Search results for: artificial neural network modeling

Authors: Guillaume P. Fernandez

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Individual language use is a matter of choice in particular interactions. The paper proposes a conceptual and theoretical framework with methodological consideration to develop how language produced in dyadic relations is to be considered and situated in the larger social configuration the interaction is embedded within. An integrated and comprehensive view is taken: social interactions are expected to be ruled by a normative context, defined by the chain of interdependences that structures the personal network. In this approach, the determinants of discursive practices are not only constrained by the moment of production and isolated from broader influences. Instead, the position the individual and the dyad have in the personal network influences the discursive practices in a twofold manner: on the one hand, the network limits the access to linguistic resources available within it, and, on the other hand, the structure of the network influences the agency of the individual, by the social control inherent to particular network characteristics. Concretely, we investigate how and to what extent consistent ego is from one interaction to another in his or her use of adverbs. To do so, social network analysis (SNA) methods are mobilized. Participants (N=130) are college students recruited in the french speaking part of Switzerland. The personal network of significant ones of each individual is created using name generators and edge interpreters, with a focus on social support and conflict. For the linguistic parts, respondents were asked to record themselves with five of their close relations. From the recordings, we computed an average similarity score based on the adverb used across interactions. In terms of analyses, two are envisaged: First, OLS regressions including network-level measures, such as density and reciprocity, and individual-level measures, such as centralities, are performed to understand the tenets of linguistic similarity from one interaction to another. The second analysis considers each social tie as nested within ego networks. Multilevel models are performed to investigate how the different types of ties may influence the likelihood to use adverbs, by controlling structural properties of the personal network. Primary results suggest that the more cohesive the network, the less likely is the individual to change his or her manner of speaking, and social support increases the use of adverbs in interactions. While promising results emerge, further research should consider a longitudinal approach to able the claim of causality.

Keywords: personal network, adverbs, interactions, social influence

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Authors: Ram Narayan Khare, Adhyatma Khare, Aradhna Shrivastava

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In this study, numerical modeling of ‘Lime Surkhi’ masonry building has been carried out for a prototype ancient building situated at seismic zone III using the Finite Element Method by Abaqus software. The model is designed in order to get the failure envelope and then decide the best method of retrofitting the structure so that the structure is made to withstand more decades, given its historical background. Previously, due to a lack of technologies, it was difficult to determine the mode of failure. Present technological development can predict the mode of failure, and subsequently, the structure can be refabricated accordingly. The study makes an important addition to the understanding of retrofitting ancient and old buildings based on the results of FEM modeling.

Keywords: seismic retrofitting, Abaqus, FEM, historic building, Lime Surkhi masonry

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Authors: Yuan Xu, Xun Liang, Meina Zhang

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The rise and attenuation of the public opinion broadcast of an emergent accident, in the social network, has a close relationship with the dynamic development of its subevents cluster. In this article, we take Tianjin Port explosion's subevents as an example to research the dynamic propagation discipline of Internet public opinion in a sudden accident, and analyze the overall structure of dynamic propagation to propose four different routes for subevents clusters propagation. We also generate network diagrams for the dynamic public opinion propagation, analyze each propagation type specifically. Based on this, suggestions on the supervision and guidance of Internet public opinion broadcast can be made.

Keywords: network dynamic transmission modes, emergent subevents clusters, Tianjin Port explosion, public opinion supervision

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Anyeres N. Atehortua Jimenez, J. David Lambraño, Juan Carlos Muñoz

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Navier-Stokes equations (NSE), which describe the dynamics of a fluid, have an important application on modeling waves used for data inversion techniques as full waveform inversion (FWI). In this work a linearized version of NSE and its variables, neglecting deviatoric terms of stress tensor, is presented. In order to get a theoretical modeling of pressure p(x,t) and wave velocity profile c(x,t), a wave equation of visco-acoustic medium (VAE) is written. A change of variables p(x,t)=q(x,t)h(ρ), is made on the equation for the VAE leading to a well known Klein-Gordon equation (KGE) describing waves propagating in variable density medium (ρ) with dispersive term α^2(x). KGE is reduced to a Poisson equation and solved by proposing a specific function for α^2(x) accounting for the energy dissipation and dispersion. Finally, an integral form solution is derived for p(x,t), c(x,t) and kinematics variables like particle velocity v(x,t), displacement u(x,t) and bulk modulus function k_b(x,t). Further, it is compared this visco-acoustic formulation with another form broadly used in the geophysics; it is argued that this formalism is more general and, given its integral form, it may offer several advantages from the modern parallel computing point of view. Applications to minimize the errors in modeling for FWI applied to oils resources in geophysics are discussed.

Keywords: Navier-Stokes equations, modeling, visco-acoustic, inversion FWI

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Authors: Wisam H. Muragaa, Kamaruzzaman Seman, Mohd Fadzli Marhusin

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Software Defined Networking (SDN) is a paradigm designed to facilitate the way of controlling the network dynamically and with more agility. Network traffic is a set of flows, each of which contains a set of packets. In SDN, a matching process is performed on every packet coming to the network in the SDN switch. Only the headers of the new packets will be forwarded to the SDN controller. In terminology, the flow header fields are called tuples. Basically, these tuples are 5-tuple: the source and destination IP addresses, source and destination ports, and protocol number. This flow information is used to provide an overview of the network traffic. Our module is meant to extract this 5-tuple with the packets and flows numbers and show them as a list. Therefore, this list can be used as a first step in the way of detecting the DDoS attack. Thus, this module can be considered as the beginning stage of any flow-based DDoS detection method.

Keywords: matching, OpenFlow tables, POX controller, SDN, table-miss

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Authors: Cheikh Bamba Dione, Alla Lo, Elhadji Mamadou Nguer, Siley O. Ba

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In this paper, we propose two neural machine translation (NMT) systems (French-to-Wolof and Wolof-to-French) based on sequence-to-sequence with attention and transformer architectures. We trained our models on a parallel French-Wolof corpus of about 83k sentence pairs. Because of the low-resource setting, we experimented with advanced methods for handling data sparsity, including subword segmentation, back translation, and the copied corpus method. We evaluate the models using the BLEU score and find that transformer outperforms the classic seq2seq model in all settings, in addition to being less sensitive to noise. In general, the best scores are achieved when training the models on word-level-based units. For subword-level models, using back translation proves to be slightly beneficial in low-resource (WO) to high-resource (FR) language translation for the transformer (but not for the seq2seq) models. A slight improvement can also be observed when injecting copied monolingual text in the target language. Moreover, combining the copied method data with back translation leads to a substantial improvement of the translation quality.

Keywords: backtranslation, low-resource language, neural machine translation, sequence-to-sequence, transformer, Wolof

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Authors: Kyoung-Jae Kim

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Collaborative Filtering (CF) is a popular technique for the personalization in the E-commerce domain to reduce information overload. In general, CF provides recommending items list based on other similar users’ preferences from the user-item matrix and predicts the focal user’s preference for particular items by using them. Many recommender systems in real-world use CF techniques because it’s excellent accuracy and robustness. However, it has some limitations including sparsity problems and complex dimensionality in a user-item matrix. In addition, traditional CF does not consider the emotional interaction between users. In this study, we propose recommender systems using social network and singular value decomposition (SVD) to alleviate some limitations. The purpose of this study is to reduce the dimensionality of data set using SVD and to improve the performance of CF by using emotional information from social network data of the focal user. In this study, we test the usability of hybrid CF, SVD and social network information model using the real-world data. The experimental results show that the proposed model outperforms conventional CF models.

Keywords: recommender systems, collaborative filtering, social network information, singular value decomposition

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Authors: Purva Joshi, Rohit Thanki, Omar Hanif

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Unmanned aerial vehicles (UAVs) are becoming increasingly important in various industrial applications and sectors. Nowadays, a multi UAV network is used for specific types of communication (e.g., military) and monitoring purposes. Therefore, it is critical to reducing propagation delay during communication between UAVs, which is essential in a multi UAV network. This paper presents how the propagation delay between the base station (BS) and the UAVs is reduced using a searching algorithm. Furthermore, the iterative-based K-nearest neighbor (k-NN) algorithm and Travelling Salesmen Problem (TSP) algorthm were utilized to optimize the distance between BS and individual UAV to overcome the problem of propagation delay in multi UAV networks. The simulation results show that this proposed method reduced complexity, improved reliability, and reduced propagation delay in multi UAV networks.

Keywords: multi UAV network, optimal distance, propagation delay, K - nearest neighbor, traveling salesmen problem

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Authors: Stamatis Zoras, Vasilis Evagelopoulos, Theodoros Staurakas

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The association between elevated air pollution levels and extreme climate conditions (temperature, particulate matter, ozone levels, etc.) and mental consequences has been, recently, the focus of significant number of studies. It varies depending on the time of the year it occurs either during the hot period or cold periods but, specifically, when extreme air pollution and weather events are observed, e.g. air pollution episodes and persistent heatwaves. It also varies spatially due to different effects of air quality and climate extremes to human health when considering metropolitan or rural areas. An air pollutant concentration and a climate extreme are taking a different form of impact if the focus area is countryside or in the urban environment. In the built environment the climate extreme effects are driven through the formed microclimate which must be studied more efficiently. Variables such as biological, age groups etc may be implicated by different environmental factors such as increased air pollution/noise levels and overheating of buildings in comparison to rural areas. Gridded air quality and climate variables derived from the land surface observations network of West Macedonia in Greece will be analysed against mortality data in a spatial format in the region of West Macedonia. Artificial intelligence (AI) tools will be used for data correction and prediction of health deterioration with climatic conditions and air pollution at local scale. This would reveal the built environment implications against the countryside. The air pollution and climatic data have been collected from meteorological stations and span the period from 2000 to 2009. These will be projected against the mortality rates data in daily, monthly, seasonal and annual grids. The grids will be operated as AI-based warning models for decision makers in order to map the health conditions in rural and urban areas to ensure improved awareness of the healthcare system by taken into account the predicted changing climate conditions. Gridded data of climate conditions, air quality levels against mortality rates will be presented by AI-analysed gridded indicators of the implicated variables. An Al-based gridded warning platform at local scales is then developed for future system awareness platform for regional level.

Keywords: air quality, artificial inteligence, climatic conditions, mortality

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Authors: Michael Fundator

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Groundbreaking application of biomathematical and biochemical research in neural networks processes to formation of non-canonical bases, islands, and analog bases in DNA and mRNA at or near the transcription that contradicts the long anticipated statistical assumptions for the distribution of bases and analog bases compounds is implemented through statistical and stochastic methods apparatus with addition of quantum principles, where the usual transience of Poisson spike train becomes very instrumental tool for finding even almost periodical type of solutions to Fokker-Plank stochastic differential equation. Present article develops new multidimensional methods of finding solutions to stochastic differential equations based on more rigorous approach to mathematical apparatus through Kolmogorov-Chentsov continuity theorem that allows the stochastic processes with jumps under certain conditions to have γ-Holder continuous modification that is used as basis for finding analogous parallels in dynamics of neutral networks and formation of analog bases and transcription in DNA.

Keywords: Fokker-Plank stochastic differential equation, Kolmogorov-Chentsov continuity theorem, neural networks, translation and transcription

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Authors: Deniz Erçelen, Özlem Selcuk Bozkurt

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The course of daily human life calls for the need for memories and remembering the time and place for certain events. Recalling memories takes up a substantial amount of time for an individual. Unfortunately, scientists lack the proper technology to fully understand and observe different brain regions that interact to form or retrieve memories. The hippocampus, a complex brain structure located in the temporal lobe, plays a crucial role in memory. The hippocampus forms memories as well as allows the brain to retrieve them by ensuring that neurons fire together. This process is called “neural synchronization.” Sadly, the hippocampus is known to deteriorate often with age. Proteins and hormones, which repair and protect cells in the brain, typically decline as the age of an individual increase. With the deterioration of the hippocampus, an individual becomes more prone to memory loss. Many memory loss starts off as mild but may evolve into serious medical conditions such as dementia and Alzheimer’s disease. In their quest to fully comprehend how memories work, scientists have created many different kinds of technology that are used to examine the brain and neural pathways. For instance, Magnetic Resonance Imaging - or MRI- is used to collect detailed images of an individual's brain anatomy. In order to monitor and analyze brain functions, a different version of this machine called Functional Magnetic Resonance Imaging - or fMRI- is used. The fMRI is a neuroimaging procedure that is conducted when the target brain regions are active. It measures brain activity by detecting changes in blood flow associated with neural activity. Neurons need more oxygen when they are active. The fMRI measures the change in magnetization between blood which is oxygen-rich and oxygen-poor. This way, there is a detectable difference across brain regions, and scientists can monitor them. Electroencephalography - or EEG - is also a significant way to monitor the human brain. The EEG is more versatile and cost-efficient than an fMRI. An EEG measures electrical activity which has been generated by the numerous cortical layers of the brain. EEG allows scientists to be able to record brain processes that occur after external stimuli. EEGs have a very high temporal resolution. This quality makes it possible to measure synchronized neural activity and almost precisely track the contents of short-term memory. Science has come a long way in monitoring memories using these kinds of devices, which have resulted in the inspections of neurons and neural pathways becoming more intense and detailed.

Keywords: brain, EEG, fMRI, hippocampus, memories, neural pathways, neurons

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Authors: Panprae Bunyapukkna

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The purpose of this research is to analyze the process that most of the Thai film industries commonly use in order to find the right cast to play the role. The result proved that most of the low-budget film productions find the cast by asking from the crew’s friends or friend of friend. Therefore, finding the cast in low-budget film productions normally has only few people shown up for the auditions and sometimes either none of them has acting knowledge or their appearances do not match the character. However, since most of the low-budget film productions do not have much ability to find members of the cast, thus some of them still will be selected. On the other hand, most of the high-budget film productions use modeling companies to find the cast for them. However, most of modeling agencies in Thailand seek and select their cast members from the cast’s appearances or talents rather than the knowledge of acting.

Keywords: casting for film, modeling business, acting, film, performing arts, film business

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Authors: Giulia I. Pintea

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The Roma people are a nomadic ethnic group native to India, and they are one of the most prevalent minorities in Europe. In the past, Roma were enslaved and they were imprisoned in concentration camps during the Holocaust; today, Roma are subject to hate crimes and are denied access to healthcare, education, and proper housing. The aim of this project is to analyze how the public perception of the Roma people may be influenced by antiziganist and pro-Roma institutions in Europe. In order to carry out this project, we used social network analysis to build two large social networks: The antiziganist network, which is composed of institutions that oppress and racialize Roma, and the pro-Roma network, which is composed of institutions that advocate for and protect Roma rights. Measures of centrality, density, and modularity were obtained to determine which of the two social networks is exerting the greatest influence on the public’s perception of Roma in European societies. Furthermore, data on hate crimes on Roma were gathered from the Organization for Security and Cooperation in Europe (OSCE). We analyzed the trends in hate crimes on Roma for several European countries for 2009-2015 in order to see whether or not there have been changes in the public’s perception of Roma, thus helping us evaluate which of the two social networks has been more influential. Overall, the results suggest that there is a greater and faster exchange of information in the pro-Roma network. However, when taking the hate crimes into account, the impact of the pro-Roma institutions is ambiguous, due to differing patterns among European countries, suggesting that the impact of the pro-Roma network is inconsistent. Despite antiziganist institutions having a slower flow of information, the hate crime patterns also suggest that the antiziganist network has a higher impact on certain countries, which may be due to institutions outside the political sphere boosting the spread of antiziganist ideas and information to the European public.

Keywords: applied mathematics, oppression, Roma people, social network analysis

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Afshin Moazami, Andrea Schiffauerova

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The objective of this work is to investigate the development and the role of collaboration networks in the creation of knowledge and innovations in the US and Canada, with a special focus on Quebec. In order to create scientific networks, the data on journal articles were extracted from SCOPUS, and the networks were built based on the co-authorship of the journal papers. For innovation networks, the USPTO database was used, and the networks were built on the patent co-inventorship. Various indicators characterizing the evolution of the network structure and the positions of the researchers and inventors in the networks were calculated. The comparison between the United States, Canada, and Quebec was then carried out. The preliminary results show that the nature of scientific collaboration networks differs from the one seen in innovation networks. Scientists work in bigger teams and are mostly interconnected within one giant network component, whereas the innovation network is much more clustered and fragmented, the inventors work more repetitively with the same partners, often in smaller isolated groups. In both Canada and the US, an increasing tendency towards collaboration was observed, and it was found that networks are getting bigger and more centralized with time. Moreover, a declining share of knowledge transfers per scientist was detected, suggesting an increasing specialization of science. The US collaboration networks tend to be more centralized than the Canadian ones. Quebec shares a lot of features with the Canadian network, but some differences were observed, for example, Quebec inventors rely more on the knowledge transmission through intermediaries.

Keywords: Canada, collaboration, innovation network, scientific network, Quebec, United States

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Authors: G. Baraneedaran, Deepak Singh, Kollipara Tejesh

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The wireless sensors network has been an active research area over the y-ear passed. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively, an energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. A spontaneous reconstruction mechanism for Local topology is designed additionally. In this experiment, FAF-EBRM is compared with LEACH and EECU, experimental results show that FAF-EBRM outperforms LEACH and EECU, which balances the energy consumption, prolongs the function lifetime and guarantees high Qos of WSN.

Keywords: energy balance, forward-aware factor (FAF), forward energy density, link weight, network performance

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Authors: A. Kardi, R. Zagrouba, M. Alqahtani

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The Internet of Everything (IoE) presents today a very attractive and motivating field of research. It is basically based on Wireless Sensor Networks (WSNs) in which the routing task is the major analysis topic. In fact, it directly affects the effectiveness and the lifetime of the network. This paper, developed from recent works and based on extensive researches, proposes a taxonomy of routing protocols in WSNs. Our main contribution is that we propose a classification model based on nine classes namely application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next hop selection and latency-awareness and energy-efficient routing protocols. In order to provide a total classification pattern to serve as reference for network designers, each class is subdivided into possible subclasses, presented, and discussed using different parameters such as purposes and characteristics.

Keywords: routing, sensor, survey, wireless sensor networks, WSNs

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Authors: Andre Slonopas, Zona Kostic, Warren Thompson

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Obfuscation is one of the most useful tools to prevent network compromise. Previous research focused on the obfuscation of the network communications between external-facing edge devices. This work proposes the use of two edge devices, external and internal facing, which communicate via private IPv4 addresses in a software-defined pseudo-random IP hopping. This methodology does not require additional IP addresses and/or resources to implement. Statistical analyses demonstrate that the hopping surface must be at least 1e3 IP addresses in size with a broad standard deviation to minimize the possibility of coincidence of monitored and communication IPs. The probability of breaking the hopping algorithm requires a collection of at least 1e6 samples, which for large hopping surfaces will take years to collect. The probability of dropped packets is controlled via memory buffers and the frequency of hops and can be reduced to levels acceptable for video streaming. This methodology provides an impenetrable layer of security ideal for information and supervisory control and data acquisition systems.

Keywords: moving target defense, cybersecurity, network security, hopping randomization, software defined network, network security theory

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Authors: Muntadher Sallal, Gareth Owenson, Mo Adda, Safa Shubbar

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Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a delay overhead in information propagation, which makes the system vulnerable to double-spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, that are based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In MNBC protocol, physical internet connectivity increases, as well as the number of hops between nodes, decreases through assigning nodes to be responsible for maintaining clusters based on physical internet proximity. We show, through simulations, that the proposed protocol defines better clustering structures that optimize the performance of the transaction propagation over the Bitcoin protocol. The evaluation of partition attacks in the MNBC protocol, as well as the Bitcoin network, was done in this paper. Evaluation results prove that even though the Bitcoin network is more resistant against the partitioning attack than the MNBC protocol, more resources are needed to be spent to split the network in the MNBC protocol, especially with a higher number of nodes.

Keywords: Bitcoin network, propagation delay, clustering, scalability

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Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

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This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

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Authors: Samia Rabet, Rachida Chemini, Gerhard Schäfer, Farid Aiouache

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The textile industry generates large quantities of wastewater, which poses significant environmental problems due to its complex composition and high levels of pollutants loaded principally with heavy metals, large amounts of COD, and dye. Separation treatment methods are often known for their effectiveness in removing contaminants whereas membrane separation techniques are a promising process for the treatment of textile effluent due to their versatility, efficiency, and low energy requirements. This study focuses on the modeling and simulation of membrane separation technologies with a cross-flow filtration process for textile effluent treatment. It aims to explore the application of mathematical models and computational simulations using ASPEN Plus Software in the prediction of a complex and real effluent separation. The results demonstrate the effectiveness of modeling and simulation techniques in predicting pollutant removal efficiencies with a global deviation percentage of 1.83% between experimental and simulated results; membrane fouling behavior, and overall process performance (hydraulic resistance, membrane porosity) were also estimated and indicating that the membrane losses 10% of its efficiency after 40 min of working.

Keywords: membrane separation, ultrafiltration, textile effluent, modeling, simulation

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Authors: Abbasi Fakhrossadat, Moharreri Mohammadamir, Shadmanmahani Mohammadjavad

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The development of cities can be influenced by various factors, including air pollution. In this study, the focus is on the city of Mashhad, which has four large power plants operating. The emission of pollutants from these power plants can have a significant impact on the quality of life and health of the city's residents. Therefore, modeling and analyzing the emission pattern of pollutants can provide useful information for urban decision-makers and help in estimating the urban development model. The aim of this research is to determine the direction of city development based on the modeling of pollutant emissions (NOX, CO, and PM10) from power plants in Mashhad. By using the AERMOD software, the release of these pollutants will be modeled and analyzed.

Keywords: emission of air pollution, thermal power plant, urban development, AERMOD

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Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

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Authors: Mohsen Khaledian, Razali Ismail, Mehdi Saeidmanesh, Mahdiar Hosseinghadiry

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A semi-analytical model for impact ionization coefficient of graphene nanoribbon (GNR) is presented. The model is derived by calculating probability of electrons reaching ionization threshold energy Et and the distance traveled by electron gaining Et. In addition, ionization threshold energy is semi-analytically modeled for GNR. We justify our assumptions using analytic modeling and comparison with simulation results. Gaussian simulator together with analytical modeling is used in order to calculate ionization threshold energy and Kinetic Monte Carlo is employed to calculate ionization coefficient and verify the analytical results. Finally, the profile of ionization is presented using the proposed models and simulation and the results are compared with that of silicon.

Keywords: nanostructures, electronic transport, semiconductor modeling, systems engineering

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Authors: M. Kebisek, L. Spendla, M. Kopcek, T. Skulavik

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In our paper we describe the security capabilities of data collection. Data are collected with probes located in the near and distant surroundings of the company. Considering the numerous obstacles e.g. forests, hills, urban areas, the data collection is realized in several ways. The collection of data uses connection via wireless communication, LAN network, GSM network and in certain areas data are collected by using vehicles. In order to ensure the connection to the server most of the probes have ability to communicate in several ways. Collected data are archived and subsequently used in supervisory applications. To ensure the collection of the required data, it is necessary to propose algorithms that will allow the probes to select suitable communication channel.

Keywords: communication, computer network, data collection, probe

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Authors: Lai Wenfang

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Tthis study will describe how to use artificial intelligence (AI) technology to build the user-oriented platform for integrated archival service. The platform will be launched in 2020 by the National Archives Administration (NAA) in Taiwan. With the progression of information communication technology (ICT) the NAA has built many systems to provide archival service. In order to cope with new challenges, such as new ICT, artificial intelligence or blockchain etc. the NAA will try to use the natural language processing (NLP) and machine learning (ML) skill to build a training model and propose suggestions based on the data sent to the platform. NAA expects the platform not only can automatically inform the sending agencies’ staffs which records catalogues are against the transfer or destroy rules, but also can use the model to find the details hidden in the catalogues and suggest NAA’s staff whether the records should be or not to be, to shorten the auditing time. The platform keeps all the users’ browse trails; so that the platform can predict what kinds of archives user could be interested and recommend the search terms by visualization, moreover, inform them the new coming archives. In addition, according to the Archives Act, the NAA’s staff must spend a lot of time to mark or remove the personal data, classified data, etc. before archives provided. To upgrade the archives access service process, the platform will use some text recognition pattern to black out automatically, the staff only need to adjust the error and upload the correct one, when the platform has learned the accuracy will be getting higher. In short, the purpose of the platform is to deduct the government digital transformation and implement the vision of a service-oriented smart government.

Keywords: artificial intelligence, natural language processing, machine learning, visualization

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Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

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Authors: M. Hussaina, K. Mahboobb, S. Z. Ilyasa, S. Shaheena

Abstract:

Plume dispersion modeling is a computational procedure to establish a relationship between emissions, meteorology, atmospheric concentrations, deposition and other factors. The emission characteristics (stack height, stack diameter, release velocity, heat contents, chemical and physical properties of the gases/particle released etc.), terrain (surface roughness, local topography, nearby buildings) and meteorology (wind speed, stability, mixing height, etc.) are required for the modeling of the plume dispersion and estimation of ground and air concentration. During the early phase of Fukushima accident, plume dispersion modeling and decisions were taken for the implementation of protective measures. A difference in estimated results and decisions made by different countries for taking protective actions created a concern in local and international community regarding the exact identification of the safe zone. The current study is focused to highlight the importance of accurate and exact weather data availability, scientific approach for decision making for taking urgent protective actions, compatible and harmonized approach for plume dispersion modeling during a nuclear emergency. As a case study, the influence of meteorological data on plume dispersion modeling and decision-making process has been performed.

Keywords: decision making process, radiation doses, nuclear emergency, meteorological implications

Procedia PDF Downloads 182

Authors: Ghada Moussa, Mamoud Owais

Abstract:

Transit Route Network Design Problem (TrNDP) is the most important component in Transit planning, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a novel solution methodology for the TrNDP, which goes beyond pervious traditional sophisticated approaches. The novelty of the solution methodology, adopted in this paper, stands on the deterministic operators which are tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. The solution methodology has been tested through Mandl’s benchmark network problem. The test results showed that the methodology developed in this research is able to improve the given network solution in terms of number of constructed routes, direct transit service coverage, transfer directness and solution reliability. Although the set of routes resulted from the methodology would stand alone as a final efficient solution for TrNDP, it could be used as an initial solution for meta-heuristic procedures to approach global optimal. Based on the presented methodology, a more robust network optimization tool would be produced for public transportation planning purposes.

Keywords: integer programming, transit route design, transportation, urban planning

Procedia PDF Downloads 273