Search results for: the connectivity of innovative network

Authors: Jie Liu, Jinqu Cheng, Qiyuan Peng, Yong Yin

Abstract:

Urban rail transit (URT) plays a significant role in dealing with traffic congestion and environmental problems in cities. However, equipment failure and obstruction of links often lead to URT links’ capacities loss in daily operation. It affects the reliability and transport service quality of URT network seriously. In order to measure the influence of links’ capacities loss on reliability and transport service quality of URT network, passengers are divided into three categories in case of links’ capacities loss. Passengers in category 1 are less affected by the loss of links’ capacities. Their travel is reliable since their travel quality is not significantly reduced. Passengers in category 2 are affected by the loss of links’ capacities heavily. Their travel is not reliable since their travel quality is reduced seriously. However, passengers in category 2 still can travel on URT. Passengers in category 3 can not travel on URT because their travel paths’ passenger flow exceeds capacities. Their travel is not reliable. Thus, the proportion of passengers in category 1 whose travel is reliable is defined as reliability indicator of URT network. The transport service quality of URT network is related to passengers’ travel time, passengers’ transfer times and whether seats are available to passengers. The generalized travel cost is a comprehensive reflection of travel time, transfer times and travel comfort. Therefore, passengers’ average generalized travel cost is used as transport service quality indicator of URT network. The impact of links’ capacities loss on transport service quality of URT network is measured with passengers’ relative average generalized travel cost with and without links’ capacities loss. The proportion of the passengers affected by links and betweenness of links are used to determine the important links in URT network. The stochastic user equilibrium distribution model based on the improved logit model is used to determine passengers’ categories and calculate passengers’ generalized travel cost in case of links’ capacities loss, which is solved with method of successive weighted averages algorithm. The reliability and transport service quality indicators of URT network are calculated with the solution result. Taking Wuhan Metro as a case, the reliability and transport service quality of Wuhan metro network is measured with indicators and method proposed in this paper. The result shows that using the proportion of the passengers affected by links can identify important links effectively which have great influence on reliability and transport service quality of URT network; The important links are mostly connected to transfer stations and the passenger flow of important links is high; With the increase of number of failure links and the proportion of capacity loss, the reliability of the network keeps decreasing, the proportion of passengers in category 3 keeps increasing and the proportion of passengers in category 2 increases at first and then decreases; When the number of failure links and the proportion of capacity loss increased to a certain level, the decline of transport service quality is weakened.

Keywords: urban rail transit network, reliability, transport service quality, links’ capacities loss, important links

Procedia PDF Downloads 114

Authors: Wei Quan, Liu Chao, Mohammed N. Afsar

Abstract:

The dielectric properties and ionic conductivity of novel "ceramic state" polymer electrolytes for high capacity lithium battery are characterized by radio-frequency and Microwave methods in two broad frequency ranges from 50 Hz to 20 KHz and 4 GHz to 40 GHz. This innovative solid polymer electrolyte which is highly ionic conductive (10-3 S/cm at room temperature) from -40 oC to +150 oC and can be used in any battery application. Such polymer exhibits properties more like a ceramic rather than polymer. The various applied measurement methods produced accurate dielectric results for comprehensive analysis of electrochemical properties and ion transportation mechanism of this newly invented polymer electrolyte. Two techniques and instruments employing air gap measurement by capacitance bridge and inwave guide measurement by vector network analyzer are applied to measure the complex dielectric spectra. The complex dielectric spectra are used to determine the complex alternating current electrical conductivity and thus the ionic conductivity.

Keywords: polymer electrolyte, dielectric permittivity, lithium battery, ionic relaxation, microwave measurement

Procedia PDF Downloads 459

Authors: Sudheer Dhume, T. Prasad

Abstract:

There are distinct signs of Business Education losing its sheen. It is more so in developing countries. One of the reasons is the value addition at the end of 2 year MBA program is not matching with the requirements of present times and expectations of the students. In this backdrop, Pedagogy Innovation has become prerequisite for making our MBA programs relevant and useful. This paper is the description and analysis of innovative Action Learning pedagogical approach adopted by a group of faculty members at NITIE Mumbai. It not only promotes multidisciplinary research but also enhances integration of the functional areas skillsets in the students. The paper discusses the theoretical bases of this pedagogy and evaluates the effectiveness of it vis-à-vis conventional pedagogical tools. The evaluation research using Bloom’s taxonomy framework showed that this blended method of Business Education is much superior as compared to conventional pedagogy.

Keywords: action learning, blooms taxonomy, business education, innovation, pedagogy

Procedia PDF Downloads 252

Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

Abstract:

The physics-informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on a strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary conditions to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of the Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful in studying various optical phenomena.

Keywords: deep learning, optical soliton, physics informed neural network, partial differential equation

Procedia PDF Downloads 59

Authors: Firas M. Tuaimah

Abstract:

This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

Procedia PDF Downloads 437

Authors: Wenyang Wan

Abstract:

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 new 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 the 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, and 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

Procedia PDF Downloads 64

Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

Abstract:

Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

Procedia PDF Downloads 104

Authors: R. Byler

Abstract:

Innovation, particularly in technology development, is a crucial aspect of nanotechnology R&D and, although several approaches to effective innovation management exist, organizational structures that promote knowledge exchange have been found to be most effect in supporting new and emerging technologies. This paper discusses Integrated Knowledge Networks (IKNs) and evaluates its use within nanotechnology R&D to increase technology innovation. Specifically, this paper reviews the role of IKNs in bolstering national and international nanotechnology development and in enhancing nanotechnology innovation. Both physical and virtual IKNs, particularly IT-based network platforms for community-based innovation, offer strategies for enhanced technology innovation, interdisciplinary cooperation, and enterprise development. Effectively creating and managing technology R&D networks can facilitate successful knowledge exchange, enhanced innovation, commercialization, and technology transfer. As such, IKNs are crucial to technology development processes and, thus, in increasing the quality and access to new, innovative nanoscience and technologies worldwide.

Keywords: community-based innovation, integrated knowledge networks, nanotechnology, technology innovation

Procedia PDF Downloads 388

Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

Abstract:

In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: video tracking, particle filter, greedy snake, neural network

Procedia PDF Downloads 323

Authors: Kristine Joy Y. Sumanga, Clarissa Mae D. Derecho

Abstract:

Telemedicine is providing health care services using electronic means at a distance, including the diagnosis, treatment, and prevention of diseases as well as the research and evaluation and education of health care providers. The role of telemedicine in this time of the COVID-19 pandemic is vital, especially in the practice of medicine. General Objective: To determine the extent of knowledge, preparedness and perception of telemedicine among Family Medicine Resident Physicians in different training institutions in Cebu City during the Coronavirus Disease 19 pandemic. Methods: A descriptive, cross-sectional survey research study was conducted in four hospital training institutions in Cebu City. A total of 41 respondents gave their consent and were given the online survey questionnaire pertaining to the extent of knowledge, preparedness and perceptions on telemedicine, including respondents’ demographic data and problems encountered in Telemedicine. Results: Out of the 41 respondents, 56.10% were young adults (26 to 30 years old), mostly females (70.73%), single (68.29%), first-year residents (43.90%), employed at a government hospital (70.73%) and are in the traditional residency pathway (82.93%). On relevant experience, 82.93% experienced telemedicine during residency, with 100% on follow-up consultations, and 95% were consulted due to infections. Respondents’ extent of knowledge was average, while the extent of preparedness and perception were great. Problems with low connectivity (80.48%) were noted by most of the respondents. Conclusion: Resident physicians moderately understood the information about telemedicine but with a great extent of preparedness and perception. They are always prepared for telemedicine modality because they are fully aware of its existence and need in the delivery of health care services among their patients at the time of the pandemic. Challenges to low connectivity and handling patients’ data privacy were the major concerns met by the resident physicians in the use of telemedicine.

Keywords: telemedicine, knowledge, preparedness, perception, family medicine, residents, COVID 19

Procedia PDF Downloads 62

Authors: Janet Kaningen, Richard N. Kaningen, Jonas Kaningen

Abstract:

This paper aims to discover the best ways to improve production efficiency, cost efficiency, community cohesion, and long-term sustainability in Ghana's housing delivery. Advanced Construction Technologies (ACTs) are set to become the sustainable mainstay of the construction industry due to the demand for innovative housing solutions. Advances in material science, building component production, and assembly technologies are leading to the development of next-generation materials such as polymeric-fiber-based products, light-metal alloys, and eco-materials. Modular housing construction has become more efficient and cost-effective than traditional building methods and is becoming increasingly popular for commercial, industrial, and residential projects. Effective project management and logistics will be imperative in the future speed and cost of modular construction housing.

Keywords: technology blending, sustainability, housing, Ghana

Procedia PDF Downloads 68

Authors: Salwa Karboune, Amanda Waglay

Abstract:

Recent studies indicate that health-promoting functional ingredients and nutraceuticals can help support and improve the overall public health, which is timely given the aging of the population and the increasing cost of health care. The development of novel ‘natural’ functional ingredients is increasingly challenging. Biocatalysis offers powerful approaches to achieve this goal. Our recent research has been focusing on the development of innovative biocatalytic approaches towards the isolation of protein isolates from potato by-products and the generation of peptides. Potato is a vegetable whose high-quality proteins are underestimated. In addition to their high proportion in the essential amino acids, potato proteins possess angiotensin-converting enzyme-inhibitory potency, an ability to reduce plasma triglycerides associated with a reduced risk of atherosclerosis, and stimulate the release of the appetite regulating hormone CCK. Potato proteins have long been considered not economically feasible due to the low protein content (27% dry matter) found in tuber (Solanum tuberosum). However, potatoes rank the second largest protein supplying crop grown per hectare following wheat. Potato proteins include patatin (40-45 kDa), protease inhibitors (5-25 kDa), and various high MW proteins. Non-destructive techniques for the extraction of proteins from potato pulp and for the generation of peptides are needed in order to minimize functional losses and enhance quality. A promising approach for isolating the potato proteins was developed, which involves the use of multi-enzymatic systems containing selected glycosyl hydrolase enzymes that synergistically work to open the plant cell wall network. This enzymatic approach is advantageous due to: (1) the use of milder reaction conditions, (2) the high selectivity and specificity of enzymes, (3) the low cost and (4) the ability to market natural ingredients. Another major benefit to this enzymatic approach is the elimination of a costly purification step; indeed, these multi-enzymatic systems have the ability to isolate proteins, while fractionating them due to their specificity and selectivity with minimal proteolytic activities. The isolated proteins were used for the enzymatic generation of active peptides. In addition, they were applied into a reduced gluten cookie formulation as consumers are putting a high demand for easy ready to eat snack foods, with high nutritional quality and limited to no gluten incorporation. The addition of potato protein significantly improved the textural hardness of reduced gluten cookies, more comparable to wheat flour alone. The presentation will focus on our recent ‘proof-of principle’ results illustrating the feasibility and the efficiency of new biocatalytic processes for the production of innovative functional food ingredients, from potato by-products, whose potential health benefits are increasingly being recognized.

Keywords: biocatalytic approaches, functional ingredients, potato proteins, peptides

Procedia PDF Downloads 363

Authors: Cristian F.T. Montenegro, Luís F. N. Lourenço, Maurício B. C. Salles, Renato M. Monaro

Abstract:

More PV systems have been connected to the electrical network each year. As the number of PV systems increases, some issues affecting grid operations have been identified. This paper studied the impacts related to changes in solar irradiance on a distribution/sub-transmission network, considering variations due to moving clouds and daily cycles. Using MATLAB/Simulink software, a solar farm of 30 MWp was built and then implemented to a test network. From simulations, it has been determined that irradiance changes can have a significant impact on the grid by causing voltage fluctuations outside the allowable thresholds. This work discussed some local control strategies and grid reinforcements to mitigate the negative effects of the irradiance changes on the grid.

Keywords: reactive power control, solar irradiance, utility-scale PV systems, voltage fluctuations

Procedia PDF Downloads 440

Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

Abstract:

5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

Procedia PDF Downloads 38

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

Abstract:

The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

Procedia PDF Downloads 135

Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

Abstract:

In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

Procedia PDF Downloads 339

Authors: Zhang Shuqi, Liu Dan

Abstract:

For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

Procedia PDF Downloads 85

Authors: Elabed Saud

Abstract:

This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

Procedia PDF Downloads 701

Authors: Sana Abusin

Abstract:

Food security in Qatar is a research priority of Qatar University (2021-2025) and all national strategies, including the Qatar National Vision 2030 and food security strategy (2018-2023). Achieving food security requires three actions: 1) transforming surplus food to those who are insecure; 2) reducing food loss and waste by recycling food into valuable resources such as compost (“green fertilizer”) that can be used in growing food; and, finally, 3) establishing strong enforcement agencies to protect consumers from outdated food and promote healthy food. Currently, these objectives are approached separately and not in a sustainable fashion. Food security in Qatar is a research priority of Qatar University (2021-2025) and all national strategies, including the Qatar National Vision 2030 and food security strategy (2018-2023). The study aims to develop an innovative mobile application that supports a sustainable solution to food insecurity and food waste in Qatar. The application will provide a common solution for many different users. For producers, it will facilitate easy disposal of excess food. For charities, it will notify them about surplus food ready for redistribution. The application will also benefit the second layer of end-users in the form of food recycling companies, who will receive information about available food waste that is unable to be consumed. We will use self-exoplanetary diagrams and digital pictures to show all the steps to the final stage. The aim is to motivate the young generation toward innovation and creation, and to encourage public-private collaboration in this sector.

Keywords: food security, innovative technology, sustainability, food waste, Qatar

Procedia PDF Downloads 94

Authors: Jorge Fernandes Teixeira Filho, André Oliveira Alcantara Fontenele, Érick Aragão Ribeiro

Abstract:

The constant emergence of new technologies used in automated processes makes it necessary for teachers and traders to apply new technologies in their classes. This paper presents an application of a new technology that will be employed in a didactic plant, which represents an effluent treatment process located in a laboratory of a federal educational institution. At work were studied in the first place, all components to be placed on automation laboratory in order to determine ways to program, parameterize and organize the plant. New technologies that have been implemented to the process are basically an AS-i network and a Profinet network, a SCADA system, which represented a major innovation in the laboratory. The project makes it possible to carry out in the laboratory various practices of industrial networks and SCADA systems.

Keywords: automation, industrial networks, SCADA systems, lab automation

Procedia PDF Downloads 522

Authors: Wenyang Wan

Abstract:

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

Procedia PDF Downloads 55

Authors: Mehdi Montakhabrazlighi, Ercan Balikci

Abstract:

The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783.

Keywords: neural network, rupture strength, superalloy, thermocalc

Procedia PDF Downloads 290

Authors: Thiago M. R. Dias, Patrícia M. Dias, Gray F. Moita

Abstract:

The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords.

Keywords: bibliometrics, data analysis, extraction and data integration, scientometrics

Procedia PDF Downloads 227

Authors: Desmond Okorie, Emmanuel Prince

Abstract:

Availability of portable water has been a global challenge especially to the developing continents/nations such as Africa/Nigeria. The World Health Organization WHO has produced the guideline for drinking water quality GDWQ which aims at ensuring water safety from source to consumer. Portable water parameters test include physical (colour, odour, temperature, turbidity), chemical (PH, dissolved solids) biological (algae, plytoplankton). This paper discusses the use of wireless sensor networks to monitor water quality using efficient and effective sensors that have the ability to sense, process and transmit sensed data. The integration of wireless sensor network to a portable sensing device offers the feasibility of sensing distribution capability, on site data measurements and remote sensing abilities. The current water quality tests that are performed in government water quality institutions in Benue State Nigeria are carried out in problematic locations that require taking manual water samples to the institution laboratory for examination, to automate the entire process based on wireless sensor network, a system was designed. The system consists of sensor node containing one PH sensor, one temperature sensor, a microcontroller, a zigbee radio and a base station composed by a zigbee radio and a PC. Due to the advancement of wireless sensor network technology, unexpected contamination events in water environments can be observed continuously. local area network (LAN) wireless local area network (WLAN) and internet web-based also commonly used as a gateway unit for data communication via local base computer using standard global system for mobile communication (GSM). The improvement made on this development show a water quality monitoring system and prospect for more robust and reliable system in the future.

Keywords: local area network, Ph measurement, wireless sensor network, zigbee

Procedia PDF Downloads 152

Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

Abstract:

Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

Procedia PDF Downloads 194

Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

Abstract:

Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

Procedia PDF Downloads 187

Authors: Alokananda Ghosh, Shraban Sarkar

Abstract:

The study aims to identify suitable sites for new hospitals with critical obstetric care facilities in Birbhum, one of the vulnerable and underserved districts of Eastern India, considering six main and 14 sub-criteria, using GIS-based Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) approach. The criteria were identified through field surveys and previous literature. After collecting expert decisions, a pairwise comparison matrix was prepared using the Saaty scale to calculate the weights through AHP. On the contrary, objective weighting methods, i.e., Entropy and Criteria Importance through Interaction Correlation (CRITIC), were used to perform the MAUT. Finally, suitability maps were prepared by weighted sum analysis. Sensitivity analyses of AHP were performed to explore the effect of dominant criteria. Results from AHP reveal that ‘maternal death in transit’ followed by ‘accessibility and connectivity’, ‘maternal health care service (MHCS) coverage gap’ were three important criteria with comparatively higher weighted values. Whereas ‘accessibility and connectivity’ and ‘maternal death in transit’ were observed to have more imprint in entropy and CRITIC, respectively. While comparing the predictive suitable classes of these three models with the layer of existing hospitals, except Entropy-MAUT, the other two are pointing towards the left-over underserved areas of existing facilities. Only 43%-67% of existing hospitals were in the moderate to lower suitable class. Therefore, the results of the predictive models might bring valuable input in future planning.

Keywords: hospital site suitability, analytic hierarchy process, multi-attribute utility theory, entropy, criteria importance through interaction correlation, multi-criteria decision analysis

Procedia PDF Downloads 38

Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

Abstract:

This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

Procedia PDF Downloads 129

Authors: Farha Sattar, Laurence Tamatea, Muhammad Nawaz

Abstract:

Drones, the Unmanned Aerial Vehicles are playing an important role in real-world problem-solving. With the new advancements in technology, drones are becoming available, affordable and user- friendly. Use of drones in education is opening new trends in teaching and learning practices in an innovative and engaging way. Drones vary in types and sizes and possess various characteristics and capabilities which enhance their potential to be used in education from basic to advanced and challenging learning activities which are suitable for primary, middle and high school level. This research aims to provide an insight to explore different types of drones and their compatibility to be used in teaching different subjects at various levels. Research focuses on integrating the drone technology along with Australian curriculum content knowledge to reinforce the understanding of the fundamental concepts and helps to develop the critical thinking and reasoning in the learning process.

Keywords: critical thinking, drone technology, drone types, innovative learning

Procedia PDF Downloads 290

Authors: Zhenyu Zhang, Hsi-Hsien Wei

Abstract:

Highway networks play a vital role in post-disaster recovery for disaster-damaged areas. Damaged bridges in such networks can disrupt the recovery activities by impeding the transportation of people, cargo, and reconstruction resources. Therefore, rapid restoration of damaged bridges is of paramount importance to long-term disaster recovery. In the post-disaster recovery phase, the key to restoration scheduling for a highway network is prioritization of bridge-repair tasks. Resilience is widely used as a measure of the ability to recover with which a network can return to its pre-disaster level of functionality. In practice, highways will be temporarily blocked during the downtime of bridge restoration, leading to the decrease of highway-network functionality. The failure to take downtime effects into account can lead to overestimation of network resilience. Additionally, post-disaster recovery of highway networks is generally divided into emergency bridge repair (EBR) in the response phase and long-term bridge repair (LBR) in the recovery phase, and both of EBR and LBR are different in terms of restoration objectives, restoration duration, budget, etc. Distinguish these two phases are important to precisely quantify highway network resilience and generate suitable restoration schedules for highway networks in the recovery phase. To address the above issues, this study proposes a novel resilience quantification method for the optimization of long-term bridge repair schedules (LBRS) taking into account the impact of EBR activities and restoration downtime on a highway network’s functionality. A time-dependent integer program with recursive functions is formulated for optimally scheduling LBR activities. Moreover, since uncertainty always exists in the LBRS problem, this paper extends the optimization model from the deterministic case to the stochastic case. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. The proposed methods are tested using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that, in this case, neglecting the bridge restoration downtime can lead to approximately 15% overestimation of highway network resilience. Moreover, accounting for the impact of EBR on network functionality can help to generate a more specific and reasonable LBRS. The theoretical and practical values are as follows. First, the proposed network recovery curve contributes to comprehensive quantification of highway network resilience by accounting for the impact of both restoration downtime and EBR activities on the recovery curves. Moreover, this study can improve the highway network resilience from the organizational dimension by providing bridge managers with optimal LBR strategies.

Keywords: disaster management, highway network, long-term bridge repair schedule, resilience, restoration downtime

Procedia PDF Downloads 127