Search results for: complex networks

Authors: Pavel Shcherban, Viktoria Ivanova, Alexander Neprokin, Vladislav Golovanov

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Currently, LLC «Lukoil-Kaliningradmorneft» is implementing a comprehensive program for the development of offshore fields of the Kaliningrad region. This is largely associated with the depletion of the resource base of land in the region, as well as the positive results of geological investigation surrounding the Baltic Sea area and the data on the volume of hydrocarbon recovery from a single offshore field are working on the Kaliningrad region – D-6 «Kravtsovskoye».The article analyzes the main stages of the LLC «Lukoil-Kaliningradmorneft»’s development program for the development of the hydrocarbon resources of the region's shelf and suggests an optimization algorithm that allows managing a multi-criteria process of development of shelf deposits. The algorithm is formed on the basis of the problem of sequential decision making, which is a section of dynamic programming. Application of the algorithm during the consolidation of the initial data, the elaboration of project documentation, the further exploration and development of offshore fields will allow to optimize the complex of technical and technological solutions and increase the economic efficiency of the field development project implemented by LLC «Lukoil-Kaliningradmorneft».

Keywords: offshore fields of hydrocarbons of the Baltic Sea, development of offshore oil and gas fields, optimization of the field development scheme, solution of multicriteria tasks in oil and gas complex, quality management in oil and gas complex

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Authors: Hefzollah Mohammadian, Mohammad Bagher Heidari

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This paper deals with the technical and economic effects of various parameters on the performance of heat recovery system on gas complex turbo generator. Given the importance of this issue, that is the main goal of economic efficiency and reduces costs; this project has been implemented similar plans in which the target is the implementation of specific patterns. The project will also help us in the process of gas refineries and the actual efficiency of the process after adding a system to analyze the turbine and predict potential problems and fix them and take appropriate measures according to the results of simulation analysis and results of the process gain. The results of modeling and the effect of different parameters on this line, have been done using Thermo Flow.

Keywords: turbo compressor, turbo generator, heat recovery boiler, gas turbines

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Authors: Mohamad Bagher Heidari, Hefzollah Mohammadian

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New discoveries in materials on the nanometer-length scale are expected to play an important role in addressing ongoing and future challenges in the field of communication. Devices and systems for ultra-high speed short and long range communication links, portable and power efficient computing devices, high-density memory and logics, ultra-fast interconnects, and autonomous and robust energy scavenging devices for accessing ambient intelligence and needed information will critically depend on the success of next-generation emerging nonmaterials and devices. This article presents some exciting recent developments in nonmaterials that have the potential to play a critical role in the development and transformation of future intelligent communication and telecom networks in the gas refinery. The industry is benefiting from nanotechnology advances with numerous applications including those in smarter sensors, logic elements, computer chips, memory storage devices, optoelectronics.

Keywords: nonmaterial, intelligent communication, nanoscale, nanophotonic, telecom

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Authors: Roya Moradifar, Bijan Honarvar, Masoumeh Zabihi

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The personal residential camps of South Pars gas complex are one of the few places where electric energy is used for the bath water heating. The widespread use of these devices is mainly responsible for the high peak of the electricity demand in the residential sector. In an attempt to deal with this issue, to reduce the electricity usage of the hot water, as an option, solar hot water systems have been proposed. However, despite the high incidence of solar radiation on the Assaloyeh about 20 MJ/m²/day, currently, there is no technical assessment quantifying the economic benefits on the region. The present study estimates the economic impacts resulting by the deployment of solar hot water systems in residential camp. Hence, the feasibility study allows assessing the potential of solar water heating as an alternative to reduce the peak on the electricity demand. In order to examine the potential of using solar energy in Bidkhoon residential camp two solar water heater packages as pilots were installed for restaurant and building. Restaurant package was damaged due to maintenance problems, but for the building package, we achieved the result of the solar fraction total 83percent and max energy saving 2895 kWh, the maximum reduction in CO₂ emissions calculated as 1634.5 kg. The results of this study can be used as a support tool to spread the use solar water heaters and create policies for South Pars Gas Complex.

Keywords: electrical energy, hot water, solar, South Pars Gas complex

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Authors: Shaimaa Eltanani, Thangal Yumnamcha, Ahmed S. Ibrahim

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Purpose: Mitochondria dysfunction is central to breaking the barrier integrity of retinal endothelial cells (RECs) in various blinding eye diseases such as diabetic retinopathy and retinopathy of prematurity. Therefore, we aimed to dissect the role of different mitochondrial components, specifically, those of oxidative phosphorylation (OxPhos), in maintaining the barrier function of RECs. Methods: Electric cell-substrate impedance sensing (ECIS) technology was used to assess in real-time the role of different mitochondrial components in the total impedance (Z) of human RECs (HRECs) and its components; the capacitance (C) and the total resistance (R). HRECs were treated with specific mitochondrial inhibitors that target different steps in OxPhos: Rotenone for complex I; Oligomycin for ATP synthase; and FCCP for uncoupling OxPhos. Furthermore, data were modeled to investigate the effects of these inhibitors on the three parameters that govern the total resistance of cells: cell-cell interactions (Rb), cell-matrix interactions (α), and cell membrane permeability (Cm). Results: Rotenone (1 µM) produced the greatest reduction in the Z, followed by FCCP (1 µM), whereas no reduction in the Z was observed after the treatment with Oligomycin (1 µM). Following this further, we deconvoluted the effect of these inhibitors on Rb, α, and Cm. Firstly, rotenone (1 µM) completely abolished the resistance contribution of Rb, as the Rb became zero immediately after the treatment. Secondly, FCCP (1 µM) eliminated the resistance contribution of Rb only after 2.5 hours and increased Cm without considerable effect on α. Lastly, Oligomycin had the lowest impact among these inhibitors on Rb, which became similar to the control group at the end of the experiment without noticeable effects on Cm or α. Conclusion: These results demonstrate differential roles for complex I, complex V, and coupling of OxPhos in maintaining the barrier functionality of HRECs, in which complex I being the most important component in regulating the barrier functionality and the spreading behavior of HRECs. Such differences can be used in investigating gene expression as well as for screening selective agents that improve the functionality of complex I to be used in the therapeutic approach for treating REC-related retinal diseases.

Keywords: human retinal endothelial cells (hrecs), rotenone, oligomycin, fccp, oxidative phosphorylation, oxphos, capacitance, impedance, ecis modeling, rb resistance, α resistance, and barrier integrity

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Authors: Aiswarea Mathew, Smita Mohanty, Jr., S. K. Nayak

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Polyurethane networks based on castor oil (CO) as a renewable resource polyol were synthesized. Polyurethane/epoxy resin interpenetrating network nanocomposites containing modified montmorillonite organoclay (C30B-PU/EP nanocomposites) were prepared by an in situ intercalation method. The conventional spectroscopic characterization of the synthesized samples using FT-IR confirms the existence of the proposed castor oil based PU structure and also showed that strong interactions existed between C30B and EP/PU matrix. The dispersion degree of C30B in EP/PU matrix was characterized by X-Ray diffraction (XRD) method. Scanning electronic microscopy analysis showed that the interpenetrating process of PU and EP increases the exfoliation degree of C30B, and it improves the compatibility and the phase structure of polyurethane/epoxy resin interpenetrating polymer networks (PU/EP IPNs). The thermal stability improves compared to the polyurethane when the PU/EP IPN is formed. Mechanical properties including the Young’s modulus and tensile strength reflected marked improvement with addition of C30B.

Keywords: castor oil, epoxy, montmorillonite, polyurethane

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Authors: Alica Höpken, Hergen Pargmann

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The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

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Authors: M. Benbouzid, Q. Bresson, A. Duclos, K. Longo, Q. Morel

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Currently, electricity suppliers must predict the consumption of their customers in order to deduce the power they need to produce. It is, then, important in a first step to optimize household consumption to obtain more constant curves by limiting peaks in energy consumption. Here centralized real time scheduling is proposed to manage the equipment's starting in parallel. The aim is not to exceed a certain limit while optimizing the power consumption across a habitat. The Raspberry Pi is used as a box; this scheduler interacts with the various sensors in 6LoWPAN. At the scale of a single dwelling, household consumption decreases, particularly at times corresponding to the peaks. However, it would be wiser to consider the use of a residential complex so that the result would be more significant. So, the ceiling would no longer be fixed. The scheduling would be done on two scales, firstly, per dwelling, and secondly, at the level of a residential complex.

Keywords: smart grid, energy box, scheduling, Gang Model, energy consumption, energy management system, wireless sensor network

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Authors: H. Li, L. Ji, J. Su

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Forward osmosis is an emerging technology for separation and has great potential in the concentration of liquid products such as protein, pharmaceutical, and natural products. In pharmacy industry, one of the very tough talks is to concentrate the product in a gentle way since some of the key components may lose bioactivity when exposed to heating or pressurization. Therefore, forward osmosis (FO), which uses inherently existed osmosis pressure instead of externally applied hydraulic pressure, is attractive for pharmaceutical enrichments in a much efficient and energy-saving way. Recently, coordination complexes have been explored as the new class of draw solutes in FO processes due to their bulky configuration and excellent performance in terms of high water flux and low reverse solute flux. Among these coordination complexes, ferric citrate complex with lots of hydrophilic groups and ionic species which make them good solubility and high osmotic pressure in aqueous solution, as well as its low toxicity, has received much attention. However, the chemistry of ferric complexation by citrate is complicated, and disagreement prevails in the literature, especially for the structure of the ferric citrate. In this study, we investigated the chemical reaction with various molar ratio of iron and citrate. It was observed that the ferric citrate complex (Fe-CA2) with molar ratio of 1:1 for iron and citrate formed at the beginning of the reaction, then Fecit would convert to ferric citrate complex at the molar ratio of 1:2 with the proper excess of citrate in the base solution. The structures of the ferric citrate complexes synthesized were systematically characterized by X-ray diffraction (XRD), UV-vis spectroscopy, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR) and Thermogravimetric analysis (TGA). Fe-CA2 solutions exhibit osmotic pressures more than twice of that for NaCl solutions at the same concentrations. Higher osmotic pressure means higher driving force, and this is preferable for the FO process. Fe-CA2 and NaCl draw solutions were prepared with the same osmotic pressure and used in FO process for BSA protein concentration. Within 180 min, BSA concentration was enriched from 0.2 to 0.27 L using Fe-CA draw solutions. However, it was only increased from 0.20 to 0.22 g/L using NaCl draw solutions. A reverse flux of 11 g/m²h was observed for NaCl draw solutes while it was only 0.1 g/m²h for Fe-CA2 draw solutes. It is safe to conclude that Fe-CA2 is much better than NaCl as draw solute and it is suitable for the enrichment of liquid product.

Keywords: draw solutes, ferric citrate complex, forward osmosis, protein enrichment

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Authors: Sara Fioretti

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In the 2nd millennium BCE, maritime networks became essential to the Mediterranean lifestyle, creating an interconnected world. This phenomenon of interconnected cultures has often been misinterpreted as an “effect” of the Mycenaean “influence” without considering the complexity and role of regional and cross-cultural exchanges. This paper explores the socio-economic relationships, in both cross-cultural and potentially inter-regional settings, present within the archaeological repertoire of the southern Italian Late Bronze Age (LBA 1600 -1140 BCE). The emergence of economic relations within the connectivity of the regional settlements is explored through ceramic contexts found in the case studies Punta di Zambrone, Broglio di Trebisacce, and Nuraghe Antigori. This work-in-progress research is situated in the shifting theoretical views of the last ten years that discuss the Late Bronze Age’s connectivity through Social Networks, Entanglement, and Assertive Objects combined with a comparative statistical study of ceramic frequency distribution. Applying these theoretical frameworks with a quantitative approach demonstrates the specific regional economic relationships that shaped the cultural interactions of the Late Bronze Age. Through this intersection of theory and statistical analysis, the case studies establish a small percentage of pottery as imported, whilst assertive productions have a relatively higher quantity. Overall, the majority still adheres to regional Italian traditions. Therefore, we can dissect the rhizomatic relationships cultivated by the Italian coasts and Mycenaeans and their roles within their networks through the intersection of theoretical and statistical analysis. This research offers a new perspective on the connectivity of the Late Bronze Age relational structures.

Keywords: late bronze age, mediterranean archaeology, exchanges and trade, frequency distribution of ceramic assemblages

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Naum Simanovsky MD, Michael Zaidman MD, Vladimir Goldman MD.

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105 plain AP radiographs of normal adult pelvises (210 hips) were evaluated. Different measurements of normal and dysplastic hip joints in 45 patients were analyzed. Attempt was made to establish reproducible, easy applicable in practice approach for evaluation and follow up of patients with hip dysplasia. The youngest of our patients was 11 years and the oldest was 47 years. Only one of our patients needed conversion to total hip replacement (THR) during ten years of follow-up. It was emphasized that selected set of measurements was built for purpose to serve, especially those who’s scheduled or undergone PAO. This approach was based on concept of acetabulum-femoral head complex and importance of reliable reference points of measurements. Comparative analysis of measured parameters between normal and dysplastic hips was performed. Among 10 selected parameters, we use already well established such as lateral center edge angle and head extrusion index, but to serve specific group of patients with PAO, new parameters were considered such as complex lateralization and complex proximal migration. By our opinion proposed approach is easy applicable in busy clinical practice, satisfactorily delineate hip pathology and give to surgeon who’s going to perform PAO guidelines in condensed form. It is also useful tools for postoperative follow up after PAO.

Keywords: periacetabular osteotomy, plain radiograph’s measurements, adolescents, adult

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Authors: Amal Khalifa, Nicolas Vana Santos

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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

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In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

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Authors: Louise Moles, Steve Riley, Sarah D. Lichenstein, Marzieh Babaeianjelodar, Robert Kohler, Annie Cheng, Corey Horien Abigail Greene, Wenjing Luo, Jonathan Ahern, Bohan Xu, Yize Zhao, Chun Chieh Fan, R. Todd Constable, Sarah W. Yip

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Background: Risks for depression are myriad and include both genetic and brain-based factors. However, relationships between these systems are poorly understood, limiting understanding of disease etiology, particularly at the developmental level. Methods: We use a data-driven machine learning approach connectome-based predictive modeling (CPM) to identify functional connectivity signatures associated with polygenic risk scores for depression (DEP-PRS) among youth from the Adolescent Brain and Cognitive Development (ABCD) study across diverse brain states, i.e., during resting state, during affective working memory, during response inhibition, during reward processing. Results: Using 10-fold cross-validation with 100 iterations and permutation testing, CPM identified connectivity signatures of DEP-PRS across all examined brain states (rho’s=0.20-0.27, p’s<.001). Across brain states, DEP-PRS was positively predicted by increased connectivity between frontoparietal and salience networks, increased motor-sensory network connectivity, decreased salience to subcortical connectivity, and decreased subcortical to motor-sensory connectivity. Subsampling analyses demonstrated that model accuracies were robust across random subsamples of N’s=1,000, N’s=500, and N’s=250 but became unstable at N’s=100. Conclusions: These data, for the first time, identify neural networks of polygenic depression risk in a large sample of youth before the onset of significant clinical impairment. Identified networks may be considered potential treatment targets or vulnerability markers for depression risk.

Keywords: genetics, functional connectivity, pre-adolescents, depression

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Authors: M. Kosacka, I. Kudelska

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Vehicle is one of the most influential and complex product worldwide, which affects people’s life, state of the environment and condition of the economy (all aspects of sustainable development concept) during each stage of lifecycle. With the increase of vehicles’ number, there is growing potential for management of End of Life Vehicle (ELV), which is hazardous waste. From one point of view, the ELV should be managed to ensure risk elimination, but from another point, it should be treated as a source of valuable materials and spare parts. In order to obtain materials and spare parts, there are established recycling networks, which are an example of sustainable policy realization at the national level. The basic object in the polish recycling network is dismantling facility. The output material streams in dismantling stations include waste, which very often generate costs and spare parts, that have the biggest potential for revenues creation. Both outputs are stored into warehouses, according to the law. In accordance to the revenue creation and sustainability potential, it has been placed a strong emphasis on storage process. We present the concept of storage method, which takes into account the specific of the dismantling facility in order to support decision-making process with regard to the principles of sustainable development. The method was developed on the basis of case study of one of the greatest dismantling facility in Poland.

Keywords: dismantling, end of life vehicles, sustainability, storage

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Authors: John Dimopoulos

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This proposal attempts a refabrication of Heidegger’s classic thing-being and object-being analysis in order to provide better ontological tools for understanding contemporary culture, technology, and society. In his work, Heidegger sought to understand and comment on the problem of technology in an era of rampant innovation and increased perils for society and the planet. Today we seem to be at another crossroads in this course, coming after postmodernity, during which dreams and dangers of modernity augmented with critical speculations of the post-war era take shape. The new era which we are now living in, referred to as hypermodernity by researchers in various fields such as architecture and cultural theory, is defined by the horizontal implementation of digital technologies, cybernetic networks, and mixed reality. Technology today is rapidly approaching a turning point, namely the point of no return for humanity’s supervision over its creations. The techno-scientific civilization of the 21st century creates a series of problems, progressively more difficult and complex to solve and impossible to ignore, climate change, data safety, cyber depression, and digital stress being some of the most prevalent. Humans often have no other option than to address technology-induced problems with even more technology, as in the case of neuron networks, machine learning, and AI, thus widening the gap between creating technological artifacts and understanding their broad impact and possible future development. As all technical disciplines and particularly design, become enmeshed in a matrix of digital hyper-objects, a conceptual toolbox that allows us to handle the new reality becomes more and more necessary. Weaponized design, prevalent in many fields, such as social and traditional media, urban planning, industrial design, advertising, and the internet in general, hints towards an increase in conflicts. These conflicts between tech companies, stakeholders, and users with implications in politics, work, education, and production as apparent in the cases of Amazon workers’ strikes, Donald Trump’s 2016 campaign, Facebook and Microsoft data scandals, and more are often non-transparent to the wide public’s eye, thus consolidating new elites and technocratic classes and making the public scene less and less democratic. The new category proposed, weapon-being, is outlined in respect to the basic function of reducing complexity, subtracting materials, actants, and parameters, not strictly in favor of a humanistic re-orientation but in a more inclusive ontology of objects and subjects. Utilizing insights of Object-Oriented Ontology (OOO) and its schematization of technological objects, an outline for a radical ontology of technology is approached.

Keywords: design, hypermodernity, object-oriented ontology, weapon-being

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Authors: Anusha M., V. Srikanth

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Maximizing spectrum usage and numerous applications of the wireless communication networks have forced to a high interest of available spectrum. Cognitive Radio control its receiver and transmitter features exactly so that they can utilize the vacant approved spectrum without impacting the functionality of the principal licensed users. The Use of various channels assists to address interferences thereby improves the whole network efficiency. The MAC protocol in cognitive radio network explains the spectrum usage by interacting with multiple channels among the users. In this paper we studied about the architecture of cognitive wireless mesh network and traditional TDMA dependent MAC method to allocate channels dynamically. The majority of the MAC protocols suggested in the research are operated on Common-Control-Channel (CCC) to handle the services between Cognitive Radio secondary users. In this paper, an extensive study of Multi-Channel Multi-Radios or frequency range channel allotment and continually synchronized TDMA scheduling are shown in summarized way.

Keywords: TDMA, MAC, multi-channel, multi-radio, WMN’S, cognitive radios

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Authors: Dehini Rachid, Ferdi Brahim

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The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.

Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: Maricarmen Herrera, Pierre Chymkowitch, Joe Robertson, Jens Eriksson, Jorrit Enserink

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tRNA genes are transcribed by RNA polymerase III. During recent years, it has become clear that tDNA transcription fluctuates during the cell cycle. However, the mechanism by which the cell cycle controls the amplitude of tDNA transcription remains unknown. We found that the cyclin Clb5 recruits the cyclin dependent kinase Cdk1 to tRNA genes to sharply increase tRNA synthesis during a brief interval in the cell cycle. We show that Cdk1 promotes the interaction of TFIIIB with TFIIIC, that it stimulates the recruitment of TFIIIC to tRNA genes, that it prevents the formation of an overly stable TFIIIB-tDNA complex and that it augments the dynamics of RNA polymerase III. Furthermore, we identify Bdp1 as a novel Cdk1 substrate, and phosphorylation of Bdp1 is required for the cell cycle-dependent increase in tDNA transcription. In addition, we show that phosphorylation of the Cdk1 substrate Nup60 mediates formation of a Nup60-Nup2 complex at tRNA genes, which is also required for cell cycle-dependent tDNA transcription. Together, our findings indicate that Cdk1 activity gates tRNA synthesis by regulating the dynamics of the TFIIIB-TFIIIC-RNAPIII complex, and that it may promote the formation of a nuclear pore microenvironment conducive to efficient tDNA transcription.

Keywords: Cdk1, cell cycle, RNAPIII machinery, tRNA

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Authors: Zeina Bou Diab, Arij Daou

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The neural mechanisms of sequential behaviors are intensively studied, with songbirds a focus for learned vocal production. We are studying the premotor nucleus HVC at a nexus of multiple pathways contributing to song learning and production. The HVC consists of multiple classes of neuronal populations, each has its own cellular, electrophysiological and functional properties. During singing, a large subset of motor cortex analog-projecting HVCRA neurons emit a single 6-10 ms burst of spikes at the same time during each rendition of song, a large subset of basal ganglia-projecting HVCX neurons fire 1 to 4 bursts that are similarly time locked to vocalizations, while HVCINT neurons fire tonically at average high frequency throughout song with prominent modulations whose timing in relation to song remains unresolved. This opens the opportunity to define models relating explicit HVC circuitry to how these neurons work cooperatively to control learning and singing. We developed conductance-based Hodgkin-Huxley models for the three classes of HVC neurons (based on the ion channels previously identified from in vitro recordings) and connected them in several physiologically realistic networks (based on the known synaptic connectivity and specific glutaminergic and gabaergic pharmacology) via different architecture patterning scenarios with the aim to replicate the in vivo firing patterning behaviors. We are able, through these networks, to reproduce the in vivo behavior of each class of HVC neurons, as shown by the experimental recordings. The different network architectures developed highlight different mechanisms that might be contributing to the propagation of sequential neural activity (continuous or punctate) in the HVC and to the distinctive firing patterns that each class exhibits during singing. Examples of such possible mechanisms include: 1) post-inhibitory rebound in HVCX and their population patterns during singing, 2) different subclasses of HVCINT interacting via inhibitory-inhibitory loops, 3) mono-synaptic HVCX to HVCRA excitatory connectivity, and 4) structured many-to-one inhibitory synapses from interneurons to projection neurons, and others. Replication is only a preliminary step that must be followed by model prediction and testing.

Keywords: computational modeling, neural networks, temporal neural sequences, ionic currents, songbird

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Authors: Y. A. R. Oliveira, M. A. Couto dos Santos, N. B. C. Júnior, S. J. L. Ribeiro, L. D. Carlos

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The Eu(TTA)3(H2O)2 complex (TTA = thenoyltrifluoroacetone) pure (EuTTA) and incorporated in an organicinorganic hybrid material (EuTTA-hyb) are revisited, this time from the crystal field parameters (CFP) and Judd-Ofelt intensity parameters (Ωλ) point of view. A detailed analysis of the emission spectra revealed that the EuTTA phase still remains in the hybrid phase. Sparkle Model calculations of the EuTTA ground state geometry have been performed and satisfactorily compared to the X-ray structure. The observed weaker crystal field strength of the phase generated by the incorporation is promptly interpreted through the existing EXAFS results of the EuTTA-hyb structure. Satisfactory predictions of the CFP, of the 7F1 level splitting and of the Ωλ in all cases were obtained by using the charge factors and polarizabilities as degrees of freedom of non-parametric models.

Keywords: crystal field parameters, europium complexes, Judd-Ofelt intensity parameters

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Authors: Wei Li, Tuo Yang

Abstract:

In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: Maryam Goodarzi, Mahdieh Sheikhi, Mehdi Goodarzi

Abstract:

This study intends to offer an appropriate strategy development framework for a telecommunication firm (as a case study) which works on Information and Communication Technology (ICT) projects, development of telecommunication networks, and maintenance of local networks, according to its dominant condition. In this approach, first, the objectives were set and the mission was defined. Then, the capability was assessed by SWOT matrix. Using SPACE matrix, the strategy of the company was determined. The strategic direction is set and an appropriate and superior strategy was developed and offered employing QSPM matrix. The theoretical framework or conceptual model of the present study first involves 4 stages of framework development and then from stage 3 (assessing capability) onward, a strategic management model by Fred R. David. In this respect, the tools and methods offered in the framework are appropriate for all kinds of organizations, particularly small firms, and help strategists identify, evaluate, and select strategies.

Keywords: strategy formulation, firm mission, strategic direction, space diagram, quantitative strategic planning matrix, SWOT matrix

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

Abstract:

Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: K. Sangeeth, P. Rekha, P. Preeja, P. Divya, R. Arya, R. Maneesha

Abstract:

Wireless sensor networks (WSN) are small or tiny devices that consists of different sensors to sense physical parameters like air pressure, temperature, vibrations, movement etc., process these data and sends it to the central data center to take decisions. The WSN domain, has wide range of applications such as monitoring and detecting natural hazards like landslides, forest fire, avalanche, flood monitoring and also in healthcare applications. With such different applications, it is being taught in undergraduate/post graduate level in many universities under department of computer science. But the cost and infrastructure required to purchase WSN nodes for having the students getting hands on expertise on these devices is expensive. This paper gives overview about the remote triggered lab that consists of more than 100 WSN nodes that helps the students to remotely login from anywhere in the world using the World Wide Web, configure the nodes and learn the WSN concepts in intuitive way. It proposes new way called over the air programming (OTAP) and its internals that program the 100 nodes simultaneously and view the results without the nodes being physical connected to the computer system, thereby allowing for sparse deployment.

Keywords: WSN, over the air programming, virtual lab, AT45DB

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Authors: Hyunsun Lee, Yi Zhu

Abstract:

Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.

Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles

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Authors: Yujiang Wu

Abstract:

As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

Procedia PDF Downloads 78