is relatively new on agenda and very few studies have been conducted on this topic. The aim of this study was to determine 30-day readmission rates and risk factors for readmission. Whether readmission was planned, related to the prior admission and avoidable or not was also assessed. The study was designed as a ‘prospective cohort study.’ 472 patients hospitalized in internal medicine departments of a university hospital in Turkey between February 1, 2015 and April 30, 2015 were followed up. Analyses were conducted using IBM SPSS Statistics version 22.0 and SPSS Modeler 16.0. Average age of the patients was 56 and 56% of the patients were female. Among these patients 95 were readmitted. Overall readmission rate was calculated as 20% (95/472). However, only 31 readmissions were unplanned. Unplanned readmission rate was 6.5% (31/472). Out of 31 unplanned readmission, 24 was related to the prior admission. Only 6 related readmission was avoidable. To determine risk factors for readmission we constructed Chi-square automatic interaction detector (CHAID) decision tree algorithm. CHAID decision trees are nonparametric procedures that make no assumptions of the underlying data. This algorithm determines how independent variables best combine to predict a binary outcome based on ‘if-then’ logic by portioning each independent variable into mutually exclusive subsets based on homogeneity of the data. Independent variables we included in the analysis were: clinic of the department, occupied beds/total number of beds in the clinic at the time of discharge, age, gender, marital status, educational level, distance to residence (km), number of people living with the patient, any person to help his/her care at home after discharge (yes/no), regular source (physician) of care (yes/no), day of discharge, length of stay, ICU utilization (yes/no), total comorbidity score, means for each 3 dimensions of Readiness for Hospital Discharge Scale (patient’s personal status, patient’s knowledge, and patient’s coping ability) and number of daycare admissions within 30 days of discharge. In the analysis, we included all 95 readmitted patients (46.12%), but only 111 (53.88%) non-readmitted patients, although we had 377 non-readmitted patients, to balance data. The risk factors for readmission were found as total comorbidity score, gender, patient’s coping ability, and patient’s knowledge. The strongest identifying factor for readmission was comorbidity score. If patients’ comorbidity score was higher than 1, the risk for readmission increased. The results of this study needs to be validated by other data–sets with more patients. However, we believe that this study will guide further studies of readmission and CHAID is a useful tool for identifying risk factors for readmission.

Keywords: decision tree, hospital, internal medicine, readmission

Procedia PDF Downloads 251