Search results for: optimized network

Authors: Jisong Zhang, Yinghua Zhao

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The present study investigated the mechanical properties and microstructure of Ultra-High Performance Concrete (UHPC) containing supplementary cementitious materials (SCMs), such as fly ash (FA) and silica fume (SF), and to verify the synergistic effect in the ternary system. On the basis of 30% fly ash replacement, the incorporation of either 10% SF or 20% SF show a better performance compared to the reference sample. The efficiency factor (k-value) was calculated as a synergistic effect to predict the compressive strength of UHPC with these SCMs. The SEM of micrographs and pore volume from BJH method indicate a high correlation with compressive strength. Further, an artificial neural networks model was constructed for prediction of the compressive strength of UHPC containing these SCMs.

Keywords: artificial neural network, fly ash, mechanical properties, ultra-high performance concrete

Procedia PDF Downloads 418

Authors: Umang Varshney, Amit Karamchandani, Rohit Kapoor

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Practitioners and researchers have realized that Blockchain is not limited to currency. Blockchain as a distributed ledger can ensure a transparent and traceable supply chain. Due to Blockchain-enabled IoTs, a firm’s information management system can now take inputs from other supply chain partners in real-time. This study aims to provide empirical evidence of dimensions responsible for blockchain implemented firm’s growth and highlight how sector (manufacturing or service), state's regulatory environment, and choice of blockchain network affect the blockchain's usefulness. This post-adoption study seeks to validate the findings of pre-adoption studies done on the blockchain. Data will be collected through a survey of managers working in blockchain implemented firms and analyzed through PLS-SEM.

Keywords: blockchain, information management system, PLS-SEM, firm's growth

Procedia PDF Downloads 128

Authors: Chen Yao, Wang Ke

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Small towns are the most basic administrative institutions in our country, which are connected with cities and rural areas. Small towns play an important role in promoting local urban and rural economic development, providing the main public services and maintaining social stability in social governance. With the vigorous development of small towns and the transformation of industrial structure, the changes of social structure, spatial structure, and lifestyle are lagging behind, causing that the spatial form and landscape style do not belong to both cities and rural areas, and seriously affecting the quality of people’s life space and environment. The rural economy in Zhejiang Province has started, the society and the population are also developing in relative stability. In September 2016, Zhejiang Province set out the 'Technical Guidelines for Comprehensive Environmental Remediation of Small Towns in Zhejiang Province,' so as to comprehensively implement the small town comprehensive environmental remediation with the main content of strengthening the plan and design leading, regulating environmental sanitation, urban order and town appearance. In November 2016, Huzhou City started the comprehensive environmental improvement of small towns, strived to use three years to significantly improve the 115 small towns, as well as to create a number of high quality, distinctive and beautiful towns with features of 'clean and livable, rational layout, industrial development, poetry and painting style'. This paper takes Meixi Town, Zhangwu Town and Sanchuan Village in Huzhou City as the empirical cases, analyzes the small town public space by applying the relative theory of actor-network and space syntax. This paper also analyzes the spatial composition in actor and social structure elements, as well as explores the relationship of actor’s spatial practice and public open space by combining with actor-network theory. This paper introduces the relevant theories and methods of spatial syntax, carries out research analysis and design planning analysis of small town spaces from the perspective of quantitative analysis. And then, this paper proposes the effective updating strategy for the existing problems in public space. Through the planning and design in the building level, the dissonant factors produced by various spatial combination of factors and between landscape design and urban texture during small town development will be solved, inhabitant quality of life will be promoted, and town development vitality will be increased.

Keywords: small towns, urbanization, public space, updating

Procedia PDF Downloads 237

Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

Procedia PDF Downloads 81

Authors: David B. Tsuanyo, Didier Aussel, Yao Azoumah, Pierre Neveu

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An innovative concept called “Flexy-Energy”is developing at 2iE. This concept aims to produce electricity at lower cost by smartly mix different available energies sources in accordance to the load profile of the region. With a higher solar irradiation and due to the fact that Diesel generator are massively used in sub-Saharan rural areas, PV/Diesel hybrid systems could be a good application of this concept and a good solution to electrify this region, provided they are reliable, cost effective and economically attractive to investors. Presentation of the developed approach is the aims of this paper. The PV/Diesel hybrid system designed consists to produce electricity and/or heat from a coupling between Diesel gensets and PV panels without batteries storage, while ensuring the substitution of gasoil by bio-fuels available in the area where the system will be installed. The optimal design of this system is based on his technical performances; the Life Cycle Cost (LCC) and Levelized Cost of Energy are developed and use as economic criteria. The Net Present Value (NPV), the internal rate of return (IRR) and the discounted payback (DPB) are also evaluated according to dual electricity pricing (in sunny and unsunny hours). The PV/Diesel hybrid system obtained is compared to the standalone Diesel gensets. The approach carried out in this paper has been applied to Siby village in Mali (Latitude 12 ° 23'N 8 ° 20'W) with 295 kWh as daily demand. This approach provides optimal physical characteristics (size of the components, number of component) and dynamical characteristics in real time (number of Diesel generator on, their load rate, fuel specific consumptions, and PV penetration rate) of the system. The system obtained is slightly cost effective; but could be improved with optimized tariffing strategies.

Keywords: investments criteria, optimization, PV hybrid, sizing, rural electrification

Procedia PDF Downloads 443

Authors: Christine Fräulin, Daniela Schurr, Hamed Shahidi Rad, Gerrit Waters, Günter Rinke, Roland Dittmeyer, Michael Nilles

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The reaction mechanisms of many liquid-phase reactions in organic chemistry have not yet been sufficiently clarified. Process conditions of several hundred degrees celsius and pressures to ten megapascals complicate the sampling and the determination of kinetic data. Space resolved in-situ measurements promises new insights. A non-invasive in-situ measurement technique has the advantages that no sample preparation is necessary, there is no change in sample mixture before analysis and the sampling do no lead to interventions in the flow. Thus, the goal of our research was the development of a contact-free spatially resolved measurement technique for kinetic studies of liquid phase reaction under process conditions. Therefore we used laser Raman spectroscopy combined with an optical transparent microchannel reactor. To show the performance of the system we choose the oxidation of cyclohexane as sample reaction. Cyclohexane oxidation is an economically important process. The products are intermediates for caprolactam and adipic acid, which are starting materials for polyamide 6 and 6.6 production. To maintain high selectivities of 70 to 90 %, the reaction is performed in industry at a low conversion of about six percent. As Raman spectroscopy is usually very selective but not very sensitive the detection of the small product concentration in cyclohexane oxidation is quite challenging. To meet these requirements, an optical experimental setup was optimized to determine the concentrations by laser Raman spectroscopy with respect to good detection sensitivity. With this measurement technique space resolved kinetic studies of uncatalysed and homogeneous catalyzed cyclohexane oxidation were carried out to obtain details about the reaction mechanism.

Keywords: in-situ laser raman spectroscopy, space resolved kinetic measurements, homogeneous catalysis, chemistry

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Authors: Supattra Kanchanopast

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The purpose of this research is to develop and design a management information system for 7 eleven shop in Bangkok. The system was designed and developed to meet users’ requirements via the internet network by use of application software such as My SQL for database management, Apache HTTP Server for Web Server and PHP Hypertext Preprocessor for an interface between web server, database and users. The system was designed into two subsystems as the main system, or system for head office, and the branch system for branch shops. These consisted of three parts which are classified by user management as shop management, inventory management and Point of Sale (POS) management. The implementation of the MIS for the mini-mart shop, can lessen the amount of paperwork and reduce repeating tasks so it may decrease the capital of the business and support an extension of branches in the future as well.

Keywords: convenience store, the management information system, inventory management, 7 eleven shop

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Authors: Emineh Tsegahun Gedif

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Zinc oxide nanoparticles (ZnO NPs) have garnered significant attention due to their diverse applications encompassing catalytic, optical, photonic, and antibacterial properties. In this study, we successfully synthesized zinc oxide nanoparticles using a rapid, environmentally benign, and cost-effective method. Neem (Azadirachta indica) leaf extract served as the reducing agent for Zn (NO₃)₂.6H2O solution under optimized conditions (pH = 9). Qualitative screening techniques and FT-IR Spectroscopy confirmed the presence of active biomolecules such as flavonoids, phenolic groups, alkaloids, terpenoids, and tannins within the Neem leaf extract, both before and after reduction. The formation of ZnO NPs was visually evident through a distinct color change from colorless to light yellow. The biosynthesized nanoparticles underwent comprehensive characterization through UV-visible, FT-IR, and XRD spectroscopies. The reduction process proved to be straightforward and user-friendly, with UV-visible spectroscopy demonstrating a surface plasmon resonance (SPR) at 321 nm, unequivocally confirming the ZnO NP formation. X-ray diffraction analysis elucidated the crystal structure, revealing an average particle size of approximately 20 nm using Scherrer's equation based on the line width of the plane. Furthermore, the synthesized zinc oxide nanoparticles were evaluated for their antimicrobial properties against both Gram-positive and Gram-negative bacteria. The results showcased significant inhibitory activity, with the highest zone of inhibition observed against Escherichia coli (15 mm) and comparatively lower activity against Staphylococcus aureus. This research underscores the potential of Neem leaf extract-mediated synthesis of ZnO NPs as an eco-friendly and effective approach for various applications, including antibacterial agents.

Keywords: zinc oxide nanoparticles (ZnO NPs), bioreducing agent, green synthesis, antibacterial activity

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

Procedia PDF Downloads 191

Authors: Wenyang Wan

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The study of urban land use and landscape pattern is the current hotspot in the fields of planning and design, ecology, etc., which is of great significance for the construction of the overall humanistic ecosystem of the city and optimization of the urban spatial structure. Banan District, as the main part of the eastern eco-city planning of Chongqing Municipality, is a high ground for highlighting the ecological characteristics of Chongqing, realizing effective transformation of ecological value, and promoting the integrated development of urban and rural areas. The analytical methods of land use transfer matrix (GIS) and landscape pattern index (Fragstats) were used to study the characteristics and laws of the evolution of land use landscape pattern in Banan District from 2000 to 2020, which provide some reference value for Banan District to alleviate the ecological contradiction of landscape. The results of the study show that ① Banan District is rich in land use types, of which the area of cultivated land will still account for 57.15% of the total area of the landscape until 2020, accounting for an absolute advantage in land use structure of Banan District; ② From 2000 to 2020, land use conversion in Banan District is characterized as Cropland > woodland > grassland > shrubland > built-up land > water bodies > wetlands, with cropland converted to built-up land being the largest; ③ From 2000 to 2020, the landscape elements of Banan District were distributed in a balanced way, and the landscape types were rich and diversified, but due to the influence of human interference, it also presented the characteristics that the shape of the landscape elements tended to be irregular, and the dominant patches were distributed in a scattered manner, and the patches had poor connectivity. It is recommended that in future regional ecological construction, the layout should be rationally optimized, the relationship between landscape components should be coordinated, the connectivity between landscape patches should be strengthened, and the degree of landscape fragmentation should be reduced.

Keywords: land use transfer, landscape pattern evolution, GIS and Fragstats, Banan district

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Seyedeh Mozhgan Seyed Talebi, Chih -Hao Lee

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With an abrupt rise in the power conservation efficiency (PCE) of perovskite solar cells (PSCs) within a short span of time, the toxicity of lead was raised as a major hurdle in the path toward their commercialization. In the present research, a systematic investigation of the electrical and optical characteristics of the all-inorganic CsSnI₃ perovskite absorber layer was performed with the Vienna Ab Initio Simulation Package (VASP) using the projector-augmented wave method. The presence of inorganic halide perovskite offers the advantages of enhancing the degradation resistance of the device, reducing the cost of cells, and minimizing the recombination of generated carriers. The simulated standard device using a 1D simulator like solar cell capacitance simulator (SCAPS) version 3308 involves FTO/n-TiO₂/CsSnI₃ Perovskite absorber/Spiro OmeTAD HTL/Au contact layer. The variation in the device design key parameters such as the thickness and defect density of perovskite absorber, hole transport layer and electron transport layer and interfacial defects are examined with their impact on the photovoltaic characteristic parameters. The effect of an increase in operating temperature from 300 K to 400 K on the performance of CsSnI3-based perovskite devices is also investigated. The optimized standard device at room temperature shows the highest PCE of 25.18 % with FF of 75.71 %, Voc of 0.96 V, and Jsc of 34.67 mA/cm². The outcomes and interpretation of different inorganic Cu-based HTLs presence, such as CuSCN, Cu₂O, CuO, CuI, SrCu₂O₂, and CuSbS₂, here represent a critical avenue for the possibility of fabricating high PCE perovskite devices made of stable, low-cost, efficient, safe, and eco-friendly all-inorganic materials like CsSnI₃ perovskite light absorber.

Keywords: CsSnI₃, hole transporting layer (HTL), lead-free perovskite solar cell, SCAPS-1D software

Procedia PDF Downloads 90

Authors: Dariush Jafari, Seyed Ali Jafari

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The recommended limit for cadmium concentration in potable water is less than 0.005 mg/L. A continuous biosorption process using indigenous red seaweed, Gracilaria corticata, was performed to remove cadmium from the potable water. The process was conducted under fixed conditions and the breakthrough curves were achieved for three consecutive sorption-desorption cycles. A modeling based on Artificial Neural Network (ANN) was employed to fit the experimental breakthrough data. In addition, a simplified semi empirical model, Thomas, was employed for this purpose. It was found that ANN well described the experimental data (R2>0.99) while the Thomas prediction were a bit less successful with R2>0.97. The adjusted design parameters using the nonlinear form of Thomas model was in a good agreement with the experimentally obtained ones. The results approve the capability of ANN to predict the cadmium concentration in potable water.

Keywords: ANN, biosorption, cadmium, packed-bed, potable water

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Authors: M. Caramihai, Irina Severin

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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

Procedia PDF Downloads 428

Authors: Sagar Hedaoo, Fazle Baki, Ahmed Azab

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In supply chain management, network design for scalable manufacturing facilities is an emerging field of research. Facility location allocation assigns facilities to customers to optimize the overall cost of the supply chain. To further optimize the costs, capacities of these facilities can be changed in accordance with customer demands. A mathematical model is formulated to fully express the problem at hand and to solve small-to-mid range instances. A dedicated constraint has been developed to restrict emissions in line with the Kyoto protocol. This problem is NP-Hard; hence, a simulated annealing metaheuristic has been developed to solve larger instances. A case study on the USA-Canada cross border crossing is used.

Keywords: emission, mixed integer linear programming, metaheuristic, simulated annealing

Procedia PDF Downloads 313

Authors: Sonia Ben Hamza, Sabra Habli, Nejla Mahjoub Saïd, Hervé Bournot, Georges Le Palec

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This paper spots light on the effect of a point source pollution on streams, stemming out from intentional release caused by unconscious facts. The consequences of such contamination on ecosystems are very serious. Accordingly, effective tools are highly demanded in this respect, which enable us to come across an accurate progress of pollutant and anticipate different measures to be applied in order to limit the degradation of the environmental surrounding. In this context, we are eager to model a pollutant dispersion of a free surface flow which is ejected by an outfall sewer of an urban sewerage network in coastal water taking into account the influence of climatic parameters on the spread of pollutant. Numerical results showed that pollutant dispersion is merely due to the presence of vortices and turbulence. Hence, it was realized that the pollutant spread in seawater is strongly correlated with climatic conditions in this region.

Keywords: coastal waters, numerical simulation, pollutant dispersion, turbulent flows

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Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

Procedia PDF Downloads 109

Authors: Nursac Akyol, Merve S. Turan, Mustafa Ozcelik, Erdogan Kucukoner, Erkan Karacabey

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Traditional raisin production is a long time drying process under sunlight. During this procedure, grapes are open to some environmental effects besides the adverse effects of the long drying period. Thus, there is a need to develop an alternative method being applicable instead of traditional one. To this extent, a combination of a potential pretreatment (carbonic maceration, CM) with convectional oven drying was examined. CM application was used in raisin production (grape drying) as a pretreatment process before oven drying. Pressure, temperature and time were examined as application parameters of CM. In conventional oven drying, the temperature is a process variable. The aim is to find out how CM and convectional drying processes affect the drying characteristics of grapes as well as their physical and chemical properties. For this purpose, the response surface method was used to determine both the effects of the variables and the optimum pretreatment and drying conditions. The optimum conditions of CM for raisin production were 0.3 MPa of pressure value, 4°C of application temperature and 8 hours of application time. The optimized drying temperature was 77°C. The results showed that the application of CM before the drying process improved the drying characteristics. Drying took only 389 minutes for grapes pretreated by CM under optimum conditions and 495 minutes for the control group dried only by the conventional drying process. According to these results, a decrease of 21% was achieved in the time requirement for raisin production. Also, it was observed that the samples dried under optimum conditions had similar physical properties as those the control group had. It was seen that raisin, which was dried under optimum conditions were in better condition in terms of some of the bioactive contents compared to control groups. In light of all results, it is seen that CM has an important potential in the industrial drying of grape samples. The current study was financially supported by TUBITAK, Turkey (Project no: 116R038).

Keywords: drying time, pretreatment, response surface methodlogy, total phenolic

Procedia PDF Downloads 142

Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Kainat Affrin, P. Reshma, G. Narendra Kumar

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Optimization of timetable is the need of the day for the rescheduling and routing of trains in real time. Trains are scheduled in parallel with the road transport vehicles to the same destination. As the number of trains is restricted due to single track, customers usually opt for road transport to use frequently. The air pollution increases as the density of vehicles on road transport is increased. Use of an alternate mode of transport like train helps in reducing air-pollution. This paper mainly aims at attracting the passengers to Train transport by proper rescheduling of trains using hybrid of stop-skip algorithm and iterative convex programming algorithm. Rescheduling of train bi-directionally is achieved on a single track with dynamic dual time and varying stops. Introduction of more trains attract customers to use rail transport frequently, thereby decreasing the pollution. The results are simulated using Network Simulator (NS-2).

Keywords: air pollution, AODV, re-scheduling, WSNs

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Authors: Ragi Poyyara, Vijaya Patnana, Mohammed Alam

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When acid is pumped into damaged reservoirs for damage removal/stimulation, distorted inflow of acid into the formation occurs caused by acid preferentially traveling into highly permeable regions over low permeable regions, or (in general) into the path of least resistance. This can lead to poor zonal coverage and hence warrants diversion to carry out an effective placement of acid. Diversion is desirably a reversible technique of temporarily reducing the permeability of high perm zones, thereby forcing the acid into lower perm zones. The uniqueness of each reservoir can pose several challenges to engineers attempting to devise optimum and effective diversion strategies. Diversion techniques include mechanical placement and/or chemical diversion of treatment fluids, further sub-classified into ball sealers, bridge plugs, packers, particulate diverters, viscous gels, crosslinked gels, relative permeability modifiers (RPMs), foams, and/or the use of placement techniques, such as coiled tubing (CT) and the maximum pressure difference and injection rate (MAPDIR) methodology. It is not always realized that the effectiveness of diverters greatly depends on reservoir properties, such as formation type, temperature, reservoir permeability, heterogeneity, and physical well characteristics (e.g., completion type, well deviation, length of treatment interval, multiple intervals, etc.). This paper reviews the mechanisms by which each variety of diverter functions and discusses the effect of various reservoir properties on the efficiency of diversion techniques. Guidelines are recommended to help enhance productivity from zones of interest by choosing the best methods of diversion while pumping an optimized amount of treatment fluid. The success of an overall acid treatment often depends on the effectiveness of the diverting agents.

Keywords: diversion, reservoir, zonal coverage, carbonate, sandstone

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Authors: Deybith Venegas-Rojas, Jens Budde, Dominik Nörz, Manfred Jücker, Hoc Khiem Trieu

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Microfluidics is a promising approach for biomedicine cell culture experiments with microfluidic bioreactors (MBR), which can provide high precision in volume and time control over mass transport and microenvironments in small-scale studies. Nevertheless, shear stress and oxygen concentration are important factors that affect the microenvironment and then the cell culture. It is presented a novel MBR design in which differences in geometry, shear stress, and oxygen concentration were studied and optimized for cell culture. The aim is to mimic the in vivo condition with biocompatible materials and continuous perfusion of nutrients, a healthy shear stress, and oxygen concentration. The design consists of a capture system of PDMS micropillars which keep cells in place, so it is not necessary any hydrogel or complicated scaffolds for cells immobilization. Besides, the design allows continuous supply with nutrients or even any other chemical for cell experimentation. Finite element method simulations were used to study and optimize the effect of parameters such as flow rate, shear stress, oxygen concentration, micropillars shape, and dimensions. The micropillars device was fabricated with microsystem technology such as soft-lithography, deep reactive ion etching, self-assembled monolayer, replica molding, and oxygen plasma bonding. Eight different geometries were fabricated and tested, with different flow rates according to the simulations. During the experiments, it was observed the effect of micropillars size, shape, and configuration for stability and shear stress control when increasing flow rate. The device was tested with several successful HepG2 3D cell cultures. With this MBR, the aforementioned parameters can be controlled in order to keep a healthy microenvironment according to specific necessities of different cell types, with no need of hydrogels and can be used for a wide range of experiments with cells.

Keywords: cell culture, micro-bioreactor, microfluidics, micropillars, oxygen concentration, shear stress

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Authors: Anisuzzaman Khan, A. J. K. Masud

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Bangladesh is the largest delta in the world predominantly comprising large network of rives and wetlands. Wetlands in Bangladesh are represented by inland freshwater, estuarine brakishwater and tidal salt-water coastal wetlands. Bangladesh possesses enormous area of wetlands including rivers and streams, freshwater lakes and marshes, haors, baors, beels, water storage reservoirs, fish ponds, flooded cultivated fields and estuarine systems with extensive mangrove swamps. The past, present, and future of Bangladesh, and its people’s livelihoods are intimately connected to its relationship with water and wetlands. More than 90% of the country’s total area consists of alluvial plains, crisscrossed by a complex network of rivers and their tributaries. Floodplains, beels (low-lying depressions in the floodplain), haors (deep depression) and baors (oxbow lakes) represent the inland freshwater wetlands. Over a third of Bangladesh could be termed as wetlands, considering rivers, estuaries, mangroves, floodplains, beels, baors and haors. The country’s wetland ecosystems also offer critical habitats for globally significant biological diversity. Of these the deeply flooded basins of north-east Bangladesh, known as haors, are a habitat of wide range of wild flora and fauna unique to Bangladesh. The haor basin lies within the districts of Sylhet, Sunamgonj, Netrokona, Kishoregonj, Habigonj, Moulvibazar, and Brahmanbaria in the Northeast region of Bangladesh comprises the floodplains of the Meghna tributaries and is characterized by the presence of numerous large, deeply flooded depressions, known as haors. It covers about around 8,568 km2 area of Bangladesh. The topography of the region is steep at around foothills in the north and slopes becoming mild and milder gradually at downstream towards south. Haor is a great reservoir of aquatic biological resources and acts as the ecological safety net to the nature as well as to the dwellers of the haor. But in reality, these areas are considered as wastelands and to make these wastelands into a productive one, a one sided plan has been implementing since long. The programme is popularly known as Flood Control, Drainage and Irrigation (FCDI) which is mainly devoted to increase the monoculture rice production. However, haor ecosystem is a multiple-resource base which demands an integrated sustainable development approach. The ongoing management approach is biased to only rice production through FCDI. Thus this primitive mode of action is diminishing other resources having more economic potential ever thought.

Keywords: freshwater wetlands, biological diversity, biological resources, conservation and sustainable development

Procedia PDF Downloads 335

Authors: El Asnaoui Khalid, Chawki Youness, Aksasse Brahim, Ouanan Mohammed

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In these recent decades, the important and fast growth in the development and demand of multimedia products is contributing to an insufficient in the bandwidth of device and network storage memory. Consequently, the theory of data compression becomes more significant for reducing the data redundancy in order to save more transfer and storage of data. In this context, this paper addresses the problem of the lossless and the near-lossless compression of images. This proposed method is based on Block SVD Power Method that overcomes the disadvantages of Matlab's SVD function. The experimental results show that the proposed algorithm has a better compression performance compared with the existing compression algorithms that use the Matlab's SVD function. In addition, the proposed approach is simple and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.

Keywords: image compression, SVD, block SVD power method, lossless compression, near lossless

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Authors: Kai Zhu, Shanhua He, Yujiao Chang

Abstract:

This paper explores the virtual linguistic landscape of an emerging virtual language community in TikTok, a language community realizing immediate and non-immediate communication without a precise Spatio-temporal domain or a specific socio-cultural boundary or interpersonal network. This kind of language community generates a large number and various forms of virtual linguistic landscape, with which we conducted a virtual ethnographic survey together with telephone interviews to collect data from coping. We have been following two language communities in TikTok for several months so that we can illustrate the composition of the two language communities and some typical virtual language landscapes in both language communities first. Then we try to explore the reasons why and how they are formed through the organization, transcription, and analysis of the interviews. Our analysis reveals the richness and diversity of the virtual linguistic landscape, and finally, we summarize some of the characteristics of this language community.

Keywords: virtual linguistic landscape, virtual language community, virtual ethnographic survey, TikTok

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Authors: Chih-Fong Tsai, Ya-Han Hu

Abstract:

The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

Abstract:

For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

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Authors: Michael Naderhirn, Marco Pavone

Abstract:

Problem statement, motivation, and aim of work: So far, the development of control algorithms was done by control engineers in a way that the controller would fit a specification by testing. When it comes to the certification of an autonomous car in highly complex scenarios, the challenge is much higher since such a controller must mathematically guarantee to implement the rules of the road while on the other side guarantee aspects like safety and real time executability. What if it becomes reality to solve this demanding problem by combining Formal Verification and System Theory? The aim of this work is to present a workflow to solve the above mentioned problem. Summary of the presented results / main outcomes: We show the usage of an English like language to transform the rules of the road into system specification for an autonomous car. The language based specifications are used to define system functions and interfaces. Based on that a formal model is developed which formally correctly models the specifications. On the other side, a mathematical model describing the systems dynamics is used to calculate the systems reachability set which is further used to determine the system input boundaries. Then a motion planning algorithm is applied inside the system boundaries to find an optimized trajectory in combination with the formal specification model while satisfying the specifications. The result is a control strategy which can be applied in real time independent of the scenario with a mathematical guarantee to satisfy a predefined specification. We demonstrate the applicability of the method in simulation driving scenarios and a potential certification. Originality, significance, and benefit: To the authors’ best knowledge, it is the first time that it is possible to show an automated workflow which combines a specification in an English like language and a mathematical model in a mathematical formal verified way to synthesizes a controller for potential real time applications like autonomous driving.

Keywords: formal system verification, reachability, real time controller, hybrid system

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Authors: Prakher Mishra, Dikshant Bodana, Saloni Desai, Sudhanshu Dixit, Sopan Agarwal, Shriraj Patel

Abstract:

Railways are sometimes lifeline of nations as they consist of huge network of rail lines and bridges. Reportedly many of the bridges are aging, weak, distressed and accident prone. It becomes a really challenging task for Engineers and workers to keep up a regular maintenance schedule for proper functioning which itself is quite a hard hitting job. In this paper we have come up with an innvovative wireless system of maintenance called BriMAIN. In this system we have installed two types of sensors, first one is called a force sensor which will continously analyse the readings of pressure at joints of the bridges and secondly an MPU-6050 triaxial gyroscope+accelerometer which will analyse the deflection of the deck of the bridge. Apart from this a separate database is also being made at the server room so that the data can be visualized by the engineers and a warning can be issued in case reading of the sensors goes above threshold.

Keywords: Accelerometer, B-MAIN, Gyroscope, MPU-6050

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Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe

Abstract:

The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

Procedia PDF Downloads 217