Search results for: satellite technologies

Authors: Collins C. Chiemeke, A. Onugba, P. Sule

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High resolution seismic reflection has recently been carried out on Zaria batholith, with the aim of characterizing the granitic Zaria batholiths in terms of its lithology. The geology of the area has revealed that the older granite outcrops in the vicinity of Zaria are exposures of a syntectonics to late-tectonic granite batholiths which intruded a crystalline gneissic basement during the Pan-African Orogeny. During the data acquisition the geophone were placed at interval of 1 m, variable offset of 1 and 10 m was used. The common midpoint (CMP) method with 12 fold coverage was employed for the survey. Analysis of the generated 3D surface of the p wave velocities from different profiles for densities and bulk modulus revealed that the rock material is more consolidated in South East part of the batholith and less consolidated in the North Western part. This was in conformity with earlier identified geology of the area, with the South Eastern part majorly of granitic outcrop, while the North Western part is characterized with the exposure of gneisses and thick overburden cover. The difference in lithology was also confirmed by the difference in seismic sections and Arial satellite photograph. Hence two major lithologies were identified, the granitic and gneisses complex which are characterized by gradational boundaries.

Keywords: basement complex, batholith, high resolution, lithologies, seismic reflection

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Authors: Fernando Vega, Esmeralda Portillo, Sara Camino, Benito Navarrete, Elena Montavez

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The European Union aims at strongly reducing their CO₂ emissions from energy and industrial sector by 2030. The energy sector contributes with more than two-thirds of the CO₂ emission share derived from anthropogenic activities. Although efforts are mainly focused on the use of renewables by energy production sector, carbon capture and storage (CCS) remains as a frontline option to reduce CO₂ emissions from industrial process, particularly from fossil-fuel power plants and cement production. Among the most feasible and near-to-market CCS technologies, namely post-combustion and oxy-combustion, partial oxy-combustion is a novel concept that can potentially reduce the overall energy requirements of the CO₂ capture process. This technology consists in the use of higher oxygen content in the oxidizer that should increase the CO₂ concentration of the flue gas once the fuel is burnt. The CO₂ is then separated from the flue gas downstream by means of a conventional CO₂ chemical absorption process. The production of a higher CO₂ concentrated flue gas should enhance the CO₂ absorption into the solvent, leading to further reductions of the CO₂ separation performance in terms of solvent flow-rate, equipment size, and energy penalty related to the solvent regeneration. This work evaluates a portfolio of CCS technologies applied to fossil-fuel power plants. For this purpose, an economic evaluation methodology was developed in detail to determine the main economical parameters for CO₂ emission removal such as the levelized cost of electricity (LCOE) and the CO₂ captured and avoided costs. ASPEN Plus™ software was used to simulate the main units of power plant and solve the energy and mass balance. Capital and investment costs were determined from the purchased cost of equipment, also engineering costs and project and process contingencies. The annual capital cost and operating and maintenance costs were later obtained. A complete energy balance was performed to determine the net power produced in each case. The baseline case consists of a supercritical 500 MWe coal-fired power plant using anthracite as a fuel without any CO₂ capture system. Four cases were proposed: conventional post-combustion capture, oxy-combustion and partial oxy-combustion using two levels of oxygen-enriched air (40%v/v and 75%v/v). CO₂ chemical absorption process using monoethanolamine (MEA) was used as a CO₂ separation process whereas the O₂ requirement was achieved using a conventional air separation unit (ASU) based on Linde's cryogenic process. Results showed a reduction of 15% of the total investment cost of the CO₂ separation process when partial oxy-combustion was used. Oxygen-enriched air production also reduced almost half the investment costs required for ASU in comparison with oxy-combustion cases. Partial oxy-combustion has a significant impact on the performance of both CO₂ separation and O₂ production technologies, and it can lead to further energy reductions using new developments on both CO₂ and O₂ separation processes.

Keywords: carbon capture, cost methodology, economic evaluation, partial oxy-combustion

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Authors: Ibrahim Tiryakioglu, Mehmet Ali Ugur, Caglar Ozkaymak

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The development of GNSS technology has led to increasingly widespread and successful applications of GNSS surveys for monitoring crustal movements. However, multi-period GPS survey solutions have not been applied in monitoring vertical surface deformation. This study uses long-term GNSS time series that are required to determine vertical deformations. In recent years, the surface deformations that are parallel and semi-parallel to Bolvadin fault have occurred in Western Anatolia. These surface deformations have continued to occur in Bolvadin settlement area that is located mostly on alluvium ground. Due to these surface deformations, a number of cracks in the buildings located in the residential areas and breaks in underground water and sewage systems have been observed. In order to determine the amount of vertical surface deformations, two continuous GNSS stations have been established in the region. The stations have been operating since 2015 and 2017, respectively. In this study, GNSS observations from the mentioned two GNSS stations were processed with GAMIT/GLOBK (GNSS Analysis Massachusetts Institute of Technology/GLOBal Kalman) program package to create a coordinate time series. With the time series analyses, the GNSS stations’ behavior models (linear, periodical, etc.), the causes of these behaviors, and mathematical models were determined. The study results from the time series analysis of these two 2 GNSS stations shows approximately 50-80 mm/yr vertical movement.

Keywords: Bolvadin fault, GAMIT, GNSS time series, surface deformations

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Authors: Vani Chintapally

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The expansion of modest and minimized Global Positioning System (GPS) beneficiaries has prompted most Automatic Vehicle Location (AVL) frameworks today depending solely on satellite-based finding frameworks, as GPS is the most stable usage of these. This paper shows the attributes of a proposed framework for following and dissecting open transport in a run of the mill medium-sized city and complexities the qualities of such a framework to those of broadly useful AVL frameworks. Particular properties of the courses broke down by the AVL framework utilized for the examination of open transport in our study incorporate cyclic vehicle courses, the requirement for particular execution reports, and so forth. This paper particularly manages vehicle movement forecasts and the estimation of station landing time, combined with consequently produced reports on timetable conformance and other execution measures. Another side of the watched issue is proficient exchange of information from the vehicles to the control focus. The pervasiveness of GSM bundle information exchange advancements combined with decreased information exchange expenses have brought on today's AVL frameworks to depend predominantly on parcel information exchange administrations from portable administrators as the correspondences channel in the middle of vehicles and the control focus. This methodology brings numerous security issues up in this conceivably touchy application field.

Keywords: automatic vehicle location (AVL), expectation of landing times, AVL security, data administrations, wise transport frameworks (ITS), guide coordinating

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Authors: Ping Bo, Meng Yunshan

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Satellite-derived sea surface temperature (SST) is a key parameter for many operational and scientific applications. However, the disadvantage of SST data is a high percentage of missing data which is mainly caused by cloud coverage. Data Interpolating Empirical Orthogonal Function (DINEOF) algorithm is an EOF-based technique for reconstructing the missing data and has been widely used in oceanographic field. The reconstruction of SST images within a long time series using DINEOF can cause large discontinuities and one solution for this problem is to filter the temporal covariance matrix to reduce the spurious variability. Based on the previous researches, an algorithm is presented in this paper to improve the temporal correlations in EOF expansion. Similar with the previous researches, a filter, such as Laplacian filter, is implemented on the temporal covariance matrix, but the temporal relationship between two consecutive images which is used in the filter is considered in the presented algorithm, for example, two images in the same season are more likely correlated than those in the different seasons, hence the latter one is less weighted in the filter. The presented approach is tested for the monthly nighttime 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST for the long-term period spanning from 1989 to 2006. The results obtained from the presented algorithm are compared to those from the original DINEOF algorithm without filtering and from the DINEOF algorithm with filtering but without taking temporal relationship into account.

Keywords: data interpolating empirical orthogonal function, image reconstruction, sea surface temperature, temporal filter

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Authors: Hossein Afzali

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Increasingly, decision analytic models are used to inform decisions about whether or not to publicly fund new health technologies. It is well noted that the accuracy of model predictions is strongly influenced by the appropriateness of model structuring. However, there is relatively inadequate methodological guidance surrounding this issue in guidelines developed by national funding bodies such as the Australian Pharmaceutical Benefits Advisory Committee (PBAC) and The National Institute for Health and Care Excellence (NICE) in the UK. This presentation aims to discuss issues around model structuring within decision making with a focus on (1) the need for a transparent and evidence-based model structuring process to inform the most appropriate set of structural aspects as the base case analysis; (2) the need to characterise structural uncertainty (If there exist alternative plausible structural assumptions (or judgements), there is a need to appropriately characterise the related structural uncertainty). The presentation will provide an opportunity to share ideas and experiences on how the guidelines developed by national funding bodies address the above issues and identify areas for further improvements. First, a review and analysis of the literature and guidelines developed by PBAC and NICE will be provided. Then, it will be discussed how the issues around model structuring (including structural uncertainty) are not handled and justified in a systematic way within the decision-making process, its potential impact on the quality of public funding decisions, and how it should be presented in submissions to national funding bodies. This presentation represents a contribution to the good modelling practice within the decision-making process. Although the presentation focuses on the PBAC and NICE guidelines, the discussion can be applied more widely to many other national funding bodies that use economic evaluation to inform funding decisions but do not transparently address model structuring issues e.g. the Medical Services Advisory Committee (MSAC) in Australia or the Canadian Agency for Drugs and Technologies in Health.

Keywords: decision-making process, economic evaluation, good modelling practice, structural uncertainty

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: A. Bouzekri, H. Benmassaud

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Aurès region is one of the arid and semi-arid areas that have suffered climate crises and overexploitation of natural resources they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and its spatiotemporal changes in the Aurès between 1987 and 2013, for this work, we adopted a method of analysis based on the exploitation of the images satellite Landsat TM 1987 and Landsat OLI 2013, from the supervised classification likelihood coupled with field surveys of the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover maps from 1987 and 2013, one can extract a spatial map change of different land cover units. The results show that between 1987 and 2013 vegetation has suffered negative changes are the significant degradation of forests and steppe rangelands, and sandy soils and bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013 allows us to understand the extensive or regressive orientation of vegetation and soil, this map shows that dense forests give his place to clear forests and steppe vegetation develops from a degraded forest vegetation and bare, sandy soils earn big steppe surfaces that explain its remarkable extension. The analysis of remote sensing data highlights the profound changes in our environment over time and quantitative monitoring of the risk of desertification.

Keywords: remote sensing, spatiotemporal, land use, Aurès

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Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

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Authors: Vishal Pareek, Shubham Gupta, Sushil Chandra Jain

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With increasing high-speed computation demand the power consumption, heat dissipation and chip size issues are posing challenges for logic design with conventional technologies. Recovery of bit loss and bit errors is other issues that require reversibility and fault tolerance in the computation. The reversible computing is emerging as an alternative to conventional technologies to overcome the above problems and helpful in a diverse area such as low-power design, nanotechnology, quantum computing. Bit loss issue can be solved through unique input-output mapping which require reversibility and bit error issue require the capability of fault tolerance in design. In order to incorporate reversibility a number of combinational reversible logic based circuits have been developed. However, very few sequential reversible circuits have been reported in the literature. To make the circuit fault tolerant, a number of fault model and test approaches have been proposed for reversible logic. In this paper, we have attempted to incorporate fault tolerance in sequential reversible building blocks such as D flip-flop, T flip-flop, JK flip-flop, R-S flip-flop, Master-Slave D flip-flop, and double edge triggered D flip-flop by making them parity preserving. The importance of this proposed work lies in the fact that it provides the design of reversible sequential circuits completely testable for any stuck-at fault and single bit fault. In our opinion our design of reversible building blocks is superior to existing designs in term of quantum cost, hardware complexity, constant input, garbage output, number of gates and design of online testable D flip-flop have been proposed for the first time. We hope our work can be extended for building complex reversible sequential circuits.

Keywords: parity preserving gate, quantum computing, fault tolerance, flip-flop, sequential reversible logic

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Authors: Hasan Muslemani, Xi Liang, Kathi Kaesehage, Francisco Ascui, Jeffrey Wilson

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The creation of a market for lower-carbon steel products, here called ‘green steel’, has been identified as an important means to support the introduction of breakthrough emission reduction technologies into the steel sector. However, the definition of what ‘green’ entails in the context of steel production, the implications on the competitiveness of green steel products in local and international markets, and the necessary market mechanisms to support their successful market penetration remain poorly explored. This paper addresses this gap by holding semi-structured interviews with international sustainability experts and commercial managers from leading steel trade associations, research institutes and steelmakers. Our findings show that there is an urgent need to establish a set of standards to define what ‘greenness’ means in the steelmaking context; standards that avoid market disruptions, unintended consequences, and opportunities for greenwashing. We also highlight that the introduction of green steel products will have implications on product competitiveness on three different levels: 1) between primary and secondary steelmaking routes, 2) with traditional, lesser green steel, and 3) with other substitutable materials (e.g. cement and plastics). This paper emphasises the need for steelmakers to adopt a transitional approach in deploying different low-carbon technologies, based on their stage of technological maturity, applicability in certain country contexts, capacity to reduce emissions over time, and the ability of the investment community to support their deployment. We further identify market mechanisms to support green steel production, including carbon border adjustments and public procurement, highlighting a need for implementing a combination of complementary policies to ensure the products’ roll-out. The study further shows that the auto industry is a likely candidate for green steel consumption, where a market would be supported by price premiums paid by willing consumers, such as those of high-end luxury vehicles.

Keywords: green steel, decarbonisation, business model innovation, market analysis

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Authors: J. Bahrawi, M. Elhag

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Estimation of evapotranspiration is always a major component in water resources management. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are thus used to overcome difficulties in obtaining daily evapotranspiration measurements on regional scale. The Surface Energy Balance System (SEBS) model was adopted to estimate daily evapotranspiration and relative evaporation along with other land surface energy fluxes. The model requires agro-climatic data that improve the model outputs. Advance Along Track Scanning Radiometer (AATSR) and Medium Spectral Resolution Imaging Spectrometer (MERIS) imageries were used to estimate the daily evapotranspiration and relative evaporation over the entire Nile Delta region in Egypt supported by meteorological data collected from six different weather stations located within the study area. Daily evapotranspiration maps derived from SEBS model show a strong agreement with actual ground-truth data taken from 92 points uniformly distributed all over the study area. Moreover, daily evapotranspiration and relative evaporation are strongly correlated. The reliable estimation of daily evapotranspiration supports the decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes.

Keywords: daily evapotranspiration, relative evaporation, SEBS, AATSR, MERIS, Nile Delta

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Authors: Chitra Rajagopal, Indra Deo Kumar, Ila Chauhan, Ruchi Joshi, Binoy Bhargavan

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Reliability and safety are essential for the success of mission-critical and safety-critical defense systems. Humans are part of the entire life cycle of defense systems development and deployment. The majority of industrial accidents or disasters are attributed to human errors. Therefore, considerations of human performance and human reliability are critical in all complex systems, including defense systems. Defense systems are operating from the ground, naval and aerial platforms in diverse conditions impose unique physical and psychological challenges to the human operators. Some of the safety and mission-critical defense systems with human-machine interactions are fighter planes, submarines, warships, combat vehicles, aerial and naval platforms based missiles, etc. Human roles and responsibilities are also going through a transition due to the infusion of artificial intelligence and cyber technologies. Human operators, not accustomed to such challenges, are more likely to commit errors, which may lead to accidents or loss events. In such a scenario, it is imperative to understand the human factors in defense systems for better systems performance, safety, and cost-effectiveness. A case study using Task Analysis (TA) based methodology for assessment and reduction of human errors in the Air and Missile Defense System in the context of emerging technologies were presented. Action-oriented task analysis techniques such as Hierarchical Task Analysis (HTA) and Operator Action Event Tree (OAET) along with Critical Action and Decision Event Tree (CADET) for cognitive task analysis was used. Human factors assessment based on the task analysis helps in realizing safe and reliable defense systems. These techniques helped in the identification of human errors during different phases of Air and Missile Defence operations, leading to meet the requirement of a safe, reliable and cost-effective mission.

Keywords: defence systems, reliability, risk, safety

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Authors: Cansu Bozer, Saadet İrem Turgut

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Artificial intelligence (AI) has become one of the most important technologies of the digital age and is transforming many sectors, including education. The advantages offered by AI, such as automation, personalised learning, and data analytics, create new opportunities for both teachers and students in education systems. Preschool education plays a fundamental role in the cognitive, social, and emotional development of children. In this period, the foundations of children's creative thinking, problem-solving, and critical thinking skills are laid. Educational technologies, especially artificial intelligence-based applications, are thought to contribute to the development of these skills. For example, artificial intelligence-supported digital learning tools can support learning processes by offering activities that can be customised according to the individual needs of each child. However, the successful use of artificial intelligence-based applications in preschool education can be realised under the guidance of teachers who have the right knowledge about this technology. Therefore, it is of great importance to measure preschool teachers' awareness levels of artificial intelligence and to understand which variables affect this awareness. The aim of this study is to measure preschool teachers' awareness levels of artificial intelligence and to determine which factors are related to this awareness. In line with this purpose, teachers' level of knowledge about artificial intelligence, their thoughts about the role of artificial intelligence in education, and their attitudes towards artificial intelligence will be evaluated. The study will be conducted with 100 teachers working in Turkey using a descriptive survey model. In this context, ‘Artificial Intelligence Awareness Level Scale for Teachers’ developed by Ferikoğlu and Akgün (2022) will be used. The collected data will be analysed using SPSS (Statistical Package for the Social Sciences) software. Descriptive statistics (frequency, percentage, mean, standard deviation) and relationship analyses (correlation and regression analyses) will be used in data analysis. As a result of the study, the level of artificial intelligence awareness of preschool teachers will be determined, and the factors affecting this awareness will be identified. The findings obtained will contribute to the determination of studies that can be done to increase artificial intelligence awareness in preschool education.

Keywords: education, child development, artificial intelligence, preschool teachers

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Authors: Ashima Anurag Sharma

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Optical fiber based networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This research paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparison between various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Sidney A. Lima, Hermann J. H. Kux, Elcio H. Shiguemori

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The Unmanned Aircraft Systems (UAS) usually navigate through the Global Navigation Satellite System (GNSS) associated with an Inertial Navigation System (INS). However, GNSS can have its accuracy degraded at any time or even turn off the signal of GNSS. In addition, there is the possibility of malicious interferences, known as jamming. Therefore, the image navigation system can solve the autonomy problem, because if the GNSS is disabled or degraded, the image navigation system would continue to provide coordinate information for the INS, allowing the autonomy of the system. This work aims to evaluate the accuracy of the positioning though photogrammetry concepts. The methodology uses orthophotos and Digital Surface Models (DSM) as a reference to represent the object space and photograph obtained during the flight to represent the image space. For the calculation of the coordinates of the perspective center and camera attitudes, it is necessary to know the coordinates of homologous points in the object space (orthophoto coordinates and DSM altitude) and image space (column and line of the photograph). So if it is possible to automatically identify in real time the homologous points the coordinates and attitudes can be calculated whit their respective accuracies. With the methodology applied in this work, it is possible to verify maximum errors in the order of 0.5 m in the positioning and 0.6º in the attitude of the camera, so the navigation through the image can reach values equal to or higher than the GNSS receivers without differential correction. Therefore, navigating through the image is a good alternative to enable autonomous navigation.

Keywords: autonomy, navigation, security, photogrammetry, remote sensing, spatial resection, UAS

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Authors: Jisun Lee, Jay Hyoun Kwon

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As an alternative way to compensate the INS (inertial navigation system) error in non-GNSS (Global Navigation Satellite System) environment, geophysical database referenced navigation is being studied. In this study, both gravity gradient and terrain data were combined to complement the weakness of sole geophysical data as well as to improve the stability of the positioning. The main process to compensate the INS error using geophysical database was constructed on the basis of the EKF (Extended Kalman Filter). In detail, two type of combination method, centralized and decentralized filter, were applied to check the pros and cons of its algorithm and to find more robust results. The performance of each navigation algorithm was evaluated based on the simulation by supposing that the aircraft flies with precise geophysical DB and sensors above nine different trajectories. Especially, the results were compared to the ones from sole geophysical database referenced navigation to check the improvement due to a combination of the heterogeneous geophysical database. It was found that the overall navigation performance was improved, but not all trajectories generated better navigation result by the combination of gravity gradient with terrain data. Also, it was found that the centralized filter generally showed more stable results. It is because that the way to allocate the weight for the decentralized filter could not be optimized due to the local inconsistency of geophysical data. In the future, switching of geophysical data or combining different navigation algorithm are necessary to obtain more robust navigation results.

Keywords: Extended Kalman Filter, geophysical database referenced navigation, gravity gradient, terrain

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Authors: Paramjit Mahey, Monica Sharma, Jasbinder Singh

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Donglian Sun, Yu Li, Paul Houser, Xiwu Zhan

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California experienced severe drought conditions in the past years. In this study, the drought conditions in California are analyzed using soil moisture anomalies derived from integrated optical and microwave satellite observations along with auxiliary land surface data. Based on the U.S. Drought Monitor (USDM) classifications, three typical drought conditions were selected for the analysis: extreme drought conditions in 2007 and 2013, severe drought conditions in 2004 and 2009, and normal conditions in 2005 and 2006. Drought is defined as negative soil moisture anomaly. To estimate soil moisture at high spatial resolutions, three approaches are explored in this study: the universal triangle model that estimates soil moisture from Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST); the basic model that estimates soil moisture under different conditions with auxiliary data like precipitation, soil texture, topography, and surface types; and the refined model that uses accumulated precipitation and its lagging effects. It is found that the basic model shows better agreements with the USDM classifications than the universal triangle model, while the refined model using precipitation accumulated from the previous summer to current time demonstrated the closest agreements with the USDM patterns.

Keywords: soil moisture, high resolution, regional drought, analysis and monitoring

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Authors: Maciej Jedrzejczyk, Karolina Marzantowicz

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Traditional utility value chain is being transformed during last few years into unbundled markets. Increased distributed generation of energy is one of considerable challenges faced by Smart Grid networks. New sources of energy introduce volatile demand response which has a considerable impact on traditional middlemen in E&U market. The purpose of this research is to search for ways to allow near-real-time electricity markets to transact with surplus energy based on accurate time synchronous measurements. A proposed framework evaluates the use of secure peer-2-peer (P2P) communication and distributed transaction ledgers to provide flat hierarchy, and allow real-time insights into present and forecasted grid operations, as well as state and health of the network. An objective is to achieve dynamic grid operations with more efficient resource usage, higher security of supply and longer grid infrastructure life cycle. Methods used for this study are based on comparative analysis of different distributed ledger technologies in terms of scalability, transaction performance, pluggability with external data sources, data transparency, privacy, end-to-end security and adaptability to various market topologies. An intended output of this research is a design of a framework for safer, more efficient and scalable Smart Grid network which is bridging a gap between traditional components of the energy network and individual energy producers. Results of this study are ready for detailed measurement testing, a likely follow-up in separate studies. New platforms for Smart Grid achieving measurable efficiencies will allow for development of new types of Grid KPI, multi-smart grid branches, markets, and businesses.

Keywords: autonomous agents, Distributed computing, distributed ledger technologies, large scale systems, micro grids, peer-to-peer networks, Self-organization, self-stabilization, smart grids

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Authors: Mansoureh Zangiabadi, Shamil Velji, Rajendra Kelkar, Neal Wade, Volker Pickert

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The EU has set itself a long-term goal of reducing greenhouse gas emissions by 80-95% of the 1990 levels by 2050 as set in the Energy Roadmap 2050. This paper reports on the European Union H2020 funded E-Lobster project which demonstrates tools and technologies, software and hardware in integrating the grid distribution, and the railway power systems with power electronics technologies (Smart Soft Open Point - sSOP) and local energy storage. In this context this paper describes the existing policies and regulatory frameworks of the energy market at European level with a special focus then at National level, on the countries where the members of the consortium are located, and where the demonstration activities will be implemented. By taking into account the disciplinary approach of E-Lobster, the main policy areas investigated includes electricity, energy market, energy efficiency, transport and smart cities. Energy storage will play a key role in enabling the EU to develop a low-carbon electricity system. In recent years, Energy Storage System (ESSs) are gaining importance due to emerging applications, especially electrification of the transportation sector and grid integration of volatile renewables. The need for storage systems led to ESS technologies performance improvements and significant price decline. This allows for opening a new market where ESSs can be a reliable and economical solution. One such emerging market for ESS is R+G management which will be investigated and demonstrated within E-Lobster project. The surplus of energy in one type of power system (e.g., due to metro braking) might be directly transferred to the other power system (or vice versa). However, it would usually happen at unfavourable instances when the recipient does not need additional power. Thus, the role of ESS is to enhance advantages coming from interconnection of the railway power systems and distribution grids by offering additional energy buffer. Consequently, the surplus/deficit of energy in, e.g. railway power systems, is not to be immediately transferred to/from the distribution grid but it could be stored and used when it is really needed. This will assure better energy management exchange between the railway power systems and distribution grids and lead to more efficient loss reduction. In this framework, to identify the existing policies and regulatory frameworks is crucial for the project activities and for the future development of business models for the E-Lobster solutions. The projections carried out by the European Commission, the Member States and stakeholders and their analysis indicated some trends, challenges, opportunities and structural changes needed to design the policy measures to provide the appropriate framework for investors. This study will be used as reference for the discussion in the envisaged workshops with stakeholders (DSOs and Transport Managers) in the E-Lobster project.

Keywords: light railway, electrical distribution network, Electrical Energy Storage, policy

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Authors: Dovile Petkeviciute-Barysiene

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Legal technologies not only create new ways for accessing and providing legal services but also transform the role of legal practitioners. Both lawyers and users of legal services expect automated solutions to outperform people with objectivity and impartiality. Although fairness of the automated decisions is crucial, research on assessing various characteristics of automated processes related to the perceived fairness has only begun. One of the major obstacles to this research is the lack of comprehensive understanding of what legal actions are automated and could be meaningfully automated, and to what extent. Neither public nor legal practitioners oftentimes cannot envision technological input due to the lack of general without illustrative examples. The aim of this study is to map decision making stages and automation levels which are and/or could be achieved in legal actions related to pre-trial and trial processes. Major legal decisions and judgments are identified during the consultations with legal practitioners. The dual-process model of information processing is used to describe cognitive processes taking place while making legal decisions and judgments during pre-trial and trial action. Some of the existing legal technologies are incorporated into the analysis as well. Several published automation level taxonomies are considered because none of them fit well into the legal context, as they were all created for avionics, teleoperation, unmanned aerial vehicles, etc. From the information processing perspective, analysis of the legal decisions and judgments expose situations that are most sensitive to cognitive bias, among others, also help to identify areas that would benefit from the automation the most. Automation level analysis, in turn, provides a systematic approach to interaction and cooperation between humans and algorithms. Moreover, an integrated map of legal decisions and judgments, information processing characteristics, and automation levels all together provide some groundwork for the research of legal technology perceived fairness and acceptance. Acknowledgment: This project has received funding from European Social Fund (project No 09.3.3-LMT-K-712-19-0116) under grant agreement with the Research Council of Lithuania (LMTLT).

Keywords: automation levels, information processing, legal judgment and decision making, legal technology

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Authors: Tomokazu Shirai, Akihiko Kondo

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Technologies of metabolic engineering or synthetic biology are essential for effective microbial bioproduction. It is especially important to develop an in silico tool for designing a metabolic pathway producing an unnatural and valuable chemical such as fossil materials of fuel or plastics. We here demonstrated two in silico tools for designing novel metabolic pathways: BioProV and HyMeP. Furthermore, we succeeded in creating an artificial metabolic pathway by enzyme engineering.

Keywords: bioinformatics, metabolic engineering, synthetic biology, genome scale model

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Authors: Niamh Higgins, Dawn Howard

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The Irish agri-food sector is of major importance to Ireland’s manufacturing sector and to the Irish economy through employment and the exporting of animal products worldwide. Infectious diseases and parasites have an impact on farm animal health causing profitability and productivity to be affected. For the sustainability of Irish dairy farming, there must be the highest standard of animal health. There can be a lack of information in accounting for > 1% of complete microbial diversity in an environment. There is the tendency of culture-based methods of microbial identification to overestimate the prevalence of species which grow easily on an agar surface. There is a need for new technologies to address these issues to assist with animal health. Metagenomic approaches provide information on both the whole genome and transcriptome present through DNA sequencing of total DNA from environmental samples producing high determination of functional and taxonomic information. Nanopore Next Generation Technologies have the ability to be powerful sequencing technologies. They provide high throughput, low material requirements and produce ultra-long reads, simplifying the experimental process. The aim of this study is to use a metagenomics approach to analyze dairy cattle faecal samples using the Oxford Nanopore MinION Next Generation Sequencer and to establish an in-house pipeline for metagenomic characterization of complex samples. Faecal samples will be obtained from Irish dairy farms, DNA extracted and the MinION will be used for sequencing, followed by bioinformatics analysis. Of particular interest, will be the parasite Buxtonella sulcata, which there has been little research on and which there is no research on its presence on Irish dairy farms. Preliminary results have shown the ability of the MinION to produce hundreds of reads in a relatively short time frame of eight hours. The faecal samples were obtained from 90 dairy cows on a Galway farm. The results from Oxford Nanopore ‘What’s in my pot’ (WIMP) using the Epi2me workflow, show that from a total of 926 classified reads, 87% were from the Kingdom Bacteria, 10% were from the Kingdom Eukaryota, 3% were from the Kingdom Archaea and < 1% were from the Kingdom Viruses. The most prevalent bacteria were those from the Genus Acholeplasma (71 reads), Bacteroides (35 reads), Clostridium (33 reads), Acinetobacter (20 reads). The most prevalent species present were those from the Genus Acholeplasma and included Acholeplasma laidlawii (39 reads) and Acholeplasma brassicae (26 reads). The preliminary results show the ability of the MinION for the identification of microorganisms to species level coming from a complex sample. With ongoing optimization of the pipe-line, the number of classified reads are likely to increase. Metagenomics has the potential in animal health for diagnostics of microorganisms present on farms. This would support wprevention rather than a cure approach as is outlined in the DAFMs National Farmed Animal Health Strategy 2017-2022.

Keywords: animal health, buxtonella sulcata, infectious disease, irish dairy cattle, metagenomics, minION, next generation sequencing

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Authors: Sahar Majed Asad

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Industrial structures have historically focused on functionality and efficiency, often disregarding aesthetics and human experience. However, a new approach is emerging that prioritizes humanizing industrial architecture and creating spaces that promote well-being, sustainability, and social responsibility. This study explores the motivations and design strategies behind this shift towards more human-centered industrial environments, providing practical guidance for architects, designers, and other stakeholders interested in incorporating these principles into their work. Through in-depth interviews with architects, designers, and industry experts, as well as a review of relevant literature, this study uncovers the reasons for this change in industrial design. The findings reveal that this shift is driven by a desire to create environments that prioritize the needs and experiences of the people who use them. The study identifies strategies such as incorporating natural elements, flexible design, and advanced technologies as crucial in achieving human-centric industrial design. It also emphasizes that effective communication and collaboration among stakeholders are crucial for successful human-centered design outcomes. This paper provides a comprehensive analysis of the motivations and design strategies behind the humanization of industrial architecture. It begins by examining the history of industrial architecture and highlights the focus on functionality and efficiency. The paper then explores the emergence of human-centered design principles in industrial architecture, discussing the benefits of this approach, including creating more sustainable and socially responsible environments.The paper explains specific design strategies that prioritize the human experience of industrial spaces. It outlines how incorporating natural elements like greenery and natural lighting can create more visually appealing and comfortable environments for industrial workers. Flexible design solutions, such as movable walls and modular furniture, can make spaces more adaptable to changing needs and promote a sense of ownership and creativity among workers. Advanced technologies, such as sensors and automation, can improve the efficiency and safety of industrial spaces while also enhancing the human experience. To provide practical guidance, the paper offers recommendations for incorporating human-centered design principles into industrial structures. It emphasizes the importance of understanding the needs and experiences of the people who use these spaces and provides specific examples of how natural elements, flexible design, and advanced technologies can be incorporated into industrial structures to promote human well-being. In conclusion, this study demonstrates that the humanization of industrial architecture is a growing trend that offers tremendous potential for creating more sustainable and socially responsible built environments. By prioritizing the human experience of industrial spaces, designers can create environments that promote well-being, sustainability, and social responsibility. This research study provides practical guidance for architects, designers, and other stakeholders interested in incorporating human-centered design principles into their work, demonstrating that a human-centered approach can lead to functional and aesthetically pleasing industrial spaces that promote human well-being and contribute to a better future for all.

Keywords: human-centered design, industrial architecture, sustainability, social responsibility

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Authors: Aadiv Shah, Hari Nair, Vedant Mittal, Alice Cheeran

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Present-day agricultural practices such as blanket spraying not only lead to excessive usage of pesticides but also harm the overall crop yield. This paper introduces an algorithm to optimize the traversal of an unmanned aerial vehicle (UAV) in constrained environments. The proposed system focuses on the agricultural application of targeted spraying for locust elimination. Given a satellite image of a farm, target zones that are prone to locust swarm formation are detected through the calculation of the normalized difference vegetation index (NDVI). This is followed by determining the optimal path for traversal of a UAV through these target zones using the proposed algorithm in order to perform pesticide spraying in the most efficient manner possible. Unlike the classic travelling salesman problem involving point-to-point optimization, the proposed algorithm determines an optimal path for multiple regions, independent of its geometry. Finally, the paper explores the idea of implementing reinforcement learning to model complex environmental behaviour and make the path planning mechanism for UAVs agnostic to external environment changes. This system not only presents a solution to the enormous losses incurred due to locust attacks but also an efficient way to automate agricultural practices across the globe in order to improve farmer ergonomics.

Keywords: locust, NDVI, optimization, path planning, reinforcement learning, UAV

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Authors: Pavlína Částková, Jiří Kropáč, Jan Kubrický

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The paper deals with the current issue of Information and Communication Technologies and their implementation into the educational activities of preschool children. The issue is addressed in the context of technical education and the specifics of its implementation in a kindergarten. One of the main topics of this paper is a technical game activity of a preschool child, and its possibilities, benefits and risks. The paper presents games/toys as one of the means of exploring and understanding technology as an essential part of human culture.

Keywords: ICT, technical education, pre-school age, technical games

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Authors: Chetna Khandelwal

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This research aims to study the contentions that Farm Animal Sanctuaries pose to human-animal relationships in modernity, which have developed as a result of globalisation of the meat industry and advancements in technology. The sociological history of human-animal relationships in farming is contextualised in order to set a foundation for the follow-up examination of challenges to existing human-(farm)animal relationships by Farm Animal Sanctuaries. The methodology was influenced by relativism, and the method involved three semi-structured small-group interviews, conducted at locations of sanctuaries. The sample was chosen through purposive sampling and varied by location and size of the sanctuary. Data collected were transcribed and qualitatively coded to generate themes. Findings revealed that sanctuary contentions to established human-animal relationships by factory farming could be divided into 4 broad categories – Revealing horrors of factory farming (involving uncovering power relations in agribusiness); transforming relationships with animals (including letting them emotionally heal in accordance with their individual personalities and treating them as partial-pets); educating the public regarding welfare conditions in factory farms as well as animal sentience through practical experience or positive imagery of farm animals, and addressing retaliation made by agribusiness in the form of technologies or discursive strategies. Hence, this research concludes that The human-animal relationship in current times has been characterised by – (ideological and physical) distance from farm animals, commodification due to increased chasing of profits over welfare and exploitation using technological advancements, creating unequal power dynamics that rid animals of any agency. Challenges to this relationship can be influenced by local populations around the sanctuary but not so dependent upon the size of it. This research can benefit from further academic exploration into farm animal sanctuaries and their role in feminist animal rights activism to enrich the ongoing fight against intensive farming.

Keywords: animal rights, factory farming, farm animal sanctuaries, human-animal relationships

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Authors: Vakhtang Charaia, Mariam Lashkhi

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Foreign direct investment (FDI) plays a vital role in global business. It provides firms with new markets and advertising channels, cheaper production facilities, admission to new technology, products, skills and financing. FDI can provide a recipient country/company with a source of new technologies, capital, practice, products, management skills, and as such can be a powerful drive for economic development. It is one of the key elements of stable economic development in many countries, especially in developing ones. Therefore the size of FDI inflow is one of the most crustal factors for economic perfection in small economy countries (like, Georgia), while most of developed ones are net exporters of FDI. Since, FDI provides firms with new markets; admission to new technologies, products and management skills; marketing channels; cheaper production facilities, and financing opportunities. It plays a significant role in Georgian economic development. Increasing FDI inflows from all over the world to Georgia in last decade was achieved with the outstanding reforms managed by the Georgian government. However, such important phenomenon as world financial crisis and Georgian-Russian war put its consequence on the over amount of FDI inflow in Georgia in the last years. It is important to mention that the biggest investor region for Georgia is EU, which is interested in Georgia not only from the economic points of view but from political. The case studies from main EU investor countries show that Georgia has a big potential of investment in different areas, such as; financial sector, energy, construction, tourism industry, transport and communications. Moreover, signing of Association Agreement between Georgia and EU will further boost all the fields of economy in Georgia in both short and long terms. It will attract more investments from different countries and especially from EU. The last, but not least important issue is the calculation of annual FDI inflow to Georgia, which it is calculated differently by different organizations, based on different methodologies, but what is more important is that all of them show significant increase of FDI in last decade, which gives a positive signal to investors and underlines necessity of further improvement of investment climate in the same direction.

Keywords: foreign direct investment (FDI), Georgia, investment development path, investment climate

Procedia PDF Downloads 274