Search results for: long short-term memory networks

Authors: Richard Grant, Landolf Rhode-Barbarigos, Shouraseni Sen Roy, Lucas Brittan, Change Li, Aiden Rowe

Abstract:

Ports are critical for the US economy, connecting farmers, manufacturers, retailers, consumers and an array of transport and storage operators. Port facilities vary widely in terms of their productivity, footprint, specializations, and governance. In this context, Port Miami is considered as one of the busiest ports providing both cargo and cruise services in connecting the wider region of the Caribbean and Mesoamerica to the global networks. It is considered as the “Cruise Capital of the World and Global Gateway of the Americas” and “leading container port in Florida.” Furthermore, it has also been ranked as one of the top container ports in the world and the second most efficient port in North America. In this regard, Port Miami has made significant investments in the strategic and capital infrastructure of about US$1 billion, including increasing the channel depth and other onshore infrastructural enhancements. Therefore, this study involves a detailed analysis of Port Miami’s network, using publicly available multiple years of data about marine vessel traffic, cargo, and connectivity and performance indices from 2015-2021. Through the analysis of cargo and cruise vessels to and from Port Miami and its relative performance at the global scale from 2015 to 2021, this study examines the port’s long-term resilience and future growth potential. The main results of the analyses indicate that the top category for both inbound and outbound cargo is manufactured products and textiles. In addition, there are a lot of fresh fruits, vegetables, and produce for inbound and processed food for outbound cargo. Furthermore, the top ten port connections for Port Miami are all located in the Caribbean region, the Gulf of Mexico, and the Southeast USA. About half of the inbound cargo comes from Savannah, Saint Thomas, and Puerto Plata, while outbound cargo is from Puerto Corte, Freeport, and Kingston. Additionally, for cruise vessels, a significantly large number of vessels originate from Nassau, followed by Freeport. The number of passenger's vessels pre-COVID was almost 1,000 per year, which dropped substantially in 2020 and 2021 to around 300 vessels. Finally, the resilience and competitiveness of Port Miami were also assessed in terms of its network connectivity by examining the inbound and outbound maritime vessel traffic. It is noteworthy that the most frequent port connections for Port Miami were Freeport and Savannah, followed by Kingston, Nassau, and New Orleans. However, several of these ports, Puerto Corte, Veracruz, Puerto Plata, and Santo Thomas, have low resilience and are highly vulnerable, which needs to be taken into consideration for the long-term resilience of Port Miami in the future.

Keywords: port, Miami, network, cargo, cruise

Procedia PDF Downloads 76

Authors: Piotr J. Balwierz, Mikhail Pachkov, Phil Arnold, Andreas J. Gruber, Mihaela Zavolan, Erik van Nimwegen

Abstract:

Understanding the key players and interactions in the regulatory networks that control gene expression and chromatin state across different cell types and tissues in metazoans remains one of the central challenges in systems biology. Our laboratory has pioneered a number of methods for automatically inferring core gene regulatory networks directly from high-throughput data by modeling gene expression (RNA-seq) and chromatin state (ChIP-seq) measurements in terms of genome-wide computational predictions of regulatory sites for hundreds of transcription factors and micro-RNAs. These methods have now been completely automated in an integrated webserver called ISMARA that allows researchers to analyze their own data by simply uploading RNA-seq or ChIP-seq data sets and provides results in an integrated web interface as well as in downloadable flat form. For any data set, ISMARA infers the key regulators in the system, their activities across the input samples, the genes and pathways they target, and the core interactions between the regulators. We believe that by empowering experimental researchers to apply cutting-edge computational systems biology tools to their data in a completely automated manner, ISMARA can play an important role in developing our understanding of regulatory networks across metazoans.

Keywords: gene expression analysis, high-throughput sequencing analysis, transcription factor activity, transcription regulation

Procedia PDF Downloads 60

Authors: Liu Yi Hui, Chun-Liang Lai, Jhang Yu Cih, He You Jing, Chiu Fan-Yun, Lin Yu Fang

Abstract:

For universities offering long-term care education, the inclusion of adulting learning and practices in professional courses as appropriate based on holistic design and evaluation could improve talent empowerment by leveraging social capital. Moreover, it could make the courses and materials used in long-term care education responsive to real-life needs. A mixed research method was used in the research design. A quantitative study was also conducted using a questionnaire survey, and the data were analyzed by SPSS 22.0 Chinese version. The qualitative data included students’ learning files (learning reflection notes, course reports, and experience records).

Keywords: adult learning, community empowerment, social capital, mixed research

Procedia PDF Downloads 147

Authors: Zhe Li, Aihua Cai

Abstract:

Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

Procedia PDF Downloads 76

Authors: Heidi Ahonen

Abstract:

Since antiquity, many cultures have seemingly known the power of low frequencies, incorporating them in healing practices through drumming, singing, humming, etc. Many music therapists recognize there is something in music that is transformative enough to make a difference in people’s lives. This paper summarizes the key findings of several low-frequency research with various client populations conducted by the author. Utilizing low-frequency sound (30 or 40 Hz) may have diverse therapeutic impacts: (1) Calming effect – decreased agitation (autism, brain injury, AD, dementia) (2) Muscle relaxation (CP & spasticity & pain/after surgery patients, MS, fibromyalgia) (3) Relaxation/stress release (anxiety, stress, PTSD, trauma, insomnia) (4) Muscular/motor functioning/ decrease of tremor (CP, MS, Parkinson) (5) Increase in alertness, cognitive awareness & short-term memory function (brain injury, severe global developmental delay, AD) (6) Increased focus (AD, PTSD, trauma). The paper will conclude by presenting ideas informing the clinical practice. Future studies need to investigate what frequencies are effective for particular client populations and why, what theories can explain the effect, and finally, something that has been long debated - is it auditive or kinaesthetic stimulation or the combination of both that is effective?

Keywords: low frequency, 40 Hz, sound, neuro disability

Procedia PDF Downloads 105

Authors: Lenka Matulova

Abstract:

Geopolymers are alumosilicate materials that have long been studied. Despite this fact, little is known about the long-term stability of geopolymer mechanical and structural properties, so crucial for their successful industrial application. To improve understanding, we investigated the effect of four different types of environments on the mechanical and structural properties of a metakaolin-based geopolymer (MK GP). The MK GP samples were stored in laboratory conditions (control samples), in water at 20 °C, in water at 80 °C, and outside exposed to the weather. Compressive and tensile strengths were measured after 28, 56, 90, and 360 days. In parallel, structural properties were analyzed using XRD, SEM, and mercury intrusion porosimetry. Whereas the mechanical properties of the samples in laboratory conditions and in 20 °C water were stable, the mechanical properties of the outdoor samples and the samples 80 °C water decreased noticeably after 360 days. Structural analyses were focused on changes in sample microstructure (developing microcrack network, porosity) and identifying zeolites, the presence of which would indicate detrimental processes in the structure that can change it from amorphous to crystalline. No zeolites were found during the 360-day period in MK GP samples, but the reduction in mechanical properties coincided with a developing network of microcracks and changes in pore size distribution.

Keywords: geopolymer, long-term properties, mechanical properties, metakaolin, structural properties

Procedia PDF Downloads 234

Authors: Chia-Chi Chang, Chuan-Bi Lin, Chia-Min Chan

Abstract:

Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system.

Keywords: ZigBee, Li-ion battery, solar panel, CC2530

Procedia PDF Downloads 372

Authors: Nouria Bouchikhi, Soufiane Bedjaoui, C. Tewfik Bouchaour, Lamia Alachaher Bedjaoui, Ulrich Maschke

Abstract:

Swelling properties and phase diagrams of binary systems composed of liquid crystalline networks and a low molecular mass liquid crystal (LMWLC) have been investigated. The networks were prepared by ultraviolet (UV) irradiation of reactive mixtures including a monomer, a cross-linking agent and a photo-initiator. These networks were prepared using two cross-linking agents: 1,6 hexanedioldiacrylate (HDDA) and a mesogenic acrylic acid 6-(4’-(6-acryloyloxy-hexyloxy) biphenyl-4-yl oxy) hexyl ester (AHBH). The obtained dry networks were characterized by differential scanning calorimetry, and immersed in an excess of a LMWLC solvent 4-cyano-4’-pentylbiphenyl (5CB), forming polymer gels. A detailed study by polarized optical microscopy allowed to determine the swelling degree of the gels and to follow the phase behavior of the solvent inside the polymer matrix in a wide range of temperature. It has been found that the gels undergo a sharp decrease of their swelling degree in response to an infinitesimal change of temperature. This finding adds new and interesting aspects on the actuators applications. We have subsequently explored the effect of different parameters on volume phase transition of these liquid crystalline materials. Such as the cross-linking density (CD), a nature of cross-linking agent and the photo initiator concentration.

Keywords: cross-linking density, liquid crystalline elastomers, phase diagrams, swelling

Procedia PDF Downloads 326

Authors: S. Neelima, P. S. Subramanyam

Abstract:

The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

Procedia PDF Downloads 431

Authors: Jung-Yoon Lee, Jongmun Kim, Hyo-Jun Chang, Jung-Min Kim

Abstract:

People’s tendency towards living in apartment houses is increasing in a densely populated country. However, some residents living in apartment houses are bothered by noise coming from the houses above. In order to reduce noise pollution, the communities are increasingly imposing a bylaw, including the limitation of floor impact sound, minimum thickness of floors, and floor soundproofing solutions. This research effort focused on the specific long-time deflection of resilient materials in the floor sound insulation systems of apartment houses. The experimental program consisted of testing nine floor sound insulation specimens subjected to sustained load for 45 days. Two main parameters were considered in the experimental investigation: three types of resilient materials and magnitudes of loads. The test results indicated that the structural behavior of the floor sound insulation systems under long-time load was quite different from that the systems under short-time load. The loading period increased the deflection of floor sound insulation systems and the increasing rate of the long-time deflection of the systems with ethylene vinyl acetate was smaller than that of the systems with low density ethylene polystyrene.

Keywords: resilient materials, floor sound insulation systems, long-time deflection, sustained load, noise pollution

Procedia PDF Downloads 265

Authors: Redouane Tlemsani, Abdelkader Benyettou

Abstract:

Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, computer vision

Procedia PDF Downloads 424

Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

Abstract:

In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

Procedia PDF Downloads 136

Authors: W. Hashim, H. Burok, N. Ghazaly, H. Ahmad Nasir, N. Mohamad Anas, A. F. Ismail, K. L. Yau

Abstract:

Future communication networks require devices that are able to work on a single platform but support heterogeneous operations which lead to service diversity and functional flexibility. This paper proposes two cognitive mechanisms termed cognitive hybrid function which is applied in multiple broadband user terminals in order to maintain reliable connectivity and preventing unnecessary interferences. By employing such mechanisms especially for future hyper-dense network, we can observe their performances in terms of optimized speed and power saving efficiency. Results were obtained from several empirical laboratory studies. It was found that selecting reliable network had shown a better optimized speed performance up to 37% improvement as compared without such function. In terms of power adjustment, our evaluation of this mechanism can reduce the power to 5dB while maintaining the same level of throughput at higher power performance. We also discuss the issues impacting future telecommunication standards whenever such devices get in place.

Keywords: dense network, intelligent network selection, multiple networks, transmit power adjustment

Procedia PDF Downloads 372

Authors: Farahnaz Karami

Abstract:

Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio.

Keywords: mobile ad hoc networks, routing, quality of service, ant colony, fuzzy logic

Procedia PDF Downloads 56

Authors: Shaharuddin Salleh, Sara Marouf, Hosein Mohammadi

Abstract:

Directional sensor networks (DSNs) have recently attracted a great deal of attention due to their extensive applications in a wide range of situations. One of the most important problems associated with DSNs is covering a set of targets in a given area and, at the same time, maximizing the network lifetime. This is due to limitation in sensing angle and battery power of the directional sensors. This problem gets more complicated by the possibility that targets may have different coverage requirements. In the present study, this problem is referred to as priority-based target coverage (PTC). As sensors are often densely deployed, organizing the sensors into several cover sets and then activating these cover sets successively is a promising solution to this problem. In this paper, we propose a learning automata-based algorithm to organize the directional sensors into several cover sets in such a way that each cover set could satisfy coverage requirements of all the targets. Several experiments are conducted to evaluate the performance of the proposed algorithm. The results demonstrated that the algorithms were able to contribute to solving the problem.

Keywords: directional sensor networks, target coverage problem, cover set formation, learning automata

Procedia PDF Downloads 407

Authors: Dawit Dibekulu, Tesfaye Dagnew, Tesfamaryam G. Meskel

Abstract:

Echoing with memories of loss and whispers of despair, this study delves into the poignant world of melancholy in Sisay Nigusu's contemporary Amharic novel, ‘Yäqənat Zār’ (‘Zār of Jealousy’). Employing a psychoanalytic lens focused on Freud and Klein's theories of mourning and melancholia, we explore the psychological depths of characters ravaged by grief. Through an interpretive paradigm and descriptive research design, we unpack the intricate tapestry of the novel, revealing how love's ashes morph into melancholic despair. The loss of loved ones, be it sudden death or betrayal, casts long shadows on the characters' souls, distorting their behavior and twisting their narratives. Altered thoughts, self-blame, and paralyzing yearning become their companions, weaving a tragic dance of longing and despair. ‘Yäqənat Zār’ serves as a powerful testament to the transformative power of storytelling, allowing us to navigate the labyrinthine paths of melancholia and gain a glimpse into the Ethiopian soul grappling with loss. This study not only sheds light on the individual's struggle with sadness but also illuminates the cultural fabric of grief and melancholia intricately woven into Ethiopian society.

Keywords: melancholy, loss, psychoanalysis, grief, identity

Procedia PDF Downloads 58

Authors: Julien Romieux, Fabio Verdicchio

Abstract:

Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: cooperative signal processing, signal representation and approximation, power management, wireless sensor networks

Procedia PDF Downloads 386

Authors: Ren Moses

Abstract:

The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

Procedia PDF Downloads 304

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

Abstract:

The analysis of scientific collaboration networks has contributed significantly to improving the understanding of how does the process of collaboration between researchers and also to understand how the evolution of scientific production of researchers or research groups occurs. However, the identification of collaborations in large scientific databases is not a trivial task given the high computational cost of the methods commonly used. This paper proposes a method for identifying collaboration in large data base of curriculum researchers. The proposed method has low computational cost with satisfactory results, proving to be an interesting alternative for the modeling and characterization of large scientific collaboration networks.

Keywords: extraction, data integration, information retrieval, scientific collaboration

Procedia PDF Downloads 388

Authors: Mai Hussein

Abstract:

'Littoral' is an original voice in Québécois theatre, spanning the cultural gaps that can exist between the playwrights’ native Lebanon, North America, Quebec, and Europe. Littoral is a 'crossroad' of cultures and themes, a 'bridge' connecting cultures and languages. It represents a new form of theatrical writing that combines the verbal, the vocal and the pantomimic, calling upon the stage to question the real, to engage characters in a quest, in a journey of mourning, of reconstructing identity and a collective memory despite ruins and wars. A theatre of witness, a theatre denouncing irrationality of racism and war, a theatre 'performing' the symptoms of the stress disorders of characters passing from resistance and anger to reconciliation and giving voice to the silenced victims, these are some of the pillars that this play has to offer. In this corrida between life and death, the identity seems like a work-in-progress that is shaped in the presence of the Self and the Other. This trajectory will lead to re-open widely the door to questions, interrogations, and reflections to show how this play is at the nexus of contemporary preoccupations of the 21st century: the importance of memory, the search for meaning, the pursuit of the infinite. It also shows how a play can create bridges between languages, cultures, societies, and movements. To what extent does it mediate between the words and the silence, and how does it burn the bridges or the gaps between the textual and the performative while investigating the power of intermediality to confront racism and segregation. It also underlines the centrality of confrontation between cultures, languages, writing and representation techniques to challenge the characters in their quest to restructure their shattered, but yet intertwined identities. The goal of this theatre would then be to invite everyone involved in the process of a journey of self-discovery away from their comfort zone. Everyone will have to explore the liminal space, to read in between the lines of the written text as well as in between the text and the performance to explore the gaps and the tensions that exist between what is said, and what is played, between the 'parole' and the performative body.

Keywords: identity, memory, performance, testimony, trauma

Procedia PDF Downloads 110

Authors: Khushalini N. Ulman, Samane Maroufi, Veena H. Sahajwalla

Abstract:

Globally, electronic waste (e-waste) continues to grow at an alarming rate. Several technologies have been developed to recover valuable materials from e-waste, however, their efficiency can be increased with a better knowledge of the e-waste components. Random access memory cards (RAMs) are considered as high value scrap for the e-waste recyclers. Despite their high precious metal content, RAMs are still recycled in a conventional manner resulting in huge loss of resources. Our research work highlights the precious metal rich components of a RAM. Inductively coupled plasma (ICP) analysis of RAMs of six different generations have been carried out and the trends in their metal content have been investigated. Over the past decade, the copper content of RAMs has halved and their tin content has increased by 70 %. The stricter environmental laws have facilitated ~96 % drop in the lead content of RAMs. To comprehend the fundamentals of thermal transformation of RAMs, our research provides their detailed kinetic study. This can assist the e-waste recyclers in optimising their metal recovery processes. Thus, understanding the chemical and thermal behaviour of RAMs can open new avenues for efficient e-waste recycling.

Keywords: electronic waste, kinetic study, recycling, thermal transformation

Procedia PDF Downloads 142

Authors: Mallikarjuna Rao Yamarthy, Subramanyam Makam Venkata, Satya Prasad Kodati

Abstract:

The main objective of the proposed work is to reduce the overall network traffic incurred by mobility management, packet delivery cost and to increase the resource utilization. The proposed algorithm, An Efficient Resource Management Algorithm (ERMA) for mobility management in wireless mesh networks, relies on pointer based mobility management scheme. Whenever a mesh client moves from one mesh router to another, the pointer is set up dynamically between the previous mesh router and current mesh router based on the distance constraints. The algorithm evaluated for signaling cost, data delivery cost and total communication cost performance metrics. The proposed algorithm is demonstrated for both internet sessions and intranet sessions. The proposed algorithm yields significantly better performance in terms of signaling cost, data delivery cost, and total communication cost.

Keywords: data delivery cost, mobility management, pointer forwarding, resource management, wireless mesh networks

Procedia PDF Downloads 361

Authors: Navneet Kaur, Amarpreet Singh

Abstract:

In Vehicular Adhoc Networks, Data Dissemination is a challenging task. There are number of techniques, types and protocols available for disseminating the data but in order to preserve limited bandwidth and to disseminate maximum data over networks makes it more challenging. There are broadcasting, multicasting and geocasting based protocols. Multicasting based protocols are found to be best for conserving the bandwidth. One such protocol named BEAM exists that improves the performance of Vehicular Adhoc Networks by reducing the number of in-network message transactions and thereby efficiently utilizing the bandwidth during an emergency situation. But this protocol may result in multicar chain collision as there was no V2V communication. So, this paper proposes a new protocol named Enhanced Bandwidth Efficient Cluster Based Multicasting Protocol (EBECM) that will overcome the limitations of existing BEAM protocol. And Simulation results will show the improved performance of EBECM in terms of Routing overhead, throughput and PDR when compared with BEAM protocol.

Keywords: BEAM, data dissemination, emergency situation, vehicular adhoc network

Procedia PDF Downloads 344

Authors: Wahab Ali Shah, Junjia He

Abstract:

In this work, we performed a large-scale investigation into leader development in a 10 m rod-plane gap under a long front positive impulse. To describe the leader propagation under slow front impulse voltages, we recorded the leader propagation with a high-speed charge coupled device (CCD) camera. It is important to figure out this phenomenon to deepen our understanding of leader discharge. The observation results showed that the leader mechanism is a very complex physical phenomenon; it could be categorized into two types of leader process, namely, continuous and the discontinuous leader streamer-leader propagation. Furthermore, we studied the continuous leader development parameters, including two-dimensional (2-D) leader length, injected charge, and final jump stage, as well as leader velocity for rod–plane configuration. We observed that the discontinuous leader makes an important contribution to the appearance of channel re-illuminations of the positive leader. The comparative study shows better results in terms of standard switch impulse and long front positive impulse. Finally, the results are presented with a view toward improving our understanding of propagation mechanisms related to restrike phenomena, which are rarely reported. To clarify the above doubts under long front cases, we carried out extensive experiments in this study.

Keywords: continuous and discontinuous leader, high-speed photographs, long air gap, positive long front impulse, restrike phenomena

Procedia PDF Downloads 165

Authors: Shara Toibayeva, Irbulat Utepbergenov, Lyazzat Issabekova, Aidana Bodesova

Abstract:

The article discusses the importance of quality assessment as a strategic tool in business and emphasizes the significance of the effectiveness of quality management systems (QMS) for enterprises. The evaluation of these systems takes into account the specificity of quality indicators, the multilevel nature of the system, and the need for optimal selection of the number of indicators and evaluation of the system state, which is critical for making rational management decisions. Methods and models of automated enterprise quality management are proposed, including an intelligent automated quality management system integrated with the Management Information and Control System. These systems make it possible to automate the implementation and support of QMS, increasing the validity, efficiency, and effectiveness of management decisions by automating the functions performed by decision makers and personnel. The paper also emphasizes the use of recurrent neural networks to improve automated quality management. Recurrent neural networks (RNNs) are used to analyze and process sequences of data, which is particularly useful in the context of document quality assessment and non-conformance detection in quality management systems. These networks are able to account for temporal dependencies and complex relationships between different data elements, which improves the accuracy and efficiency of automated decisions. The project was supported by a grant from the Ministry of Education and Science of the Republic of Kazakhstan under the Zhas Galym project No. AR 13268939, dedicated to research and development of digital technologies to ensure consistency of QMS regulatory documents.

Keywords: automated control system, quality management, document structure, formal language

Procedia PDF Downloads 26

Authors: Ben Khayut, Lina Fabri, Maya Avikhana

Abstract:

The models of the modern Artificial Narrow Intelligence (ANI) cannot: a) independently and continuously function without of human intelligence, used for retraining and reprogramming the ANI’s models, and b) think, understand, be conscious, cognize, infer, and more in state of Uncertainty, and changes in situations, and environmental objects. To eliminate these shortcomings and build a new generation of Artificial Intelligence systems, the paper proposes a Conception, Model, and Method of Computational Psycholinguistic Cognitive Situational-Fuzzy Self-Controlled Brain and Mind System (CPCSFSCBMSUU) using a neural network as its computational memory, operating under uncertainty, and activating its functions by perception, identification of real objects, fuzzy situational control, forming images of these objects, modeling their psychological, linguistic, cognitive, and neural values of properties and features, the meanings of which are identified, interpreted, generated, and formed taking into account the identified subject area, using the data, information, knowledge, and images, accumulated in the Memory. The functioning of the CPCSFSCBMSUU is carried out by its subsystems of the: fuzzy situational control of all processes, computational perception, identifying of reactions and actions, Psycholinguistic Cognitive Fuzzy Logical Inference, Decision making, Reasoning, Systems Thinking, Planning, Awareness, Consciousness, Cognition, Intuition, Wisdom, analysis and processing of the psycholinguistic, subject, visual, signal, sound and other objects, accumulation and using the data, information and knowledge in the Memory, communication, and interaction with other computing systems, robots and humans in order of solving the joint tasks. To investigate the functional processes of the proposed system, the principles of Situational Control, Fuzzy Logic, Psycholinguistics, Informatics, and modern possibilities of Data Science were applied. The proposed self-controlled System of Brain and Mind is oriented on use as a plug-in in multilingual subject Applications.

Keywords: computational brain, mind, psycholinguistic, system, under uncertainty

Procedia PDF Downloads 167

Authors: Tafadzwa Shumba, Rose C. Nyatondo, Regret Sunge

Abstract:

This research analyses the impact of budget deficits on the economic performance of Zimbabwe. The study employs the autoregressive distributed lag (ARDL) confines testing method to co-integration and long-run estimation using time series data from 1980-2018. The Augmented Dick Fuller (ADF) and the Granger approach were used to testing for stationarity and causality among the factors. Co-integration test results affirm a long term association between GDP development rate and descriptive factors. Causality test results show a unidirectional connection between budget shortfall to GDP development and bi-directional causality amid debt and budget deficit. This study also found unidirectional causality from debt to GDP growth rate. ARDL estimates indicate a significantly positive long term and significantly negative short term impact of budget shortfall on GDP. This suggests that budget deficits have a short-run growth retarding effect and a long-run growth-inducing effect. The long-run results follow the Keynesian theory that posits that fiscal deficits result in an increase in GDP growth. Short-run outcomes follow the neoclassical theory. In light of these findings, the government is recommended to minimize financing of recurrent expenditure using a budget deficit. To achieve sustainable growth and development, the government needs to spend an absorbable budget deficit focusing on capital projects such as the development of human capital and infrastructure.

Keywords: ARDL, budget deficit, economic performance, long run

Procedia PDF Downloads 87

Authors: Van Son Nguyen, Van Huy Mai, Alec Moradpour, Pascale Auban Senzier, Claude Pasquier, Kang Wang, Pierre-Antoine Albouy, Marcelo J. Rozenberg, John Giapintzakis, Christian N. Mihailescu, Charis M. Orfanidou, Thomas Maroutian, Philippe Lecoeur, Guillaume Agnus, Pascal Aubert, Sylvain Franger, Raphaël Salot, Nathalie Brun, Katia March, David Alamarguy, Pascal ChréTien, Olivier Schneegans

Abstract:

In the field of Nanoelectronics, a major research activity is being developed towards non-volatile memories. To face the limitations of existing Flash memory cells (endurance, downscaling, rapidity…), new approaches are emerging, among them resistive switching memories (Re-RAM). In this work, we analysed the behaviour of LixCoO2 oxide thin films in electrode/film/electrode devices. Preliminary results have been obtained concerning the influence of bias pulses characteristics (duration, value) on some performance parameters, such as endurance and resistance ratio (ROFF/RON). Besides, Conducting Probe Atomic Force Microscopy (CP-AFM) characterizations of the devices have been carried out to better understand some causes of performance failure, and thus help optimizing the switching performance of such devices.

Keywords: non volatile resistive memories, resistive switching, thin films, endurance

Procedia PDF Downloads 601

Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

Abstract:

A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

Procedia PDF Downloads 121

Authors: Hend Alrasheed

Abstract:

Due to their different properties that have led to avoid several limitations of classic client/server systems, there has been a great interest in the development and the improvement of different peer to peer systems. Understanding the properties of complex peer to peer networks is essential for their future improvements. It was shown that the performances of peer to peer protocols are directly related to their underlying topologies. Therefore, multiple efforts have analyzed the topologies of different peer to peer systems. This study presents an overview of major findings of close experimental analyses to different topologies of three unstructured peer to peer systems: BitTorrent, Gnutella, and FreeNet.

Keywords: peer to peer networks, network topology, graph diameter, clustering coefficient, small-world property, random graph, degree distribution

Procedia PDF Downloads 376